Estimation of high-resolution surface soil moisture through GIS-based frequency ratio modeling

Estimation of high-resolution surface soil moisture through GIS-based frequency ratio modeling | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Estimation of high-resolution surface soil moisture through GIS-based frequency ratio modeling SAILESH SAMANTA This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4626766/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This research established an empirical methodology for estimating higher-resolution soil moisture using GIS and frequency ratio (FR) modeling techniques. Soil moisture active passive (SMAP) Level-4 global 3-hourly 9 km spatial resolution surface and root zone soil moisture datasets were used as reference data. A total of 283 reference points were selected through spatial fishnet analysis with the root zone soil moisture over 0.35 and surface soil moisture over 0.30. Eighty percent (80%) of these reference points served as inputs to the FR model, with the remaining twenty percent (20%) reserved for validation. Key independent variables incorporated in the FR modeling process included land use land cover, soil texture, normalized difference vegetation index, land surface temperature, topographic wetness index, rainfall, elevation, slope, and distance from rivers. The study area encompassed the final drainage basin of the Markham River catchment, situated in the Morobe Province of Papua New Guinea. The high-resolution developed database on surface soil moisture was reclassified into five basic zones segmenting on the FR index value, namely very low (less than 6), low ( 6 – 7 ), moderate ( 7 – 8 ), high ( 8 – 9 ), and very high (More than 9). The result indicates almost 26.10% of the land area is classified as a high soil moisture class and 56.89% as a very high soil moisture class. The FR model evinced a prediction accuracy of 93.98% along with a succession rate of 91.59%. These results provide useful data for scientific applications in various domains, specifically in the agricultural sector, local government administrator, researcher, and planner. Soil moisture Frequency ratio High-resolution Remote sensing Geographic information system Markham River Figures Figure 1 Figure 2 Figure 3 Introduction Soil moisture plays an important role in optimal crop production, drought and flood forecasting, water supply management, and agricultural monitoring (Pegram et al. 2010; Zhu et al. 2019). The earlier report and research indicated that agriculture production consumed 70% of total freshwater (Pimentel et al. 2004; Connor 2015; Paquin and Cosgrove 2016) and it may increase to 90% by 2050 (Sophocleous 2004). The amount of water in the upper soil is called soil moisture (Svetlitchnyi et al. 2003). This surface soil moisture interacts with the transpiration and evaporation process (Lawrence et al. 2007). The surface soil moisture refers to the presence of water in the top 10cm of the soil and the root zone soil moisture is available in the upper 200 cm of soil (Manfreda et al. 2014; Yinglan et al. 2022). Measuring soil moisture in rugged terrain can prove difficult using traditional field methods (Famiglietti et al. 2008; Crow et al. 2012). In such cases, remote sensing, geographic information systems (GIS), and spatial modeling have increased importance for assessing soil moisture conditions (Mulder et al. 2011; Rani et al. 2022). There are several comprehensive tools available, which were used by researchers to assess or model soil moisture content. Soil and water assessment tool (SWAT) can estimate soil moisture content based on the standard rainfall index (Havrylenko et al. 2016). The soil conservation service curve number (SCS-CN) is used by the SWAT model to predict stream flow estimation (Wang et al. 2008). The SCS-CN method can estimate surface runoff based on soil and land use land cover information in a specific rainfall (Pal and Samanta 2011). Topography based hydrological model (TOPMODEL) is another model, which was used by many researchers to simulate soil moisture and runoff in watershed areas (Tombul 2007; Zhu et al. 2009; Fu et al. 2018). The bridging event and continuous hydrological (BEACH) model can estimate soil moisture conditions with acceptable accuracy based on meteorological data, soil properties, topographic data, and crop characteristics (Sheikh et al. 2009). Presently uses of optical, thermal, and microwave remote sensing technologies have proven the reliability of soil moisture prediction. There is a strong relationship between the land surface temperature (LST) and soil moisture content (Cammalleri and Vogt 2015; Ghahremanloo et al. 2019). A most recent study has been conducted to model soil moisture using climate data and normalized difference vegetation index (NDVI) based on machine learning algorithms (random forest) in alpine grassland (Wang and Fu 2023). Temperature vegetation dryness index (TVDI) is a factor of NDVI and LST, which has been used in the detection of soil moisture at a larger spatial resolution (Sandholt et al. 2002; Park et al. 2014; Zhao et al. 2021). Several researches have been carried out in the recent past to estimate soil moisture at relatively low spatial (1 km) resolution (Zeng et al. 2015; Meng et al. 2019). The spatial resolution of microwave-based soil moisture measurements is relatively coarse (3 to 9 km) (Entekhabi et al. 2010; Zhang et al. 2020; Nguyen et al. 2023). Several researchers confirmed that different models, tools, and algorithms estimate soil moisture, but it is not clear which is the best method (Nguyen et al. 2022; Tramblay and Segui 2022; Kisekka et al. 2022). Frequency ratio (FR) modeling is a statistical, quantitative, and probability approach for simulating environmental conditions (Laaidi et al. 2003; Tehrany et al. 2018), flood hazard assessment (Samanta et al. 2018; Arabameri et al. 2019), landslide susceptibility (Arabameri et al. 2019; Mersha and Meten 2020) mapping based on topographical and environmental conditions. Different statistical modeling approaches have been applied for the estimation of soil moisture, modeling of soil moisture in the recent past (Lookingbill and Urban 2004; Ahmad et al. 2010; Hosseini et al. 2015; Pal et al. 2016; Aires et al. 2021). This research is an ensemble method to estimate high-spatial resolution soil moisture (30 m) based on the frequency ratio (FR) statistical approach based on several environmental and topographic parameters like land use land cover (LULC), NDVI, LST, topographic wetness index (TWI), elevation, slope, rainfall, soil texture, and distance from river. The goal of this study was to examine the effectiveness of the frequency ratio (FR) model and GIS in the estimation and spatial mapping of soil moisture in the final catchment of the Markham River basin under the Morobe Province of Papua New Guinea. The research aimed to estimate surface soil moisture zones in the final flow Basin of Markham River. The objectives of this study were to create wall-to-wall datasets on conditioning environmental and topographic factors into the FR model, create a higher spatial resolution (30 m) soil moisture zone database, and finally, validate the FR model based on prediction accuracy and succession rate. Study location and materials The research area is situated in the Morobe Province of Papua New Guinea (PNG), in the lower basin of the Markham River. The last sub-basin number 14, which has a land area of 1806.85 square kilometers, is situated between longitudes 146.09º E and 174.04º E and latitudes 6.23º S and 6.78º S (Fig. 1 ). The Finisterre range is the source of the fourth-longest river in Papua New Guinea, the Markham River, which empties into the Huon Gulf (Renagi et al. 2010). The upper basin region spans approximately 12800 square kilometers and is characterized by steep slopes, rough terrain, thick forest, and drainage (Sam et al. 2020). The study area experiences hot and humid weather throughout the year (Ningal et al. 2008). In the research region, 4200 mm of total rainfall falls annually. Most of the basin's soils have a modest amount of drainage. The "dry season," which lasts from June to October, is usually when the North-West monsoon influences the rainfall (Prentice and Hope 2007). Because of the decreased rainfall, a progressive deterioration in soil moisture conditions was also observed. This has a significant impact on crop output, thus farmers should think about moisture-saving management techniques. Several active and passive microwave sensors are currently employed in accruing global soil moisture data at a spatial resolution of around 9 km. Soil Moisture Active Passive (SMAP) is an Earth-orbiting observatory satellite mission designed to measure the amount of water in the surface soil of the Earth (Reichle et al. 2014). The SMAP instrument incorporates an L-band radar and L - band radiometer, which can detect emitted radiation in the frequency range of 1–2 GHz and have a wavelength range of 30 − 15 cm (Das et al. 2010; Montzka et al. 2016). L - band radiometer is used to investigate soil moisture and soil variability, sea ice thickness, moisture measurement and leakage detection in structures, and road density changes (Sales et al. 2007; Escorihuela et al. 2010). Algorithm Theoretical Basis Documents (ATBDs) provide the physical and mathematical descriptions of algorithms, which are used in the generation of SMAP science data products (Reichle et al. 2009; Chan et al. 2013). SMAP soil moisture data has two immediate parameters, namely global surface soil moisture and root zone soil moisture (Reichle et al. 2012). The latest SMAP level-4 Global 3-hourly 9 km equal-area sealable earth grid surface and root zone soil moisture geophysical data, version 7 was released in November 2022 (Reichle et al. 2022a). The past seven years of SMAP observations and model simulations (April 2015 – March 2022) are incorporated into version 7 of the database (Reichle et al. 2022b). The average of 3-hourly geophysical data of a particular day is estimated to be the 1200–1500 hours data sets. These data sets are freely available to use without restrictions for science and application users (Reichle et al. 2022a). The SMAP geospatial data set (for the date of 23rd September 2023: 1200–1500 hrs.) was downloaded from https://nsidc.org/ in h5 format (Reichle et al. 2022b) and further processed in ArcGIS v10.5 to re-format, re-project, and subset as per the study area. This data set was used as an input reference into the FR model. Advanced space-borne thermal emission and reflection radiometer (ASTER) global digital elevation model (GDEM) is a digital representation of ground surface terrain with a high spatial resolution of 30 m (Abrams et al., 2020). This data was downloaded, mosiked, and cropped using the study area boundary. Landsat 8 satellite image (30 m spatial resolution) is another data used in this research. All nine ( 9 ) spectral bands of the operational land imager (OLI) and two ( 3 ) spectral bands of a thermal infrared sensor (TIRS) were downloaded from Earth Explorer ( http://earthexplorer.usgs.gov ). Furthermore, after the radiometric enhancement and rearrangement of the spectral bands, the image was clipped as per the study area for further use. Methodology Estimation of soil moisture was carried out based on nine-fold ( 9 ) geospatial parameters based on the consultation of local soil scientists and agricultural experts. They are land use land cover (LULC), soil texture, normalized differential vegetation index (NDVI), land surface temperature (LST), topographic wetness index (TWI), rainfall, elevation, slope, and distance from the river. Wall-to-wall geospatial layers were constructed from satellite remote sensing images and the national-level GIS database of PNG. LULC map was prepared through supervised classification from Landsat 8 OLI satellite data. Soil texture map and rainfall map were developed from the national-level GIS database of PNG. The distance from the river database was generated using a drainage network through a proximity analysis process in the ArcGIS V10.5. Elevation and slope were calculated from ASTER GDEM. TWI and LST data sets were generated from DEM and TIRS data through the topographic wetness model and surface temperature model respectively. TWI was calculated based on Eq. 1 (Beven and Kirkby 1979; Samanta et al. 2018). \(\text{T}\text{W}\text{I}= \text{L}\text{n}\left(\frac{\text{a}}{\text{t}\text{a}\text{n}\text{B}}\right)\) Eq. 1 Where TWI is the topographic wetness index , a refers to the specific catchment area [a = A/L, total basin area (A) divided by length of contour (L)] and B refers to the slope in degree. Several analyses were performed to calculate the TWI, namely flow direction, flow accumulation, slope in degree, radian slope, tan slope, and scaled flow accumulation (Kopecký et al. 2021). Similarly, a series of calculations were performed to derive LST from TIRS bands, like extraction of spectral radiance value (Eq. 2), calculation of brightness temperature (Eq. 3), calculation of NDVI (Eq. 4), derived of proportion of vegetation (Eq. 5), calculation of land surface emissivity (Eq. 6) and finally obtain the land surface temperature (Eq. 7) map (Rajeshwari and Mani 2014; Avdan and Jovanovska 2016; De Jesus et al. 2017; Wang et al. 2019). \(\text{T}\text{O}\text{A} \left(\text{L}\right) = \text{M}\text{L} \text{*} \text{Q}\text{c}\text{a}\text{l} + \text{A}\text{L}\) Eq. 2 Where TOA (L) is the top of atmospheric spectral radiance, M L represents the band-specific multiplicative rescaling factor (0.0003342), Q cal is the pixel value of band 10, and A L represents the band-specific assistive rescaling factor (0.1). \(\text{B}\text{T} = (\text{K}2 / (\text{l}\text{n} (\text{K}1 / \text{L}) + 1\left)\right) - 273.15\) Eq. 3 Where BT is the brightness temperature, K1 is the band-specific thermal conversion constant (774.8853), K2 is the band-specific thermal conversion constant (1321.0789), and L is TOA(L). \(\text{N}\text{D}\text{V}\text{I} = \text{F}\text{l}\text{o}\text{a}\text{t}(\text{B}\text{a}\text{n}\text{d}5 – \text{B}\text{a}\text{n}\text{d}4) / \text{F}\text{l}\text{o}\text{a}\text{t}(\text{B}\text{a}\text{n}\text{d}5 + \text{B}\text{a}\text{n}\text{d}4)\) Eq. 4 Where NDVI is the normalized differential vegetation index, Band5 is the pixel value in the Near-infrared (INR) band, and Band4 is the pixel value in the RED band. \(\text{P}\text{v} = \text{S}\text{q}\text{u}\text{a}\text{r}\text{e} \left(\right(\text{N}\text{D}\text{V}\text{I} – \text{N}\text{D}\text{V}\text{I}\text{m}\text{i}\text{n}) / (\text{N}\text{D}\text{V}\text{I}\text{m}\text{a}\text{x} – \text{N}\text{D}\text{V}\text{I}\text{m}\text{i}\text{n}\left)\right)\) Eq. 5 Where P v is the proportion of vegetation, NDVI refers to the pixel-wise normalized differential vegetation index, max is the maximum value, and min is the minimum. \(\text{E} = 0.004 \text{*} \text{P}\text{v} + 0.986\) Eq. 6 Where E is the land surface emissivity, and P v is the proportion of vegetation. \(\text{L}\text{S}\text{T} = (\text{B}\text{T} / (1 + (0.00115 \text{*} \text{B}\text{T} / 1.4388) \text{*} \text{L}\text{n}\left(\text{E}\right)\left)\right)\) Eq. 7 Where LST is the land surface temperature, BT is the brightness temperature, and proportion of vegetation, and E is the land surface emissivity. The existing soil moisture inventory database is essential to generate high-resolution surface soil moisture through a frequency ratio model. SMAP Level-4 surface soil moisture and root zone soil moisture datasets were used as reference databases with a spatial resolution of 9 km. A fishnet analysis was performed to create reference points with known soil moisture values. Soil moisture value ranges from 0 to 1, where 0 indicates extreme dry conditions and 1 indicates extreme wet conditions (Saha et al. 2021). Furthermore, SM values more than 0.3 are considered favorable soil moisture (no drought) conditions. On the other hand, an SM value of less than 0.3 was classified as drought (Parida et al. 2008). A total number of 412 reference points were created through spatial fishnet analysis. 283 reference points were selected based on the root zone soil moisture over 0.35 and surface soil moisture over 0.30. Eighty percent (80%) of these reference points (226) served as inputs to the FR model, with the remaining twenty percent (20%) point ( 57 ) reserved for the validation process (Pradhan and Lee 2010; Bashir et al. 2023; Taffese and Espinosa-Leal 2023). The frequency ratio (FR) model is a quantitative bivariate statistical analysis approach (Samanta et al., 2018), that was adopted in this study to estimate surface soil moisture index. This approach offers the quantitative relationship between the soil moisture episodes and numerous conditioning parameters. FR model calculates FR value which expresses the type of correlation between parameters and potential soil moisture. The calculation of FR was processed using Eq. 8 (Bonham-Carter 1994; Samanta et al. 2018). \(\text{F}\text{R} = (\text{E}∕\text{F})∕(\text{M}∕\text{L})\) Eq. 8 Where E represents several potential surface soil moistures of more than 0.30 and root zone soil moisture of more than 0.35 for each factor; F represents the total number of potential surface soil moisture; M stands for histogram of a class and L is the total histogram of the area. The lower FR value (less than) refers to weak correlation and on the other hand higher FR value (more than 1) indicates strong correlation between the conditioning factor and potential soil moisture respectively. After the calculation of the FR value, the frequency ratio index was calculated using Eq. 9 (Tehrany et al., 2014; Samanta et al., 2018) & Eq. 10. \(\text{F}\text{R}\text{I} = {\Sigma } \text{F}\text{R}\) Eq. 9 Where FRI is the Frequency ratio index and FR is the frequency ratio for each factor \(\text{F}\text{R}\text{I} = \text{S}\text{M}\text{I}\) Eq. 10 Where FRI is the Frequency ratio index and SMI is the soil moisture index The calculated FRI refers to the soil moisture index (SMI), which indicates the type of wetness based on the soil moisture index value. Higher value indicates higher moisture content or wet soil and lower refers to the lower moisture content or dry soil. Results and discussion Different conditioning factors play specific roles in estimating high-resolution surface soil moisture databases. Nine ( 9 ) conditioning factors were selected carefully for the estimation of surface soil moisture, namely land use land cover, soil texture, normalized difference vegetation index, land surface temperature, topographic wetness index, rainfall, elevation, slope, and distance from rivers were selected. The spatial distribution pattern of these parameters was mapped and statistical databases were built with their sub-classes (Fig. 2 and Table 1 ). The land use and land cover (LULC) map was prepared by spectral classification of the Landsat satellite image. The classification produces a LULC database, which presents a total of nine ( 9 ) major classes. They are dense Forest, low dense forest, shrub land, outcrop/barren land, mountain grassland, urban and built-up, inland water, river water, and agriculture. The low dense forest is the dominant class, which covers an area of 35.38% of the study area. The low dense forest is mostly dominated in the eastern, western, and some pockets of the southern region of the study area (Fig. 2 a). The shrubland is the second largest land cover class (26%) dominated in the middle portion and some pockets of the eastern and northern parts of the study area. A total FR index of 7.44 was contributed by LULC for the FR model. The highest frequency ratio (FR) value (1.41) was calculated for shrubland (Table 1 ) based on the FR equation (Eq. 8), which indicates a higher correlation to the soil moisture content (Zhang et al. 2011; Kidron and Gutschick 2013). A soil texture map was produced based on the soil classification scheme by the United States Department of Agriculture (USDA). A total of eight ( 8 ) textural classes are found in the study area. They are silty clay, sandy loam, sandy clay loam, silty clay loam, silty loam, sandy clay, sand, and loamy sand (Fig. 2 b). Two other classes peat and lake were included in the map as additional sub-classes. Sandy clay loam is the largest soil texture class, which covers an area of 31.24% of the study area. Sandy clay loam is found in the middle portion of the study area where the river is flowing and its floodplain area. The peat class (1.13%) is found in some pockets of the eastern part of the study site. The maximum total FR index of 9.33 was contributed by the soil texture parameter into the FR model compared to other parameters. The maximum frequency ratio (FR) value of 1.57 was calculated for the Peat soil class (Table 1 ), which describes a strong correlation with the surface soil moisture (Petrone et al. 2004; Takada et al. 2009). Normalized difference vegetation index (NDVI) was calculated through the band ratio of the subtraction and addition of the near-infrared and red bands (Bhandari et al., 2012), which have a dynamic response to soil moisture variation (Ahmed et al. 2017). The output NDVI value is ranged from − 0.39 to 0.67 (Fig. 2 c). Furthermore, the study area was categorised into five ( 5 ) different zones based on the calculated NDVI value, like (i) less than 0.1 (20.24%), (ii) 0.1–0.15 (13.19%), (iii) 1.15–0.30 (18.62%), (iv) 0.30–0.45 (31.83%), and (v) more than 0.45 (16.11%). FR value of 1.41 is calculated for the 2nd category (0.1–0.15) and a total FR index of 5.20 is contributed by the soil texture parameter into the FR model (Table 1 ). Generally, the land surface temperature (LST) has a negative relationship with surface soil moisture except in high-latitude regions (Ghahremanloo et al. 2019; Jiang et al. 2023). The modeled LST of this area is varied from 9⁰ to 47⁰ centigrade (C). The spatial database on calculated LST was grouped into five ( 5 ) classes, namely (i) less than 20⁰ C (3.03%), (ii) 20⁰ C − 25⁰ C (15.36%), (iii) 25⁰ C − 30⁰ C (61.44%), (iv) 30⁰ C − 35⁰ C (17.03%), and (v) more than 35⁰ C (3.14%). The highest temperature is observed in the township area in the eastern part and some pockets of middle and northwest parts of the study area, whereas the lowest temperature is found in the higher altitude sections in the western portion of the study area (Fig. 2 d). The topographic wetness index (TWI) quantifies the topography-based soil moisture variation (Raduła et al. 2018; Kopecký et al. 2021). The TWI has a positive relationship with surface soil moisture (Maduako et al. 2017). The calculated TWI ranged from 0.47 to 42.31 with an average value of 8.38 (Fig. 2 e). The maximum range of TWI is spread over the middle part of the watershed area where the topographic slope is very gentle (Qin et al., 2011). The spatial database of TWI was further reclassified into five ( 5 ) categories, namely (i) less than 5.0 (4.43%), (ii) 5.0–7.5 (38.46%), (iii) 7.5–10.0 (38.10%), (iv) 10.0–12.5 (9.09%), and (v) more than 12,.5 (9.91%). LST and TWI contribute a total FR index of 4.76 and 4.87 to the FR model respectively (Table 1 ). Rainfall is the primary source of soil moisture (Li et al. 2016) in the study area. The relationship between rainfall and surface soil moisture is highly linear (Sehler et al. 2019). The mean annual rainfall in the study area ranged from 1350 mm to 3850 mm. The study area was classified into five ( 5 ) different classes based on rainfall intensity and the class statistics for each class was generated, specifically (i) less than 1500 mm (0.85%), (ii) 1500 mm to 2000 mm (14.55%), (iii) 72000 mm to 2500 mm (57.11%), (iv) 2500 mm to 3000 mm (10.22%), and (v) more than 3000 mm (17.27%) (Fig. 2 f and Table 1 ). Elevation and slope have greater impact on surface soil moisture (Moeslund et al. 2013). As water flows downhill under the influence of gravity, the higher elevation areas are characterized by lower soil moisture and lower elevation areas are dominated by higher moisture conditions (Qiu et al. 2001). The elevation of the study area is varied from 0 to 1789.41 m. Based on the altitude variation, the study area was categorized into five ( 5 ) different groups, namely (i) less than 200 m (63.84%), (ii) 200 m to 400 m (21.39%), (iii) 400 m to 600 m (6.54%), (iv) 600 m to 800 m (3.92%), and (v) more than 800 m (4.82%) (Fig. 2 g). The time stability of soil moisture is lowest in the gentle slope area compared to the moderate and steep slopes (Cai et al., 2019). Based on the variation of the slope, the study area was divided into five ( 5 ) categories. They are (i) less than 2⁰ (53.59%), (ii) 2⁰ to 5⁰ (14.55%), (iii) 5⁰ to 10⁰ (7.87%), (iv) 10⁰ to 20⁰ (13.43%), and (v) more than 20⁰ (10.55%). A higher slope (More than 20⁰) is found in mountain areas in the northeast, southeast, and northwest parts of the study area (Fig. 2 h). Both gentle slope (less than 2⁰) and lower altitude (less than 200 m) are located in the middle part of the basin area. Elevation and slope parameters shared a total FR index of 3.27 and 4.16 in the FR model respectively (Table 1 ). In general, soil moisture varies by distance from the river (Horvath, 2002). Soil situated near the river is characterised by higher moisture than soils located at a distance from the river (Kumar et al., 2016). Five different buffer areas were generated through proximity analysis from the river to determine the effect of river on the soil moisture, such as (i) less than 200 m (24.62%), (ii) 200 m to 400 m (14.04%), (iii) 400 m to 600 m (10.85%), (iv) 600 m to 800 m (8.40%), and (v) more than 800 m (42.09%) (Table 1 ). Table 1 Conditioning factors used for estimation of soil moisture (SM) through FR model Value Class name or Description Histogram % of Histogram Potential SM points % of Potential SM points Frequency ratio (FR) Total FR index Land use and Land cover 1 Dense Forest 47374 2.36 2 0.885 0.37 7.44 2 Low dense forest 710143 35.38 68 30.088 0.85 3 Shrub land 521832 26.00 83 36.726 1.41 4 Outcrop/barren lands 43242 2.15 2 0.885 0.41 5 Mountain grassland 421354 20.99 35 15.487 0.74 6 Urban and built-up 15443 0.77 2 0.885 1.15 7 Inland water 4765 0.24 0 0.000 0.00 8 River water 140449 7.00 19 8.407 1.20 9 Agriculture 102468 5.11 15 6.637 1.30 Soil Texture 1 Silty clay 91671 4.57 2 0.88 0.19 9.33 2 Sandy loam 176212 8.78 19 8.41 0.96 3 Sandy clay loam 627064 31.24 67 29.65 0.95 4 Silty clay loam 406699 20.26 43 19.03 0.94 5 Peat 22672 1.13 4 1.77 1.57 6 Silty loam 22603 1.13 3 1.33 1.18 7 Sandy clay 14248 0.71 0 0.00 0.00 8 Sand 555087 27.66 78 34.51 1.25 9 Lake 6567 0.33 1 0.44 1.35 10 Loamy sand 84247 4.20 9 3.98 0.95 Normalized Differential Vegetation Index (NDVI) 1 Less than 0.1 406280 20.24 47 20.80 1.03 5.20 2 0.1–0.15 264815 13.19 42 18.58 1.41 3 1.15–0.30 373813 18.62 38 16.81 0.90 4 0.30–0.45 638820 31.83 63 27.88 0.88 5 More than 0.45 323342 16.11 36 15.93 0.99 Land Surface Temperature (LST) degree C 1 Less than 20 60822 3.03 3 1.33 0.44 4.76 2 20–25 308266 15.36 20 8.85 0.58 3 25–30 1233168 61.44 149 65.93 1.07 4 30–35 341812 17.03 43 19.03 1.12 5 More than 35 63002 3.14 11 4.87 1.55 Topographic Wetness Index (TWI) 1 Less than 5.0 88958 4.43 6 2.65 0.60 4.87 2 5.0–7.5 771982 38.46 76 33.63 0.87 3 7.5–10.0 764626 38.10 95 42.04 1.10 4 10.0–12.5 182526 9.09 26 11.50 1.27 5 More than 12.5 198978 9.91 23 10.18 1.03 Rainfall in mm 1 Less than 1500 16990 0.85 2 0.88 1.05 4.65 2 1500–2000 292094 14.55 25 11.06 0.76 3 2000–2500 1146286 57.11 148 65.49 1.15 4 2500–3000 205126 10.22 22 9.73 0.95 5 More than 3000 346574 17.27 29 12.83 0.74 Elevation in m 1 Less than 200 1281891 63.87 162 71.68 1.12 3.27 2 200–400 429324 21.39 50 22.12 1.03 3 400–600 131360 6.54 10 4.42 0.68 4 600–800 78617 3.92 3 1.33 0.34 5 More than 800 85878 4.28 1 0.44 0.10 Slope in degree 1 Less than 2 1075600 53.59 144 63.72 1.19 4.16 2 2–5 292084 14.55 39 17.26 1.19 3 5–10 157971 7.87 11 4.87 0.62 4 10–20 269603 13.43 20 8.85 0.66 5 More than 20 211812 10.55 12 5.31 0.50 Distance from river in m 1 Less than 200 494112 24.62 59 26.11 1.06 5.06 2 200–400 281756 14.04 40 17.70 1.26 3 400–600 217794 10.85 21 9.29 0.86 4 600–800 168666 8.40 18 7.96 0.95 5 More than 800 844742 42.09 89 39.38 0.94 The rating was assigned to each sub-class of all the nine conditioning parameters based on the FR value presented in Table 1 . The FR value is ranged from 0 to 1.57. FR value of more than 1 indicates a strong correlation to the soil moisture event, whereas less than 1 refers to a weak correlation (Sarkar and Mondal 2020). The total FR index was calculated for each parameter after aggregation of all FR values sponsored by individual sub-classes. Finally, the overlay analysis was performed after integrating all the parameters with their FR characteristics based on Eq. 9, and the high-resolution (30 m spatial resolution) soil moisture index database was developed. The soil moisture index value is ranged from 4.57 to 11.61 with an average index value of 9.01 (Fig. 3 a). A higher soil moisture index value indicates wet soil and a lower index indicates dry soil. The resulting soil moisture index database was further reclassified into five ( 5 ) soil groups based on surface soil moisture index. They are (i) very low moisture (less than 6.0), (ii) low moisture (6.0 to 7.0), (iii) moderate moisture (7.0 to 8.0), (iv) high moisture (8.0 to 9.0), and (v) very high moisture (More than 9.0) (Fig. 2 b). The result identifies that almost 56.89% of the land areas are classified as very high moisture, 26.10% as high moisture, 10.18% as moderate, 4.71% as low moisture, and 2.12% as very low moisture (Table 2 ). The high-moisture soil and very high moisture soil classes (relatively wet soil classes) are identified in the middle section of the study area, where the Markham River and the surrounding floodplain area are located. These areas are enriched with higher moisture content because of higher topographic wetness index, lower elevation, and flat slopes (Fig. 2 ). Shrubland, sandy clay loam, lower NDVI, moderate surface temperature, and proximity to the river are the other dominant characteristics that caused higher surface soil moisture in the lower part of the Markham Valley and surrounding areas. Table 2 Spatial distribution of soil moisture zones in the study area Class no. Soil moisture class FR index range Histogram % Area 1 Very low Less than 6 42461 2.12 2 Low 6–7 94519 4.71 3 Moderate 7–8 204339 10.18 4 High 8–9 523870 26.10 5 Very high More than 9 1141881 56.89 The purpose of the study was to estimate surface high-resolution soil moisture in the final catchment of the Markham River basin through the FR statistical approach. Although the reference point datasets were generated from 9 km SMAP level-4 data through fishnet analysis, the FR model generates a high-resolution spatial surface soil moisture database at a spatial resolution of 30 meters. A statistical spatial interpolation process can predict the soil moisture at unknown locations using known soil moisture information (Srivastava et al. 2019), but the critical or location variation can’t be incorporated into the prediction. So, the FR model is an alternative statistical method was selected for this study (Snepvangers et al. 2003). The topographic slope, elevation, topographic wetness index (TWI), and land surface temperature (LST) were measured statistically using linear trend line analysis (R-squared) to determine how well they fit the regression model (Shaw et al. 2023). The coefficient of determination (R-squared) values for topographic slope, elevation, TWI, and LST were determined to be 0.669, 0.4593, 0.2823, and 0.2694, respectively. The linear regression analysis identifies topographic slope has the maximum impact on soil moisture determination, followed by topographic height, TWI, and LST. Accuracy and success rate are essential to validate model-based estimation of soil moisture (Delgoda et al. 2016; Chi et al. 2019). The high-resolution estimated soil moisture index database was validated through prediction accuracy and success rate. The success rate of the FR model was calculated using several successful input reference points (207) over the total number of input reference points (226) with known soil moisture conditions, which were used as training for the soil moisture estimation. The success rate was computed as 91.59% (Table 3 ). On the other hand, the prediction accuracy was calculated using 57 reference points, which were not used for the FR modeling. The prediction accuracy of the estimation was calculated as 93.98% (Table 3 ), which is very good evidence to validate the FR model in the estimation of high-resolution surface soil moisture. The choice of the FR model over the MCDA is the best option for estimating surface soil moisture because the FR model estimates with better efficiency compared to any GIS-based MCDA model (Khosravi 2016; Wang and Li 2017). Table 3 Prediction accuracy and succession rate for the Estimation of soil moisture Soil moisture class FR index range Validation [20% flood points] Accuracy (high and very high class) Prediction Accuracy %) Training [80% flood points] Success ((high and very high class) Success Rate (%) Very low Less than 6 0 53 93.98% 0 207 91.59% Low 6–7 1 2 Moderate 7–8 3 17 High 8–9 13 38 Very high More than 9 40 169 Total 57 226 Conclusion The estimation of surface soil moisture was conducted at the spatial resolution of 30 m (pixel size) based on LULC, soil texture NDVI, LST, TWI, rainfall, elevation, slope, and distance from the river. The spatial resolution of all the input conditioning parameters was in 30-meter pixels. It was investigated that soil moisture index distribution depends mostly on the TWI, LST, slope, soil type, proximity to the river, and vegetation. Topographic slope, elevation, TWI, and LST are important independent parameters for soil moisture estimation in selected catchment areas. A total of 283 reference points were selected in a specified interval with known soil moisture conditions. Eighty percent of these reference points (226) were used as inputs and the remaining twenty percent ( 57 ) were kept to validate the FR model estimation. The prediction accuracy of 93.98% validates the FR model prediction as acceptable and realistic. Remote sensing, GIS, and FR models are promising in hydrological research. FR model produces results with better prediction efficiency compared to any other GIS-based MCDA model (Wang and Li 2017; Samanta et al. 2017; Zeleke 2019). These results provide useful data for scientific applications in various domains, specifically in the agricultural sector, local government administrator, researcher, and planner. Soil moisture databases are efficiently used for watershed characterization, water balance studies, soil respiration, hydrology, soil health monitoring, plant growth, plant water stress, and irrigation scheduling. The establishment of ground-based combined models over larger catchment areas is required to generate accurate and precise daily, weekly, or monthly high-resolution soil moisture databases. Declarations Compliance with ethical standards Not applicable Conflict of interest The authors declare that there is no conflict of interest for the publication of this article. Funding No funding was received to conduct this research Author Contribution The author confirms sole responsibility for the following: study conceptualization, methodology, formal analysis, data collection, analysis and interpretation of results, and manuscript preparation. Acknowledgement The author is thankful to the Papua New Guinea University of Technology and to the School of Surveying and Land Studies for all the facilities made available and availed for the work as a researcher. Data Availability Data is provided within the manuscript References Abrams M, Crippen R, Fujisada H (2020) ASTER global digital elevation model (GDEM) and ASTER global water body dataset (ASTWBD). Remote Sens 12(7):1156. https://doi.org/10.3390/rs12071156 Ahmad S, Kalra A, Stephen H (2010) Estimating soil moisture using remote sensing data: A machine learning approach. 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Resources 8(2):70. https://doi.org/10.3390/resources8020070 Svetlitchnyi AA, Plotnitskiy SV, Stepovaya OY (2003) Spatial distribution of soil moisture content within catchments and its modelling on the basis of topographic data. J Hydrol 277(1–2):50–60. https://doi.org/10.1016/S0022-1694(03)00083-0 Taffese WZ, Espinosa-Leal L (2023) Multitarget regression models for predicting compressive strength and chloride resistance of concrete. J Building Eng 72:106523. https://doi.org/10.1016/j.jobe.2023.106523 Takada M, Mishima Y, Natsume S (2009) Estimation of surface soil properties in peatland using ALOS/PALSAR. Landscape Ecol Eng 5:45–58. https://doi.org/10.1007/s11355-008-0061-4 Tehrany MS, Kumar L, Jebur MN, Shabani F (2018) Evaluating the application of the statistical index method in flood susceptibility mapping and its comparison with frequency ratio and logistic regression methods. Geomatics Nat Hazards Risk. https://doi.org/10.1080/19475705.2018.1506509 Tehrany MS, Lee MJ, Pradhan B, Jebur MN, Lee S (2014) Flood susceptibility mapping using integrated bivariate and multivariate statistical models. Environ Earth Sci 72:4001–4015. https://doi.org/10.1007/s12665-014-3289-3 Tombul M (2007) Mapping field surface soil moisture for hydrological modeling. Water Resour Manage 21:1865–1880. https://doi.org/10.1007/s11269-006-9134-z Tramblay Y, Seguí PQ (2022) Estimating soil moisture conditions for drought monitoring with random forests and a simple soil moisture accounting scheme. Nat Hazards Earth Syst Sci 22:1325–1334. https://doi.org/10.5194/nhess-22-1325-2022 Wang L, Lu Y, Yao Y (2019) Comparison of three algorithms for the retrieval of land surface temperature from Landsat 8 images. Sensors 19(22):5049. https://doi.org/10.3390/s19225049 Wang Q, Li W (2017) A GIS-based comparative evaluation of analytical hierarchy process and frequency ratio models for landslide susceptibility mapping. Phys Geogr 38(4):318–337. https://doi.org/10.1080/02723646.2017.1294522 Wang S, Fu G (2023) Modelling soil moisture using climate data and normalized difference vegetation index based on nine algorithms in alpine grasslands. Front Environ Sci 11. https://doi.org/10.3389/fenvs.2023.1130448 Wang X, Shang S, Yang W, Melesse AM (2008) Simulation of an agricultural watershed using an improved curve number method in SWAT. Trans ASABE 51(4):1323–1339. https://doi.org/10.13031/2013.25248 Yinglan A, Wang G, Hu P, Lai X, Xue B, Fang Q (2022) Root-zone soil moisture estimation based on remote sensing data and deep learning. Environ Res 212:113278. https://doi.org/10.1016/j.envres.2022.113278 Zeng J, Li Z, Chen Q, Bi H, Qiu J, Zou P (2015) Evaluation of remotely sensed and reanalysis soil moisture products over the Tibetan Plateau using in-situ observations. Remote Sens Environ 163:91–110. https://doi.org/10.1016/j.rse.2015.03.008 Zhang L, Ji L, Wylie BK (2011) Response of spectral vegetation indices to soil moisture in grasslands and shrublands. Int J Remote Sens 32(18):5267–5286. https://doi.org/10.1080/01431161.2010.496471 Zhang L, Meng Q, Hu D, Zhang Y, Yao S, Chen X (2020) Comparison of different soil dielectric models for microwave soil moisture retrievals. Int J Remote Sens 41(8):3054–3069. https://doi.org/10.1080/01431161.2019.1698077 Zhao H, Li Y, Chen X, Wang H, Yao N, Liu F (2021) Monitoring monthly soil moisture conditions in China with temperature vegetation dryness indexes based on an enhanced vegetation index and normalized difference vegetation index. Theoret Appl Climatol 143:159–176. https://doi.org/10.1007/s00704-020-03422-x Zhu Q, Luo Y, Xu YP, Tian Y, Yang T (2019) Satellite soil moisture for agricultural drought monitoring: Assessment of SMAP-derived soil water deficit index in Xiang River Basin, China. Remote Sens 11(3):362. https://doi.org/10.3390/rs11030362 Zhu X, Peng W, Xu J, Yang Y (2009), November Simulating the soil moisture and runoff in Baohe catchment based on TOPMODEL and DEM. In 2009 Third International Symposium on Intelligent Information Technology Application Workshops (pp. 297–300). IEEE. https://doi.org/10.1109/IITAW.2009.52 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4626766","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":324434543,"identity":"7f953112-66fe-49c2-8f10-d293e44dd224","order_by":0,"name":"SAILESH SAMANTA","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYJCCAyCCjZ2BgZmhAshiZm4gUgszSMsZkBZGwlogAKSFsQ3EIqDFvP/4xYM/GOrk+ZiZD38unFcbzd8O1PKjYhtOLTI3cgoO8zAcNmxjZkuTnrnteO6Mw4wNjD1nbuPUIiHBk3AY6B3GNmYeM2bebcdyG4BagC7Eo4X/TALIYfZtzPyfP/POOZY7n6AWhvQDB3gYmBOBtjBI8zbU5G4gqEUih+Ewj8HhZKBfzKR5jh3I3QjUchCvX/iPP/74o6LOdn578+PPPDV1ufPOHz744EcFbi0MDDwGDAwGcN5hMHkAj3ogYH+AzKvDr3gUjIJRMApGJAAAI/9VDxwC6ZMAAAAASUVORK5CYII=","orcid":"","institution":"Papua New Guinea University of Technology","correspondingAuthor":true,"prefix":"","firstName":"SAILESH","middleName":"","lastName":"SAMANTA","suffix":""}],"badges":[],"createdAt":"2024-06-24 01:08:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4626766/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4626766/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60932824,"identity":"9faa9550-2a44-4fa3-933e-734c78a0c626","added_by":"auto","created_at":"2024-07-23 17:37:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":563960,"visible":true,"origin":"","legend":"\u003cp\u003eLocation map of the Study area (a) Papua New Guinea with the Markham watershed and (b) The study area sub-basin no.14\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4626766/v1/1def38c39c17694c82b096be.png"},{"id":60933113,"identity":"8b1c1fda-752f-4f7a-940c-7a17e450cc20","added_by":"auto","created_at":"2024-07-23 17:45:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2655453,"visible":true,"origin":"","legend":"\u003cp\u003eParameters used for FR modeling (a) land use land cover, (b) soil texture, (c) normalized difference vegetation index, (d) land surface temperature, (e) topographic wetness index, (f) rainfall, (g) elevation, and (h) slope characteristics of the study area\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4626766/v1/6705872cadfe8577dc2612ce.png"},{"id":60932416,"identity":"fc92ba97-f08b-46ba-826b-f6d9a9d0de3e","added_by":"auto","created_at":"2024-07-23 17:29:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1976610,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated surface soil moisture (a) Spatial distribution pattern of soil moisture-based FR modeling (b) validation using 20% legacy soil moisture reference point\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4626766/v1/4d55bbbea38692af44bfaf01.png"},{"id":75966112,"identity":"c0bb5fc2-247d-4b0c-83f8-75deb5065f02","added_by":"auto","created_at":"2025-02-11 04:16:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5883023,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4626766/v1/e67fc963-28ea-4e9a-ac01-7aff227eccd9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Estimation of high-resolution surface soil moisture through GIS-based frequency ratio modeling","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSoil moisture plays an important role in optimal crop production, drought and flood forecasting, water supply management, and agricultural monitoring (Pegram et al. 2010; Zhu et al. 2019). The earlier report and research indicated that agriculture production consumed 70% of total freshwater (Pimentel et al. 2004; Connor 2015; Paquin and Cosgrove 2016) and it may increase to 90% by 2050 (Sophocleous 2004). The amount of water in the upper soil is called soil moisture (Svetlitchnyi et al. 2003). This surface soil moisture interacts with the transpiration and evaporation process (Lawrence et al. 2007). The surface soil moisture refers to the presence of water in the top 10cm of the soil and the root zone soil moisture is available in the upper 200 cm of soil (Manfreda et al. 2014; Yinglan et al. 2022). Measuring soil moisture in rugged terrain can prove difficult using traditional field methods (Famiglietti et al. 2008; Crow et al. 2012). In such cases, remote sensing, geographic information systems (GIS), and spatial modeling have increased importance for assessing soil moisture conditions (Mulder et al. 2011; Rani et al. 2022). There are several comprehensive tools available, which were used by researchers to assess or model soil moisture content. Soil and water assessment tool (SWAT) can estimate soil moisture content based on the standard rainfall index (Havrylenko et al. 2016). The soil conservation service curve number (SCS-CN) is used by the SWAT model to predict stream flow estimation (Wang et al. 2008). The SCS-CN method can estimate surface runoff based on soil and land use land cover information in a specific rainfall (Pal and Samanta 2011). Topography based hydrological model (TOPMODEL) is another model, which was used by many researchers to simulate soil moisture and runoff in watershed areas (Tombul 2007; Zhu et al. 2009; Fu et al. 2018). The bridging event and continuous hydrological (BEACH) model can estimate soil moisture conditions with acceptable accuracy based on meteorological data, soil properties, topographic data, and crop characteristics (Sheikh et al. 2009).\u003c/p\u003e \u003cp\u003ePresently uses of optical, thermal, and microwave remote sensing technologies have proven the reliability of soil moisture prediction. There is a strong relationship between the land surface temperature (LST) and soil moisture content (Cammalleri and Vogt 2015; Ghahremanloo et al. 2019). A most recent study has been conducted to model soil moisture using climate data and normalized difference vegetation index (NDVI) based on machine learning algorithms (random forest) in alpine grassland (Wang and Fu 2023). Temperature vegetation dryness index (TVDI) is a factor of NDVI and LST, which has been used in the detection of soil moisture at a larger spatial resolution (Sandholt et al. 2002; Park et al. 2014; Zhao et al. 2021). Several researches have been carried out in the recent past to estimate soil moisture at relatively low spatial (1 km) resolution (Zeng et al. 2015; Meng et al. 2019). The spatial resolution of microwave-based soil moisture measurements is relatively coarse (3 to 9 km) (Entekhabi et al. 2010; Zhang et al. 2020; Nguyen et al. 2023). Several researchers confirmed that different models, tools, and algorithms estimate soil moisture, but it is not clear which is the best method (Nguyen et al. 2022; Tramblay and Segui 2022; Kisekka et al. 2022).\u003c/p\u003e \u003cp\u003eFrequency ratio (FR) modeling is a statistical, quantitative, and probability approach for simulating environmental conditions (Laaidi et al. 2003; Tehrany et al. 2018), flood hazard assessment (Samanta et al. 2018; Arabameri et al. 2019), landslide susceptibility (Arabameri et al. 2019; Mersha and Meten 2020) mapping based on topographical and environmental conditions. Different statistical modeling approaches have been applied for the estimation of soil moisture, modeling of soil moisture in the recent past (Lookingbill and Urban 2004; Ahmad et al. 2010; Hosseini et al. 2015; Pal et al. 2016; Aires et al. 2021). This research is an ensemble method to estimate high-spatial resolution soil moisture (30 m) based on the frequency ratio (FR) statistical approach based on several environmental and topographic parameters like land use land cover (LULC), NDVI, LST, topographic wetness index (TWI), elevation, slope, rainfall, soil texture, and distance from river. The goal of this study was to examine the effectiveness of the frequency ratio (FR) model and GIS in the estimation and spatial mapping of soil moisture in the final catchment of the Markham River basin under the Morobe Province of Papua New Guinea. The research aimed to estimate surface soil moisture zones in the final flow Basin of Markham River. The objectives of this study were to create wall-to-wall datasets on conditioning environmental and topographic factors into the FR model, create a higher spatial resolution (30 m) soil moisture zone database, and finally, validate the FR model based on prediction accuracy and succession rate.\u003c/p\u003e\n\u003ch3\u003eStudy location and materials\u003c/h3\u003e\n\u003cp\u003eThe research area is situated in the Morobe Province of Papua New Guinea (PNG), in the lower basin of the Markham River. The last sub-basin number 14, which has a land area of 1806.85 square kilometers, is situated between longitudes 146.09\u0026ordm; E and 174.04\u0026ordm; E and latitudes 6.23\u0026ordm; S and 6.78\u0026ordm; S (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The Finisterre range is the source of the fourth-longest river in Papua New Guinea, the Markham River, which empties into the Huon Gulf (Renagi et al. 2010). The upper basin region spans approximately 12800 square kilometers and is characterized by steep slopes, rough terrain, thick forest, and drainage (Sam et al. 2020). The study area experiences hot and humid weather throughout the year (Ningal et al. 2008). In the research region, 4200 mm of total rainfall falls annually. Most of the basin's soils have a modest amount of drainage. The \"dry season,\" which lasts from June to October, is usually when the North-West monsoon influences the rainfall (Prentice and Hope 2007). Because of the decreased rainfall, a progressive deterioration in soil moisture conditions was also observed. This has a significant impact on crop output, thus farmers should think about moisture-saving management techniques.\u003c/p\u003e \u003cp\u003eSeveral active and passive microwave sensors are currently employed in accruing global soil moisture data at a spatial resolution of around 9 km. Soil Moisture Active Passive (SMAP) is an Earth-orbiting observatory satellite mission designed to measure the amount of water in the surface soil of the Earth (Reichle et al. 2014). The \u003cem\u003eSMAP\u003c/em\u003e instrument incorporates an L-band radar and \u003cem\u003eL\u003c/em\u003e-\u003cem\u003eband radiometer, which can\u003c/em\u003e detect emitted radiation in the frequency range of 1\u0026ndash;2 GHz and have a wavelength range of 30\u0026thinsp;\u0026minus;\u0026thinsp;15 cm (Das et al. 2010; Montzka et al. 2016). \u003cem\u003eL\u003c/em\u003e-\u003cem\u003eband radiometer\u003c/em\u003e is used to investigate soil moisture and soil variability, sea ice thickness, moisture measurement and leakage detection in structures, and road density changes (Sales et al. 2007; Escorihuela et al. 2010). Algorithm Theoretical Basis Documents (ATBDs) provide the physical and mathematical descriptions of algorithms, which are used in the generation of SMAP science data products (Reichle et al. 2009; Chan et al. 2013). SMAP soil moisture data has two immediate parameters, namely global surface soil moisture and root zone soil moisture (Reichle et al. 2012). The latest SMAP level-4 Global 3-hourly 9 km equal-area sealable earth grid surface and root zone soil moisture geophysical data, version 7 was released in November 2022 (Reichle et al. 2022a). The past seven years of SMAP observations and model simulations (April 2015 \u0026ndash; March 2022) are incorporated into version 7 of the database (Reichle et al. 2022b). The average of 3-hourly geophysical data of a particular day is estimated to be the 1200\u0026ndash;1500 hours data sets. These data sets are freely available to use without restrictions for science and application users (Reichle et al. 2022a). The SMAP geospatial data set (for the date of 23rd September 2023: 1200\u0026ndash;1500 hrs.) was downloaded from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://nsidc.org/\u003c/span\u003e\u003cspan address=\"https://nsidc.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e in h5 format (Reichle et al. 2022b) and further processed in ArcGIS v10.5 to re-format, re-project, and subset as per the study area. This data set was used as an input reference into the FR model. Advanced space-borne thermal emission and reflection radiometer (ASTER) global digital elevation model (GDEM) is a digital representation of ground surface terrain with a high spatial resolution of 30 m (Abrams et al., 2020). This data was downloaded, mosiked, and cropped using the study area boundary. Landsat 8 satellite image (30 m spatial resolution) is another data used in this research. All nine (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) spectral bands of the operational land imager (OLI) and two (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) spectral bands of a thermal infrared sensor (TIRS) were downloaded from Earth Explorer (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://earthexplorer.usgs.gov\u003c/span\u003e\u003cspan address=\"http://earthexplorer.usgs.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Furthermore, after the radiometric enhancement and rearrangement of the spectral bands, the image was clipped as per the study area for further use.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eEstimation of soil moisture was carried out based on nine-fold (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) geospatial parameters based on the consultation of local soil scientists and agricultural experts. They are land use land cover (LULC), soil texture, normalized differential vegetation index (NDVI), land surface temperature (LST), topographic wetness index (TWI), rainfall, elevation, slope, and distance from the river. Wall-to-wall geospatial layers were constructed from satellite remote sensing images and the national-level GIS database of PNG. LULC map was prepared through supervised classification from Landsat 8 OLI satellite data. Soil texture map and rainfall map were developed from the national-level GIS database of PNG. The distance from the river database was generated using a drainage network through a proximity analysis process in the ArcGIS V10.5. Elevation and slope were calculated from ASTER GDEM. TWI and LST data sets were generated from DEM and TIRS data through the topographic wetness model and surface temperature model respectively. TWI was calculated based on Eq.\u0026nbsp;1 (Beven and Kirkby 1979; Samanta et al. 2018).\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\text{T}\\text{W}\\text{I}= \\text{L}\\text{n}\\left(\\frac{\\text{a}}{\\text{t}\\text{a}\\text{n}\\text{B}}\\right)\\)\u003c/span\u003e \u003c/span\u003e Eq.\u0026nbsp;1\u003c/p\u003e \u003cp\u003e \u003cem\u003eWhere TWI is the topographic wetness index\u003c/em\u003e, a \u003cem\u003erefers to the specific catchment area [a\u0026thinsp;=\u0026thinsp;A/L, total basin area (A) divided by length of contour (L)] and B refers to the slope in degree.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eSeveral analyses were performed to calculate the TWI, namely flow direction, flow accumulation, slope in degree, radian slope, tan slope, and scaled flow accumulation (Kopeck\u0026yacute; et al. 2021). Similarly, a series of calculations were performed to derive LST from TIRS bands, like extraction of spectral radiance value (Eq.\u0026nbsp;2), calculation of brightness temperature (Eq.\u0026nbsp;3), calculation of NDVI (Eq.\u0026nbsp;4), derived of proportion of vegetation (Eq.\u0026nbsp;5), calculation of land surface emissivity (Eq.\u0026nbsp;6) and finally obtain the land surface temperature (Eq.\u0026nbsp;7) map (Rajeshwari and Mani 2014; Avdan and Jovanovska 2016; De Jesus et al. 2017; Wang et al. 2019).\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\text{T}\\text{O}\\text{A} \\left(\\text{L}\\right) = \\text{M}\\text{L} \\text{*} \\text{Q}\\text{c}\\text{a}\\text{l} + \\text{A}\\text{L}\\)\u003c/span\u003e \u003c/span\u003e Eq.\u0026nbsp;2\u003c/p\u003e \u003cp\u003e \u003cem\u003eWhere TOA (L) is the top of atmospheric spectral radiance, M\u003c/em\u003e \u003csub\u003e \u003cem\u003eL\u003c/em\u003e \u003c/sub\u003e \u003cem\u003erepresents the band-specific multiplicative rescaling factor (0.0003342), Q\u003c/em\u003e\u003csub\u003e\u003cem\u003ecal\u003c/em\u003e\u003c/sub\u003e \u003cem\u003eis the pixel value of band 10, and A\u003c/em\u003e\u003csub\u003e\u003cem\u003eL\u003c/em\u003e\u003c/sub\u003e \u003cem\u003erepresents the band-specific assistive rescaling factor (0.1).\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\text{B}\\text{T} = (\\text{K}2 / (\\text{l}\\text{n} (\\text{K}1 / \\text{L}) + 1\\left)\\right) - 273.15\\)\u003c/span\u003e \u003c/span\u003e Eq.\u0026nbsp;3\u003c/p\u003e \u003cp\u003e \u003cem\u003eWhere BT is the brightness temperature, K1 is the band-specific thermal conversion constant (774.8853), K2 is the band-specific thermal conversion constant (1321.0789), and L is TOA(L).\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\text{N}\\text{D}\\text{V}\\text{I} = \\text{F}\\text{l}\\text{o}\\text{a}\\text{t}(\\text{B}\\text{a}\\text{n}\\text{d}5 \u0026ndash; \\text{B}\\text{a}\\text{n}\\text{d}4) / \\text{F}\\text{l}\\text{o}\\text{a}\\text{t}(\\text{B}\\text{a}\\text{n}\\text{d}5 + \\text{B}\\text{a}\\text{n}\\text{d}4)\\)\u003c/span\u003e \u003c/span\u003e Eq.\u0026nbsp;4\u003c/p\u003e \u003cp\u003e \u003cem\u003eWhere NDVI is the\u003c/em\u003e normalized differential vegetation index, \u003cem\u003eBand5 is the pixel value in the Near-infrared (INR) band, and Band4 is the pixel value in the RED band.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\text{P}\\text{v} = \\text{S}\\text{q}\\text{u}\\text{a}\\text{r}\\text{e} \\left(\\right(\\text{N}\\text{D}\\text{V}\\text{I} \u0026ndash; \\text{N}\\text{D}\\text{V}\\text{I}\\text{m}\\text{i}\\text{n}) / (\\text{N}\\text{D}\\text{V}\\text{I}\\text{m}\\text{a}\\text{x} \u0026ndash; \\text{N}\\text{D}\\text{V}\\text{I}\\text{m}\\text{i}\\text{n}\\left)\\right)\\)\u003c/span\u003e \u003c/span\u003e Eq.\u0026nbsp;5\u003c/p\u003e \u003cp\u003e \u003cem\u003eWhere P\u003c/em\u003e \u003csub\u003e \u003cem\u003ev\u003c/em\u003e \u003c/sub\u003e \u003cem\u003eis the proportion of vegetation, NDVI refers to the pixel-wise normalized differential vegetation index, max is the maximum value, and min is the minimum.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\text{E} = 0.004 \\text{*} \\text{P}\\text{v} + 0.986\\)\u003c/span\u003e \u003c/span\u003e Eq.\u0026nbsp;6\u003c/p\u003e \u003cp\u003e \u003cem\u003eWhere E is the land surface emissivity, and P\u003c/em\u003e \u003csub\u003e \u003cem\u003ev\u003c/em\u003e \u003c/sub\u003e \u003cem\u003eis the proportion of vegetation.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\text{L}\\text{S}\\text{T} = (\\text{B}\\text{T} / (1 + (0.00115 \\text{*} \\text{B}\\text{T} / 1.4388) \\text{*} \\text{L}\\text{n}\\left(\\text{E}\\right)\\left)\\right)\\)\u003c/span\u003e \u003c/span\u003e Eq.\u0026nbsp;7\u003c/p\u003e \u003cp\u003e \u003cem\u003eWhere LST is the land surface temperature, BT is the brightness temperature, and proportion of vegetation, and E is the land surface emissivity.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe existing soil moisture inventory database is essential to generate high-resolution surface soil moisture through a frequency ratio model. SMAP Level-4 surface soil moisture and root zone soil moisture datasets were used as reference databases with a spatial resolution of 9 km. A fishnet analysis was performed to create reference points with known soil moisture values. Soil moisture value ranges from 0 to 1, where 0 indicates extreme dry conditions and 1 indicates extreme wet conditions (Saha et al. 2021). Furthermore, SM values more than 0.3 are considered favorable soil moisture (no drought) conditions. On the other hand, an SM value of less than 0.3 was classified as drought (Parida et al. 2008). A total number of 412 reference points were created through spatial fishnet analysis. 283 reference points were selected based on the root zone soil moisture over 0.35 and surface soil moisture over 0.30. Eighty percent (80%) of these reference points (226) served as inputs to the FR model, with the remaining twenty percent (20%) point (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e) reserved for the validation process (Pradhan and Lee 2010; Bashir et al. 2023; Taffese and Espinosa-Leal 2023).\u003c/p\u003e \u003cp\u003eThe frequency ratio (FR) model is a quantitative bivariate statistical analysis approach (Samanta et al., 2018), that was adopted in this study to estimate surface soil moisture index. This approach offers the quantitative relationship between the soil moisture episodes and numerous conditioning parameters. FR model calculates FR value which expresses the type of correlation between parameters and potential soil moisture. The calculation of FR was processed using Eq.\u0026nbsp;8 (Bonham-Carter 1994; Samanta et al. 2018).\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\text{F}\\text{R} = (\\text{E}∕\\text{F})∕(\\text{M}∕\\text{L})\\)\u003c/span\u003e \u003c/span\u003e Eq.\u0026nbsp;8\u003c/p\u003e \u003cp\u003e \u003cem\u003eWhere E represents several potential surface soil moistures of more than 0.30 and root zone soil moisture of more than 0.35 for each factor; F represents the total number of potential surface soil moisture; M stands for histogram of a class and L is the total histogram of the area.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe lower FR value (less than) refers to weak correlation and on the other hand higher FR value (more than 1) indicates strong correlation between the conditioning factor and potential soil moisture respectively.\u003c/p\u003e \u003cp\u003eAfter the calculation of the FR value, the frequency ratio index was calculated using Eq.\u0026nbsp;9 (Tehrany et al., 2014; Samanta et al., 2018) \u0026amp; Eq.\u0026nbsp;10.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\text{F}\\text{R}\\text{I} = {\\Sigma } \\text{F}\\text{R}\\)\u003c/span\u003e \u003c/span\u003e Eq.\u0026nbsp;9\u003c/p\u003e \u003cp\u003e \u003cem\u003eWhere FRI is the Frequency ratio index and FR is the frequency ratio for each factor\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\text{F}\\text{R}\\text{I} = \\text{S}\\text{M}\\text{I}\\)\u003c/span\u003e \u003c/span\u003e Eq.\u0026nbsp;10\u003c/p\u003e \u003cp\u003e \u003cem\u003eWhere FRI is the Frequency ratio index and SMI is the soil moisture index\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe calculated FRI refers to the soil moisture index (SMI), which indicates the type of wetness based on the soil moisture index value. Higher value indicates higher moisture content or wet soil and lower refers to the lower moisture content or dry soil.\u003c/p\u003e"},{"header":"Results and discussion","content":"\u003cp\u003eDifferent conditioning factors play specific roles in estimating high-resolution surface soil moisture databases. Nine (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) conditioning factors were selected carefully for the estimation of surface soil moisture, namely land use land cover, soil texture, normalized difference vegetation index, land surface temperature, topographic wetness index, rainfall, elevation, slope, and distance from rivers were selected. The spatial distribution pattern of these parameters was mapped and statistical databases were built with their sub-classes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The land use and land cover (LULC) map was prepared by spectral classification of the Landsat satellite image. The classification produces a LULC database, which presents a total of nine (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) major classes. They are dense Forest, low dense forest, shrub land, outcrop/barren land, mountain grassland, urban and built-up, inland water, river water, and agriculture. The low dense forest is the dominant class, which covers an area of 35.38% of the study area. The low dense forest is mostly dominated in the eastern, western, and some pockets of the southern region of the study area (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The shrubland is the second largest land cover class (26%) dominated in the middle portion and some pockets of the eastern and northern parts of the study area. A total FR index of 7.44 was contributed by LULC for the FR model. The highest frequency ratio (FR) value (1.41) was calculated for shrubland (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) based on the FR equation (Eq.\u0026nbsp;8), which indicates a higher correlation to the soil moisture content (Zhang et al. 2011; Kidron and Gutschick 2013). A soil texture map was produced based on the soil classification scheme by the United States Department of Agriculture (USDA). A total of eight (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) textural classes are found in the study area. They are silty clay, sandy loam, sandy clay loam, silty clay loam, silty loam, sandy clay, sand, and loamy sand (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Two other classes peat and lake were included in the map as additional sub-classes. Sandy clay loam is the largest soil texture class, which covers an area of 31.24% of the study area. Sandy clay loam is found in the middle portion of the study area where the river is flowing and its floodplain area. The peat class (1.13%) is found in some pockets of the eastern part of the study site. The maximum total FR index of 9.33 was contributed by the soil texture parameter into the FR model compared to other parameters. The maximum frequency ratio (FR) value of 1.57 was calculated for the Peat soil class (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which describes a strong correlation with the surface soil moisture (Petrone et al. 2004; Takada et al. 2009).\u003c/p\u003e \u003cp\u003eNormalized difference vegetation index (NDVI) was calculated through the band ratio of the subtraction and addition of the near-infrared and red bands (Bhandari et al., 2012), which have a dynamic response to soil moisture variation (Ahmed et al. 2017). The output NDVI value is ranged from \u0026minus;\u0026thinsp;0.39 to 0.67 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Furthermore, the study area was categorised into five (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) different zones based on the calculated NDVI value, like (i) less than 0.1 (20.24%), (ii) 0.1\u0026ndash;0.15 (13.19%), (iii) 1.15\u0026ndash;0.30 (18.62%), (iv) 0.30\u0026ndash;0.45 (31.83%), and (v) more than 0.45 (16.11%). FR value of 1.41 is calculated for the 2nd category (0.1\u0026ndash;0.15) and a total FR index of 5.20 is contributed by the soil texture parameter into the FR model (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Generally, the land surface temperature (LST) has a negative relationship with surface soil moisture except in high-latitude regions (Ghahremanloo et al. 2019; Jiang et al. 2023). The modeled LST of this area is varied from 9⁰ to 47⁰ centigrade (C). The spatial database on calculated LST was grouped into five (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) classes, namely (i) less than 20⁰ C (3.03%), (ii) 20⁰ C \u0026minus;\u0026thinsp;25⁰ C (15.36%), (iii) 25⁰ C \u0026minus;\u0026thinsp;30⁰ C (61.44%), (iv) 30⁰ C \u0026minus;\u0026thinsp;35⁰ C (17.03%), and (v) more than 35⁰ C (3.14%). The highest temperature is observed in the township area in the eastern part and some pockets of middle and northwest parts of the study area, whereas the lowest temperature is found in the higher altitude sections in the western portion of the study area (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003eThe topographic wetness index (TWI) quantifies the topography-based soil moisture variation (Raduła et al. 2018; Kopeck\u0026yacute; et al. 2021). The TWI has a positive relationship with surface soil moisture (Maduako et al. 2017). The calculated TWI ranged from 0.47 to 42.31 with an average value of 8.38 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee). The maximum range of TWI is spread over the middle part of the watershed area where the topographic slope is very gentle (Qin et al., 2011). The spatial database of TWI was further reclassified into five (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) categories, namely (i) less than 5.0 (4.43%), (ii) 5.0\u0026ndash;7.5 (38.46%), (iii) 7.5\u0026ndash;10.0 (38.10%), (iv) 10.0\u0026ndash;12.5 (9.09%), and (v) more than 12,.5 (9.91%). LST and TWI contribute a total FR index of 4.76 and 4.87 to the FR model respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Rainfall is the primary source of soil moisture (Li et al. 2016) in the study area. The relationship between rainfall and surface soil moisture is highly linear (Sehler et al. 2019). The mean annual rainfall in the study area ranged from 1350 mm to 3850 mm. The study area was classified into five (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) different classes based on rainfall intensity and the class statistics for each class was generated, specifically (i) less than 1500 mm (0.85%), (ii) 1500 mm to 2000 mm (14.55%), (iii) 72000 mm to 2500 mm (57.11%), (iv) 2500 mm to 3000 mm (10.22%), and (v) more than 3000 mm (17.27%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eElevation and slope have greater impact on surface soil moisture (Moeslund et al. 2013). As water flows downhill under the influence of gravity, the higher elevation areas are characterized by lower soil moisture and lower elevation areas are dominated by higher moisture conditions (Qiu et al. 2001). The elevation of the study area is varied from 0 to 1789.41 m. Based on the altitude variation, the study area was categorized into five (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) different groups, namely (i) less than 200 m (63.84%), (ii) 200 m to 400 m (21.39%), (iii) 400 m to 600 m (6.54%), (iv) 600 m to 800 m (3.92%), and (v) more than 800 m (4.82%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg). The time stability of soil moisture is lowest in the gentle slope area compared to the moderate and steep slopes (Cai et al., 2019). Based on the variation of the slope, the study area was divided into five (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) categories. They are (i) less than 2⁰ (53.59%), (ii) 2⁰ to 5⁰ (14.55%), (iii) 5⁰ to 10⁰ (7.87%), (iv) 10⁰ to 20⁰ (13.43%), and (v) more than 20⁰ (10.55%). A higher slope (More than 20⁰) is found in mountain areas in the northeast, southeast, and northwest parts of the study area (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eh). Both gentle slope (less than 2⁰) and lower altitude (less than 200 m) are located in the middle part of the basin area. Elevation and slope parameters shared a total FR index of 3.27 and 4.16 in the FR model respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In general, soil moisture varies by distance from the river (Horvath, 2002). Soil situated near the river is characterised by higher moisture than soils located at a distance from the river (Kumar et al., 2016). Five different buffer areas were generated through proximity analysis from the river to determine the effect of river on the soil moisture, such as (i) less than 200 m (24.62%), (ii) 200 m to 400 m (14.04%), (iii) 400 m to 600 m (10.85%), (iv) 600 m to 800 m (8.40%), and (v) more than 800 m (42.09%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConditioning factors used for estimation of soil moisture (SM) through FR model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass name or Description\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHistogram\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e% of Histogram\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePotential SM points\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e% of Potential SM points\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFrequency ratio (FR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTotal FR index\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eLand use and Land cover\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDense Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003e7.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow dense forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e710143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShrub land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e521832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOutcrop/barren lands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMountain grassland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e421354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban and built-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInland water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRiver water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e140449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgriculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eSoil Texture\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSilty clay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003e9.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSandy loam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e176212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSandy clay loam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e627064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSilty clay loam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e406699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSilty loam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSandy clay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e555087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLoamy sand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eNormalized Differential Vegetation Index (NDVI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess than 0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e406280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e5.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1\u0026ndash;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e264815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.15\u0026ndash;0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e373813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.30\u0026ndash;0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e638820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMore than 0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e323342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eLand Surface Temperature (LST) degree C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess than 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e4.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e308266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1233168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e65.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u0026ndash;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e341812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMore than 35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eTopographic Wetness Index (TWI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess than 5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e4.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.0\u0026ndash;7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e771982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.5\u0026ndash;10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e764626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.0\u0026ndash;12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e182526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMore than 12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e198978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eRainfall in mm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess than 1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e4.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1500\u0026ndash;2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e292094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2000\u0026ndash;2500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1146286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e65.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2500\u0026ndash;3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e205126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMore than 3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e346574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eElevation in m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess than 200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1281891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e3.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200\u0026ndash;400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e429324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e400\u0026ndash;600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e131360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e600\u0026ndash;800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMore than 800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eSlope in degree\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess than 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1075600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e4.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e292084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e157971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e269603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMore than 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e211812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eDistance from river in m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess than 200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e494112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e5.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200\u0026ndash;400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e281756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e400\u0026ndash;600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e217794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e600\u0026ndash;800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e168666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMore than 800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e844742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe rating was assigned to each sub-class of all the nine conditioning parameters based on the FR value presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The FR value is ranged from 0 to 1.57. FR value of more than 1 indicates a strong correlation to the soil moisture event, whereas less than 1 refers to a weak correlation (Sarkar and Mondal 2020). The total FR index was calculated for each parameter after aggregation of all FR values sponsored by individual sub-classes. Finally, the overlay analysis was performed after integrating all the parameters with their FR characteristics based on Eq.\u0026nbsp;9, and the high-resolution (30 m spatial resolution) soil moisture index database was developed. The soil moisture index value is ranged from 4.57 to 11.61 with an average index value of 9.01 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). A higher soil moisture index value indicates wet soil and a lower index indicates dry soil. The resulting soil moisture index database was further reclassified into five (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) soil groups based on surface soil moisture index. They are (i) very low moisture (less than 6.0), (ii) low moisture (6.0 to 7.0), (iii) moderate moisture (7.0 to 8.0), (iv) high moisture (8.0 to 9.0), and (v) very high moisture (More than 9.0) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). The result identifies that almost 56.89% of the land areas are classified as very high moisture, 26.10% as high moisture, 10.18% as moderate, 4.71% as low moisture, and 2.12% as very low moisture (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The high-moisture soil and very high moisture soil classes (relatively wet soil classes) are identified in the middle section of the study area, where the Markham River and the surrounding floodplain area are located. These areas are enriched with higher moisture content because of higher topographic wetness index, lower elevation, and flat slopes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Shrubland, sandy clay loam, lower NDVI, moderate surface temperature, and proximity to the river are the other dominant characteristics that caused higher surface soil moisture in the lower part of the Markham Valley and surrounding areas.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSpatial distribution of soil moisture zones in the study area\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass no.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoil moisture class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFR index range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHistogram\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e% Area\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLess than 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u0026ndash;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e94519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u0026ndash;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e204339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u0026ndash;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e523870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery high\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMore than 9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1141881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e56.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe purpose of the study was to estimate surface high-resolution soil moisture in the final catchment of the Markham River basin through the FR statistical approach. Although the reference point datasets were generated from 9 km SMAP level-4 data through fishnet analysis, the FR model generates a high-resolution spatial surface soil moisture database at a spatial resolution of 30 meters. A statistical spatial interpolation process can predict the soil moisture at unknown locations using known soil moisture information (Srivastava et al. 2019), but the critical or location variation can\u0026rsquo;t be incorporated into the prediction. So, the FR model is an alternative statistical method was selected for this study (Snepvangers et al. 2003). The topographic slope, elevation, topographic wetness index (TWI), and land surface temperature (LST) were measured statistically using linear trend line analysis (R-squared) to determine how well they fit the regression model (Shaw et al. 2023). The coefficient of determination (R-squared) values for topographic slope, elevation, TWI, and LST were determined to be 0.669, 0.4593, 0.2823, and 0.2694, respectively. The linear regression analysis identifies topographic slope has the maximum impact on soil moisture determination, followed by topographic height, TWI, and LST. Accuracy and success rate are essential to validate model-based estimation of soil moisture (Delgoda et al. 2016; Chi et al. 2019). The high-resolution estimated soil moisture index database was validated through prediction accuracy and success rate. The success rate of the FR model was calculated using several successful input reference points (207) over the total number of input reference points (226) with known soil moisture conditions, which were used as training for the soil moisture estimation. The success rate was computed as 91.59% (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). On the other hand, the prediction accuracy was calculated using 57 reference points, which were not used for the FR modeling. The prediction accuracy of the estimation was calculated as 93.98% (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), which is very good evidence to validate the FR model in the estimation of high-resolution surface soil moisture. The choice of the FR model over the MCDA is the best option for estimating surface soil moisture because the FR model estimates with better efficiency compared to any GIS-based MCDA model (Khosravi 2016; Wang and Li 2017).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrediction accuracy and succession rate for the Estimation of soil moisture\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoil moisture class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFR index range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValidation\u003c/p\u003e \u003cp\u003e[20% flood points]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003cp\u003e(high and very high class)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePrediction\u003c/p\u003e \u003cp\u003eAccuracy %)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTraining\u003c/p\u003e \u003cp\u003e[80% flood points]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSuccess\u003c/p\u003e \u003cp\u003e((high and very high class)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSuccess\u003c/p\u003e \u003cp\u003eRate (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess than 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e93.98%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e91.59%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u0026ndash;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u0026ndash;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u0026ndash;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery high\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMore than 9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe estimation of surface soil moisture was conducted at the spatial resolution of 30 m (pixel size) based on LULC, soil texture NDVI, LST, TWI, rainfall, elevation, slope, and distance from the river. The spatial resolution of all the input conditioning parameters was in 30-meter pixels. It was investigated that soil moisture index distribution depends mostly on the TWI, LST, slope, soil type, proximity to the river, and vegetation. Topographic slope, elevation, TWI, and LST are important independent parameters for soil moisture estimation in selected catchment areas. A total of 283 reference points were selected in a specified interval with known soil moisture conditions. Eighty percent of these reference points (226) were used as inputs and the remaining twenty percent (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e) were kept to validate the FR model estimation. The prediction accuracy of 93.98% validates the FR model prediction as acceptable and realistic. Remote sensing, GIS, and FR models are promising in hydrological research. FR model produces results with better prediction efficiency compared to any other GIS-based MCDA model (Wang and Li 2017; Samanta et al. 2017; Zeleke 2019). These results provide useful data for scientific applications in various domains, specifically in the agricultural sector, local government administrator, researcher, and planner. Soil moisture databases are efficiently used for watershed characterization, water balance studies, soil respiration, hydrology, soil health monitoring, plant growth, plant water stress, and irrigation scheduling. The establishment of ground-based combined models over larger catchment areas is required to generate accurate and precise daily, weekly, or monthly high-resolution soil moisture databases.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompliance with ethical standards\u003c/h2\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConflict of interest\u003c/strong\u003e \u003cp\u003eThe authors declare that there is no conflict of interest for the publication of this article.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNo funding was received to conduct this research\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThe author confirms sole responsibility for the following: study conceptualization, methodology, formal analysis, data collection, analysis and interpretation of results, and manuscript preparation.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe author is thankful to the Papua New Guinea University of Technology and to the School of Surveying and Land Studies for all the facilities made available and availed for the work as a researcher.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is provided within the manuscript\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbrams M, Crippen R, Fujisada H (2020) ASTER global digital elevation model (GDEM) and ASTER global water body dataset (ASTWBD). 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IEEE. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1109/IITAW.2009.52\u003c/span\u003e\u003cspan address=\"10.1109/IITAW.2009.52\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Soil moisture, Frequency ratio, High-resolution, Remote sensing, Geographic information system, Markham River","lastPublishedDoi":"10.21203/rs.3.rs-4626766/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4626766/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis research established an empirical methodology for estimating higher-resolution soil moisture using GIS and frequency ratio (FR) modeling techniques. Soil moisture active passive (SMAP) Level-4 global 3-hourly 9 km spatial resolution surface and root zone soil moisture datasets were used as reference data. A total of 283 reference points were selected through spatial fishnet analysis with the root zone soil moisture over 0.35 and surface soil moisture over 0.30. Eighty percent (80%) of these reference points served as inputs to the FR model, with the remaining twenty percent (20%) reserved for validation. Key independent variables incorporated in the FR modeling process included land use land cover, soil texture, normalized difference vegetation index, land surface temperature, topographic wetness index, rainfall, elevation, slope, and distance from rivers. The study area encompassed the final drainage basin of the Markham River catchment, situated in the Morobe Province of Papua New Guinea. The high-resolution developed database on surface soil moisture was reclassified into five basic zones segmenting on the FR index value, namely very low (less than 6), low (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), moderate (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), high (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), and very high (More than 9). The result indicates almost 26.10% of the land area is classified as a high soil moisture class and 56.89% as a very high soil moisture class. The FR model evinced a prediction accuracy of 93.98% along with a succession rate of 91.59%. These results provide useful data for scientific applications in various domains, specifically in the agricultural sector, local government administrator, researcher, and planner.\u003c/p\u003e","manuscriptTitle":"Estimation of high-resolution surface soil moisture through GIS-based frequency ratio modeling","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-23 17:29:32","doi":"10.21203/rs.3.rs-4626766/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"55abb572-8b3b-4e69-9af1-1ec901051364","owner":[],"postedDate":"July 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-11T04:08:38+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-23 17:29:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4626766","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4626766","identity":"rs-4626766","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}