Estimation of accumulated soil organic carbon stock in Dawuro zone montane Forests southwest Ethiopia using geospatial tools

Estimation of accumulated soil organic carbon stock in Dawuro zone montane Forests southwest Ethiopia using geospatial tools | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Estimation of accumulated soil organic carbon stock in Dawuro zone montane Forests southwest Ethiopia using geospatial tools Jemal Abebe, Aziza shiferaw, Tamirat wato This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6262250/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study estimates the accumulated soil organic carbon (SOC) stock in the montane forests of the Dawuro zone, Southwest Ethiopia, utilizing geospatial tools. Digital soil mapping techniques, including remote sensing and the Normalized Difference Vegetation Index (NDVI), were employed to analyze forest canopy density and its impact on soil health—a critical factor for climate change mitigation. Data were gathered from satellite imagery (Sentinel-2A), soil samples, and field surveys. The research reveals the relationship between land use/land cover (LULC) classifications and SOC storage, highlighting that dense forests and grasslands significantly contribute to SOC accumulation, while open areas and steep slopes show limited storage due to erosion risks. Soil texture analysis indicates that high clay content enhances organic matter retention, promoting higher SOC levels. Additionally, soil pH was found to influence SOC dynamics, with moderately acidic to neutral soils supporting better microbial activity and carbon storage. The study underscores the importance of sustainable land management practices in maintaining optimal SOC levels and improving soil quality, ultimately providing valuable insights for ecological conservation and climate resilience strategies in the region. Through regression analysis, the findings establish a robust framework for predicting soil health conditions based on SOC and its influencing factors NDVI LULC BSI SOC Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Carbon inventory assessments involve estimating the stocks and net fluxes of carbon within various land use systems, considering specific areas, timeframes, and management practices (Batjes, 2011 ). Soil organic carbon (SOC) is a critical component of the global carbon cycle, representing approximately twice the atmospheric carbon content and about 75% of the total terrestrial organic carbon pool (Albaladejo et al., 2013 ). Indeed, soils constitute the largest terrestrial reservoir of organic carbon. Large-scale land use changes, such as deforestation and agricultural activities—including biomass burning, plowing, drainage, and low-input farming have significantly altered SOC pools (Lorenz & Lal, 2005 ). The storage of organic carbon in soil is governed by the balance between carbon inputs and losses. This balance is influenced by biotic factors (e.g., biomass production, microbial activity), environmental variables (e.g., mean annual precipitation and temperature), soil characteristics (e.g., texture, lithology), and anthropogenic controls (e.g., land use and management) (Albaladejo et al., 2013 ). The nature of carbon inputs differs between aboveground and belowground sources. In forests, aboveground inputs primarily consist of leaf and needle litter, supplemented by branches, bark, and fruits, while woody debris is a significant component in natural forests; herbaceous litterfall plays a minor role (Lorenz & Lal, 2005 ). Conversely, information on aboveground carbon inputs for arable and grassland ecosystems is less extensive, though inputs depend heavily on crop residue amounts and types, fertilizer application, and are significantly higher when residues are returned to the soil (Lorenz & Lal, 2005 ). SOC stocks under undisturbed, native vegetation are generally considered to be in dynamic equilibrium with other terrestrial carbon pools, primarily controlled by climate, terrain, vegetation, soil mineralogy, particle/aggregate size, and their interactions (Jenny, 1980 ; Watson et al., 2000 ; Canadell et al., 2007 ). Forests are significant contributors to the global carbon cycle, acting as major carbon sinks. The quantity of carbon exchanged between forests and the atmosphere via photosynthesis and respiration is substantial, estimated to be seven times the anthropogenic carbon emissions (Kumar et al., 2016 ). Soil respiration is a key process influencing CO2 emissions from forest ecosystems, with surface organic matter content directly related to carbon input (Kumar et al., 2016 ). Forests hold considerable carbon stocks, with soil storing two to three times more carbon than aboveground biomass and atmospheric carbon combined in these ecosystems (Kumar et al., 2013 ). The SOC pool is determined by the equilibrium between carbon inputs from vegetation and losses, primarily through microbial decomposition, which releases CO2 (Kumar et al., 2016 ). SOC, representing the carbon within soil organic matter, includes all biological material in the soil, such as plant residues, living roots, organisms, and decomposing or decomposed material (Kumar et al., 2016 ). The amount of SOC is directly linked to crop productivity, soil fertility, physical characteristics, and vegetation health. As the largest terrestrial carbon pool, SOC is a key variable in understanding terrestrial carbon dynamics. Estimating soil carbon involves field research and remote sensing applications, particularly in diverse forest types like Amazonian, Boreal, tropical, and coniferous forests (Davidson et al., 2008 ; Frolking et al., 1999 ; Cochrane et al., 1999 ; Gupta et al., 2014 ). The production and decomposition of SOC stocks are influenced by multiple factors, including land use, land cover, climate, soil texture, topography, and hydrology (Houghton and Hackler, 1999 ; IPCC, 2014 ; Rani et al., 2011; Righelato and Spracklen, 2007 ). Recent advancements in mapping and remote sensing facilitate detailed mapping of SOC, analyzing its properties, heterogeneity, and characteristics (Kumar et al., 2016 ). Digital soil mapping (DSM), integrating soil maps and field data with remote sensing techniques (Abdel-Kader, 2011 ; Ismail and Yacoub, 2012 ; Ali and Moghanm, 2013 ), is increasingly used to predict soil distribution and is crucial for evaluating forest canopy density, protecting soil, and informing climate change mitigation strategies (Cochrane et al., 1999 ; Davidson et al., 2008 ; Houghton et al., 2000 ; Hirsch et al., 2004 ; Meyfroidt and Lambin, 2008 ; Rikimaru et al., 2002 ; Vargas et al., 2008 ). The primary goal of this study is to utilize geospatial technology, incorporating the Normalized Difference Vegetation Index (NDVI), Bare Soil Index (BSI), soil pH, soil texture, and existing SOC data, to estimate the accumulated SOC in the montane forests of the Dawuro zone. 2. Methods and materials 2.1. Description of the Study area Dawuro is located between latitudes 6° 36' to 7° 21' north and longitudes 36° 68' to 37° 52' east. The Gojeb and Omo rivers encircle and divide Dawuro from northwest to southwest in a clockwise direction. Dawuro borders Konta Special Wereda to the west, Jimma Zone (Oromiya Region) to the northwest, Hadiya and Kambata-Tambaro Zones to the northeast, Wolayta Zone to the east, and Gamo-Gofa Zone to the southeast. Dawuro covers an area of 5,159.813 km² and has five woredas and one town administration: Essara, Tocha, Maraka, Genabosa, Loma, and Tarcha Town Administration. The landscape of Dawuro consists primarily of mountains, plateaus, deep gorges, and lowland plains. Dawuro's altitude ranges from 500 meters around the confluence of the Mansa and Omo rivers in the special area of Bona-Balala in Adabacho Kebele in Essara Woreda to 3,000 meters above sea level (a.s.l.) at Tuta in Tocha Woreda. Dawuro is enriched with a diverse range of tree and plant species, as well as natural vegetation and forests. For example, Chabara-Churichura National Park, along with the natural forests along the valleys of the Gojeb and Omo rivers, as well as other larger rivers in the zone such as Mansa, Zigna, and Gelo, is located in Essara and Tocha Woredas. These areas contain a large amount of natural vegetation that is important for biodiversity research. 2.2. Data used The research was conducted using satellite data and soil data field survey data. Primary data were collected during field observations through field surveys and measurements of samples collected from the study area (see Table 1 ), while satellite data used for soil carbon estimation was sentinel-2A images. Table 1 Data and their specification DATA Description Source Purpose Sentinel-2A 10m resolution Europeanspace agency (ESA) To make Lu/lc map and ndvi map Soil 250m ISRIC data sets To prepare soil PH, organic carbon and texture Ground truth data Using Garmin GPS taking Sample points The Study Area For accuracy assessment of the supervised classification. 2.3. Image interpretation The satellite imagery utilized in this study was sourced from the European Space Agency (ESA). Preprocessing of the imagery, including georeferencing, refinement, and correction, was performed using ERDAS IMAGINE 2015 software. A suite of digital preprocessing techniques was applied to enhance the imagery; these included geometric distortion removal, atmospheric correction, image registration, augmentation, and various data transformations, enabling the identification and analysis of different image elements (Pandey et al., 2012 ; Sharma et al., 2012 ). To ensure the accuracy and validity of the land use/land cover (LULC) classification, a comprehensive ground survey using GPS points was conducted to identify key features. Following preprocessing, the four spectral bands of the Sentinel-2A imagery (near-infrared, red, green, and blue) were used to identify ground objects and assess image properties. Ground truthing activities, supported by GPS data, were crucial for accurately identifying and classifying different features and LULC types within the study area (Kumar et al., 2012 ). 2.4. Data Processing and analysis Estimating accumulated soil organic carbon (SOC) requires collecting relevant data to identify land use types that store significant amounts of SOC versus those that do not, information essential for developing appropriate mitigation strategies. For this purpose, the Sentinel-2A image was processed for supervised classification using ArcGIS software. As described by Yan et al. ( 2006 ), supervised classification involves the user defining specific pixel values or spectral signatures representative of each class. This is achieved by selecting training sites—locations that serve as representative samples for known land cover types. Following the classification, a thematic land cover map was generated to identify the different land use/land cover (LULC) categories within the study area. Accumulated SOC was then assessed for each identified LULC category. Evaluating the accuracy of a map derived from remote sensing data is a critical step. The error matrix is the standard method for reporting classification accuracy, enabling the calculation of overall accuracy, user's accuracy, producer's accuracy, and the Kappa statistic. 2.5. Estimation of soil organic carbon Several processes and approaches are required to estimate the accumulated soil organic carbon (SOC) stock utilizing the NDVI (Normalized Difference Vegetation Index), BSI (Bare Soil Index), pH, SOC, and soil texture. 2.6. Bare soil index and NDVI Bare soil is the soil that remains uncovered by grass, wood chips, live ground covers, artificial turf, or similar coverings. It is the soil or sand on the Earth’s surface. The following formulas are used to calculate the bare soil index of a region. The BSI is a normalized index based on the difference sums of two separating bands of the satellite image (for vegetation). The formulas used for calculating the bare soil index are provided below (GU, 2019). BSI for sentinel-2: = ((B11 + B04) - (B08 + B02)) / ((B11 + B04) + (B08 + B02)) Where: BSI = Bare soil index, B11 = SWIR2, B4 = Read, B8 = NIR and B2 = Blue. NDVI is calculated from satellite imagery, whereby the satellite’s spectrometer or radiometric sensor measures and stores reflectance values for both red and NIR bands on two separate channels or images (Kriegler et al., 1969). The NDVI values estimate vegetated and non-vegetated regions. In general, NDVI values range from − 1.0 to 1.0, with negative values indicating clouds and water, positive values near zero indicating bare soil, and higher positive values of NDVI ranging from sparse vegetation (0.1–0.5) to dense green vegetation (0.6 and above). NDVI is also directly related to the: “leaf area index” (LAI), which is often used in crop growth models herbaceous or total green biomass (tons/ha) for given vegetation types and percent ground cover. The equation to calculate the NDVI using sentinel-2 image is given below: NDVI = (NIR-RED) / (NIR + RED) Where: NIR = B8 and RED = B4 3. Results and discussion This section details the estimation of soil organic carbon (SOC), integrating satellite imagery and soil data. The Land Use Land Cover (LULC) classification results provide insights into how the study area is utilized for different purposes. To generate a digital SOC map, the Normalized Difference Vegetation Index (NDVI) map was utilized in conjunction with specific band math equations. This resulting SOC map provides digital SOC values for the same spatial locations as the collected soil samples, facilitating their comparison through regression analysis. 3.1. Land use/land covers classification The Land Use Land Cover (LULC) map for this study was developed and validated using accurate GPS points collected during fieldwork. Half of these points were used for training the classification model, while the remaining points were reserved for validation to assess accuracy. LULC maps provide essential information on the current state of the landscape, documenting existing conditions and their changes over time (Pandey et al., 2013; Sharma et al., 2013; Kumar et al., 2017; Banerjee and Srivastava, 2013). This approach classifies the existing land resources into distinct thematic categories, including grassland, dense forest, light forest, cropland, steep slope areas, and open land surfaces. These land use categories create diverse environments, each with distinct implications for the accumulated soil organic carbon (SOC) stock. Steep slope areas, comprising 12.73% of the total land area, exhibit moderate vegetative cover but have limited SOC storage potential due to erosion risks. Conversely, light forests, covering 17.21% of the land, play a significant role in SOC accumulation. They enhance soil health and carbon storage through the input of organic matter from leaf litter and root decomposition. Grasslands, occupying 16.71% of the terrain, possess substantial potential for SOC sequestration, particularly due to their efficient carbon storage via deep root systems, especially under sustainable management. Open areas, accounting for 9.95% of the total, contribute minimally to SOC owing to their lack of vegetative cover. Dense forests, although covering only 7.68% of the total area, are crucial for SOC storage, capable of sequestering significant amounts of carbon both above- and below-ground. Their preservation is vital for maintaining these carbon stocks. Cropland represents the largest land use category at 21.15%, with SOC potential highly variable depending on management practices. While sustainable farming can significantly enhance SOC, conventional practices may lead to depletion. Finally, scrub areas, making up 14.56% of the landscape, can support SOC buildup if managed appropriately. These areas provide habitat and contribute to carbon storage through vegetation, underscoring the importance of prudent land management across all categories. Table 2 Land use land cover statics Lu/lc count Area / % Steep slope area 619535 12.73 Light forest 837315 17.21 Grass land 813398 16.71 Open area 484309 9.95 Dense forest 373995 7.68 Crop land 1029253 21.15 Scrub land 708754 14.56 Total 4866559 100 3.2. Bare soil index and NDVI maps The Normalized Difference Vegetation Index (NDVI) map, derived from the red and near-infrared (NIR) bands of the satellite image, is presented in Fig. 4 . This NDVI map was subsequently utilized within a band math equation to estimate soil organic carbon (SOC). Similarly, the Bare Soil Index (BSI) map for the study area was generated using its respective formula. It has been observed that lower BSI values indicate the presence of vegetation, while higher BSI values signify a greater extent of bare soil. Consequently, low BSI values are associated with soil covered by vegetation or water (corresponding to high NDVI values or negative NDVI values), whereas high BSI values indicate more open and bare soil surfaces (characterized by low NDVI values greater than zero). In summary, low BSI values typically correspond to vegetated or water-covered areas, while high BSI values are indicative of predominantly bare soil. 3.3. Soil pH and soil organic carbon Soil pH is a crucial factor influencing both soil inorganic carbon (SIC) and organic carbon (SOC) dynamics, and its impact must be considered in soil analysis. The pH of collected soil samples was assessed using standard laboratory methods. A spatial distribution map of soil pH was generated from these measured values, sourced from the ESRIC Data Hub (see Fig. 6 ). Furthermore, spatial correlation maps comparing pH with both measured and predicted SOC were developed and analyzed (see Figs. 5 and 6 ). Using ArcGIS tools, a spatial variability map illustrating the relationship between pH and predicted SOC was created to visualize how SOC accumulation varies with pH across the study area (see Fig. 6 ). Subsequently, regression analysis was performed between the spatial maps of SOC and pH (see Fig. 6 ). Statistical regression analysis between pH and SOC values revealed a strong relationship, with an R² value of 0.80 (see Table 3 ). Within the study area, soil pH is identified as a vital factor influencing SOC accumulation; with its effects varying across different pH ranges (see Fig. 6 ). In the acidic pH range of 5.1 to 5.6, decomposition rates are often slower due to reduced microbial activity, potentially leading to higher short-term SOC accumulation as organic matter decomposes more slowly. Conversely, the pH range of 5.7 to 6.2, representing moderately acidic to neutral soils, generally supports more effective SOC accumulation. In this range, typically more vigorous microbial activity enhances organic matter decomposition and promotes efficient carbon storage. Soils with a pH between 6.3 and 6.8, classified as neutral to slightly alkaline, tend to foster high microbial activity and effective nutrient cycling. This environment encourages organic matter decomposition, leading to increased SOC accumulation. Moreover, these soils are generally more fertile, supporting robust plant growth and significant inputs of organic matter. Overall, the study area exhibits optimal soil pH levels crucial for maximizing SOC stocks. High SOC levels, such as a value of 75, indicate rich organic matter content, which enhances soil structure, fertility, and water retention (see Fig. 5 ). Such levels support diverse microbial populations, improving nutrient cycling and overall soil health, suggesting the potential benefits of effective land management practices like cover cropping, reduced tillage, or organic amendments. In contrast, a low SOC value of 16 signifies a deficiency in organic matter, resulting in poor soil structure, diminished fertility, and reduced water retention. Such low SOC values may result from practices like intensive agriculture, erosion, or land degradation, which deplete organic matter over time. Analysis indicates that lower SOC values are prevalent in open areas, grasslands, croplands, and steep slopes, while the highest SOC values are observed in forested and scrubland areas, consistent with the land use and land cover patterns. Therefore, maintaining optimal soil pH and implementing sustainable land management practices are essential for enhancing SOC and promoting overall soil health. 3.4. Soil texture and soil organic carbon Soil texture significantly influences soil organic carbon (SOC) stocks due to the varying physical and chemical properties of different soil types. In the study area, a high percentage (48.17%) of the land is characterized by high clay content. Clay soils generally exhibit a greater capacity to retain organic matter, attributed to their small particle size and large surface area, correlating with SOC levels observed in forested and lightly vegetated areas as identified in the land use and land cover classification. Clay loam, comprising 51.77% of the area, is prevalent in scrubland and grassland regions. This soil type, featuring a balanced mixture of clay, silt, and sand, offers good water retention and aeration. These characteristics promote microbial activity, potentially fostering higher SOC levels. Conversely, sandy clay, covering only 0.05% of the area, has a lower SOC potential. Its larger particle sizes lead to poorer moisture retention and reduced organic matter accumulation. Furthermore, sandy clay loam, with minimal coverage (0.01%), typically displays lower SOC stocks due to its higher sand content, which further compromises moisture retention capacity. Table 3 Soil texture statistical value Name of soil texture count area/ha area/% Clay 39768 248550 48.17 Sandyclay loam 1 6.25 0.01 Clayloam4 42743 267143.8 51.77 Sandy clay 45 281.25 0.05 Total 82557 515981.3 100 4. Conclusion This study employs geospatial methods combined with a linear regression model to estimate soil organic carbon (SOC) within the study area. It also investigates the relationships between SOC and key influencing factors, including NDVI, pH, soil texture, and the Bare Soil Index (BSI). A linear model representing the correlations among these variables and SOC facilitates the assessment and prediction of regional soil conditions. The coefficient of determination (R²), traditionally used for comparing models of various forest parameters, is applied here to evaluate model performance. The BSI map (see Fig. 3 ) reveals that brown areas, indicating the lowest BSI values, correspond to regions where the soil is not bare. This aligns with the presence of scrubland, light vegetation, and dense forest. Conversely, the outer parts of the study area, depicted in green and showing the highest BSI values, correspond to crops, grasslands, and open areas. Notably, regions with low BSI values are associated with low NDVI values (-0.18) in the study area (see Fig. 4 ). Regarding soil texture, high clay content (48.17%) dominates the area. Clay soils generally possess a greater capacity for organic matter retention due to their small particle size and large surface area. Clay loam, covering 51.77% of the area and found in scrubland and grassland, contains a balanced mixture of clay, silt, and sand. This composition promotes good water retention and aeration, enhancing microbial activity and potentially leading to higher SOC levels. The accumulation of SOC is also influenced by soil pH, with effects varying across different ranges. Acidic soils tend to exhibit slower decomposition rates, potentially resulting in higher short-term SOC levels. Moderately acidic to neutral soils (pH 5.7 to 6.2) generally support better SOC accumulation due to higher microbial activity. Neutral to slightly alkaline soils (pH 6.3 to 6.8) further encourage SOC accumulation, as they support vigorous microbial activity, effective nutrient cycling, and, being more fertile, robust plant growth and significant organic matter inputs. A SOC value of 75 signifies rich organic matter, contributing to improved soil structure, fertility, and water retention. Such high SOC levels support diverse microbial populations, enhancing nutrient cycling and overall soil health. In contrast, a low SOC value indicates a deficiency in organic matter, leading to poor soil structure, reduced fertility, and lower water retention. Low SOC values, potentially resulting from intensive agriculture, erosion, or land degradation, are typically found in open areas, grasslands, croplands, and steep slopes, whereas the highest SOC values are observed in forested and scrubland areas. Abbreviations NDVI normalized difference vegetation index SOC Soil Organic Carbon BSI Bare soil index LULC land use land cover Declarations Acknowledgements: We are grateful to International Soil Reference and Information Centre and European space agency for providing material support to accomplish this research. We also received secondary data from the Central Statistical Agency (CSA). Our gratitude also goes to Dr. Ginjo Gitima for their assistance in the data analysis and for their unwavering support, sharing of information, and important advice on every aspect of our project. The authors’ contributions: JT conceived and designed the method section’s proven work. The author contributes to the article’s analysis, verification, and writing. AS conceived soil data analysis and interpretation. TW conceived reference and grammar checking. Both the authors read and approved the final manuscript. Funding: This study received no funding from any institutions, agencies, or people. Ethics and Consent: We are writing to outline the ethical considerations associated with a research titled "[Estimation of accumulated soil organic carbon stock in Dawuro zone montane Forests South West Ethiopia using geospatial tools]." This study aims to estimates the accumulated soil organic carbon (SOC) stock in the montane forests of the Dawuro zone, Southwest Ethiopia, utilizing geospatial tools. Human Subjects: our research is not involves human participants. We have obtained only geospatial data for the purpose of this study. Environmental Impact: The research has not involves any environmental assessment to evaluate potential impacts and have implemented measures to mitigate any adverse effects. Data Management: All data collected during the research will be stored securely and will be anonym zed to protect the privacy of data center. Data sharing will be conducted in accordance with relevant regulations and institutional policies. Conflict of Interest: I declare that there are no conflicts of interest associated with this research. All funding sources have been disclosed, and there are no financial incentives affecting the outcomes of the study. Sustainability Practices: We have adhered to sustainable research practices, minimizing waste and using resources efficiently throughout our study. References Abdel-Kader, Fawzy Hassan, 2011. Digital soil mapping at pilot sites in the northwest coast of Egypt: a multinomial logistic regression approach. Egypt. J. Remote Sens. Space Sci. 14 (1), 29–40. 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"Effect of iron supply on Southern Ocean CO2 uptake and implications for glacial atmospheric CO2." Nature 407.6805 (2000): 730-733. Yan, G., Mas, J. F., Maathuis, B. H. P., Xiangmin, Z., & Van Dijk, P. M. (2006). Comparison of pixel‐based and object‐oriented image classification approaches—a case study in a coal fire area, Wuda, Inner Mongolia, China. International Journal of Remote Sensing, 27(18), 4039-4055. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6262250","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":468788398,"identity":"775727ed-76f5-4ce9-a240-ef2670a7a64e","order_by":0,"name":"Jemal Abebe","email":"data:image/png;base64,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","orcid":"","institution":"space science and geospatial institute","correspondingAuthor":true,"prefix":"","firstName":"Jemal","middleName":"","lastName":"Abebe","suffix":""},{"id":468788401,"identity":"b87d0296-7a5c-4a34-af3d-9a85e680252d","order_by":1,"name":"Aziza shiferaw","email":"","orcid":"","institution":"chemical and construction research","correspondingAuthor":false,"prefix":"","firstName":"Aziza","middleName":"","lastName":"shiferaw","suffix":""},{"id":468788404,"identity":"99f26625-ac76-4648-b17d-3bf0d22de114","order_by":2,"name":"Tamirat wato","email":"","orcid":"","institution":"BONGA UNIVERSITY","correspondingAuthor":false,"prefix":"","firstName":"Tamirat","middleName":"","lastName":"wato","suffix":""}],"badges":[],"createdAt":"2025-03-19 13:53:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6262250/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6262250/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84382382,"identity":"56e9a043-34a4-42d1-80e7-0bddffee7c0d","added_by":"auto","created_at":"2025-06-11 09:23:58","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":69427,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area map\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6262250/v1/39ef6867be28bd20dfa2d161.jpg"},{"id":84382378,"identity":"74de1a67-7479-42b2-98d6-e69944260553","added_by":"auto","created_at":"2025-06-11 09:23:58","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":89566,"visible":true,"origin":"","legend":"\u003cp\u003eLand use land cover map of Dawuro zone\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6262250/v1/2892496cc72b50ac4a07906f.jpg"},{"id":84382361,"identity":"c2e1c746-f60b-4e94-ad9c-c3b5dbb1a287","added_by":"auto","created_at":"2025-06-11 09:23:54","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":56374,"visible":true,"origin":"","legend":"\u003cp\u003eBare soil index map of Dawuro zone\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6262250/v1/0b6c344eeed4744417943f3d.jpg"},{"id":84383172,"identity":"41852850-1ef5-4d14-8fe8-13edbdf6c9ab","added_by":"auto","created_at":"2025-06-11 09:31:56","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":66219,"visible":true,"origin":"","legend":"\u003cp\u003eNDVI map of Dawuro zone\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6262250/v1/2c8492d88d91f8595eca58a4.jpg"},{"id":84384155,"identity":"30171d60-f5ab-4cdc-92b7-35f46a5eed6b","added_by":"auto","created_at":"2025-06-11 09:39:56","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":56216,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot of PH and SOC\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6262250/v1/b8645df806eb18950a3d538f.jpg"},{"id":84382375,"identity":"a326f57e-0205-489f-a279-fac656dfade5","added_by":"auto","created_at":"2025-06-11 09:23:57","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":86109,"visible":true,"origin":"","legend":"\u003cp\u003eSOC Map of Dawuro zone\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6262250/v1/8da27a4a852294fd35b7e9d4.jpg"},{"id":84382394,"identity":"ac075b43-d944-4c0b-84d4-314970e3f764","added_by":"auto","created_at":"2025-06-11 09:24:00","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":106380,"visible":true,"origin":"","legend":"\u003cp\u003eSoil PH Map of Dawuro zone\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6262250/v1/3d6c2ebb4e5bcb45f19b3b42.jpg"},{"id":84382368,"identity":"c43ec107-4716-4bf4-b803-a4afa6ac10ef","added_by":"auto","created_at":"2025-06-11 09:23:56","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":122063,"visible":true,"origin":"","legend":"\u003cp\u003eSoil Texture Map of Dawuro Zone\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6262250/v1/6cf0596ae0447a632305ea52.jpg"},{"id":86633801,"identity":"c03e3cb3-03e9-4f12-b1c5-a26aef87b9c7","added_by":"auto","created_at":"2025-07-14 06:54:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1169426,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6262250/v1/7d24231b-006e-4b74-b5ef-684729c07826.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Estimation of accumulated soil organic carbon stock in Dawuro zone montane Forests southwest Ethiopia using geospatial tools","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCarbon inventory assessments involve estimating the stocks and net fluxes of carbon within various land use systems, considering specific areas, timeframes, and management practices (Batjes, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Soil organic carbon (SOC) is a critical component of the global carbon cycle, representing approximately twice the atmospheric carbon content and about 75% of the total terrestrial organic carbon pool (Albaladejo et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Indeed, soils constitute the largest terrestrial reservoir of organic carbon. Large-scale land use changes, such as deforestation and agricultural activities—including biomass burning, plowing, drainage, and low-input farming have significantly altered SOC pools (Lorenz \u0026amp; Lal, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe storage of organic carbon in soil is governed by the balance between carbon inputs and losses. This balance is influenced by biotic factors (e.g., biomass production, microbial activity), environmental variables (e.g., mean annual precipitation and temperature), soil characteristics (e.g., texture, lithology), and anthropogenic controls (e.g., land use and management) (Albaladejo et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The nature of carbon inputs differs between aboveground and belowground sources. In forests, aboveground inputs primarily consist of leaf and needle litter, supplemented by branches, bark, and fruits, while woody debris is a significant component in natural forests; herbaceous litterfall plays a minor role (Lorenz \u0026amp; Lal, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Conversely, information on aboveground carbon inputs for arable and grassland ecosystems is less extensive, though inputs depend heavily on crop residue amounts and types, fertilizer application, and are significantly higher when residues are returned to the soil (Lorenz \u0026amp; Lal, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSOC stocks under undisturbed, native vegetation are generally considered to be in dynamic equilibrium with other terrestrial carbon pools, primarily controlled by climate, terrain, vegetation, soil mineralogy, particle/aggregate size, and their interactions (Jenny, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1980\u003c/span\u003e; Watson et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Canadell et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Forests are significant contributors to the global carbon cycle, acting as major carbon sinks. The quantity of carbon exchanged between forests and the atmosphere via photosynthesis and respiration is substantial, estimated to be seven times the anthropogenic carbon emissions (Kumar et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Soil respiration is a key process influencing CO2 emissions from forest ecosystems, with surface organic matter content directly related to carbon input (Kumar et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Forests hold considerable carbon stocks, with soil storing two to three times more carbon than aboveground biomass and atmospheric carbon combined in these ecosystems (Kumar et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe SOC pool is determined by the equilibrium between carbon inputs from vegetation and losses, primarily through microbial decomposition, which releases CO2 (Kumar et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). SOC, representing the carbon within soil organic matter, includes all biological material in the soil, such as plant residues, living roots, organisms, and decomposing or decomposed material (Kumar et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The amount of SOC is directly linked to crop productivity, soil fertility, physical characteristics, and vegetation health. As the largest terrestrial carbon pool, SOC is a key variable in understanding terrestrial carbon dynamics.\u003c/p\u003e \u003cp\u003eEstimating soil carbon involves field research and remote sensing applications, particularly in diverse forest types like Amazonian, Boreal, tropical, and coniferous forests (Davidson et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Frolking et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Cochrane et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Gupta et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The production and decomposition of SOC stocks are influenced by multiple factors, including land use, land cover, climate, soil texture, topography, and hydrology (Houghton and Hackler, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; IPCC, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Rani et al., 2011; Righelato and Spracklen, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Recent advancements in mapping and remote sensing facilitate detailed mapping of SOC, analyzing its properties, heterogeneity, and characteristics (Kumar et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Digital soil mapping (DSM), integrating soil maps and field data with remote sensing techniques (Abdel-Kader, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Ismail and Yacoub, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Ali and Moghanm, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), is increasingly used to predict soil distribution and is crucial for evaluating forest canopy density, protecting soil, and informing climate change mitigation strategies (Cochrane et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Davidson et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Houghton et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Hirsch et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Meyfroidt and Lambin, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Rikimaru et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Vargas et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe primary goal of this study is to utilize geospatial technology, incorporating the Normalized Difference Vegetation Index (NDVI), Bare Soil Index (BSI), soil pH, soil texture, and existing SOC data, to estimate the accumulated SOC in the montane forests of the Dawuro zone.\u003c/p\u003e"},{"header":"2. Methods and materials","content":"\u003cp\u003e2.1. Description of the Study area\u003c/p\u003e\u003cp\u003eDawuro is located between latitudes 6° 36' to 7° 21' north and longitudes 36° 68' to 37° 52' east. The Gojeb and Omo rivers encircle and divide Dawuro from northwest to southwest in a clockwise direction. Dawuro borders Konta Special Wereda to the west, Jimma Zone (Oromiya Region) to the northwest, Hadiya and Kambata-Tambaro Zones to the northeast, Wolayta Zone to the east, and Gamo-Gofa Zone to the southeast.\u003c/p\u003e\u003cp\u003eDawuro covers an area of 5,159.813 km² and has five woredas and one town administration: Essara, Tocha, Maraka, Genabosa, Loma, and Tarcha Town Administration. The landscape of Dawuro consists primarily of mountains, plateaus, deep gorges, and lowland plains. Dawuro's altitude ranges from 500 meters around the confluence of the Mansa and Omo rivers in the special area of Bona-Balala in Adabacho Kebele in Essara Woreda to 3,000 meters above sea level (a.s.l.) at Tuta in Tocha Woreda. Dawuro is enriched with a diverse range of tree and plant species, as well as natural vegetation and forests.\u003c/p\u003e\u003cp\u003eFor example, Chabara-Churichura National Park, along with the natural forests along the valleys of the Gojeb and Omo rivers, as well as other larger rivers in the zone such as Mansa, Zigna, and Gelo, is located in Essara and Tocha Woredas. These areas contain a large amount of natural vegetation that is important for biodiversity research.\u003c/p\u003e\u003cp\u003e2.2. Data used\u003c/p\u003e\u003cp\u003eThe research was conducted using satellite data and soil data field survey data. Primary data were collected during field observations through field surveys and measurements of samples collected from the study area (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), while satellite data used for soil carbon estimation was sentinel-2A images.\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\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\u003eData and their specification\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\u003eDATA\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePurpose\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSentinel-2A\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10m resolution\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEuropeanspace agency (ESA)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTo make Lu/lc map and ndvi map\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoil\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e250m\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eISRIC data sets\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTo prepare soil PH, organic carbon and texture\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGround truth data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUsing Garmin GPS taking Sample points\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe Study Area\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFor accuracy assessment of the supervised classification.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e2.3. Image interpretation\u003c/p\u003e\u003cp\u003eThe satellite imagery utilized in this study was sourced from the European Space Agency (ESA). Preprocessing of the imagery, including georeferencing, refinement, and correction, was performed using ERDAS IMAGINE 2015 software. A suite of digital preprocessing techniques was applied to enhance the imagery; these included geometric distortion removal, atmospheric correction, image registration, augmentation, and various data transformations, enabling the identification and analysis of different image elements (Pandey et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Sharma et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). To ensure the accuracy and validity of the land use/land cover (LULC) classification, a comprehensive ground survey using GPS points was conducted to identify key features. Following preprocessing, the four spectral bands of the Sentinel-2A imagery (near-infrared, red, green, and blue) were used to identify ground objects and assess image properties. Ground truthing activities, supported by GPS data, were crucial for accurately identifying and classifying different features and LULC types within the study area (Kumar et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e2.4. Data Processing and analysis\u003c/p\u003e\u003cp\u003eEstimating accumulated soil organic carbon (SOC) requires collecting relevant data to identify land use types that store significant amounts of SOC versus those that do not, information essential for developing appropriate mitigation strategies. For this purpose, the Sentinel-2A image was processed for supervised classification using ArcGIS software. As described by Yan et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), supervised classification involves the user defining specific pixel values or spectral signatures representative of each class. This is achieved by selecting training sites—locations that serve as representative samples for known land cover types.\u003c/p\u003e\u003cp\u003eFollowing the classification, a thematic land cover map was generated to identify the different land use/land cover (LULC) categories within the study area. Accumulated SOC was then assessed for each identified LULC category. Evaluating the accuracy of a map derived from remote sensing data is a critical step. The error matrix is the standard method for reporting classification accuracy, enabling the calculation of overall accuracy, user's accuracy, producer's accuracy, and the Kappa statistic.\u003c/p\u003e\u003cp\u003e2.5. Estimation of soil organic carbon\u003c/p\u003e\u003cp\u003eSeveral processes and approaches are required to estimate the accumulated soil organic carbon (SOC) stock utilizing the NDVI (Normalized Difference Vegetation Index), BSI (Bare Soil Index), pH, SOC, and soil texture.\u003c/p\u003e\u003cp\u003e2.6. Bare soil index and NDVI\u003c/p\u003e\u003cp\u003eBare soil is the soil that remains uncovered by grass, wood chips, live ground covers, artificial turf, or similar coverings. It is the soil or sand on the Earth’s surface. The following formulas are used to calculate the bare soil index of a region. The BSI is a normalized index based on the difference sums of two separating bands of the satellite image (for vegetation). The formulas used for calculating the bare soil index are provided below (GU, 2019).\u003c/p\u003e\u003cp\u003e \u003cb\u003eBSI\u003c/b\u003e for sentinel-2: \u003cb\u003e= ((B11 + B04) - (B08 + B02)) / ((B11 + B04) + (B08 + B02))\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWhere: BSI = Bare soil index, \u003cb\u003eB11 = SWIR2, B4 = Read, B8 = NIR and B2 = Blue.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNDVI is calculated from satellite imagery, whereby the satellite’s spectrometer or radiometric sensor measures and stores reflectance values for both red and NIR bands on two separate channels or images (Kriegler et al., 1969). The NDVI values estimate vegetated and non-vegetated regions. In general, NDVI values range from − 1.0 to 1.0, with negative values indicating clouds and water, positive values near zero indicating bare soil, and higher positive values of NDVI ranging from sparse vegetation (0.1–0.5) to dense green vegetation (0.6 and above).\u003c/p\u003e\u003cp\u003eNDVI is also directly related to the:\u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003e“leaf area index” (LAI), which is often used in crop growth models\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eherbaceous or total green biomass (tons/ha) for given vegetation types and percent ground cover.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe equation to calculate the NDVI using sentinel-2 image is given below:\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e\u003cp\u003eNDVI \u003cb\u003e= (NIR-RED) / (NIR + RED)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWhere: NIR = B8 and\u003c/p\u003e\u003ch3\u003eRED = B4\u003c/h3\u003e"},{"header":"3. Results and discussion","content":"\u003cp\u003eThis section details the estimation of soil organic carbon (SOC), integrating satellite imagery and soil data. The Land Use Land Cover (LULC) classification results provide insights into how the study area is utilized for different purposes. To generate a digital SOC map, the Normalized Difference Vegetation Index (NDVI) map was utilized in conjunction with specific band math equations. This resulting SOC map provides digital SOC values for the same spatial locations as the collected soil samples, facilitating their comparison through regression analysis.\u003c/p\u003e\u003ch2\u003e3.1. Land use/land covers classification\u003c/h2\u003e\u003cp\u003eThe Land Use Land Cover (LULC) map for this study was developed and validated using accurate GPS points collected during fieldwork. Half of these points were used for training the classification model, while the remaining points were reserved for validation to assess accuracy. LULC maps provide essential information on the current state of the landscape, documenting existing conditions and their changes over time (Pandey et al., 2013; Sharma et al., 2013; Kumar et al., 2017; Banerjee and Srivastava, 2013). This approach classifies the existing land resources into distinct thematic categories, including grassland, dense forest, light forest, cropland, steep slope areas, and open land surfaces.\u003c/p\u003e\u003cp\u003eThese land use categories create diverse environments, each with distinct implications for the accumulated soil organic carbon (SOC) stock. Steep slope areas, comprising 12.73% of the total land area, exhibit moderate vegetative cover but have limited SOC storage potential due to erosion risks. Conversely, light forests, covering 17.21% of the land, play a significant role in SOC accumulation. They enhance soil health and carbon storage through the input of organic matter from leaf litter and root decomposition. Grasslands, occupying 16.71% of the terrain, possess substantial potential for SOC sequestration, particularly due to their efficient carbon storage via deep root systems, especially under sustainable management. Open areas, accounting for 9.95% of the total, contribute minimally to SOC owing to their lack of vegetative cover.\u003c/p\u003e\u003cp\u003eDense forests, although covering only 7.68% of the total area, are crucial for SOC storage, capable of sequestering significant amounts of carbon both above- and below-ground. Their preservation is vital for maintaining these carbon stocks. Cropland represents the largest land use category at 21.15%, with SOC potential highly variable depending on management practices. While sustainable farming can significantly enhance SOC, conventional practices may lead to depletion. Finally, scrub areas, making up 14.56% of the landscape, can support SOC buildup if managed appropriately. These areas provide habitat and contribute to carbon storage through vegetation, underscoring the importance of prudent land management across all categories.\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\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\u003eLand use land cover statics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLu/lc\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecount\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArea / %\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSteep slope area\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e619535\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.73\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight forest\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e837315\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.21\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrass land\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e813398\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.71\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpen area\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e484309\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.95\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDense forest\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e373995\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.68\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrop land\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1029253\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.15\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScrub land\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e708754\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.56\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4866559\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e3.2. Bare soil index and NDVI maps\u003c/p\u003e\u003cp\u003eThe Normalized Difference Vegetation Index (NDVI) map, derived from the red and near-infrared (NIR) bands of the satellite image, is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. This NDVI map was subsequently utilized within a band math equation to estimate soil organic carbon (SOC). Similarly, the Bare Soil Index (BSI) map for the study area was generated using its respective formula. It has been observed that lower BSI values indicate the presence of vegetation, while higher BSI values signify a greater extent of bare soil. Consequently, low BSI values are associated with soil covered by vegetation or water (corresponding to high NDVI values or negative NDVI values), whereas high BSI values indicate more open and bare soil surfaces (characterized by low NDVI values greater than zero). In summary, low BSI values typically correspond to vegetated or water-covered areas, while high BSI values are indicative of predominantly bare soil.\u003c/p\u003e\u003cp\u003e3.3. Soil pH and soil organic carbon\u003c/p\u003e\u003cp\u003eSoil pH is a crucial factor influencing both soil inorganic carbon (SIC) and organic carbon (SOC) dynamics, and its impact must be considered in soil analysis. The pH of collected soil samples was assessed using standard laboratory methods. A spatial distribution map of soil pH was generated from these measured values, sourced from the ESRIC Data Hub (see Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Furthermore, spatial correlation maps comparing pH with both measured and predicted SOC were developed and analyzed (see Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Using ArcGIS tools, a spatial variability map illustrating the relationship between pH and predicted SOC was created to visualize how SOC accumulation varies with pH across the study area (see Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Subsequently, regression analysis was performed between the spatial maps of SOC and pH (see Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eStatistical regression analysis between pH and SOC values revealed a strong relationship, with an R² value of 0.80 (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Within the study area, soil pH is identified as a vital factor influencing SOC accumulation; with its effects varying across different pH ranges (see Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). In the acidic pH range of 5.1 to 5.6, decomposition rates are often slower due to reduced microbial activity, potentially leading to higher short-term SOC accumulation as organic matter decomposes more slowly.\u003c/p\u003e\u003cp\u003eConversely, the pH range of 5.7 to 6.2, representing moderately acidic to neutral soils, generally supports more effective SOC accumulation. In this range, typically more vigorous microbial activity enhances organic matter decomposition and promotes efficient carbon storage. Soils with a pH between 6.3 and 6.8, classified as neutral to slightly alkaline, tend to foster high microbial activity and effective nutrient cycling. This environment encourages organic matter decomposition, leading to increased SOC accumulation. Moreover, these soils are generally more fertile, supporting robust plant growth and significant inputs of organic matter. Overall, the study area exhibits optimal soil pH levels crucial for maximizing SOC stocks.\u003c/p\u003e\u003cp\u003eHigh SOC levels, such as a value of 75, indicate rich organic matter content, which enhances soil structure, fertility, and water retention (see Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Such levels support diverse microbial populations, improving nutrient cycling and overall soil health, suggesting the potential benefits of effective land management practices like cover cropping, reduced tillage, or organic amendments. In contrast, a low SOC value of 16 signifies a deficiency in organic matter, resulting in poor soil structure, diminished fertility, and reduced water retention. Such low SOC values may result from practices like intensive agriculture, erosion, or land degradation, which deplete organic matter over time. Analysis indicates that lower SOC values are prevalent in open areas, grasslands, croplands, and steep slopes, while the highest SOC values are observed in forested and scrubland areas, consistent with the land use and land cover patterns. Therefore, maintaining optimal soil pH and implementing sustainable land management practices are essential for enhancing SOC and promoting overall soil health.\u003c/p\u003e\u003cp\u003e3.4. Soil texture and soil organic carbon\u003c/p\u003e\u003cp\u003eSoil texture significantly influences soil organic carbon (SOC) stocks due to the varying physical and chemical properties of different soil types. In the study area, a high percentage (48.17%) of the land is characterized by high clay content. Clay soils generally exhibit a greater capacity to retain organic matter, attributed to their small particle size and large surface area, correlating with SOC levels observed in forested and lightly vegetated areas as identified in the land use and land cover classification.\u003c/p\u003e\u003cp\u003eClay loam, comprising 51.77% of the area, is prevalent in scrubland and grassland regions. This soil type, featuring a balanced mixture of clay, silt, and sand, offers good water retention and aeration. These characteristics promote microbial activity, potentially fostering higher SOC levels. Conversely, sandy clay, covering only 0.05% of the area, has a lower SOC potential. Its larger particle sizes lead to poorer moisture retention and reduced organic matter accumulation. Furthermore, sandy clay loam, with minimal coverage (0.01%), typically displays lower SOC stocks due to its higher sand content, which further compromises moisture retention capacity.\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=\"left\" 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\u003eSoil texture statistical value\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\u003eName of soil texture\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecount\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003earea/ha\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003earea/%\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClay\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39768\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e248550\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.17\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSandyclay loam\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.25\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClayloam4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42743\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e267143.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.77\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSandy clay\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e281.25\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e82557\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e515981.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThis study employs geospatial methods combined with a linear regression model to estimate soil organic carbon (SOC) within the study area. It also investigates the relationships between SOC and key influencing factors, including NDVI, pH, soil texture, and the Bare Soil Index (BSI). A linear model representing the correlations among these variables and SOC facilitates the assessment and prediction of regional soil conditions. The coefficient of determination (R²), traditionally used for comparing models of various forest parameters, is applied here to evaluate model performance.\u003c/p\u003e\u003cp\u003eThe BSI map (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) reveals that brown areas, indicating the lowest BSI values, correspond to regions where the soil is not bare. This aligns with the presence of scrubland, light vegetation, and dense forest. Conversely, the outer parts of the study area, depicted in green and showing the highest BSI values, correspond to crops, grasslands, and open areas. Notably, regions with low BSI values are associated with low NDVI values (-0.18) in the study area (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRegarding soil texture, high clay content (48.17%) dominates the area. Clay soils generally possess a greater capacity for organic matter retention due to their small particle size and large surface area. Clay loam, covering 51.77% of the area and found in scrubland and grassland, contains a balanced mixture of clay, silt, and sand. This composition promotes good water retention and aeration, enhancing microbial activity and potentially leading to higher SOC levels.\u003c/p\u003e\u003cp\u003eThe accumulation of SOC is also influenced by soil pH, with effects varying across different ranges. Acidic soils tend to exhibit slower decomposition rates, potentially resulting in higher short-term SOC levels. Moderately acidic to neutral soils (pH 5.7 to 6.2) generally support better SOC accumulation due to higher microbial activity. Neutral to slightly alkaline soils (pH 6.3 to 6.8) further encourage SOC accumulation, as they support vigorous microbial activity, effective nutrient cycling, and, being more fertile, robust plant growth and significant organic matter inputs.\u003c/p\u003e\u003cp\u003eA SOC value of 75 signifies rich organic matter, contributing to improved soil structure, fertility, and water retention. Such high SOC levels support diverse microbial populations, enhancing nutrient cycling and overall soil health. In contrast, a low SOC value indicates a deficiency in organic matter, leading to poor soil structure, reduced fertility, and lower water retention. Low SOC values, potentially resulting from intensive agriculture, erosion, or land degradation, are typically found in open areas, grasslands, croplands, and steep slopes, whereas the highest SOC values are observed in forested and scrubland areas.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eNDVI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enormalized difference vegetation index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSOC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSoil Organic Carbon\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eBSI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBare soil index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLULC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eland use land cover\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements: We are grateful to\u0026nbsp;International Soil Reference and Information Centre\u0026nbsp;and\u0026nbsp;European space agency\u0026nbsp;for providing material support to accomplish this research. We also received secondary data from the Central Statistical Agency (CSA). Our gratitude also goes to Dr. Ginjo Gitima for their assistance in the data analysis and for their unwavering support, sharing of information, and important advice on every aspect of our project.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors\u0026rsquo; contributions: JT conceived and designed the method section\u0026rsquo;s proven work. The author contributes to the article\u0026rsquo;s analysis, verification, and writing. AS conceived soil data analysis and interpretation. TW conceived reference and grammar checking. Both the authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Funding: This study received no funding from any institutions, agencies, or people.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthics and Consent:\u0026nbsp;We are writing to outline the ethical considerations associated with a research titled \u0026quot;[Estimation of accumulated soil organic carbon stock in Dawuro zone montane Forests South West Ethiopia using geospatial tools].\u0026quot; This study aims to estimates the accumulated soil organic carbon (SOC) stock in the montane forests of the Dawuro zone, Southwest Ethiopia, utilizing geospatial tools.\u003c/p\u003e\n\u003cp\u003eHuman Subjects: our research is not involves human participants. We have obtained only geospatial data for the purpose of this study.\u003c/p\u003e\n\u003cp\u003eEnvironmental Impact: The research has not involves any environmental assessment to evaluate potential impacts and have implemented measures to mitigate any adverse effects.\u003c/p\u003e\n\u003cp\u003eData Management: All data collected during the research will be stored securely and will be anonym zed to protect the privacy of data center. Data sharing will be conducted in accordance with relevant regulations and institutional policies.\u003c/p\u003e\n\u003cp\u003eConflict of Interest:\u0026nbsp;I declare that there are no conflicts of interest associated with this research. All funding sources have been disclosed, and there are no financial incentives affecting the outcomes of the study.\u003c/p\u003e\n\u003cp\u003eSustainability Practices: We have adhered to sustainable research practices, minimizing waste and using resources efficiently throughout our study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdel-Kader, Fawzy Hassan, 2011. Digital soil mapping at pilot sites in the northwest coast of Egypt: a multinomial logistic regression approach. Egypt. J. Remote Sens. Space Sci. 14 (1), 29\u0026ndash;40.\u003c/li\u003e\n\u003cli\u003eAlbaladejo, J., Ortiz, R., Garcia-Franco, N., Navarro, A. R., Almagro, M., Pintado, J. G., \u0026amp; Mart\u0026iacute;nez-Mena, M. (2013). Land use and climate change impacts on soil organic carbon stocks in semi-arid Spain. Journal of Soils and Sediments, 13(2), 265\u0026ndash;277. https://doi.org/10.1007/s11368-012-0617-7\u003c/li\u003e\n\u003cli\u003eAli, R.R., Moghanm, F.S., 2013. 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International Journal of Remote Sensing, 27(18), 4039-4055.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"NDVI, LULC, BSI, SOC","lastPublishedDoi":"10.21203/rs.3.rs-6262250/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6262250/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study estimates the accumulated soil organic carbon (SOC) stock in the montane forests of the Dawuro zone, Southwest Ethiopia, utilizing geospatial tools. Digital soil mapping techniques, including remote sensing and the Normalized Difference Vegetation Index (NDVI), were employed to analyze forest canopy density and its impact on soil health\u0026mdash;a critical factor for climate change mitigation. Data were gathered from satellite imagery (Sentinel-2A), soil samples, and field surveys. The research reveals the relationship between land use/land cover (LULC) classifications and SOC storage, highlighting that dense forests and grasslands significantly contribute to SOC accumulation, while open areas and steep slopes show limited storage due to erosion risks. Soil texture analysis indicates that high clay content enhances organic matter retention, promoting higher SOC levels. Additionally, soil pH was found to influence SOC dynamics, with moderately acidic to neutral soils supporting better microbial activity and carbon storage. The study underscores the importance of sustainable land management practices in maintaining optimal SOC levels and improving soil quality, ultimately providing valuable insights for ecological conservation and climate resilience strategies in the region. Through regression analysis, the findings establish a robust framework for predicting soil health conditions based on SOC and its influencing factors\u003c/p\u003e","manuscriptTitle":"Estimation of accumulated soil organic carbon stock in Dawuro zone montane Forests southwest Ethiopia using geospatial tools","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-11 09:23:24","doi":"10.21203/rs.3.rs-6262250/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d0a022c6-05f1-4583-8f8a-7166e82c273f","owner":[],"postedDate":"June 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-14T06:54:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-11 09:23:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6262250","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6262250","identity":"rs-6262250","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}