Soil Mapping Research Articles

Interpreting and evaluating digital soil mapping prediction uncertainty: A case study using texture from SoilGrids

Soil information is critical for a wide range of land resource and environmental decisions. These decisions will be compromised when the soil information quality is unsatisfactory. Thus, users of soil information need to understand and consider the uncertainty of the available soil information and be able to judge whether it is fit for purpose. The uncertainty information provided with the SoilGrids2.0 product was examined in a case study. We hypothesised that the soil property predictions for the Netherlands (NL) might be less uncertain than those of New Zealand (NZ) because there were more relevant training data for NL than for NZ. The study objectives were to: 1)understand whether the provided uncertainty information is correct for both countries; 2)explore spatial patterns and relationships in the prediction error and uncertainty information using quantitative tools and new graphical analyses; 3)analyse whether these patterns and relations can be explained; and 4)explore how the uncertainty information and insights derived from graphical analyses might assist an end user to determine whether a map is suitable for their purpose. The study focused on soil texture.Independent datasets showed that the SoilGrids 2.0 uncertainty information was too optimistic for sand and too pessimistic for clay for both countries. The graphical analyses confirmed the initial assumption that NL predictions were more accurate than those for NZ, but they also indicated that some locations in NL have high uncertainty. The graphical analyses allowed only a limited identification of the four sources of uncertainty in digital soil maps, but were quite insightful in helping us to better understand the reliability of the information. A set of recommendations was developed for both producers and consumers of digital soil mapping (DSM) products. This includes the provision of a summary map of accuracy classes. We suggest that more research and educational effort is needed to ensure that digital soil maps are used appropriately.

Estimation of Soil Salinity by Combining Spectral and Texture Information from UAV Multispectral Images in the Tarim River Basin, China

Texture features have been consistently overlooked in digital soil mapping, especially in soil salinization mapping. This study aims to clarify how to leverage texture information for monitoring soil salinization through remote sensing techniques. We propose a novel method for estimating soil salinity content (SSC) that combines spectral and texture information from unmanned aerial vehicle (UAV) images. Reflectance, spectral index, and one-dimensional (OD) texture features were extracted from UAV images. Building on the one-dimensional texture features, we constructed two-dimensional (TD) and three-dimensional (THD) texture indices. The technique of Recursive Feature Elimination (RFE) was used for feature selection. Models for soil salinity estimation were built using three distinct methodologies: Random Forest (RF), Partial Least Squares Regression (PLSR), and Convolutional Neural Network (CNN). Spatial distribution maps of soil salinity were then generated for each model. The effectiveness of the proposed method is confirmed through the utilization of 240 surface soil samples gathered from an arid region in northwest China, specifically in Xinjiang, characterized by sparse vegetation. Among all texture indices, TDTeI1 has the highest correlation with SSC (|r| = 0.86). After adding multidimensional texture information, the R2 of the RF model increased from 0.76 to 0.90, with an improvement of 18%. Among the three models, the RF model outperforms PLSR and CNN. The RF model, which combines spectral and texture information (SOTT), achieves an R2 of 0.90, RMSE of 5.13 g kg−1, and RPD of 3.12. Texture information contributes 44.8% to the soil salinity prediction, with the contributions of TD and THD texture indices of 19.3% and 20.2%, respectively. This study confirms the great potential of introducing texture information for monitoring soil salinity in arid and semi-arid regions.

Self-supervised learning of Vision Transformers for digital soil mapping using visual data

In arid environments, prospecting cultivable land is challenging due to harsh climatic conditions and vast, hard-to-access areas. However, the soil is often bare, with little vegetation cover, making it easy to observe from above. Hence, remote sensing can drastically reduce costs to explore these areas. For the past few years, deep learning has extended remote sensing analysis, first with Convolutional Neural Networks (CNNs), then with Vision Transformers (ViTs). The main drawback of deep learning methods is their reliance on large calibration datasets, as data collection is a cumbersome and costly task, particularly in drylands. However, recent studies demonstrate that ViTs can be trained in a self-supervised manner to take advantage of large amounts of unlabelled data to pre-train models. These backbone models can then be finetuned to learn a supervised regression model with few labelled data.In our study, we trained ViTs in a self-supervised way with a 9500 km2 satellite image of dry-lands in Saudi Arabia with a spatial resolution of 1.5 m per pixel. The resulting models were used to extract features describing the bare soil and predict soil attributes (pH H2O, pH KCl, Si composition). Using only RGB data, we can accurately predict these soil properties and achieve, for instance, an RMSE of 0.40 ± 0.03 when predicting alkaline soil pH. We also assess the effectiveness of adding additional covariates, such as elevation. The pretrained models can as well be used as visual features extractors. These features can be used to automatically generate a clustered map of an area or as input of random forests models, providing a versatile way to generate maps with limited labelled data and input variables.

디지털 토양도 작성 기법을 적용한 산림 토양 탄소 저장량 평가

디지털 토양도 작성 기법을 적용한 산림 토양 탄소 저장량 평가

Quantification of Soil Erosion Using Digital Soil Mapping and RUSLE Method for Coimbatore District, Tamil Nadu, India

Quantification of Soil Erosion Using Digital Soil Mapping and RUSLE Method for Coimbatore District, Tamil Nadu, India

Insights into the prediction uncertainty of machine-learning-based digital soil mapping through a local attribution approach

Abstract. Machine learning (ML) models have become key ingredients for digital soil mapping. To improve the interpretability of their predictions, diagnostic tools such as the widely used local attribution approach known as SHapley Additive exPlanations (SHAP) have been developed. However, the analysis of ML model predictions is only one part of the problem, and there is an interest in obtaining deeper insights into the drivers of the prediction uncertainty as well, i.e. explaining why an ML model is confident given the set of chosen covariate values in addition to why the ML model delivered some particular results. In this study, we show how to apply SHAP to local prediction uncertainty estimates for a case of urban soil pollution – namely, the presence of petroleum hydrocarbons in soil in Toulouse (France), which pose a health risk via vapour intrusion into buildings, direct soil ingestion, and groundwater contamination. Our results show that the drivers of the prediction best estimates are not necessarily the drivers of confidence in these predictions, and we identify those leading to a reduction in uncertainty. Our study suggests that decisions regarding data collection and covariate characterisation as well as communication of the results should be made accordingly.

Comparison of two gamma-ray datasets measured with different methods and assessment of their performance to predict soil properties

ABSTRACT Terrain mobility is directly dependent on soil properties, i.e. stoniness, soil particle size and peat depth. Gamma-ray spectrometry has been widely used in soil mapping as gamma-rays can penetrate up to 50 cm of soil. The method has proven to be effective in gathering information on various soil properties, such as clay and silt content, organic carbon content, soil pH, and soil stoniness. The intensity of gamma radiation decreases with increasing soil moisture content, which is important when assessing the load-bearing capacity of forest terrain, which affects the quality and planning of mechanized wood harvesting. In this study, we compared two gamma-ray datasets that were acquired from the same plots but measured with different methods: airborne gamma-ray spectrometry and a handheld gamma-ray spectrometer. Regression analyses were used to examine whether the gamma-ray datasets have significant similarities. Linear discriminant analysis was then used to study the performance of the two gamma-ray datasets when predicting different soil properties (stoniness index classes (SIC), soil depth, and peat depth). Our correlation analysis produced R2 values of 0.18–0.29 and root mean square error (RMSE%) values of 0.48–0.55. Prediction of SIC was 67.1% accurate with ground gamma and 45.9% accurate with air gamma. For soil depth predictions, the accuracies were 64.7–70.0% for ground gamma and 61.7–63.5% for air gamma. Our findings showed that ground gamma-ray spectrometry is better at predicting soil properties than air gamma-ray spectrometry.

Modeling the Spatial Variability of Soil Nutrients - A Case from Soil Health Card Project, India

Ascertaining and mapping soil nutrient data is crucial for governments to maintain soil health on farmlands. As part of the soil health card project, a total of 329 geo-referenced soil samples were collected from Thaticherla village, Anantapur mandal, Andhra Pradesh, India. These samples were analyzed for various soil properties such as soil pH, electrical conductivity (EC), organic carbon (OC), available nitrogen (N), available phosphorus (P), available potassium (K), available sulphur (S), DTPA extractable micronutrients (Fe, Mn, Zn, Cu), and hot water-soluble boron (B) at a depth of 0 to 15 cm. The results showed high variability (>35%) in coefficients of variation in Cu, EC, Zn, and B. The findings indicated positive correlation between Zn and Mn; N and OC; and OC and Zn. The data underwent logarithmic and Box-Cox transformations to achieve normalization. The ordinary kriging method was employed to analyze the spatial variability. The findings revealed that exponential model was appropriate for B, Fe, Mn, Zn, and OC; Gaussian for K; J-Bessel for N; K-Bessel for Cu, P, and S; stable for EC and rational quadratic for pH, respectively. The analysis showed a strong to weak spatial dependency. In the study area, the spatial variability maps exhibited deficiencies of 97%, 96% and 40% for N, OC and Zn, respectively. Therefore, it is urgent to apply suitable manures and fertilizers in the study area to address these issues. The study area exhibited significant variation in spatial patterns, emphasizing the importance of implementing field-specific plans for soil health and environmental management.

Soil organic carbon sequestration can be promoted through the improvement of landscape configuration heterogeneity in typical agricultural regions of northeast China

Landscape heterogeneity is considered a promising option for building resilient and sustainable agroecosystems. Understanding the relationships between farmland soil organic carbon (SOC) and landscape heterogeneity can support soil carbon sequestration and better serve food security and climate change. However, the influence extent and optimal scale of farmland landscape heterogeneity (i.e. landscape composition and landscape configuration heterogeneity) on SOC remains unclear. In this study, we established the relationships between multi-scale landscape heterogeneity and SOC in a typical grain-production county of northeast China. Stepwise regression results showed that when the buffer radius was 2000 m, the interpretation of SOC by landscape heterogeneity was the largest. The effects of landscape composition and landscape configuration on SOC were further decomposed by Variance Partitioning Analysis, and we found that independent interpretation ability of landscape configuration (8%) exceeded landscape composition (7%). The result of soil mapping combined with landscape indexes also showed that landscape configuration contributed more to the increased accuracy. Moreover, we found that correlation between configuration indexes and SOC at the class level was less related than that at the landscape level, among which the two most important indexes were Mean Fractal Dimension Index (FRAC_MN) and Number of Patches (NP). FRAC_MN was even more important than natural factors, indicating the validity of landscape as an indicator of human activities should not be ignored when considering farmland SOC. Overall, the results of this study revealed that the negative effects of agricultural intensification on SOC can be buffered to a certain extent by increasing the complexity of patch shape and reducing the degree of landscape fragmentation at the landscape level, providing hope for the sustainable development in intensive agricultural areas. In addition, due to the scale effect of landscape heterogeneity on farmland SOC, we suggest that decision makers should consider the spatial scale in landscape allocation and planning. This study provides a scientific reference for realizing the balance between grain production and ecological function in intensive agricultural areas.

Quantification of the uncertainty of geoscientific maps relying on human sensory engagement

Uncertainty of a geoscientific map derived from data sampled by sensory engagement of human experts cannot be quantified from within the processing chain followed to produce the map due to subjective components in the underlying data. Nevertheless, quantified uncertainty associated with such maps would be essentially required for correct information retrieval from such maps. An approach mimicking an expert elicitation through digital agents in an information fusion procedure is suggested to quantify the trustworthiness of a partially subjective map (such as a soil map or any other geoscientific map inherently linked to partial subjectivity, e.g., a geological map). This procedure links a partially subjective map with collocated maps resulting from technical measurements provided with quantified uncertainty. Variation of methodology in this approach results in an ensemble of maps with variable degree of matching with the partially subjective map which allows for probabilistic statements about the trustworthiness of the partially subjective map. This enables including partially subjective maps in further data integration relying on numerical analysis, e.g., in the context of computational modeling or machine learning. By being able to assign a quantitative trustworthiness to a partially subjective map the risk of over-utilization of this map is reduced.

Landslide Susceptibility Mapping in Nigeria Using Remote Sensing, GIS, and Machine Learning Models

Landslide situations remain a significant concern in Nigeria, as they pose a great risk to human lives, property, and the natural environment, particularly in regions with steep slopes, heavy rainfall, and unfavorable human interference, including deforestation and urbanization. Nigeria’s diverse geographical structure, ranging from urban areas such as Lagos and Abuja to rural regions, underscores the importance of accurate predictions for risk management and avoidance techniques. This research utilized remote sensing and GIS assessment to analyze landslide susceptibility in several regions of Nigeria. Data related to terrain, vegetation, soil moisture, and ground deformation were gathered using high-resolution satellite imagery from sources such as SPOT, ASTER, differential synthetic aperture radar interferometry (D-InSAR), and Landsat TM. GIS data layers included DEM, LULC, soil and geological maps, as well as hydrological maps and data. Methods applied in this study include logistic regression, STAT-R, frequency ratio, and the Random Forest tree-based model. The research produced detailed landslide susceptibility maps for various regions in Nigeria and identified significant factors such as slope, elevation, land use, precipitation, and access to transportation facilities. The Random Forest model demonstrated the most robust predictive capability. The integration of remote sensing with GIS was particularly significant, enhancing the precision of predictions and improving the efficacy of planning and management strategies. By incorporating remote sensing, GIS, and various machine learning algorithms, the researchers have developed a reliable tool for landslide risk prediction and management in Nigeria. Future research should focus on improving data quality and enhancing the generalizability of results to other regions.

REVEALING THE FERTILITY STATUS OF KHANEWAL DISTRICT’S LANDS THROUGH GIS-BASED STUDY

A rapid increase in industrialization and urbanization and population growth requires expansion of agricultural area for food security and raise the importance of soil health assessment to ensure protection and sustainable use of agricultural lands according to their potential. For this purpose, use of digital soil mapping for the analysis of key physicochemical characteristics has been widely used. The GIS enabled the mapping of extensive areas. The purpose of this study is the spatial analysis of soil fertility indicators i.e., soil reaction (pH), Electrical Conductivity (EC), organic matter (OM), micro-macro {Zinc (Zn), Copper (Cu), Iron (Fe), Manganese (Mn), Boron (B)} nutrients in Khanewal districts using GIS-approaches. The result of study shows that soil pH and EC are normal for crop production while almost 95% of samples show low OM, 100% have low available phosphorous content, 33.3% have low K content, 71% have low Fe and 83% have low B content. Therefore, it was recommended that, to continuously incorporate farmyard manure, green manure, and crop residues into the soil over an extended period to address the organic matter deficiency. P, K, Fe and B deficient area should be fertilized with variable rate of respective fertilizer and soil should be periodically tested. It was recommended that the central parts of Khanewal district lands are cultivable as compared to boarder areas.

Mapping the soil C:N ratio at the European scale by combining multi-year Sentinel radar and optical data via cloud computing

Spatial information on the soil carbon-to-nitrogen (C:N) ratio is essential for sustainable soil use and management. The unprecedented availability of Sentinel optical and radar data on cloud computing platforms, such as the Google Earth Engine (GEE), has created new possibilities for developing soil prediction models from the local scale to the planetary scale. However, there is a paucity of literature on the effects of Sentinel sensor selection and integration and radar data utilization strategies on mapping the C:N ratio. In this study, we explored the use of multiyear Sentinel-1 radar and Sentinel-2 optical data obtained from the GEE platform combined with the digital soil mapping (DSM) technique to map the soil C:N ratio at the European scale. The performance of soil prediction models, which were constructed using two modeling techniques (random forest and support vector machine), derived under multiple scenarios based on optical, radar and commonly used auxiliary data (climatic and topographic variables) combined with the LUCAS 2018 soil dataset, was evaluated by a cross-validation technique. The results showed that the modeling performance varied with the selection and integration of Sentinel observations, as well as the configuration of the radar data. Models based on single polarization performed the worst across all scenarios related to Sentinel-1, with cross-polarization performing better than copolarization. Models that utilized Sentinel-1 data from ascending orbits outperformed those that utilized data from descending orbits. The application of Sentinel-1 backscatter information derived from different orbits and polarization modes resulted in improved prediction accuracy. Our study also demonstrated the potential of integrating multiyear Sentinel satellite data via the GEE to map the continental-scale C:N ratio. The model based on Sentinel-1 data outperformed the one built on Sentinel-2 data, whereas combining Sentinel-2 optical data with Sentinel-1 radar data led to more accurate predictions. The variable importance results indicated that optical data and backscattering information from Sentinel observations are the most important groups of variables for soil C:N ratio mapping compared to the other variable groups (terrain and climate data). The digital soil maps generated under the different scenarios exhibited detailed patterns with significant spatial variation, with similar overall trends but slightly different details.

Mapping soil erosion susceptibility: a comparison of neural networks and fuzzy-AHP techniques

Mapping soil erosion susceptibility: a comparison of neural networks and fuzzy-AHP techniques

Картографирование почв на основе аэрофотосъемки с БПЛА

The article presents the results of research on the creation of a detailed soil map (scale 1:2500) using the example of a land plot of the Federal State Budgetary Educational Institution of Higher Education Perm State Agrarian University. The purpose of the research is to improve the methodology of detailed soil mapping using an unmanned aerial vehicle (UAV) and methods of geoinformation analysis of aerial photographs. The total area of the plot is 13.4 hectares, on which a UAV survey was carried out on September 12, 2023, and a soil survey was carried out on a detailed scale from June 1 to 24, 2024. Soil mapping was carried out on the basis of field data and a digital elevation model created by photogrammetry methods based on UAV survey materials. The map creation process is automated by creating geoprocessing models in QGIS model builder. Based on the results of creating a detailed soil map, a conclusion was made about the close relationship of soils with characteristic relief elements, which are reflected in classes according to the topographic position index TPI. Keywords: UAV, GEOINFORMATIONAL MAPPING, SOIL MAP

Prediction of soil texture using remote sensing data. A systematic review

Soil particle size fractions play a critical role in determining soil health attributes, including soil aeration, water infiltration and retention capacity, nutrients, and organic matter dynamics. Traditional soil mapping methods rely predominantly on ground-based surveys and laboratory analysis which are reported to be time-consuming and expensive. To address these challenges, there has been a global shift towards digital soil mapping (DSM) techniques that utilize remote sensing data. This review, conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guideline, aims to provide a comprehensive synthesis of the current state of soil texture prediction using remote sensing data. In particular, the review extract and synthesizes the satellite images used, identify the derived environmental covariates and their relative importance, and assesses the prediction models/algorithms used in the prediction of soil texture. Synthesis and analysis of 70 articles show that clay content is the most predicted of the three soil particle fractions accounting for 37% of the reviewed studies predominantly from topsoil layer (74.29%). Sentinel 2 and Landsat 8 are reported as the most frequently used satellite images. Among the covariates derived from these images, NDVI (80.4%) and SAVI (60.8%) are by far the most derived band ratios (indices). Red (37.3%), NIR (35.3%), Green (33.3%), Blue (33.3%), and SW2 (29.4%) bands were the five most incorporated as covariates for soil texture prediction amongst individual satellite bands. Regarding the DSM algorithms, Random Forest (RF) appeared in most reviewed articles followed by Support Vector Machines (SVM), and Quantile Regression Forest (QRF). The comparative model performance analysis showed that RF and Artificial neural network (ANN) had a good trade-off across validation metrics indicating their best performance in the prediction of both clay, sand, and silt. The RF performance showed a decreasing trend with increasing depth interval for clay and sand prediction and inconsistent for silt prediction.

Machine Learning Modelling for Soil Moisture Retrieval from Simulated NASA-ISRO SAR (NISAR) L-Band Data

Soil moisture is a critical factor that supports plant growth, improves crop yields, and reduces erosion. Therefore, obtaining accurate and timely information about soil moisture across large regions is crucial. Remote sensing techniques, such as microwave remote sensing, have emerged as powerful tools for monitoring and mapping soil moisture. Synthetic aperture radar (SAR) is beneficial for estimating soil moisture at both global and local levels. This study aimed to assess soil moisture and dielectric constant retrieval over agricultural land using machine learning (ML) algorithms and decomposition techniques. Three polarimetric decomposition models were used to extract features from simulated NASA-ISRO SAR (NISAR) L-Band radar images. Machine learning techniques such as random forest regression, decision tree regression, stochastic gradient descent (SGD), XGBoost, K-nearest neighbors (KNN) regression, neural network regression, and multilinear regression were used to retrieve soil moisture from three different crop fields: wheat, soybean, and corn. The study found that the random forest regression technique produced the most precise soil moisture estimations for soybean fields, with an R2 of 0.89 and RMSE of 0.050 without considering vegetation effects and an R2 of 0.92 and RMSE of 0.042 considering vegetation effects. The results for real dielectric constant retrieval for the soybean field were an R2 of 0.89 and RMSE of 6.79 without considering vegetation effects and an R2 of 0.89 and RMSE of 6.78 with considering vegetation effects. These findings suggest that machine learning algorithms and decomposition techniques, along with a semi-empirical technique like Water Cloud Model (WCM), can be effective tools for estimating soil moisture and dielectric constant values precisely. The methodology applied in the current research contributes essential insights that could benefit upcoming missions, such as the Radar Observing System for Europe in L-band (ROSE-L) and the collaborative NASA-ISRO SAR (NISAR) mission, for future data analysis in soil moisture applications.

Efficiency Analysis of Open Polygon Method in Land Mapping in Mojoroto Kediri

In the building establishment process, the geotechnical characteristics of soil have a significant impact on the structural performance of buildings. Therefore, soil mapping becomes a critical step in understanding the properties of soil that can affect foundation and structural stability. The mapping method used is open polygon mapping, which allows flexibility in determining land boundaries. Measurements were taken at three survey points: roadside, middle of the field, and edge of the field. The mapping results reveal variations in soil characteristics that need to be considered in development planning to ensure solid and stable building foundations. The implementation of Geographic Information Systems (GIS) in this process facilitates deeper data analysis and better visualization of field conditions. The mapping results show significant variations in ground elevation at each survey point, emphasizing the importance of understanding the area's topography. Additionally, area measurements were conducted using open polygon data, providing fundamental information for area development planning. Contour maps generated through Surfer software provide clear visualization of ground elevations at each survey point, enhancing understanding of the area's topography. This research contributes significantly to understanding soil characteristics and topography in the rear area of Mojoroto. It is expected to serve as a guide for area development planning, ensuring the stability and safety of future building structures. Thus, soil mapping is a critical step in ensuring the safety and sustainability of future buildings and contributes to sustainable area development planning.

Estimation of Runoff at Shetrunji River Basin using SWAT Hydrological Modelling

The researchers and scientists have realized that this is the high time to act in accordance to the impact of change of climate. Because of climate change problem, various effects occur, one of them is hydrological extremes. This leads to the emergence of hot-spots and vulnerable areas causing difficult adaptation. The current analysis intends to estimate the influence of change in climate on the hydrological parameter on one of the most important tributaries of Saurashtra region of Gujarat, Western India. Out of number of methods available to perform this task, simulation (hydrological modelling) which is the most widely used techniques available at this time, has been adopted. Here in this research the SWAT (Soil and Water Assessment Tool) model which happens to be a semi-distributed model and integrates the GIS information with the attribute database of the River has been utilized. The watershed delineation has been done using AWD (Automatic Watershed Delineation) which is available as a plugin in ArcSWAT. It has classified the catchment into 28 subbasins. From Esri Sentinel-2 landuse map has been used and the soil map is extracted from FAO Raster world soil map. Moreover, we can classify the sub basins into 173 HRUs (Hydrological Response Unit) by the model. The weather files from 1982 to 2019 has been used from the global weather source as a seed to the model. The successful run of the model gives elucidate fluctuating runoff volumes over time, spanning from a lowest of 2.56 mm to a extreme of 503.78mm. Notably, the annual average runoff stands at 190.82 mm, representing approximately 28.34% of the cumulative rainfall experienced within the river basin. The estimation of runoff is helpful in allocating water for agriculture, household demands, and industrial requirements.

Calculating Surface Runoff by Scs-Cn Model in the Sanjai River Basin, Jharkhand

Surface runoff prediction is a very intricate, evolving, and exponential phenomenon controlled by numerous interconnected components. Regulated by numerous types of related factors, surface runoff production is a highly complex, dynamic, and non-linear phenomena. Advancement and long-term control of water resources depend on run-off forecast. For runoff forecasts, many techniques and models are at hand; Among strategies, “Soil Conservation Services curve number(SCS- CN)” approach stands out. In order to determine potential outflow from a drainage system or region, the SCS implemented the Curve Number index. It is possible to figure out the curve number of a drainage basin by taking into account the antecedent moisture conditions(AMC), the soil, land surface use and land cover (LULC). Considered also is the Hydrological Soil Group (HSG). Four kinds of soils are offered at HSG: “A, B, C, and D”. Whereas soil type D stands for low penetration rates and greater surface flow capacity, soil category A comprises higher penetration rates below the surface and less runoff potentiality. The study's research site was the Sanjai river basin in Jharkhand. Arc Gis 10.1 and Erdas 14 software helped produce thematic layers like soil maps, LULC maps, and so on. The whole catchment was divided into sub-watersheds in order to more accurately measure runoff. The findings demonstrate that the SCS-CN simulation performs productive runoff estimates. The research demonstrates that more sustainable water use results from suitable surface runoff analysis.