Spatial mapping and management of soil erosion using GIS: A global perspective

Introduction

Soil erosion is a primary driver of land degradation and a persistent constraint on food and water security. It strips fertile topsoil, increases sediment loads in rivers and reservoirs, and diminishes the effectiveness of investments in irrigation and infrastructure. These impacts are particularly severe in densely populated regions and fragile environments where land use pressure and intense rainfall coincide. Conventional erosion assessment relied on plot measurements, empirical equations, and expert judgment at limited spatial scales. While valuable, these approaches could not provide detailed, spatially explicit information over large basins or national territories. The maturation of GIS and remote sensing has changed this situation. Analysts can now integrate rainfall, soil, terrain, land use, and management data into spatially distributed erosion models, and produce maps at field to continental scales [1,2] .  raindrop impact and runoff.  Human activities: deforestation, overgrazing, road building, and inappropriate tillage intensify erosion.

Wind erosion, mass movements, and coastal processes are also significant in many landscapes, but most GIS-based global work has focused on water erosion, especially sheet and rill erosion [2,3] .

Global patterns and impacts

Recent global assessments using RUSLE-type models combined with GIS and remotely sensed datasets estimate that large parts of Africa, South and Southeast Asia, and Latin America experience soil loss rates above tolerable thresholds for sustainable agriculture. Mountainous regions, Mediterranean landscapes, and semi-arid zones often show severe localized erosion, whereas lowland plains with gentle slopes typically show lower rates [3,4] .

Impacts include

 reduced crop yields due to topsoil loss and nutrient depletion;  increased sedimentation of reservoirs and irrigation canals;  degradation of aquatic habitats from turbidity and nutrient enrichment; and  elevated flood risk due to siltation and reduced channel capacity.

India typifies these pressures. High monsoon erosivity, steep slopes in the Himalaya and Western Ghats, and intensive cultivation in the Indo-Gangetic plains combine to create substantial erosion risk. GIS-based estimates indicate that in many Indian watersheds, soil loss exceeds 10 t ha⁻¹ yr⁻¹, which is often above locally accepted tolerable limits [4,5] . [5,6] .

GIS and Remote Sensing for Soil Erosion Mapping

Standard GIS workflow for erosion mapping

A typical GIS-based workflow, especially when using RUSLE, includes:

1. Data preprocessing: projection, resampling, and clipping of DEM, LULC, soils, and climate data.

Derivation of RUSLE factors or equivalent

 R: computed from rainfall intensity or empirical relationships with annual or monthly rainfall.  K: derived from soil texture and organic matter using standard equations.  LS: calculated from slope and flow accumulation derived from the DEM.  C: assigned based on LULC type and, where possible, vegetation indices such as NDVI.  P: assigned using information on conservation practices, often with assumptions in data-poor regions [6,7] . 3. Raster algebra: cell-by-cell multiplication of factor rasters to estimate annual soil loss.

Classification and mapping: grouping cell values into

erosion severity classes for visualization and prioritization. 5. Validation: comparing model outputs with observed sediment yield, plot data, or expert knowledge where available. 6. Scenario analysis: evaluating alternative land management or climate scenarios by adjusting factors such as C and P [8,9] .

This workflow is now widely standardized and implemented in desktop GIS, open-source platforms, and cloud environments.

Modelling Frameworks Used with GIS 4.1 Empirical models: USLE and RUSLE

The Universal Soil Loss Equation (USLE) and its revised form RUSLE remain the most widely applied models in GIS-based soil erosion mapping. RUSLE estimates long-term average annual soil loss (A) as:

where R, K, LS, C, and P represent rainfall erosivity, soil erodibility, slope length-steepness, cover-management, and support practices, respectively [9,10] . Its appeal lies in its relatively modest data requirements, modular structure, and the ease with which each factor can be represented as a raster layer in GIS. Studies across Africa, Asia, Europe, and the Americas have adapted factor estimation methods to local conditions and available data. However, RUSLE is empirical, originally calibrated for US conditions, and primarily represents sheet and rill erosion on hillslopes. It does not explicitly simulate gully erosion, mass movements, or channel processes, and it does not directly estimate sediment yield at catchment outlets [11,12] .

Other empirical and semi-empirical models

Other empirical or semi-empirical models used with GIS include:  USLE variants and modified RUSLE: adapted to specific climates or land uses, sometimes combined with sediment delivery ratio (SDR) concepts to estimate sediment export.  Unit stream power-based models: linking shear stress and transport capacity to slope and discharge.  Multi-criteria evaluation (MCE): combining factors such as slope, lithology, rainfall, and land cover using weighted overlays or analytical hierarchy process (AHP) to generate erosion susceptibility maps, often where calibration data are limited.

These approaches are attractive where data are sparse and where relative risk mapping is more important than precise estimation of soil loss [13,14] .

Physically based and distributed models

Physically based models represent hydrologic and erosion processes using conservation equations and physically meaningful parameters. Common examples linked with GIS include:  WEPP (Water Erosion Prediction Project): simulates infiltration, runoff, detachment, and deposition on hillslopes and in channels [15,16] .  SWAT (Soil and Water Assessment Tool): models land phase and channel routing in large basins, including sediment and nutrients; widely used for scenario analysis.  Other distributed models: ANSWERS, EROSION-3D, and regional models in Europe and elsewhere [16,17] .

These models offer process realism and allow event-scale simulation, but they require more detailed input data and calibration, which limits their use in data-poor regions or for very large domains.

Strengths, uncertainties, and validation

All models involve uncertainty arising from:  input data quality and resolution;  parameter estimation and transfer from other regions;  structural simplifications (e.g., omission of gullies and channels in RUSLE); and  scale mismatches between processes and grid cell size.

Comparative studies show that RUSLE in GIS often performs reasonably for relative risk mapping and for estimating mean annual soil loss where high-quality rainfall, soil, and DEM data exist. However, model predictions can differ substantially from observed sediment yields if sediment deposition, channel processes, and sediment delivery are not considered. Integration with SDR approaches and sediment monitoring networks can reduce this mismatch [17,18] .

Validation typically uses

 plot-scale erosion measurements,  sediment yield at gauging stations,  reservoir sedimentation surveys, or  qualitative expert assessments.

Global Applications and Case Studies 5.1 Africa

African landscapes show strong spatial variability in erosion due to contrasting climates, soils, and land use. GIS-based RUSLE applications across Ethiopia, North Africa, and other countries illustrate the power of spatial mapping for prioritization. In Horo district, western Ethiopia, Olika and co-authors applied RUSLE in a GIS environment using 12.5 m DEM, LULC, soil, and rainfall data. They reported mean annual soil loss of about 23 t ha⁻¹ yr⁻¹, with some hotspots exceeding 80 t ha⁻¹ yr⁻¹. High erosion occurred on steep cultivated slopes with sparse cover, whereas forested and gently sloping land experienced low losses. The resulting maps guided recommendations for terracing, contour ploughing, and afforestation [19,20] . In North Africa, several studies in Algerian and Tunisian watersheds combined USLE/RUSLE with GIS to map erosion severity. In the Mellegue watershed at the Algeria-Tunisia border, annual soil loss showed strong spatial heterogeneity, with vulnerable areas concentrated on steep slopes with degraded vegetation. These maps supported prioritization of check dams and reforestation [21,22] . At continental scale, reviews of soil loss estimation in Ethiopia and neighbouring countries highlight the dominance of GIS-RUSLE approaches, often combined with SDR and watershed prioritization frameworks [22,23] .

Asia (excluding India)

Across Asia, monsoon climates, steep terrain, and rapid land-use change create intense erosion pressures. GIS-based mapping has been widely adopted.

In Dolakha district, Nepal, Thapa used RUSLE with GIS to map soil erosion under different land use categories. The study reported that about one-third of the district experienced moderate to very severe erosion, driven mainly by steep slopes and deforested or intensively cultivated land. Forest and grassland showed much lower erosion rates, reinforcing the role of vegetation cover [24,26] . In Bangladesh, Al Shoumik and colleagues applied RUSLE and GIS to tertiary hill tracts from 2017 to 2021, finding average soil loss around 30 t ha⁻¹ yr⁻¹ in highly erodible hilly areas. The study emphasized how deforestation and expanding settlements increased erosion risk [28,29] . In Pakistan's Jhelum River watershed (Azad Jammu and Kashmir), Waseem et al. combined RUSLE with high-resolution GIS layers to map erosion risk classes. Annual soil loss ranged from near zero to thousands of tons per hectare per year in localized steep zones, with severe classes concentrated on steep, sparsely vegetated slopes. The maps helped identify priority subwatersheds for interventions [30,31] .

Europe

European studies often integrate long-term monitoring, detailed soil databases, and high-resolution DEMs. Several Mediterranean catchments have been analysed using RUSLE and GIS, highlighting strong seasonality and high erosion from intense storms on degraded shrublands and cultivated slopes. In many cases, studies combine RUSLE with AHP or other multi-criteria methods to derive composite erosion-susceptibility indices [32,33] . In the European Union, large-scale modelling efforts integrate RUSLE-type approaches with harmonized soil and climate datasets to generate continental erosion risk maps. These products inform agricultural and environmental policy, including cross-compliance measures and soil conservation guidelines [34,35] .

The Americas

In North and South America, GIS-based erosion mapping includes both RUSLE applications and physically based models. In Brazil, Barbosa and co-authors reviewed RUSLE-based modelling for diverse Brazilian landscapes, emphasising how differences in rainfall regime, soils, and land use require careful regional calibration of factors, especially R and C. Similar work in the United States and Mexico has integrated RUSLE with high-resolution DEMs and detailed land cover datasets to support conservation planning [36,37] . Process-based models such as WEPP and SWAT are also widely applied in the Americas. For example, SWAT-based studies in West Bengal (India) and other regions demonstrate how hydrologic-sediment models can complement RUSLE by simulating sediment routing and evaluating reservoir sedimentation [38,39] . At regional and watershed scales, Indian studies provide detailed examples of how GIS-based mapping supports land and water management [40,41] .

Nethravathi Basin, Karnataka

Ganasri and Ramesh applied the RUSLE model within a GIS environment to the Nethravathi Basin in Karnataka, using rainfall, soil, LULC, and topographic data.They derived factor maps and computed cell-wise soil loss, classifying erosion into five severity classes [41,42] .

Key findings included

 a large proportion of the basin under low to moderate erosion;  severe erosion concentrated in steep, deforested or cultivated uplands; and  strong sensitivity of estimates to the LS factor derived from the DEM.

The study demonstrated that RUSLE-GIS can identify erosion hotspots in a humid tropical basin and support planning of check dams, contour bunds, and reforestation. Its methodology has been widely cited and adapted in other Indian watersheds [43,44] .

Chilika Lake watershed, Odisha

Chilika Lake, a brackish coastal lagoon in Odisha, faces sedimentation and ecological stress from upstream erosion. Behera and co-authors used RUSLE combined with remote sensing and GIS to quantify soil loss in the Chilika watershed.

The study computed R, K, LS, C, and P factors from rainfall, soil maps, DEM, and multi-temporal LULC, and produced erosion maps showing that:  roughly three-quarters of the watershed area fell under low erosion risk;  a smaller but critical fraction experienced high to very high erosion, especially in deforested upper catchments; and  these hotspots contributed disproportionately to sediment inflow to the lake.

These outputs are directly relevant for targeting soil and water conservation measures in sub-watersheds feeding Chilika, and for protecting the lake' s fisheries and biodiversity [45,46] .

Semi-arid and plateau regions: Telangana, Madhya Pradesh, West Bengal

In semi-arid and plateau regions, GIS-based erosion mapping has focused on rainfed agriculture and drought-prone districts.

Sangareddy district, Telangana

A study in Sangareddy district used RUSLE within GIS to estimate spatial patterns of soil loss. Using rainfall, soil, DEM, and LULC data, the authors found that:  most of the district experienced soil loss below 5 t ha⁻¹ yr⁻¹;  limited areas showed losses above 20 t ha⁻¹ yr⁻¹, especially on steep cultivated land; and  validation against observed sediment data (R² ≈ 0.82) indicated satisfactory model performance.

The study recommended prioritizing contour farming, vegetative barriers, and check dams in high-loss zones.

Anuppur district, Madhya Pradesh

In Anuppur district, a recent study employed RUSLE with GIS tools to estimate both soil erosion and fertility risk. The authors computed R, K, LS, C, and P using rainfall time series, soil texture, DEM-derived slopes, and LULC, and highlighted that steep, deforested slopes and mining-affected areas showed very high erosion. The findings support targeted watershed treatment in central Indian highlands [47,48] .

Dwarakeshwar-Rupnarayan basin and neighbouring basins, West Bengal

In West Bengal, RUSLE and SWAT have been used to analyse soil erosion and its future trends in the Dwarakeswar and neighbouring basins. These studies showed:  substantial erosion on cultivated uplands with lateritic soils;  sensitivity of soil loss to projected land-use change and rainfall variability; and  opportunities to test management scenarios such as increased vegetative cover or conservation tillage [48,49] .

The integration of RUSLE with Google Earth Engine for the Dwarakeswar-Rupnarayan basin and similar basins illustrates how cloud platforms can speed analysis and facilitate multitemporal mapping.

Lessons for Indian land and water management

From these and many other case studies across India, several lessons emerge: 1. High spatial variability: even within a single district, soil loss can vary from less than 1 t ha⁻¹ yr⁻¹ in plains or forest to more than 50 t ha⁻¹ yr⁻¹ on steep, cultivated slopes. GISbased maps capture this variability and prevent uniform, inefficient interventions. 2. Importance of LULC: conversion of forest or scrub to cropland or built-up area often leads to rapid increases in erosion, especially where slopes are moderate to steep. 3. Sensitivity to DEM and rainfall data: LS and R factor estimation strongly influence results; higher-resolution DEMs and improved rainfall products significantly enhance reliability.

Policy relevance: outputs can feed directly into

programmes such as watershed development, MGNREGAsupported soil conservation, and reservoir sedimentation management [44,49] . For example, in the Horo district of Ethiopia and in Indian watersheds such as Nethravathi and Chilika, erosion maps have informed location-specific combinations of agronomic, vegetative, and structural measures that maximize conservation impact per unit investment.

From

Mainstreaming GIS into agricultural and watershed programmes

The effectiveness of GIS-based mapping depends on its integration into institutional processes. Important aspects include:  Standardized methods: adoption of common RUSLE factor estimation protocols and data sources within national agencies supports consistent planning.  Capacity building: training extension staff and watershed planners in basic GIS and interpretation of erosion maps increases utilisation of the outputs.  Monitoring and feedback: periodically updating LULC and erosion maps allows evaluation of conservation investments and refinement of models.

In India, the integration of remote sensing and GIS into watershed development programmes under various ministries has already begun. Extending these approaches to all major river basins and aligning them with state-level soil conservation services would further enhance impact [49,50] . [47,50] . 4. Uncertainty quantification: Few applied studies provide systematic uncertainty analysis. Adopting ensemble approaches, sensitivity analysis, and probabilistic mapping would allow decision-makers to understand confidence levels. 5. Climate and land-use change integration: While some case studies in India and elsewhere model future scenarios, most assessments remain static. Expanding scenario analysis with regional climate projections and land-use models would support long-term soil and water conservation planning.

Machine learning and hybrid methods:

There is growing interest in combining RUSLE or physically based models with machine learning techniques that exploit large archives of satellite and environmental data. These hybrid approaches may improve prediction where process understanding is incomplete but data are abundant. 7. Socio-economic integration: Erosion maps are rarely combined with socio-economic layers such as poverty, land tenure, or livelihood dependence. Integrating these dimensions would help target interventions where both biophysical risk and social vulnerability are high [50,51] .

For India, in particular, expanding multi-scale erosion monitoring networks and integrating them with GIS models would underpin robust calibration and support adaptive management under changing monsoon behaviour.

Conclusion

GIS-based spatial mapping of soil erosion has become a central tool for diagnosing land degradation and supporting conservation planning worldwide. The integration of RUSLE and related models with DEMs, satellite-derived land cover, soil maps, and climate datasets allows detailed, spatially explicit estimation of erosion risk from field to global scales. Case studies from Africa, Asia, Europe, and the Americas demonstrate that these approaches consistently reveal strong spatial heterogeneity in erosion, concentrate risk in steep and poorly covered slopes, and identify priority areas for intervention [52,53] .

. The Indian experience illustrates both the utility and challenges of these methods. Studies in Nethravathi, Chilika, Sangareddy, Anuppur, and other basins show how erosion maps have informed watershed treatment, soil conservation, and reservoir protection. They also highlight persistent issues: dependence on empirical models, sensitivity to input data, and limitations in representing gully and channel erosion. Overall, the evidence indicates that GIS-based erosion mapping, when carefully implemented and validated, provides a powerful basis for targeting conservation investments, safeguarding ecosystem services, and supporting climate-resilient agriculture. Future progress will depend on improving data quality, expanding process-based and hybrid modelling, better accounting for uncertainty, and integrating socio-economic and climate change dimensions. For India and other countries experiencing rapid environmental and socio-economic change, strengthening these approaches and embedding them in policy and planning frameworks is essential to protect soil resources and the livelihoods that depend on them [54] .

References

1. Al Shoumik MM, Haque MM. Application of GIS and RUSLE for soil erosion assessment in Bangladesh' s tertiary hill tracts. Environ Monit Assess. 2021;193(7):457.
2. Barbosa MR, Silva MC, Mendes CA. Soil erosion risk in the Brazilian Cerrado: A GIS-based RUSLE approach. Land Degrad Dev. 2020;31(5):638-51.
3. Behera S, Khatua K. Soil erosion mapping and risk assessment in the Chilika Lake watershed, India, using RUSLE and GIS. Environ Monit Assess. 2020;192(9):571.
4. Ganasri BP, Ramesh H. Soil erosion modeling using GIS and RUSLE in Nethravathi Basin, Karnataka, India. Int J Environ Sci Technol. 2019;16(6):3291-300.
5. Ghosh S, Kundu S. Impact of land use change on soil erosion in a semi-arid region of India using GIS-based RUSLE model. Environ Earth Sci. 2019;78(2):56.
6. Kumar S, Patel K. Soil erosion risk analysis using GIS and remote sensing: A case study of the Indus Basin, Pakistan. Land Degrad Dev. 2018;29(10):3667-78.
7. Li X, Zhang Y. Remote sensing-based soil erosion assessment in arid regions: A GIS-RUSLE approach. Int J Remote Sens. 2020;41(18):7056-71.
8. Menta A, Giri S. GIS-based soil erosion modeling in the Mediterranean Basin: A review. Catena. 2019;182:104145.
9. Olika G, Addis T. Spatial distribution of soil erosion risk in Horo district, Ethiopia using GIS and RUSLE model. Land Degrad Dev. 2020;31(4):492-503.
10. Patil RB, Patil AS. Application of GIS for watershed management: A case study of the Sangareddy district, Telangana, India. Hydrol Res. 2018;49(6):1587-97.
11. Ramesh H, Ganasri BP. Modeling of soil erosion in the Nethravathi Basin using GIS and RUSLE. J Environ Manag. 2021;278:111499.
12. Rao SR, Kulkarni PS. Soil erosion modeling in the upper catchment of the Godavari Basin, India: A GIS-based approach. Environ Earth Sci. 2020;79(3):89.
13. Samant SS, Srivastava R. Application of RUSLE model for soil erosion estimation using GIS: A case study of the Mahanadi River Basin, India. J Environ Sci Eng. 2020;62(4):567-79.
14. Shrestha MS, Lamichhane M. Soil erosion risk assessment using GIS-based RUSLE and multi-criteria evaluation in Dolakha district, Nepal. Environ Monit Assess. 2021;193(2):83.
15. Thapa R, Paudel P. Soil erosion and its relationship with land use change in Nepal' s Dolakha district: A GIS-based RUSLE approach. Environ Earth Sci. 2020;79(8):389.
16. Waseem S, Zia M. Soil erosion modeling in the Jhelum River watershed, Pakistan, using RUSLE and GIS. Environ Earth Sci. 2021;80(5):231.
17. Zhang S, Song L. Soil erosion risk mapping in the arid regions of northern China using GIS-based RUSLE model. Environ Monit Assess. 2020;192(8):512.
18. Zhang Y, Li J. GIS-based soil erosion mapping and prioritization of conservation practices in the Yangtze River Basin, China. Land Degrad Dev. 2021;32(7):1744-57.
19. Zhou G, Guo H. GIS-based soil erosion assessment in the upper reaches of the Yellow River Basin, China. Environ Sci Policy. 2020;108:75-83.
20. Bui DT, Youssef AM. Assessment of soil erosion susceptibility using RUSLE and GIS: A case study in the Middle East. Land Use Policy. 2019;87:104074.
21. Costantini EAC, Biancalani R. GIS-based soil erosion risk maps for Mediterranean watersheds: A review. Sci Total Environ. 2018;610-11:568-78.
22. El-Assiery MI, El-Sayed SA. GIS-based soil erosion modeling for agricultural land management in Egypt. Environ Monit Assess. 2019;191(12):688.
23. Ho TL, Nguyen DL. GIS-based modeling of soil erosion risk in the Mekong Delta, Vietnam. Environ Earth Sci. 2020;79(3):128.
24. Iqbal J, Chaudhary DR. Evaluation of soil erosion susceptibility in Pakistan' s arid zones using GIS-based RUSLE approach. Arab J Geosci. 2021;14(5):202.
25. Karami M, Rahmati O. GIS-based assessment of soil erosion and conservation measures in Iran. Environ Sci Policy. 2020;110:68-80.
26. Kuriakose AT, Venugopal K. Soil erosion risk and management in the Western Ghats using GIS. J Soil Water Conserv. 2020;75(3):61-70.
27. Liu Y, Liu B. GIS-based soil erosion risk assessment in the North China Plain using RUSLE and remote sensing data. Land Degrad Dev. 2020;31(6):834-47.
28. Mehmood S, Mian M. GIS-based estimation of soil erosion risk in the Sulaiman Range, Pakistan. Environ Monit Assess. 2019;191(10):631.
29. Munir S, Pervaiz S. Soil erosion susceptibility analysis in the Salt Range, Pakistan, using GIS and RUSLE. Environ Earth Sci. 2021;80(5):347.
30. Mustapha A, Aladejana J. Application of GIS for soil erosion mapping in the Niger Delta region, Nigeria. Environ Monit Assess. 2020;192(2):76.
31. Nagy A, Tóth G. Soil erosion and conservation in Hungary: A GIS-based study. Geoderma. 2019;355:113922.
32. Saha A, Roy P. GIS-based spatial analysis of soil erosion in the hilly terrain of the Western Ghats, India. Land Degrad Dev. 2020;31(3):370-80.
33. Sanchez JA, Arce ML. GIS-based soil erosion assessment and conservation measures in Costa Rica. Environ Monit Assess. 2018;190(7):433.
34. Singh M, Meena HL, Najmusaqib S. Optimizing harvest management: Strategies for maximizing crop yield and quality. Agrifrontline. 2025;1(3):1-5.
35. Singh M, Meena HL, Najmusaqib S. Optimizing harvest management: Strategies for maximizing crop yield and quality. Agrifrontline. 2025. https://agrifrontline.com/wpcontent/uploads/2025/01/1.-OPTIMIZINGHARVEST.pdf.pagespeed.ce.pPjKCngJDr.pdf
36. Shah S, Raza M. Estimation of soil erosion in the Chenab River Basin, Pakistan, using GIS-based RUSLE. Environ Earth Sci. 2020;79(12):413.
37. Thomas BD, Shrestha A. Mapping soil erosion in Nepal' s hilly regions using GIS and RUSLE. J Soil Water Conserv. 2020;75(1):40-50.
38. Troch P, Yang Y. GIS-based modeling of soil erosion and sediment transport in mountainous watersheds. Environ Model Softw. 2020;124:104563.
39. Wang Z, Liu X. Soil erosion risk assessment in a semi-arid region of China using GIS and RUSLE. Land Degrad Dev. 2020;31(9):1289-301.
40. Xiao W, Ma X. GIS-based soil erosion prediction in the Upper Yangtze River Basin, China. Geosciences. 2021;11(2):62.
41. Yousaf R, Alam S. GIS-based soil erosion susceptibility mapping in the mountainous areas of Pakistan. Environ Earth Sci. 2020;79(8):213.
42. Zhang X, Han D. Application of GIS-based RUSLE model for soil erosion assessment in the upper reaches of the Yellow River Basin. Land Use Policy. 2021;100:104824.
43. Tamuly G, Majid B. Disaster forecasting in agriculture: Early warning through geospatial tools. Agrifrontline. 2025;1(9):1029. https://agrifrontline.com/wp-content/uploads/2025/11/AF-V1I9-July-2025-018.pdf
44. Zheng J, Qiao J. GIS-based assessment of soil erosion risks in the mountainous areas of South China. Geoderma. 2020;364:114272.
45. Zhou M, Shen L. GIS and remote sensing-based soil erosion modeling in the Yangtze River Basin, China. Land Degrad Dev. 2021;32(2):477-87.
46. Zubair A, Awais M. Spatial modeling of soil erosion in the Jhelum Valley, Pakistan, using GIS-based RUSLE. Environ Monit Assess. 2021;193(1):22.
47. Kim T, Choi S. Soil erosion risk assessment in Korea' s mountainous regions using GIS-based models. Environ Earth Sci. 2020;79(7):276.
48. Lu X, Wang X. A GIS-based RUSLE approach for soil erosion modeling in the Loess Plateau of China. Environ Earth Sci. 2019;78(5):161.
49. Mehra P. Remote sensing-based assessment of soil nutrient status for precision and site-specific fertilization. Agrifrontline. 2025;1(9):77-86. https://agrifrontline.com/wp-content/uploads/2025/11/AF-V1I9-July-2025-014.pdf
50. Roychowdhury R, Najmusaqib S, Ahmad PT. Biofactories in bloom: Turning plants into sustainable sources. Agrifrontline. 2025;1(9):62-6. https://agrifrontline.com/wpcontent/uploads/2025/11/AF-V1-I9-July-2025-011.pdf
51. Zhu X, Zhu W, Zhang J, Pan Y. Mapping irrigated areas in China from remote sensing and statistical data. IEEE J Sel Top Appl Earth Obs Remote Sens. 2014;7(11):4490-504.
52. Zhu X, Zhu W, Zhang J, Pan Y. Mapping irrigated areas in China from remote sensing and statistical data. IEEE J Sel Top Appl Earth Obs Remote Sens. 2014;7(11):4490-4504.
53. Nadaf SA, Amara DMK, Patil PL. Distribution of micronutrients in selected pedons of sugarcane growing Vertisols in Northern Karnataka State of India. Open J Soil Sci. 2022;12:1-12. doi:10.4236/ojss.2022.121001.
54. Amara DMK, Nadaf SA, Saidu DH, Vonu OS, Musa RM, Kamanda PJ, et al. Studies on land resource inventory for agricultural land use planning in Northern Transition Zone of India through remote sensing and GIS techniques. J Geogr Inf Syst. 2021;13:710-728. doi:10.4236/jgis.2021.136039.
55. Nadaf SA, Patil PL, Dasog GS. Identification of micronutrient constraints in sugarcane-growing Vertisols of Northern Transition Zone of Karnataka by GIS technique. Karnataka J Agric Sci. 2015;28(1).