Change detection analysis of surface water bodies using pixel-based model in Jammu district, India

Change detection analysis of surface water bodies using pixel-based model in Jammu district, India | 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 Change detection analysis of surface water bodies using pixel-based model in Jammu district, India Rashid Latief Bhatt, Shashi Prabha, Mohammad Aithsham, Priyanka Dogra This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6920200/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 16 You are reading this latest preprint version Abstract This study investigates the spatiotemporal dynamics of surface water bodies in Jammu District, India, from 2000 to 2020, utilizing a pixel-based model with Landsat time-series data. The research employs the Normalized Difference Water Index (NDWI) and the Automated Water Extraction Index (AWEI) to quantify changes in water body extent, revealing a significant decline across the district. Approximately 18–20 km² of water bodies were lost, primarily due to rapid urbanization, intensified agricultural practices, and climatic variability, including altered precipitation patterns. In contrast, only 12–13 km² of new water bodies were accreted, indicating limited expansion. Notable fragmentation and desiccation were observed, particularly in the northern and central regions, where ponds, lakes, and riverine networks have diminished, highlighting a critical hydrological imbalance. Both NDWI and AWEI demonstrated high accuracy (above 85%), with AWEI excelling in urban and shadowed environments due to its multi-band approach. Spatial distribution maps and temporal change detection highlight a consistent reduction in water body coverage, driven by anthropogenic pressures such as groundwater over-extraction, pollution, and land-use changes, alongside natural factors like reduced monsoon intensity. These findings underscore the urgent need for integrated water resource management and sustainable land-use planning to mitigate further degradation. By providing a detailed assessment of water body changes in a semi-arid region, this study offers critical insights for policymakers and lays the groundwork for geographically targeted conservation strategies to preserve Jammu’s hydrological balance. Surface water AWEI NDWI water bodies change detection urbanization Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction The hydrological system and human life depend on the many water bodies found on the earth's surface, including lakes, ponds, rivers, and reservoirs" (Jawak et al., 2015 ; Chave, 2001 ; Acharya et al., 2016 ; Ahmmad et al., 2020). "The survival of humans and society depends on surface water as a natural resource" (Ridd and Liu, 1998 ; Rokni et al., 2014 ). "Intensified farming (Haack 1996 ) and rapid urbanization (Du et al., 2016 ) have led to the depletion and deterioration of surface water bodies," which has had a significant effect on the local climate, quality of life, and public health (Gober et al., 2009 ). Therefore, extraction and identification of surface water is necessary in various aspects such as for the policy formulation, (Gulcan Sarp and Mehmet 2016 ) estimation of water zones (Acharya et al., 2016 ; Rover et al., 2012 ; Alsdorf et al., 2007 ), flood monitoring regions; (Jain et al., 2005 ; Chignell et al., 2015 ), wetland inventory levels; (Rebelo et al., 2009 ; Ozesmi and Bauer, 2014), change detection dynamics ( Rokni et al., 2014 ; Du et al., 2012 ), sustainable management and monitoring of natural resources (Hassan et al., 2016 ). Remote sensing technology is crucial for measuring and monitoring surface water. Readily accessible high-spatial resolution optical satellite data, (Pekel et al., 2016 ; Yang et al., 2018) such as Landsat images” (Tulbure and Broich, 2013 ; Singh et al., 2015 ; Acharya et al., 2016 ) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images are particularly essential (Sivanpillai and Miller, 2010 ; Zhou et al., 2014 ; Yang et al., 2018). However, the Landsat series has been used extensively for determining water bodies ( Rover et al., 2012 ; Alsdorf et al., 2007 ). Many image processing algorithms have been developed for obtaining water features from satellite data, which include single-band, multi-band, and classification methods. Earlier studies suggest that surface water change detection involves extracting individual water features from multi-temporal satellite data, analysing them, and then comparing them to identify changes (Alesheikh et al., 2007 ; Du et al., 2012 ; El-Asmar and Hereher, 2010 ; López-Caloca et al., 2008; Zhang et al., 2009; Rokni et al., 2014 ). Several multiband water index methods are widely used, including Normalized Differential Water Index NDWI (McFeeters, 1996 ), Modified Normalized Differential Water Index MNDWI (Xu, 2006 ), and Automated Water Extraction Index AWEI (Feyisa et al., 2013, T. D. Acharya et al., 2019 )”. The Normalized Difference Water Index (NDWI) is dependent on to water characteristics obtained from Landsat TM (McFeeters, 1996 , Ali et al., 2019 ). “NDWI is also dependent upon built-up regions and often exaggerates water bodies in metropolitan settings” (Huang et al., 2015 , Yang et al., 201 After examined NDWI, MNDWI, NDMI, WRI, NDVI, and AWEI for obtaining surface water from Landsat data and used an entirely new surface water change detection process based on the principal elements of multi-temporal NDWI (Xu, 2006 b). This study used the pixel-level combination of multitemporal satellite images approach for detecting surface water changes. Pixel-level image fusion, also known as Pan sharpening, involves combining multiple images of a scene to create one integrated image that is more informative than the individual images (Pohl and Van Genderen, 1998 ). Pixel-based models have been used in several studies to analyse surface water bodies for change in a variety of locations, including India (Kumar et al., 2018 ; Patel et al., 2019 ). For instance, Kumar et al. ( 2018 ) examined changes in surface water bodies in the Chenab River basin using a pixel-based model. The study discovered that human activity and climate change had significantly altered the extent of surface water bodies. In a similar vein, Patel et al. ( 2019 ) examined changes in surface water bodies in the Narmada River basin in India using a pixel-based model. Jammu District, located in the Indian union territory of Jammu and Kashmir, is home to several significant surface water bodies, including the Tawi River and the Ranbir Canal (Kumar et al., 2018 )”. However, these water bodies are facing significant threats, including pollution, sedimentation, and changes in water levels. According to a recent article in The Hindu, "Jammu's water bodies face threat of pollution, encroachment" (The Hindu, 2020 ). The article highlights the alarming rate of pollution in Jammu's water bodies, citing the discharge of untreated sewage and industrial effluents as major contributors. Similarly, an article in The Times of India reports on the pollution of surface water bodies in Jammu, stating that "Pollution choking Jammu's rivers" (The Times of India, 2020 ). The article attributes the pollution to the lack of effective waste management systems and the indiscriminate dumping of waste into water bodies. A report by the Indian Express highlights the decline of water bodies in Jammu and Kashmir, stating that "Jammu and Kashmir's water bodies have declined by 50% in the past two decades" (Indian Express, 2020 ). The report cites climate change, human activity, and climate change have significantly altered the depth and extent of surface water bodies... factors contributing to the decline. Existing research on surface water in Jammu District has predominantly focused on the water quality of major rivers, particularly the Tawi River, with studies highlighting issues such as pollution from untreated sewage and industrial effluents (e.g., The Hindu, 2020 ; The Times of India, 2020 ). However, the change detection of smaller water bodies, such as ponds and lakes, has been largely overlooked in the geographic domain, especially within Jammu’s diverse topography and land-use patterns. These smaller water bodies are vital for local ecosystems, groundwater recharge, and community water security but are often excluded from large-scale analyses due to their size and fragmented distribution. Furthermore, there is a lack of localized studies integrating high-resolution satellite imagery with socio-economic data to explore micro-level drivers of water body decline, such as urban encroachment, groundwater over-extraction, or changes in agricultural practices. Comparative evaluations of NDWI and AWEI in semi-arid, urbanizing regions with mixed irrigation and rainfed agriculture are also limited. This study addresses these gaps by focusing on district-level change detection, including smaller water bodies, and emphasizes the need for future research to utilize finer-scale imagery and ground-level socio-economic data to develop precise, geographically targeted interventions for sustainable water management in Jammu District The present study aims to (i) analyse the status of water bodies in the study (ii) Examine the performance of the most frequently employed water indices using Landsat time series data to observe surface water variation between 2000 to 2020. These findings will help us better comprehend the changes occurred in the water bodies in Jammu district. Study area Jammu is situated between longitudes 74° 24' and 75° 18' East and latitudes 32° 50' and 33° 30' North. It lies approximately 650 kilometres from New Delhi, accessible via a National Highway (Sudan, S., 2015 . To the north and northeast, Jammu borders the Tehsil of Reasi district; to the east and southeast, it adjoins the Tehsil of Ramnagar, also in Udhampur district. To the south, it is bordered by the Tehsil of Billawar in the Kathua district, which also borders Jammu to the north. The district can be classified into two distinct regions. The Kandi area, located north of the Jammu-Chhamb Road and north of the Jammu-Pathankot route, is characterized by its insufficient and predominantly rainfed conditions. In contrast, the land situated south of these routes benefits from more robust irrigation infrastructure, primarily consisting of canals and tube wells (CGWB, 2019). Jammu district spans an area of 3165 square kilometres, including 1165 square kilometres of hilly terrain and 2000 square kilometres of adjoining plains, encompassing the Kandi and Sirowal belts. With a population of approximately 1.588 million, Jammu district is located within the semi-arid Shivalik’s piedmont zone (Sharma et al., 2016 ). The district is administratively divided into eight blocks, which exhibit varying irrigation methods some are irrigated through canals and sub-surface systems, while others rely on rainfed agriculture. Consequently, agricultural practices and crop cultivation are unevenly distributed across the region. Data acquisition and image processing The study area requires only one image. (Path/row-149/037). Images were from the years 2000 to 2020 at 10-year intervals. Landsat 5 TM and Landsat 8 OLI were used in the study area. The Landsat 5 TM and Landsat 8 OLI images were retrieved from the USGS Earth Resources Observation Systems Data Centre (Rokni et al., 2014 ; Sekertekin et al., 2017). The images from the same month were chosen to coincide with the same vegetation season. The analysis included all visible and infrared bands, excluding thermal infrared. All images have been shown at UTM Zone 43N. The Landsat images were processed employing ArcGIS 10.3 (Sekertekin et al., 2017; Ahmmad et al., 2020). The present paper uses Thematic Mapper (TM) and Operational Land Imager (OLI) sensors to examine water body detection in the Jammu district of India. We used bands 1,2,4,5 and 7 for Thematic Mapper (TM) and 2,3,5,6 and 7 for Operational Land Imager (OLI) sensors (Rokni et al., 2014 ; Sekertekin et al., 2017 Ahmmad et al., 2020). The Data collection date and resolution of satellite sensors used in the paper are shown in the Table 1 . Table 1 Dates of Landsat TM and Landsat OLI satellite image with spatial resolution Sensor Path/Row Date captured Spatial resolution Total no. of bands No. of bands used Landsat-5 TM 149/037 24 Feb 2000 30m 7 1, 2, 4, 5 and 7 Landsat-5 TM 149/037 03 Feb 2010 30m 7 1, 2, 4, 5 and 7 Landsat-8 OLI 149/037 15 Feb 2020 30m 7 2, 3, 5, 6 and 7 Methodology Water Indices Normalized Difference Water Index (NDWI) NDWI = \(\:\frac{\left({{\rho\:}}_{\left\{\text{g}\text{r}\text{e}\text{e}\text{n}\right\}}-\:{{\rho\:}}_{\left\{\text{N}\text{I}\text{R}\right\}}\right)}{\left({{\rho\:}}_{\left\{\text{g}\text{r}\text{e}\text{e}\text{n}\right\}}+\:{{\rho\:}}_{\left\{\text{N}\text{I}\text{R}\right\}}\right)}\) …… Eq. (1) Automated Water Extraction Index (AWEI) The Automated Water Extraction Index (AWEI) [Eq. (2)] was first proposed by Feyisa, Meinhardt, and Buchroithner (2014) as a novel technique for mapping surface water using Landsat Satellite imagery. The index was introduced to address the drawbacks of previously used water indices by minimizing interference of surface water bodies with shadows and dark surfaces, offering improved accuracy in both shadowed (AWEI sh ) and non-shadowed areas (AWEI nsh ). AWEI sh = \(\:{{\rho\:}}_{\left\{\text{B}\text{L}\text{U}\text{E}\right\}}+\:2.5\:\times\:\:{{\rho\:}}_{\left\{\text{G}\text{R}\text{E}\text{E}\text{N}\right\}}-\:1.5\:\times\:\:\left({{\rho\:}}_{\left\{\text{N}\text{I}\text{R}\right\}}+\:{{\rho\:}}_{\left\{\text{S}\text{W}\text{I}\text{R}1\right\}}\right)-\:0.25\:\times\:\:{{\rho\:}}_{\left\{\text{S}\text{W}\text{I}\text{R}2\right\}}\) ... Eq. (2) Binary classification based on reclassified Pixel model Initially, NDWI (Normalized Difference Water Index) maps were processed utilizing ArcGIS toolboxes. The subsequent phase involved reclassifying these maps using Spatial Analyst tools. The reclassification resulted in two distinct classes: water and non-water, based on threshold values ranging from − 1 to + 1. To identify water bodies, a binary classification approach was applied where pixels were reclassified as follows: ≤0 were categorized as non-water (0), and > 0 were classified as water (1). Similarly, AWEI (Automated Water Extraction Index) maps were generated using the ArcGIS Toolbox. The methodology followed the same pattern, involving raster calculations and reclassification into binary classes: water (1) and non-water (0). A consistent threshold was applied, classifying pixel values of ≤ 0 as non-water and values greater than 0 as water bodies. Subsequently, the binary classified NDWI and AWEI maps, which delineated detected water bodies for the years 2000, 2010, and 2020, were converted from raster to polygon format using conversion tools Calculation of area of each pixel based binary classes for water and non- water areas The analysis of water bodies was conducted using the geoprocessing toolbar, which facilitated the intersection of detected water features. This process generated fields representing various transitions: water to non-water, water to water, non-water to water, and non-water to non-water. Each of these fields was accompanied by calculations of the proportional change in area over three-time intervals: from 2000 to 2010, from 2010 to 2020, and from 2000 to 2020. Specifically, the transition from water to non-water was categorized as "Abolished Water", while the transition from non-water to water was labelled as "Accreted Water". Result and discussion Landsat TM imagery from 2000 and 2010, as well as Landsat OLI imagery from 2020, were used in this study. A pixel-based model was employed to identify and analyse water bodies. Surface water bodies were identified using the Normalized Difference Water Index (NDWI) and Automated Water Extraction Index (AWEI) obtained from Landsat satellite images. NDWI is generally used to monitor and identify minor changes in the water content of water bodies. NDWI may increase water bodies in a satellite picture by using the NIR (near-infrared) and green spectral bands (McFeeters, 1996 , Deoli et al., 2022 ). AWEI is a method that improves water extraction accuracy by increasing spectral difference between water and non-water surfaces, particularly in areas with shadows and urban influence that are often major causes of low classification accuracy (Feyisa et al., 2014 ). The images have been classified into two categories: water and non-water objects. NDWI values were positive in water areas, but negative in vegetation and urban areas (McFeeters, 1996 ). For AWEI, the binary classes were achieved utilizing multiple spectral bands (blue, green, NIR, SWIR1 and SWIR2) and stabilizing a threshold of zero which is used to distinguish non water and water pixels by aggregating non-water pixels below 0 (≤ 0) and water pixels above 0 (> 0). Tables 2 and 3 depicts changes in surface water bodies in India's Jammu district over twenty years, 2000 to 2020. NDWI and AWEI findings indicate a small decline in the number of surface water bodies in Jammu district during this period. Spatial Distribution and Extent of Water Bodies The NDWI and AWEI results shows that water bodies in 2000 were more evenly distributed over the study area with a larger density in the northern and central sections as shown Fig. 3 (d, e, f) and Fig. 4 (d,e,f). The geographical expanse indicates the presence of several ponds, lakes, and potentially riverine networks. By 2010, the size of water bodies had shrunk notably, as indicated by both the water detection indices, in the north with fewer and smaller water bodies as compared to the year 2000, reflecting the fragmentation, erosion and decline in size. This decrease may suggest a lack of connection in surface water systems, resulting in the desiccation of smaller ponds and lakes. The decreased extent might reflect changes in the pattern of precipitation. The 2020 map indicates continuing reduction of water bodies, with many previously wet places now seeming dry. The considerable drop in water bodies during the 20-year period indicates a potentially dangerous hydrological imbalance. Water body fragmentation may be caused by increased water extraction for agriculture or development, and a reduction in wetland habitats. Temporal Change Detection There has been a noticeable decrease in the number of water bodies between 2000 and 2010, especially in the district's northern and centre areas, as indicated by both the water detection indices. The alterations point to a tendency land use changes that affect the retention of surface water. The temporal study points to a potential shift in the hydrological regime of the district, most likely because of altered patterns of land use, such as urbanization, deforestation, or intensification of agriculture, which reduces the retention of surface water. Water bodies continue to decline from 2010 to 2020, resulting in a major loss of coverage. The continuous drop in water bodies might be related to increased climatic variability, such as longer dry periods or lower monsoon strength. Furthermore, human activities such as groundwater over-extraction, dam construction, or changes in drainage patterns might have contributed to the observed trend. Table 2 Change detection (NDWI) in area (in Sq. Kms) Change Detection Years 2000–2010 2010–2020 2000–2020 Non-water to Non-Water 2357.13783 2360.266243 2359.012984 Water - Non-Water 16.76205541 16.7773838 18.09022362 Non-Water - Water 14.76313585 13.6268242 12.94203726 Water to Water 20.81979926 18.80330803 19.48587439 Table 3 Change detection (AWEI) in area (in Sq. Kms) Change Detection Years 2000–2010 2010–2020 2000–2020 Non-water to Non-Water 2361.491081 2368.26572 2355.680495 Water to Non-water 12.469069 10.751307 20.031582 Non-water to Water 17.592138 15.003978 13.41319 Water to Water 16.657714 15.500092 19.100642 Analysis of the Change Detection Data (2000–2020) Table 4 and Table 5 depicts land cover changes, calculated each for NDWI and AWEI indices, with an emphasis on transitions between water and non-water regions throughout three time periods: 2000–2010, 2010–2020, and 2000–2020. Derived results of NDWI calculations indicated that from 2000–2010, the Non-Water to Non-Water area was 2357.13783 km², whereas from 2010–2020 it was 2360.266243 km². From 2000–2020, it was 2359.012984 km². The AWEI results indicate nearly identical area figures for Non-Water to Non-Water transitions: 2361.491081 km² for the period 2000–2010, 2368.265720 km² for 2010–2020, and 2355.680495 km² for the entire period from 2000–2020. These values are slightly similar to those calculated using the NDWI. The Stability of Non-Water Areas and the high values over all three time periods suggest that a significant portion of Jammu District's land area has remained non-water (i.e., land) across time. This shows that these places have generally consistent land cover, possibly comprising of agricultural fields, urban areas, forests, or barren lands that have not changed much in surface water presence. The little alterations in the data indicate that land usage in these locations has remained stable, with little conversion to water-bodies. Stability in non-water locations may imply effective management or inherent resistance to change, such as consistent farming methods or continuous dry circumstances. Water to Water (Unchanged/Common) ratio, as per NDWI index of surface water detection, was 20.81979926 km³ between 2000 and 2010; this slightly decreased to 18.80330803 km³ between 2010 and 2020, whereas the overall trend for the same time between 2000 and 2020, was 19.48587439 km². In contrast to the NDWI-derived values, the AWEI index yielded slightly different estimates for the respective land cover transitions. For Water to Water transitions, the AWEI-calculated areas were 16.657714 km² for 2000–2010, 15.500092 km² for 2010–2020, and 19.100642 km² for the entire 2000–2020 period. Similarly, for Water to Non-Water transitions, the AWEI results indicated 12.469069 km² for 2000–2010, 10.751307 km² for 2010–2020, and 20.031582 km² for 2000–2020. These figures demonstrate slight variations when compared to those obtained using the NDWI index. According to the statistics, several bodies of water have not altered considerably over time It is important to note that NDWI and AWEI utilize different spectral bands of satellite imagery, which accounts for the observed discrepancies. Nonetheless, the data calculated for each change detection category was nearly identical for the 2000–2020 period, likely due to the enhanced quality and resolution of satellite imagery available in 2020. The data reveals a predominant stability in non-water areas, with minimal expansion of water bodies and a steady conversion of existing water bodies to non-water areas. The changes suggest localized environmental and anthropogenic influences that have led to a gradual reduction in water bodies. The Fig. 5 provides quantitative insights into the changes in water bodies in Jammu District from 2000 to 2020. By combining the analysis of the NDWI and AWEI maps with the Table 2 and 3 , we can gain a deeper understanding of the spatial and temporal dynamics of water bodies. The data retrieved from NDWI analysis indicates that approximately 18.09 square kilometres of water bodies were abolished during the 2000–2020 period .The abolished areas are marked in yellow as shown in the Fig. 6 (a, b, c). Whereas the AWEI analysis indicates approximately 20 square kilometres of water bodies eroded or abolished during the 2000–2020 period. The same has been indicated in Fig. 7 (d, e, f) in which the abolished areas are marked in red. These areas are likely dispersed throughout the district, with a possible concentration near urban centres or agricultural zones, where human activities tend to be more intense. Table 4 Change detection (NDWI) in water area (in Sq. Kms) Period Water area abolished Water area accreted Water Area unchanged 2000–2010 16.76205541 14.76313585 20.81979926 2010–2020 16.7773838 13.6268242 18.80330803 2000–2020 18.09022362 12.94203726 19.48587439 Table 5 Change detection (AWEI) in water area (in Sq. Kms) Period Water area abolished Water area accreted Water Area unchanged 2000–2010 12.469069 17.592138 16.657714 2010–2020 10.751307 15.003978 15.500092 2000–2020 20.031582 13.41319 19.100642 The data derived from the NDWI calculations (Table 4 ) shows that around 12.94 square kilometres of new water bodies were created or expanded during the same period. The accreted areas, also marked in yellow on the second map, as shown in the Fig. 6 (d, e, f). They could be in lower-lying areas or regions with enhanced water retention capabilities. Most of the water bodies, covering about 19.48 square kilometres, remained unchanged during this period. For the data obtained from AWEI analysis (Table 5 ), it can be observed that about 13.41 square kilometres of new water bodies were a created or expanded during the 20 year period. These accreted areas are marked in green as shown in the Fig. 7 (d, e, f). This calculated value of the area of expanded water bodies is nearly equivalent to the area of expanded water bodies calculated by NDWI index. Also a similar data figure can be seen calculated by both the methods for tracing the water area that has remain unchanged.These areas represent stable water bodies that were neither abolished nor expanded. They are crucial for maintaining the region's hydrological balance and likely represent well-established lakes, ponds, or rivers that have not been significantly impacted by human activities or natural changes. The slight overall loss of water bodies (net loss of about 6 km²)as shown in the table is a key finding that reflects both human and natural influences on the landscape. The spatial distribution of abolished and accreted areas highlights the regions most affected by these dynamics. Accuracy assessment Table 6 displays the confusion matrix for seasonal surface water detection, calculated through NDWI method, obtained from the images shown in Fig. 3 . The surface water mapping has an overall accuracy (OA), Kappa coefficient, producer accuracy (PA), and user accuracy (UA) more than 85%. As a result, the approach given in this work is one of the most successful in extracting surface water in the study region. Table 6 Accuracy % of the PBM Model for NDWI Year User Producer Overall Kappa Coefficient 2000 90.2 95.8 93 0.86 2010 90 96 95 0.90 2020 90 97.6 90 0.87 Similarly, Table 7 displays the confusion matrix for surface water detection, calculated through AWEI method of surface water detection, obtained from the images shown in Fig. 6 . The user accuracy for this method has fallen slightly as compared to the previous one, but the overall producer accuracy is improved. This implies a trade-off between commission and omission errors. A lower user accuracy usually indicates more false positives, whereas lower produced accuracy indicates more false negatives. In simple words, a method with higher accuracy is more reliable for applications where you want to be assured that detected water is actually water, whereas the method that has higher produced accuracy is better at not missing any water bodies. This trade-off between commission and omission errors is because the second method we used was specifically designed for calculating surface water in shadowed areas. Table 7 Accuracy % of the PBM Model for AWEI Year User Producer Overall Kappa Coefficient 2000 84 97.8 91 0.88 2010 82 100 91 0.82 2020 88 97.8 93 0.86 Producer Accuracy: For NDWI, producer accuracy has steadily increased throughout the years, reaching its peak in 2020. This shows that the model has grown better at forecasting producer behaviour. Whereas, For AWEI, user accuracy has slightly fallen as compared to the previous method but the producer accuracy is nearly consistent and is significantly better than the previous one. User Accuracy: User accuracy has remained very consistent over the years for NDWI but varied for AWEI peaking in 2020, which might be an outcome of improved satellite sensors in 2020. Overall Accuracy: The overall accuracy has varied, peaking in 2010 for NDWI. For AWEI, the overall accuracy has remained consistent throughout the years. The Kappa coefficient has remained reasonably steady, showing that the model's predictions and actual values are consistently in accord in both of the methods. Over time, the PBM model has improved producer accuracy while maintaining a rather consistent level of user accuracy and overall agreement. Conclusion This study employed a pixel-based model to investigate surface water body changes in Jammu District, India, from 2000 to 2020. Landsat satellite images shows a net loss of around 6 square kilometres of water bodies over the last two decades (USGS, 2020 ). The Normalized Difference Water Index (NDWI) along with the Automated Water Extraction Index (AWEI) indicates a more significant declining trend in the district's centre and northern sections (McFeeters, 1996 ). Human activities like as urbanization (Foley et al., 2005 ) and increasing agricultural water extraction (Postel, 1999 ) have been highlighted as key reasons of this reduction. Climate variability, such as variations in precipitation patterns (IPCC, 2013), also contributed to the reduction of water bodies. The decline of water resources has serious environmental and social consequences for populations who rely on them (Gleick, 1998 ). The pixel-based model detected surface water bodies with excellent accuracy (85%), making it a viable tool for monitoring changes in water resources (Ouma and Tateishi, 2014 ). The study's finding has far-reaching consequences for water resource management and regional sustainability. The depletion of surface water bodies in Jammu District is a serious problem that requires quick response. Our research serves as a wake-up call for the government, communities, and policymakers to pay attention and collaborate on finding effective solutions for managing the water resources. Declarations Funding No funding was received to carry out this study Ethical approval All the ethical standards of research publishing were taken care of during this study Conflict of interest The authors declare no conflict of interest Consent for publication All the authors have read and agreed to publish the manuscript. Author Contribution The first author has contributed to writing the research paperThe corresponding author has contributed to the map preparationThe second author has contributed to supervision and reviewing the paperThe third author has contributed to the introduction, references, and conclusion References Acharya, T. D., Lee, D. H., Yang, I. T., & Lee, J. K. (2016). Identification of water bodies in a Land sat 8 OLI image using a J48 decision tree. Sensors, 16(7), 1075. https://doi.org/10.3390/s16071075 Acharya, T. D., Lee, D. H., Yang, I. T., & Lee, J. K. (2019). Application of water indices in surface water change detection using Landsat imagery in Nepal. Sensors and Materials, 31(5), 1429–1442. https://doi.org/10.18494/SAM.2019.2229 Ahammad, T., Rahaman, H., Faisal, B., & Sultana, N. (2020). Model-based change detection of water body using Landsat imagery: A case study of Rajshahi, Bangladesh. Environment and Natural Resources Journal, 18, 345–355. https://doi.org/10.32526/ennrj.18.4.2020.33 Alesheikh, A. A., Ghorbanali, A., & Nouri, N. (2007). Coastline change detection using remote sensing. International Journal of Environmental Science & Technology, 4, 61–66. Ali, M., Dirawan, G., Hasim, A., & Abidin, M. (2019). Detection of changes in surface water bodies urban area with NDWI and MNDWI methods. International Journal on Advanced Science, Engineering and Information Technology, 9(3), 946–951. https://doi.org/10.18517/ijaseit.9.3.8692 Alsdorf, D. E., Rodríguez, E., & Lettenmaier, D. P. (2007). Measuring surface water from space. Reviews of Geophysics, 45(2). https://doi.org/10.1029/2006RG000197 Chave, P. (2001). The EU Water Framework Directive: An Introduction. IWA Publishing. Chignell, S. M., Zhu, Z., McCarty, J. L., Huang, C., & Rowland, J. (2015). Developing a remote sensing based surface water extent product for the conterminous United States. Remote Sensing of Environment, 164, 318–330. https://doi.org/10.1016/j.rse.2015.04.016 Deoli, V., Kumar, D., & Kuriqi, A. (2022). Detection of water spread area changes in eutrophic lake using Land sat data. Sensors, 22(18), 6827. https://doi.org/10.3390/s22186827 Du, Y., Zhang, Y., Ling, F., Wang, Q., Li, W., & Li, X. (2016). Water bodies’ mapping from Sentinel-2 imagery with modified normalized difference water index at 10-m spatial resolution. Remote Sensing, 8(4), 354. https://doi.org/10.3390/rs8040354 Du, Z., Linghu, B., Ling, F., Li, W., Tian, W., Wang, H., ... & Zhang, X. (2012). Estimating surface water area changes using time-series Landsat data in the Qingjiang River Basin, China. Journal of Applied Remote Sensing, 6, 063609. El-Asmar, H. M., & Hereher, M. E. (2010). Change detection of the coastal zone east of the Nile Delta using remote sensing. Environmental Earth Sciences, 62(4), 769–777. https://doi.org/10.1007/s12665-010-0564-9 Feyisa, G. L., Meilby, H., Fensholt, R., & Proud, S. R. (2014). Automated Water Extraction Index: A new technique for surface water mapping using Landsat imagery. Remote Sensing of Environment, 140, 23–35. https://doi.org/10.1016/j.rse.2013.08.029 Foley, J. A., DeFries, R., Asner, G. P., Barford, C., Bonan, G., Carpenter, S. R., ... & Helkowski, J. (2005). Global consequences of land use. Science, 309(5734), 570–574. Gleick, P. H. (1998). Water in crisis: Paths to sustainable water management. In Water in Crisis (pp. 1–12). Oxford University Press. Gober, P., Brazel, A. J., Quay, R., Myint, S. W., Grossman-Clarke, S., Miller, A., & Rossi, S. (2009). Using watered landscapes to manipulate urban heat island effects: How much water will it take to cool Phoenix? Journal of the American Planning Association, 76(1), 109–121. Ground Water Information Booklet Jammu District, Jammu & Kashmir. (2019). Government of India, Central Ground Water Board, Ministry of Water Resources. Gulcan Sarp and Mehmet, 2016. Water body extraction and change detection using time series; A case study of Lake Burdur, Turkey .Environmental Monitoring and assessment ,188(2),105 Hassan et al.,2016 Hassan, Q.K.,Bourque,C.P,A.,&Meng,F.R.(2016).Applications of remote sensing for forest and fire monitoring. Haack, B. (1996). Monitoring wetland changes with remote sensing: An East African example. Environmental Management, 20(3), 411–419. https://doi.org/10.1007/BF01203848 Huang, X., Xie, C., Fang, X., & Zhang, L. (2015). Combining pixel- and object-based machine learning for identification of water-body types from urban high-resolution remote-sensing imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(5), 2097–2110. https://doi.org/10.1109/JSTARS.2015.24207 Indian Express. (2020). Jammu and Kashmir’s water bodies have declined by 50% in the past two decades. Intergovernmental Panel on Climate Change (IPCC). (2013). Climate Change 2013: The Physical Science Basis. Cambridge University Press. Jain, S. K., Singh, R. D., Jain, M. K., & Lohani, A. K. (2005). Delineation of flood-prone areas using remote sensing techniques. Water Resources Management, 19(4), 333–347. Jawak, S. D., Kulkarni, K., & Luis, A. J. (2015). A review on extraction of lakes from remotely sensed optical satellite data with a special focus on cryospheric lakes. Advances in Remote Sensing, 4, 196–213. Kumar, P., Kumar, V., & Sharma, R. (2018). Assessment of water quality of rivers in Jammu District, Jammu and Kashmir, India. Journal of Environmental Science and Health, Part B, 53, 347–355. Laonamsai, J., Julphunthong, P., Saprathet, T., Kimmany, B., Ganchanasuragit, T., Chomcheawchan, P., & Tomun, N. (2023). Utilizing NDWI, MNDWI, SAVI, WRI, and AWEI for estimating erosion and deposition in Ping River in Thailand. Hydrology, 10(3), 70. https://doi.org/10.3390/hydrology10030070 Lu, D., Mausel, P., Brondizio, E., & Moran, E. (2004). Change detection techniques. International Journal of Remote Sensing, 25(9), 2365–2407. McFeeters, S. K. (1996). The use of the normalized difference water index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7), 1425–1432. Millennium Ecosystem Assessment (MEA). (2005). Ecosystems and Human Well-being: Synthesis. Island Press. Ouma, Y. O., & Tateishi, R. (2014). A water index for rapid mapping of shoreline changes in Lake Victoria. International Journal of Remote Sensing, 35(10), 3534–3551. Patel, D. P., Dholakia, M., Naresh, N., & Srivastava, P. K. (2019). Change detection analysis of surface water bodies in the Narmada River basin, India. Journal of Environmental Management, 235, 345–355. Pekel, J. F., Cottam, A., Gorelick, N., & Belward, A. S. (2016). High-resolution mapping of global surface water and its long-term changes. Nature, 540, 418–422. Pohl, C., & Van Genderen, J. L. (1998). Multisensory image fusion in remote sensing: Concepts, methods and applications. International Journal of Remote Sensing, 19(5), 823–854. Postel, S. L. (1999). Pillar of Sand: Can the Irrigation Miracle Last? W.W. Norton & Company. Rebelo, L. M., McCartney, M. P., & Finlayson, C. M. (2009). Wetlands of sub-Saharan Africa: Distribution and contribution to livelihoods. Wetlands Ecology and Management, 18, 557–572. Ridd, M. K., & Liu, J. (1998). A comparison of four algorithms for change detection in an urban environment. Remote Sensing of Environment, 63(2), 95–100. Rokni, K., Ahmad, A., Selamat, A., & Hazini, S. (2014). Water feature extraction and change detection using multitemporal Landsat imagery. Remote Sensing, 6(5), 4173–4189. Rover, J., Ji, L., Wylie, B. K., & Tieszen, L. L. (2012). Establishing water body areal extent trends in Interior Alaska from multi-temporal Landsat data. Remote Sensing Letters, 3(7), 595–604. Sekertekin, A., & Kutoglu, S. H. (2017). Mapping water surface using Landsat 8 OLI imagery and NDWI index: A case study of Lake Beyşehir, Turkey. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-2/W7, 285–289. Sharma, R., Kumar, V., & Kumar, P. (2016). Status of water bodies in Jammu District. Journal of Environmental Sciences, 6(3), 456–463. Singh, A., Kumar, P., & Singh, R. (2015). Change detection in land use/land cover using satellite remote sensing. International Journal of Advanced Remote Sensing and GIS, 4(1), 1230–1242. Sivanpillai, R., & Miller, S. N. (2010). Improvements in mapping water bodies using ASTER data. Ecological Informatics, 5, 73–78. Sudan, S. (2015). Assessment of groundwater quality: A case study of Jammu District. International Journal of Science and Research, 4(1), 2319–7064. The Hindu. (2020). Jammu’s water bodies face threat of pollution, encroachment. The Times of India. (2020). Pollution choking Jammu’s rivers. Tulbure, M. G., & Broich, M. (2013). Spatiotemporal dynamics of surface water bodies using Landsat time-series data from 1999 to 2011. ISPRS Journal of Photogrammetry and Remote Sensing, 79, 44–52. USGS. (2020). Earth Explorer – United States Geological Survey. https://earthexplorer.usgs.gov Vorosmarty, C. J., McIntyre, P. B., Gessner, M. O., Dudgeon, D., Prusevich, A., Green, P., ... & Davies, P. M. (2010). Global threats to human water security and river biodiversity. Nature, 467, 555–561. Xu, H. (2006). Modification of normalized difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14), 3025–3033. Zhang, Y., Gao, J., & Wang, J. (2007). Detailed mapping of a salt farm from Land sat TM imagery using neural network and maximum likelihood classifiers: A comparison. International Journal of Remote Sensing, 28(10), 2077–2089. Zhou, Y., Zhang, X., Li, W., & Yu, L. (2014). Monitoring and modelling water quality using remote sensing and GIS: A case study of the Han River in Wuhan, China. Environmental Monitoring and Assessment, 186, 353–366. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 03 Sep, 2025 Reviews received at journal 20 Aug, 2025 Reviews received at journal 16 Aug, 2025 Reviews received at journal 12 Aug, 2025 Reviews received at journal 11 Aug, 2025 Reviewers agreed at journal 09 Aug, 2025 Reviewers agreed at journal 07 Aug, 2025 Reviewers agreed at journal 07 Aug, 2025 Reviewers agreed at journal 07 Aug, 2025 Reviewers agreed at journal 07 Aug, 2025 Reviewers agreed at journal 07 Aug, 2025 Reviewers invited by journal 07 Aug, 2025 Editor invited by journal 22 Jul, 2025 Editor assigned by journal 22 Jun, 2025 Submission checks completed at journal 22 Jun, 2025 First submitted to journal 18 Jun, 2025 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6920200","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":498133075,"identity":"a64eb14b-dc1b-40b6-9d2f-4feca024eff6","order_by":0,"name":"Rashid Latief Bhatt","email":"","orcid":"","institution":"University of Jammu","correspondingAuthor":false,"prefix":"","firstName":"Rashid","middleName":"Latief","lastName":"Bhatt","suffix":""},{"id":498133076,"identity":"c53407f8-9950-44c2-a573-ec554393952b","order_by":1,"name":"Shashi Prabha","email":"","orcid":"","institution":"University of Jammu","correspondingAuthor":false,"prefix":"","firstName":"Shashi","middleName":"","lastName":"Prabha","suffix":""},{"id":498133078,"identity":"0a94aa41-94d6-45cc-9ee0-6c80c0969578","order_by":2,"name":"Mohammad Aithsham","email":"data:image/png;base64,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","orcid":"","institution":"University of Jammu","correspondingAuthor":true,"prefix":"","firstName":"Mohammad","middleName":"","lastName":"Aithsham","suffix":""},{"id":498133080,"identity":"9691435e-d4e7-4dba-b520-9d68ba43087d","order_by":3,"name":"Priyanka Dogra","email":"","orcid":"","institution":"University of Jammu","correspondingAuthor":false,"prefix":"","firstName":"Priyanka","middleName":"","lastName":"Dogra","suffix":""}],"badges":[],"createdAt":"2025-06-18 07:23:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6920200/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6920200/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88920663,"identity":"3dc50a6e-c98d-49ef-a132-316142602c34","added_by":"auto","created_at":"2025-08-12 17:14:28","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":155837,"visible":true,"origin":"","legend":"\u003cp\u003eLocation map of the study area\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6920200/v1/6ed05bd7afa81836021a1794.jpg"},{"id":88920664,"identity":"408d28c3-e218-4765-a1e4-b9476988063f","added_by":"auto","created_at":"2025-08-12 17:14:28","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":140633,"visible":true,"origin":"","legend":"\u003cp\u003eOverall flowchart adopted in this study\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6920200/v1/156600af11d4b2f843f3b60a.jpg"},{"id":88921200,"identity":"3e083b16-2218-4c0c-9bff-3fc3a94b08ac","added_by":"auto","created_at":"2025-08-12 17:22:28","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":150307,"visible":true,"origin":"","legend":"\u003cp\u003e(a), (b) and (c) shows the false colour composites (FCC) of the Jammu district in 2000, 2010\u003c/p\u003e\n\u003cp\u003eand 2020. Figures (d), (e), and (f) show the detected water bodies for each year in the Jammu District\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6920200/v1/109d6387ca3f85724c6c2a7c.jpg"},{"id":88922193,"identity":"9f3ec38d-69b9-4e79-a08a-b7d8477513bb","added_by":"auto","created_at":"2025-08-12 17:38:29","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":133393,"visible":true,"origin":"","legend":"\u003cp\u003e(a), (b) and (c) shows the False Colour Composites (FCC) of Jammu district in 2000, 2010 and 2020. (d), (e) and (f) indicates the detected water bodies for each year in Jammu district\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6920200/v1/2469d05501d84d739ae5c4a1.jpg"},{"id":88920667,"identity":"73fab50d-351a-4def-8183-c713f1371e8d","added_by":"auto","created_at":"2025-08-12 17:14:28","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":108951,"visible":true,"origin":"","legend":"\u003cp\u003e(a) and (b) shows the change detection bar graphs for NDWI and AWEI respectively\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6920200/v1/540fd03eaa8eca93eea115fc.jpg"},{"id":88920669,"identity":"715817eb-23e5-4879-bd3d-23cf07283504","added_by":"auto","created_at":"2025-08-12 17:14:28","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":149966,"visible":true,"origin":"","legend":"\u003cp\u003e(a), (b) and (c) indicates the abolished water bodies and (d), (e) and (f) indicates the accreted water bodies of the year 2000, 2010 and 2020 respectively\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6920200/v1/5f3ca340f5a159934e127726.jpg"},{"id":88922252,"identity":"d54a2c49-86c5-4471-b763-37a4b3dc974a","added_by":"auto","created_at":"2025-08-12 17:38:29","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":147431,"visible":true,"origin":"","legend":"\u003cp\u003e(a), (b) and (c) indicates the abolished water bodies and (d), (e) and (f) indicates the accreted water bodies of the year 2000, 2020 and 2020 respectively\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6920200/v1/32e5d45d760b88c21015815a.jpg"},{"id":88921791,"identity":"a60c6885-36fb-4d18-802f-6ea7d6b39ba3","added_by":"auto","created_at":"2025-08-12 17:30:28","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":71995,"visible":true,"origin":"","legend":"\u003cp\u003e(a), (b) shows the status of change detection bar graphs for NDWI and AWEI respectively\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6920200/v1/34417261389458d9a860ce95.jpg"},{"id":88922399,"identity":"494ce91a-d600-4731-99e5-1b8843065e5e","added_by":"auto","created_at":"2025-08-12 17:46:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1935330,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6920200/v1/64716ec5-23a6-4ff8-9fd4-eecf0fccb6d1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Change detection analysis of surface water bodies using pixel-based model in Jammu district, India","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe hydrological system and human life depend on the many water bodies found on the earth's surface, including lakes, ponds, rivers, and reservoirs\" (Jawak et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Chave, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Acharya et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ahmmad et al., 2020). \"The survival of humans and society depends on surface water as a natural resource\" (Ridd and Liu, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Rokni et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). \"Intensified farming (Haack \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) and rapid urbanization (Du et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) have led to the depletion and deterioration of surface water bodies,\" which has had a significant effect on the local climate, quality of life, and public health (Gober et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Therefore, extraction and identification of surface water is necessary in various aspects such as for the policy formulation, (Gulcan Sarp and Mehmet \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) estimation of water zones (Acharya et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Rover et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Alsdorf et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), flood monitoring regions; (Jain et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Chignell et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), wetland inventory levels; (Rebelo et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Ozesmi and Bauer, 2014), change detection dynamics ( Rokni et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Du et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), sustainable management and monitoring of natural resources (Hassan et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Remote sensing technology is crucial for measuring and monitoring surface water. Readily accessible high-spatial resolution optical satellite data, (Pekel et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Yang et al., 2018) such as Landsat images\u0026rdquo; (Tulbure and Broich, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Acharya et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images are particularly essential (Sivanpillai and Miller, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Yang et al., 2018). However, the Landsat series has been used extensively for determining water bodies ( Rover et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Alsdorf et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Many image processing algorithms have been developed for obtaining water features from satellite data, which include single-band, multi-band, and classification methods. Earlier studies suggest that surface water change detection involves extracting individual water features from multi-temporal satellite data, analysing them, and then comparing them to identify changes (Alesheikh et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Du et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; El-Asmar and Hereher, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; L\u0026oacute;pez-Caloca et al., 2008; Zhang et al., 2009; Rokni et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Several multiband water index methods are widely used, including Normalized Differential Water Index NDWI (McFeeters, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1996\u003c/span\u003e), Modified Normalized Differential Water Index MNDWI (Xu, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), and Automated Water Extraction Index AWEI (Feyisa et al., 2013, T. D. Acharya et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u0026rdquo;. The Normalized Difference Water Index (NDWI) is dependent on to water characteristics obtained from Landsat TM (McFeeters, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1996\u003c/span\u003e, Ali et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). \u0026ldquo;NDWI is also dependent upon built-up regions and often exaggerates water bodies in metropolitan settings\u0026rdquo; (Huang et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Yang et al., 201 After examined NDWI, MNDWI, NDMI, WRI, NDVI, and AWEI for obtaining surface water from Landsat data and used an entirely new surface water change detection process based on the principal elements of multi-temporal NDWI (Xu, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2006\u003c/span\u003eb). This study used the pixel-level combination of multitemporal satellite images approach for detecting surface water changes. Pixel-level image fusion, also known as Pan sharpening, involves combining multiple images of a scene to create one integrated image that is more informative than the individual images (Pohl and Van Genderen, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1998\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePixel-based models have been used in several studies to analyse surface water bodies for change in a variety of locations, including India (Kumar et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Patel et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For instance, Kumar et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) examined changes in surface water bodies in the Chenab River basin using a pixel-based model. The study discovered that human activity and climate change had significantly altered the extent of surface water bodies. In a similar vein, Patel et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) examined changes in surface water bodies in the Narmada River basin in India using a pixel-based model. Jammu District, located in the Indian union territory of Jammu and Kashmir, is home to several significant surface water bodies, including the Tawi River and the Ranbir Canal (Kumar et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u0026rdquo;. However, these water bodies are facing significant threats, including pollution, sedimentation, and changes in water levels. According to a recent article in The Hindu, \"Jammu's water bodies face threat of pollution, encroachment\" (The Hindu, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The article highlights the alarming rate of pollution in Jammu's water bodies, citing the discharge of untreated sewage and industrial effluents as major contributors. Similarly, an article in The Times of India reports on the pollution of surface water bodies in Jammu, stating that \"Pollution choking Jammu's rivers\" (The Times of India, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The article attributes the pollution to the lack of effective waste management systems and the indiscriminate dumping of waste into water bodies. A report by the Indian Express highlights the decline of water bodies in Jammu and Kashmir, stating that \"Jammu and Kashmir's water bodies have declined by 50% in the past two decades\" (Indian Express, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The report cites climate change, human activity, and climate change have significantly altered the depth and extent of surface water bodies... factors contributing to the decline. Existing research on surface water in Jammu District has predominantly focused on the water quality of major rivers, particularly the Tawi River, with studies highlighting issues such as pollution from untreated sewage and industrial effluents (e.g., The Hindu, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; The Times of India, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, the change detection of smaller water bodies, such as ponds and lakes, has been largely overlooked in the geographic domain, especially within Jammu\u0026rsquo;s diverse topography and land-use patterns. These smaller water bodies are vital for local ecosystems, groundwater recharge, and community water security but are often excluded from large-scale analyses due to their size and fragmented distribution. Furthermore, there is a lack of localized studies integrating high-resolution satellite imagery with socio-economic data to explore micro-level drivers of water body decline, such as urban encroachment, groundwater over-extraction, or changes in agricultural practices. Comparative evaluations of NDWI and AWEI in semi-arid, urbanizing regions with mixed irrigation and rainfed agriculture are also limited. This study addresses these gaps by focusing on district-level change detection, including smaller water bodies, and emphasizes the need for future research to utilize finer-scale imagery and ground-level socio-economic data to develop precise, geographically targeted interventions for sustainable water management in Jammu District\u003c/p\u003e\u003cp\u003eThe present study aims to (i) analyse the status of water bodies in the study (ii) Examine the performance of the most frequently employed water indices using Landsat time series data to observe surface water variation between 2000 to 2020. These findings will help us better comprehend the changes occurred in the water bodies in Jammu district.\u003c/p\u003e"},{"header":"Study area","content":"\u003cp\u003eJammu is situated between longitudes 74\u0026deg; 24' and 75\u0026deg; 18' East and latitudes 32\u0026deg; 50' and 33\u0026deg; 30' North. It lies approximately 650 kilometres from New Delhi, accessible via a National Highway (Sudan, S., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2015\u003c/span\u003e. To the north and northeast, Jammu borders the Tehsil of Reasi district; to the east and southeast, it adjoins the Tehsil of Ramnagar, also in Udhampur district. To the south, it is bordered by the Tehsil of Billawar in the Kathua district, which also borders Jammu to the north. The district can be classified into two distinct regions. The Kandi area, located north of the Jammu-Chhamb Road and north of the Jammu-Pathankot route, is characterized by its insufficient and predominantly rainfed conditions. In contrast, the land situated south of these routes benefits from more robust irrigation infrastructure, primarily consisting of canals and tube wells (CGWB, 2019).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eJammu district spans an area of 3165 square kilometres, including 1165 square kilometres of hilly terrain and 2000 square kilometres of adjoining plains, encompassing the Kandi and Sirowal belts. With a population of approximately 1.588\u0026nbsp;million, Jammu district is located within the semi-arid Shivalik\u0026rsquo;s piedmont zone (Sharma et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The district is administratively divided into eight blocks, which exhibit varying irrigation methods some are irrigated through canals and sub-surface systems, while others rely on rainfed agriculture. Consequently, agricultural practices and crop cultivation are unevenly distributed across the region.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData acquisition and image processing\u003c/h2\u003e\u003cp\u003eThe study area requires only one image. (Path/row-149/037). Images were from the years 2000 to 2020 at 10-year intervals. Landsat 5 TM and Landsat 8 OLI were used in the study area. The Landsat 5 TM and Landsat 8 OLI images were retrieved from the USGS Earth Resources Observation Systems Data Centre (Rokni et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Sekertekin et al., 2017). The images from the same month were chosen to coincide with the same vegetation season. The analysis included all visible and infrared bands, excluding thermal infrared. All images have been shown at UTM Zone 43N. The Landsat images were processed employing ArcGIS 10.3 (Sekertekin et al., 2017; Ahmmad et al., 2020). The present paper uses Thematic Mapper (TM) and Operational Land Imager (OLI) sensors to examine water body detection in the Jammu district of India. We used bands 1,2,4,5 and 7 for Thematic Mapper (TM) and 2,3,5,6 and 7 for Operational Land Imager (OLI) sensors (Rokni et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Sekertekin et al., 2017 Ahmmad et al., 2020). The Data collection date and resolution of satellite sensors used in the paper are shown in the Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\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\u003eDates of Landsat TM and Landsat OLI satellite image with spatial resolution\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSensor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePath/Row\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDate captured\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSpatial resolution\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTotal no. of bands\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo. of bands used\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLandsat-5 TM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e149/037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24 Feb 2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1, 2, 4, 5 and 7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLandsat-5 TM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e149/037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e03 Feb 2010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1, 2, 4, 5 and 7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLandsat-8 OLI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e149/037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 Feb 2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2, 3, 5, 6 and 7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eWater Indices\u003c/h2\u003e\u003cp\u003eNormalized Difference Water Index (NDWI)\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eNDWI = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\left({{\\rho\\:}}_{\\left\\{\\text{g}\\text{r}\\text{e}\\text{e}\\text{n}\\right\\}}-\\:{{\\rho\\:}}_{\\left\\{\\text{N}\\text{I}\\text{R}\\right\\}}\\right)}{\\left({{\\rho\\:}}_{\\left\\{\\text{g}\\text{r}\\text{e}\\text{e}\\text{n}\\right\\}}+\\:{{\\rho\\:}}_{\\left\\{\\text{N}\\text{I}\\text{R}\\right\\}}\\right)}\\)\u003c/span\u003e\u003c/span\u003e …… Eq.\u0026nbsp;(1)\u003c/p\u003e\u003cp\u003eAutomated Water Extraction Index (AWEI)\u003c/p\u003e\u003cp\u003eThe Automated Water Extraction Index (AWEI) [Eq.\u0026nbsp;(2)] was first proposed by Feyisa, Meinhardt, and Buchroithner (2014) as a novel technique for mapping surface water using Landsat Satellite imagery. The index was introduced to address the drawbacks of previously used water indices by minimizing interference of surface water bodies with shadows and dark surfaces, offering improved accuracy in both shadowed (AWEI\u003csub\u003esh\u003c/sub\u003e) and non-shadowed areas (AWEI\u003csub\u003ensh\u003c/sub\u003e).\u003c/p\u003e\u003cp\u003eAWEI\u003csub\u003esh\u003c/sub\u003e = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\rho\\:}}_{\\left\\{\\text{B}\\text{L}\\text{U}\\text{E}\\right\\}}+\\:2.5\\:\\times\\:\\:{{\\rho\\:}}_{\\left\\{\\text{G}\\text{R}\\text{E}\\text{E}\\text{N}\\right\\}}-\\:1.5\\:\\times\\:\\:\\left({{\\rho\\:}}_{\\left\\{\\text{N}\\text{I}\\text{R}\\right\\}}+\\:{{\\rho\\:}}_{\\left\\{\\text{S}\\text{W}\\text{I}\\text{R}1\\right\\}}\\right)-\\:0.25\\:\\times\\:\\:{{\\rho\\:}}_{\\left\\{\\text{S}\\text{W}\\text{I}\\text{R}2\\right\\}}\\)\u003c/span\u003e\u003c/span\u003e ... Eq.\u0026nbsp;(2)\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eBinary classification based on reclassified Pixel model\u003c/h3\u003e\n\u003cp\u003eInitially, NDWI (Normalized Difference Water Index) maps were processed utilizing ArcGIS toolboxes. The subsequent phase involved reclassifying these maps using Spatial Analyst tools. The reclassification resulted in two distinct classes: water and non-water, based on threshold values ranging from − 1 to + 1. To identify water bodies, a binary classification approach was applied where pixels were reclassified as follows: ≤0 were categorized as non-water (0), and \u0026gt; 0 were classified as water (1). Similarly, AWEI (Automated Water Extraction Index) maps were generated using the ArcGIS Toolbox. The methodology followed the same pattern, involving raster calculations and reclassification into binary classes: water (1) and non-water (0). A consistent threshold was applied, classifying pixel values of ≤ 0 as non-water and values greater than 0 as water bodies.\u003c/p\u003e\u003cp\u003eSubsequently, the binary classified NDWI and AWEI maps, which delineated detected water bodies for the years 2000, 2010, and 2020, were converted from raster to polygon format using conversion tools\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eCalculation of area of each pixel based binary classes for water and non- water areas\u003c/h3\u003e\n\u003cp\u003eThe analysis of water bodies was conducted using the geoprocessing toolbar, which facilitated the intersection of detected water features. This process generated fields representing various transitions: water to non-water, water to water, non-water to water, and non-water to non-water. Each of these fields was accompanied by calculations of the proportional change in area over three-time intervals: from 2000 to 2010, from 2010 to 2020, and from 2000 to 2020. Specifically, the transition from water to non-water was categorized as \"Abolished Water\", while the transition from non-water to water was labelled as \"Accreted Water\".\u003c/p\u003e"},{"header":"Result and discussion","content":"\u003cp\u003eLandsat TM imagery from 2000 and 2010, as well as Landsat OLI imagery from 2020, were used in this study. A pixel-based model was employed to identify and analyse water bodies.\u003c/p\u003e\u003cp\u003eSurface water bodies were identified using the Normalized Difference Water Index (NDWI) and Automated Water Extraction Index (AWEI) obtained from Landsat satellite images. NDWI is generally used to monitor and identify minor changes in the water content of water bodies. NDWI may increase water bodies in a satellite picture by using the NIR (near-infrared) and green spectral bands (McFeeters, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1996\u003c/span\u003e, Deoli et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAWEI is a method that improves water extraction accuracy by increasing spectral difference between water and non-water surfaces, particularly in areas with shadows and urban influence that are often major causes of low classification accuracy (Feyisa et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The images have been classified into two categories: water and non-water objects. NDWI values were positive in water areas, but negative in vegetation and urban areas (McFeeters, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). For AWEI, the binary classes were achieved utilizing multiple spectral bands (blue, green, NIR, SWIR1 and SWIR2) and stabilizing a threshold of zero which is used to distinguish non water and water pixels by aggregating non-water pixels below 0 (≤ 0) and water pixels above 0 (\u0026gt; 0). Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e depicts changes in surface water bodies in India's Jammu district over twenty years, 2000 to 2020. NDWI and AWEI findings indicate a small decline in the number of surface water bodies in Jammu district during this period.\u003c/p\u003e\u003ch3\u003eSpatial Distribution and Extent of Water Bodies\u003c/h3\u003e\u003cp\u003eThe NDWI and AWEI results shows that water bodies in 2000 were more evenly distributed over the study area with a larger density in the northern and central sections as shown Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (d, e, f) and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (d,e,f). The geographical expanse indicates the presence of several ponds, lakes, and potentially riverine networks. By 2010, the size of water bodies had shrunk notably, as indicated by both the water detection indices, in the north with fewer and smaller water bodies as compared to the year 2000, reflecting the fragmentation, erosion and decline in size. This decrease may suggest a lack of connection in surface water systems, resulting in the desiccation of smaller ponds and lakes. The decreased extent might reflect changes in the pattern of precipitation. The 2020 map indicates continuing reduction of water bodies, with many previously wet places now seeming dry. The considerable drop in water bodies during the 20-year period indicates a potentially dangerous hydrological imbalance. Water body fragmentation may be caused by increased water extraction for agriculture or development, and a reduction in wetland habitats.\u003c/p\u003e\u003ch3\u003eTemporal Change Detection\u003c/h3\u003e\u003cp\u003eThere has been a noticeable decrease in the number of water bodies between 2000 and 2010, especially in the district's northern and centre areas, as indicated by both the water detection indices. The alterations point to a tendency land use changes that affect the retention of surface water. The temporal study points to a potential shift in the hydrological regime of the district, most likely because of altered patterns of land use, such as urbanization, deforestation, or intensification of agriculture, which reduces the retention of surface water. Water bodies continue to decline from 2010 to 2020, resulting in a major loss of coverage. The continuous drop in water bodies might be related to increased climatic variability, such as longer dry periods or lower monsoon strength. Furthermore, human activities such as groundwater over-extraction, dam construction, or changes in drainage patterns might have contributed to the observed trend.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\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\u003eChange detection (NDWI) in area (in Sq. Kms)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eChange Detection\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eYears\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2000–2010\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2010–2020\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2000–2020\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-water to Non-Water\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2357.13783\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2360.266243\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2359.012984\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWater - Non-Water\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16.76205541\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16.7773838\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18.09022362\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Water - Water\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14.76313585\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.6268242\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.94203726\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWater to Water\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.81979926\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18.80330803\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19.48587439\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\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\u003eChange detection (AWEI) in area (in Sq. Kms)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eChange Detection\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eYears\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2000–2010\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2010–2020\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2000–2020\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-water to Non-Water\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2361.491081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2368.26572\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2355.680495\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWater to Non-water\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12.469069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.751307\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20.031582\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-water to Water\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17.592138\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.003978\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.41319\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWater to Water\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16.657714\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.500092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19.100642\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003ch2\u003eAnalysis of the Change Detection Data (2000–2020)\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e depicts land cover changes, calculated each for NDWI and AWEI indices, with an emphasis on transitions between water and non-water regions throughout three time periods: 2000–2010, 2010–2020, and 2000–2020. Derived results of NDWI calculations indicated that from 2000–2010, the Non-Water to Non-Water area was 2357.13783 km², whereas from 2010–2020 it was 2360.266243 km². From 2000–2020, it was 2359.012984 km². The AWEI results indicate nearly identical area figures for Non-Water to Non-Water transitions: 2361.491081 km² for the period 2000–2010, 2368.265720 km² for 2010–2020, and 2355.680495 km² for the entire period from 2000–2020. These values are slightly similar to those calculated using the NDWI. The Stability of Non-Water Areas and the high values over all three time periods suggest that a significant portion of Jammu District's land area has remained non-water (i.e., land) across time. This shows that these places have generally consistent land cover, possibly comprising of agricultural fields, urban areas, forests, or barren lands that have not changed much in surface water presence. The little alterations in the data indicate that land usage in these locations has remained stable, with little conversion to water-bodies. Stability in non-water locations may imply effective management or inherent resistance to change, such as consistent farming methods or continuous dry circumstances.\u003c/p\u003e\u003cp\u003eWater to Water (Unchanged/Common) ratio, as per NDWI index of surface water detection, was 20.81979926 km³ between 2000 and 2010; this slightly decreased to 18.80330803 km³ between 2010 and 2020, whereas the overall trend for the same time between 2000 and 2020, was 19.48587439 km². In contrast to the NDWI-derived values, the AWEI index yielded slightly different estimates for the respective land cover transitions. For Water to Water transitions, the AWEI-calculated areas were 16.657714 km² for 2000–2010, 15.500092 km² for 2010–2020, and 19.100642 km² for the entire 2000–2020 period. Similarly, for Water to Non-Water transitions, the AWEI results indicated 12.469069 km² for 2000–2010, 10.751307 km² for 2010–2020, and 20.031582 km² for 2000–2020.\u003c/p\u003e\u003cp\u003eThese figures demonstrate slight variations when compared to those obtained using the NDWI index. According to the statistics, several bodies of water have not altered considerably over time It is important to note that NDWI and AWEI utilize different spectral bands of satellite imagery, which accounts for the observed discrepancies. Nonetheless, the data calculated for each change detection category was nearly identical for the 2000–2020 period, likely due to the enhanced quality and resolution of satellite imagery available in 2020.\u003c/p\u003e\u003cp\u003eThe data reveals a predominant stability in non-water areas, with minimal expansion of water bodies and a steady conversion of existing water bodies to non-water areas. The changes suggest localized environmental and anthropogenic influences that have led to a gradual reduction in water bodies.\u003c/p\u003e\u003cp\u003eThe Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e provides quantitative insights into the changes in water bodies in Jammu District from 2000 to 2020. By combining the analysis of the NDWI and AWEI maps with the Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, we can gain a deeper understanding of the spatial and temporal dynamics of water bodies.\u003c/p\u003e\u003cp\u003eThe data retrieved from NDWI analysis indicates that approximately 18.09 square kilometres of water bodies were abolished during the 2000–2020 period .The abolished areas are marked in yellow as shown in the Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e (a, b, c). Whereas the AWEI analysis indicates approximately 20 square kilometres of water bodies eroded or abolished during the 2000–2020 period. The same has been indicated in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e(d, e, f) in which the abolished areas are marked in red. These areas are likely dispersed throughout the district, with a possible concentration near urban centres or agricultural zones, where human activities tend to be more intense.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eChange detection (NDWI) in water area (in Sq. Kms)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePeriod\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWater area abolished\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWater area accreted\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWater Area unchanged\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2000–2010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16.76205541\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.76313585\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20.81979926\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2010–2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16.7773838\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.6268242\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18.80330803\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2000–2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18.09022362\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.94203726\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19.48587439\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eChange detection (AWEI) in water area (in Sq. Kms)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePeriod\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWater area abolished\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWater area accreted\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWater Area unchanged\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2000–2010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12.469069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17.592138\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16.657714\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2010–2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10.751307\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.003978\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.500092\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2000–2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.031582\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.41319\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19.100642\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eThe data derived from the NDWI calculations (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) shows that around 12.94 square kilometres of new water bodies were created or expanded during the same period. The accreted areas, also marked in yellow on the second map, as shown in the Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e (d, e, f). They could be in lower-lying areas or regions with enhanced water retention capabilities. Most of the water bodies, covering about 19.48 square kilometres, remained unchanged during this period.\u003c/p\u003e\u003cp\u003eFor the data obtained from AWEI analysis (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), it can be observed that about 13.41 square kilometres of new water bodies were a created or expanded during the 20 year period. These accreted areas are marked in green as shown in the Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e (d, e, f). This calculated value of the area of expanded water bodies is nearly equivalent to the area of expanded water bodies calculated by NDWI index. Also a similar data figure can be seen calculated by both the methods for tracing the water area that has remain unchanged.These areas represent stable water bodies that were neither abolished nor expanded. They are crucial for maintaining the region's hydrological balance and likely represent well-established lakes, ponds, or rivers that have not been significantly impacted by human activities or natural changes.\u003c/p\u003e\u003cp\u003eThe slight overall loss of water bodies (net loss of about 6 km²)as shown in the table is a key finding that reflects both human and natural influences on the landscape. The spatial distribution of abolished and accreted areas highlights the regions most affected by these dynamics.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAccuracy assessment\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e displays the confusion matrix for seasonal surface water detection, calculated through NDWI method, obtained from the images shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The surface water mapping has an overall accuracy (OA), Kappa coefficient, producer accuracy (PA), and user accuracy (UA) more than 85%. As a result, the approach given in this work is one of the most successful in extracting surface water in the study region.\u003c/p\u003e\u003cdiv class=\"gridtable\"\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\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAccuracy % of the PBM Model for NDWI\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUser\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eProducer\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eKappa Coefficient\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2000\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e90.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2010\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2020\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e97.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eSimilarly, Table \u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e displays the confusion matrix for surface water detection, calculated through AWEI method of surface water detection, obtained from the images shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The user accuracy for this method has fallen slightly as compared to the previous one, but the overall producer accuracy is improved. This implies a trade-off between commission and omission errors. A lower user accuracy usually indicates more false positives, whereas lower produced accuracy indicates more false negatives. In simple words, a method with higher accuracy is more reliable for applications where you want to be assured that detected water is actually water, whereas the method that has higher produced accuracy is better at not missing any water bodies. This trade-off between commission and omission errors is because the second method we used was specifically designed for calculating surface water in shadowed areas.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAccuracy % of the PBM Model for AWEI\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUser\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eProducer\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eKappa Coefficient\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2000\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e97.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2010\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2020\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e97.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eProducer Accuracy: For NDWI, producer accuracy has steadily increased throughout the years, reaching its peak in 2020. This shows that the model has grown better at forecasting producer behaviour. Whereas, For AWEI, user accuracy has slightly fallen as compared to the previous method but the producer accuracy is nearly consistent and is significantly better than the previous one.\u003c/p\u003e\u003cp\u003eUser Accuracy: User accuracy has remained very consistent over the years for NDWI but varied for AWEI peaking in 2020, which might be an outcome of improved satellite sensors in 2020.\u003c/p\u003e\u003cp\u003eOverall Accuracy: The overall accuracy has varied, peaking in 2010 for NDWI. For AWEI, the overall accuracy has remained consistent throughout the years.\u003c/p\u003e\u003cp\u003eThe Kappa coefficient has remained reasonably steady, showing that the model's predictions and actual values are consistently in accord in both of the methods.\u003c/p\u003e\u003cp\u003eOver time, the PBM model has improved producer accuracy while maintaining a rather consistent level of user accuracy and overall agreement.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study employed a pixel-based model to investigate surface water body changes in Jammu District, India, from 2000 to 2020. Landsat satellite images shows a net loss of around 6 square kilometres of water bodies over the last two decades (USGS, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The Normalized Difference Water Index (NDWI) along with the Automated Water Extraction Index (AWEI) indicates a more significant declining trend in the district's centre and northern sections (McFeeters, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Human activities like as urbanization (Foley et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and increasing agricultural water extraction (Postel, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) have been highlighted as key reasons of this reduction. Climate variability, such as variations in precipitation patterns (IPCC, 2013), also contributed to the reduction of water bodies.\u003c/p\u003e\u003cp\u003eThe decline of water resources has serious environmental and social consequences for populations who rely on them (Gleick, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). The pixel-based model detected surface water bodies with excellent accuracy (85%), making it a viable tool for monitoring changes in water resources (Ouma and Tateishi, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The study's finding has far-reaching consequences for water resource management and regional sustainability. The depletion of surface water bodies in Jammu District is a serious problem that requires quick response. Our research serves as a wake-up call for the government, communities, and policymakers to pay attention and collaborate on finding effective solutions for managing the water resources.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received to carry out this study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the ethical standards of research publishing were taken care of during this study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors have read and agreed to publish the manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThe first author has contributed to writing the research paperThe corresponding author has contributed to the map preparationThe second author has contributed to supervision and reviewing the paperThe third author has contributed to the introduction, references, and conclusion\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAcharya, T. D., Lee, D. H., Yang, I. T., \u0026amp; Lee, J. K. (2016). Identification of water bodies in a Land sat 8 OLI image using a J48 decision tree. Sensors, 16(7), 1075. https://doi.org/10.3390/s16071075\u003c/li\u003e\n\u003cli\u003eAcharya, T. D., Lee, D. H., Yang, I. T., \u0026amp; Lee, J. K. (2019). Application of water indices in surface water change detection using Landsat imagery in Nepal. Sensors and Materials, 31(5), 1429\u0026ndash;1442. https://doi.org/10.18494/SAM.2019.2229\u003c/li\u003e\n\u003cli\u003eAhammad, T., Rahaman, H., Faisal, B., \u0026amp; Sultana, N. (2020). Model-based change detection of water body using Landsat imagery: A case study of Rajshahi, Bangladesh. Environment and Natural Resources Journal, 18, 345\u0026ndash;355. https://doi.org/10.32526/ennrj.18.4.2020.33\u003c/li\u003e\n\u003cli\u003eAlesheikh, A. A., Ghorbanali, A., \u0026amp; Nouri, N. (2007). Coastline change detection using remote sensing. International Journal of Environmental Science \u0026amp; Technology, 4, 61\u0026ndash;66.\u003c/li\u003e\n\u003cli\u003eAli, M., Dirawan, G., Hasim, A., \u0026amp; Abidin, M. (2019). Detection of changes in surface water bodies urban area with NDWI and MNDWI methods. International Journal on Advanced Science, Engineering and Information Technology, 9(3), 946\u0026ndash;951. https://doi.org/10.18517/ijaseit.9.3.8692\u003c/li\u003e\n\u003cli\u003eAlsdorf, D. E., Rodr\u0026iacute;guez, E., \u0026amp; Lettenmaier, D. P. (2007). Measuring surface water from space. Reviews of Geophysics, 45(2). https://doi.org/10.1029/2006RG000197\u003c/li\u003e\n\u003cli\u003eChave, P. (2001). The EU Water Framework Directive: An Introduction. IWA Publishing. \u003c/li\u003e\n\u003cli\u003eChignell, S. M., Zhu, Z., McCarty, J. L., Huang, C., \u0026amp; Rowland, J. (2015). Developing a remote sensing based surface water extent product for the conterminous United States. Remote Sensing of Environment, 164, 318\u0026ndash;330. https://doi.org/10.1016/j.rse.2015.04.016\u003c/li\u003e\n\u003cli\u003eDeoli, V., Kumar, D., \u0026amp; Kuriqi, A. (2022). Detection of water spread area changes in eutrophic lake using Land sat data. Sensors, 22(18), 6827. https://doi.org/10.3390/s22186827\u003c/li\u003e\n\u003cli\u003eDu, Y., Zhang, Y., Ling, F., Wang, Q., Li, W., \u0026amp; Li, X. (2016). Water bodies\u0026rsquo; mapping from Sentinel-2 imagery with modified normalized difference water index at 10-m spatial resolution. Remote Sensing, 8(4), 354. https://doi.org/10.3390/rs8040354\u003c/li\u003e\n\u003cli\u003eDu, Z., Linghu, B., Ling, F., Li, W., Tian, W., Wang, H., ... \u0026amp; Zhang, X. (2012). Estimating surface water area changes using time-series Landsat data in the Qingjiang River Basin, China. Journal of Applied Remote Sensing, 6, 063609.\u003c/li\u003e\n\u003cli\u003eEl-Asmar, H. M., \u0026amp; Hereher, M. E. (2010). Change detection of the coastal zone east of the Nile Delta using remote sensing. Environmental Earth Sciences, 62(4), 769\u0026ndash;777. https://doi.org/10.1007/s12665-010-0564-9\u003c/li\u003e\n\u003cli\u003eFeyisa, G. L., Meilby, H., Fensholt, R., \u0026amp; Proud, S. R. (2014). Automated Water Extraction Index: A new technique for surface water mapping using Landsat imagery. Remote Sensing of Environment, 140, 23\u0026ndash;35. https://doi.org/10.1016/j.rse.2013.08.029\u003c/li\u003e\n\u003cli\u003eFoley, J. A., DeFries, R., Asner, G. P., Barford, C., Bonan, G., Carpenter, S. R., ... \u0026amp; Helkowski, J. (2005). Global consequences of land use. Science, 309(5734), 570\u0026ndash;574.\u003c/li\u003e\n\u003cli\u003eGleick, P. H. (1998). Water in crisis: Paths to sustainable water management. In Water in Crisis (pp. 1\u0026ndash;12). Oxford University Press.\u003c/li\u003e\n\u003cli\u003eGober, P., Brazel, A. J., Quay, R., Myint, S. W., Grossman-Clarke, S., Miller, A., \u0026amp; Rossi, S. (2009). Using watered landscapes to manipulate urban heat island effects: How much water will it take to cool Phoenix? Journal of the American Planning Association, 76(1), 109\u0026ndash;121.\u003c/li\u003e\n\u003cli\u003eGround Water Information Booklet Jammu District, Jammu \u0026amp; Kashmir. (2019). Government of India, Central Ground Water Board, Ministry of Water Resources.\u003c/li\u003e\n\u003cli\u003eGulcan Sarp and Mehmet, 2016. Water body extraction and change detection using time series; A case study of Lake Burdur, Turkey .Environmental Monitoring and assessment ,188(2),105\u003c/li\u003e\n\u003cli\u003eHassan et al.,2016 Hassan, Q.K.,Bourque,C.P,A.,\u0026amp;Meng,F.R.(2016).Applications of remote sensing for forest and fire monitoring.\u003c/li\u003e\n\u003cli\u003eHaack, B. (1996). Monitoring wetland changes with remote sensing: An East African example. Environmental Management, 20(3), 411\u0026ndash;419. https://doi.org/10.1007/BF01203848\u003c/li\u003e\n\u003cli\u003eHuang, X., Xie, C., Fang, X., \u0026amp; Zhang, L. (2015). Combining pixel- and object-based machine learning for identification of water-body types from urban high-resolution remote-sensing imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(5), 2097\u0026ndash;2110. https://doi.org/10.1109/JSTARS.2015.24207\u003c/li\u003e\n\u003cli\u003eIndian Express. (2020). Jammu and Kashmir\u0026rsquo;s water bodies have declined by 50% in the past two decades.\u003c/li\u003e\n\u003cli\u003eIntergovernmental Panel on Climate Change (IPCC). (2013). Climate Change 2013: The Physical Science Basis. Cambridge University Press.\u003c/li\u003e\n\u003cli\u003eJain, S. K., Singh, R. D., Jain, M. K., \u0026amp; Lohani, A. K. (2005). Delineation of flood-prone areas using remote sensing techniques. Water Resources Management, 19(4), 333\u0026ndash;347.\u003c/li\u003e\n\u003cli\u003eJawak, S. D., Kulkarni, K., \u0026amp; Luis, A. J. (2015). A review on extraction of lakes from remotely sensed optical satellite data with a special focus on cryospheric lakes. Advances in Remote Sensing, 4, 196\u0026ndash;213.\u003c/li\u003e\n\u003cli\u003eKumar, P., Kumar, V., \u0026amp; Sharma, R. (2018). Assessment of water quality of rivers in Jammu District, Jammu and Kashmir, India. Journal of Environmental Science and Health, Part B, 53, 347\u0026ndash;355.\u003c/li\u003e\n\u003cli\u003eLaonamsai, J., Julphunthong, P., Saprathet, T., Kimmany, B., Ganchanasuragit, T., Chomcheawchan, P., \u0026amp; Tomun, N. (2023). Utilizing NDWI, MNDWI, SAVI, WRI, and AWEI for estimating erosion and deposition in Ping River in Thailand. Hydrology, 10(3), 70. https://doi.org/10.3390/hydrology10030070\u003c/li\u003e\n\u003cli\u003eLu, D., Mausel, P., Brondizio, E., \u0026amp; Moran, E. (2004). Change detection techniques. International Journal of Remote Sensing, 25(9), 2365\u0026ndash;2407.\u003c/li\u003e\n\u003cli\u003eMcFeeters, S. K. (1996). The use of the normalized difference water index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7), 1425\u0026ndash;1432.\u003c/li\u003e\n\u003cli\u003eMillennium Ecosystem Assessment (MEA). (2005). Ecosystems and Human Well-being: Synthesis. Island Press.\u003c/li\u003e\n\u003cli\u003eOuma, Y. O., \u0026amp; Tateishi, R. (2014). A water index for rapid mapping of shoreline changes in Lake Victoria. International Journal of Remote Sensing, 35(10), 3534\u0026ndash;3551.\u003c/li\u003e\n\u003cli\u003ePatel, D. P., Dholakia, M., Naresh, N., \u0026amp; Srivastava, P. K. (2019). Change detection analysis of surface water bodies in the Narmada River basin, India. Journal of Environmental Management, 235, 345\u0026ndash;355.\u003c/li\u003e\n\u003cli\u003ePekel, J. F., Cottam, A., Gorelick, N., \u0026amp; Belward, A. S. (2016). High-resolution mapping of global surface water and its long-term changes. Nature, 540, 418\u0026ndash;422.\u003c/li\u003e\n\u003cli\u003ePohl, C., \u0026amp; Van Genderen, J. L. (1998). Multisensory image fusion in remote sensing: Concepts, methods and applications. International Journal of Remote Sensing, 19(5), 823\u0026ndash;854.\u003c/li\u003e\n\u003cli\u003ePostel, S. L. (1999). Pillar of Sand: Can the Irrigation Miracle Last? W.W. Norton \u0026amp; Company.\u003c/li\u003e\n\u003cli\u003eRebelo, L. M., McCartney, M. P., \u0026amp; Finlayson, C. M. (2009). Wetlands of sub-Saharan Africa: Distribution and contribution to livelihoods. Wetlands Ecology and Management, 18, 557\u0026ndash;572.\u003c/li\u003e\n\u003cli\u003eRidd, M. K., \u0026amp; Liu, J. (1998). A comparison of four algorithms for change detection in an urban environment. Remote Sensing of Environment, 63(2), 95\u0026ndash;100.\u003c/li\u003e\n\u003cli\u003eRokni, K., Ahmad, A., Selamat, A., \u0026amp; Hazini, S. (2014). Water feature extraction and change detection using multitemporal Landsat imagery. Remote Sensing, 6(5), 4173\u0026ndash;4189.\u003c/li\u003e\n\u003cli\u003eRover, J., Ji, L., Wylie, B. K., \u0026amp; Tieszen, L. L. (2012). Establishing water body areal extent trends in Interior Alaska from multi-temporal Landsat data. Remote Sensing Letters, 3(7), 595\u0026ndash;604.\u003c/li\u003e\n\u003cli\u003eSekertekin, A., \u0026amp; Kutoglu, S. H. (2017). Mapping water surface using Landsat 8 OLI imagery and NDWI index: A case study of Lake Beyşehir, Turkey. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-2/W7, 285\u0026ndash;289.\u003c/li\u003e\n\u003cli\u003eSharma, R., Kumar, V., \u0026amp; Kumar, P. (2016). Status of water bodies in Jammu District. Journal of Environmental Sciences, 6(3), 456\u0026ndash;463.\u003c/li\u003e\n\u003cli\u003eSingh, A., Kumar, P., \u0026amp; Singh, R. (2015). Change detection in land use/land cover using satellite remote sensing. International Journal of Advanced Remote Sensing and GIS, 4(1), 1230\u0026ndash;1242.\u003c/li\u003e\n\u003cli\u003eSivanpillai, R., \u0026amp; Miller, S. N. (2010). Improvements in mapping water bodies using ASTER data. Ecological Informatics, 5, 73\u0026ndash;78.\u003c/li\u003e\n\u003cli\u003eSudan, S. (2015). Assessment of groundwater quality: A case study of Jammu District. International Journal of Science and Research, 4(1), 2319\u0026ndash;7064.\u003c/li\u003e\n\u003cli\u003eThe Hindu. (2020). Jammu\u0026rsquo;s water bodies face threat of pollution, encroachment.\u003c/li\u003e\n\u003cli\u003eThe Times of India. (2020). Pollution choking Jammu\u0026rsquo;s rivers.\u003c/li\u003e\n\u003cli\u003eTulbure, M. G., \u0026amp; Broich, M. (2013). Spatiotemporal dynamics of surface water bodies using Landsat time-series data from 1999 to 2011. ISPRS Journal of Photogrammetry and Remote Sensing, 79, 44\u0026ndash;52.\u003c/li\u003e\n\u003cli\u003eUSGS. (2020). Earth Explorer \u0026ndash; United States Geological Survey. https://earthexplorer.usgs.gov\u003c/li\u003e\n\u003cli\u003eVorosmarty, C. J., McIntyre, P. B., Gessner, M. O., Dudgeon, D., Prusevich, A., Green, P., ... \u0026amp; Davies, P. M. (2010). Global threats to human water security and river biodiversity. Nature, 467, 555\u0026ndash;561.\u003c/li\u003e\n\u003cli\u003eXu, H. (2006). Modification of normalized difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14), 3025\u0026ndash;3033.\u003c/li\u003e\n\u003cli\u003eZhang, Y., Gao, J., \u0026amp; Wang, J. (2007). Detailed mapping of a salt farm from Land sat TM imagery using neural network and maximum likelihood classifiers: A comparison. International Journal of Remote Sensing, 28(10), 2077\u0026ndash;2089.\u003c/li\u003e\n\u003cli\u003eZhou, Y., Zhang, X., Li, W., \u0026amp; Yu, L. (2014). Monitoring and modelling water quality using remote sensing and GIS: A case study of the Han River in Wuhan, China. Environmental Monitoring and Assessment, 186, 353\u0026ndash;366.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-geoscience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Geoscience](https://www.springer.com/journal/44288)","snPcode":"44288","submissionUrl":"https://submission.nature.com/new-submission/44288","title":"Discover Geoscience","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Surface water, AWEI, NDWI, water bodies, change detection, urbanization","lastPublishedDoi":"10.21203/rs.3.rs-6920200/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6920200/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the spatiotemporal dynamics of surface water bodies in Jammu District, India, from 2000 to 2020, utilizing a pixel-based model with Landsat time-series data. The research employs the Normalized Difference Water Index (NDWI) and the Automated Water Extraction Index (AWEI) to quantify changes in water body extent, revealing a significant decline across the district. Approximately 18\u0026ndash;20 km\u0026sup2; of water bodies were lost, primarily due to rapid urbanization, intensified agricultural practices, and climatic variability, including altered precipitation patterns. In contrast, only 12\u0026ndash;13 km\u0026sup2; of new water bodies were accreted, indicating limited expansion. Notable fragmentation and desiccation were observed, particularly in the northern and central regions, where ponds, lakes, and riverine networks have diminished, highlighting a critical hydrological imbalance. Both NDWI and AWEI demonstrated high accuracy (above 85%), with AWEI excelling in urban and shadowed environments due to its multi-band approach. Spatial distribution maps and temporal change detection highlight a consistent reduction in water body coverage, driven by anthropogenic pressures such as groundwater over-extraction, pollution, and land-use changes, alongside natural factors like reduced monsoon intensity. These findings underscore the urgent need for integrated water resource management and sustainable land-use planning to mitigate further degradation. By providing a detailed assessment of water body changes in a semi-arid region, this study offers critical insights for policymakers and lays the groundwork for geographically targeted conservation strategies to preserve Jammu\u0026rsquo;s hydrological balance.\u003c/p\u003e","manuscriptTitle":"Change detection analysis of surface water bodies using pixel-based model in Jammu district, India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-12 17:14:23","doi":"10.21203/rs.3.rs-6920200/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-03T23:05:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-20T06:15:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-16T11:31:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-12T18:01:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-12T03:10:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"64978848689678849180565937642461313027","date":"2025-08-09T13:47:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"57760656811254185743802417980757360130","date":"2025-08-07T08:21:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"66884654340015659468209268219879433815","date":"2025-08-07T07:52:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"260868245190877571594671548434139013154","date":"2025-08-07T06:27:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"272187053851502989276407269123705913272","date":"2025-08-07T06:20:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"108091865724406219951845356066410393180","date":"2025-08-07T06:06:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-07T05:55:36+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-22T15:24:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-23T03:59:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-23T03:56:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Geoscience","date":"2025-06-18T07:14:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-geoscience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Geoscience](https://www.springer.com/journal/44288)","snPcode":"44288","submissionUrl":"https://submission.nature.com/new-submission/44288","title":"Discover Geoscience","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0e73c549-ec5f-4985-8f0a-258d15c3af64","owner":[],"postedDate":"August 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-30T11:08:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-12 17:14:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6920200","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6920200","identity":"rs-6920200","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}