Estimation of Soil Loss using RUSLE, GIS, and Remote Sensing: A Case Study of Sangli District, Maharashtra


  • Pranjali D. Patil Sahyadri College of Agricultural Engineering, Yeshwantnagar, Karad, Maharashtra, India Author
  • Nitin G. Patil Director, ICAR- National Bureau of Soil Survey and Land Use Planning, Nagpur, Maharashtra, India Author
  • Atul A. Atre Professor, Department of Soil and Water Conservation Engineering, Mahatma Phule Krishi Vidyapeeth, Rahuri Author



GIS, land use, RS, RUSLE, Sangli district, soil loss


Accurate estimation of soil loss is essential for watershed managers and planners to identify the priority areas for soil and water conservation measures. This study was undertaken to estimate the average annual soil loss in the study area of Sangli district, Maharashtra by using the Revised Universal Soil Loss Equation (RUSLE) in conjunction with Geographic Information System (GIS) and Remote Sensing (RS) data. The five potential factors of RUSLE impacting soil erosion were estimated through remote sensing data, enabling a comprehensive and informed assessment of soil erosion. The results of the analysis revealed that the average annual soil loss from the study area varied between 0 t.ha-1.yr-1 and 202.10 t.ha-1.yr-1. Higher annual soil loss was estimated in the western part of the study area, which ranged from 15 t.ha-1.yr-1 to 25 t.ha-1.yr-1 as compared to other parts of the study area. The Sangali district, in general, can be categorised as a low erosion potential district (0-5 t.ha-1.yr-1). The generated information can be utilised for the implementation of soil and water management and conservation measures in the western part of Sangli district, where there is a large area under the forest and agricultural land.


Babu R; Dhyani B L; Kumar N. 2004. Assessment of erodibility status and refined iso-erodent map of India. Indian J. Soil Conserv., 32(3), 171-177.

Bagwan W A; Gavali R S. 2020. Delineating changes in soil erosion risk zones using RUSLE model based on confusion matrix for the Urmodi river watershed, Maharashtra, India. Model. Earth Syst. Environ., 7, 2113- 2126.

CGWB. 2022. Aquifer Maps and Ground Water Management Plan- Sangli District, Maharashtra. Central Ground Water Board, Central Region, Nagpur, Department of Water Resources, River Development and Ganga Rejuvenation, Ministry of Jal Shakti, Govt. of India, (AAP 2021-22), 2431/NQM/2022, pp: 114.

Chanu N B; Mani A; Raghu Babu M; Suneela M V. 2022. Impact of land use land cover on soil erosion in Krishna lower subbasin. J. Agric. Eng., 55(2), 52-60.

Chatterjee S; Krishna A P; Sharma A P. 2014. Geospatial assessment of soil erosion vulnerability at watershed level in some sections of the Upper Subarnarekha River basin, Jharkhand, India. Environ. Earth Sci., 71, 357-374.

Choudhury B U; Nengzouzam G; Islam A. 2022a. Evaluation of climate change impact on soil erosion in the integrated farming system based hilly micro-watersheds using Revised Universal Soil Loss Equation. CATENA, 214,106306.

Choudhury B U; Nengzouzam G; Islam A. 2022b. Runoff and soil erosion in the integrated farming systems based on micro-watersheds under projected climate change scenarios and adaptation strategies in the eastern Himalayan Mountain ecosystem (India). J. Environ. Manage., 309, 114667.

Efthimiou N; Lykoudi E; Karavitis C. 2014. Soil erosion assessment using the RUSLE model and GIS. Eur. Water, 47, 15-30.

Ganasri B P; Ramesh H. 2015. Assessment of soil erosion by RUSLE model using remote sensing and GIS - A case study of Nethravathi Basin. Geosci. Frontiers, 7, 953-961.

Gaubi I; Chaabani A; Ben Mammou A; Hamza M H. 2016. A GIS-based soil erosion prediction using the Revised Universal Soil Loss Equation (RUSLE) (Lebna watershed, Cap Bon, Tunisia). Nat. Hazards, 86, 219-239.

Getu L A; Nagy A; Addis H K. 2022. Soil loss estimation and severity mapping using the RUSLE model and GIS in Megech watershed, Ethiopia. Environ. Challenges, 8, 100560.

Gupta S; Kumar S. 2017. Simulating climate change impact on soil erosion using RUSLE model − A case study in a watershed of mid-Himalayan landscape. J. Earth Syst. Sci., 126(3), Article No. 43.

Haan C T; Barfeld B J; Hayes J C. 1994. Design Hydrology and Sedimentology for Small Catchments. Academic Press, New York, pp: 588.

Jain M K; Das D. 2010. Estimation of sediment yield and areas of soil erosion and deposition for watershed prioritization using GIS and remote sensing. Water Resour. Manage., 24(10), 2091-2112.

Jat M. 2017. Tutorial for soil loss estimation in ArcGIS. [Accessed on 09/08/2023 09:30]

Jiang L; Yao Z; Liu Z; Wu S; Wang R; Wang L. 2015. Estimation of soil erosion in some sections of Lower Jinsha River based on RUSLE. Nat Hazards, 76(3), 1831-1847.

Joshi V; Susware N; Sinha D. 2016. Estimating soil loss from a watershed in Western Deccan, India, using Revised Universal Soil Loss Equation. Landscape Environ., 10 (1), 13-25.

Kamble S; Wankhede D M. 2018. Soil loss estimation of Bhatghar reservoir by RUSLE model using remote sensing and GIS technique. Rev. Res., 7(9), 5-13.

Kashiwar S R; Dongarwar U R; Kundu M C; Kumar D; Dongarwar L; Verma H; Awatade S. 2018. Evaluation of long-term rainfall variability of Bhandara (Maharashtra), India using GIS. Int. J. Curr. Microbiol. Appl. Sci., 7(7), 3846-3854.

Kashiwar S R; Kundu M C; Dongarwar U R. 2021. Soil erosion estimation of Bhandara region of Maharashtra, India, by integrated use of RUSLE, remote sensing, and GIS. Nat. Hazards, 110, 937-959.

Khare D; Mondal A; Kundu S; Mishra P. 2017. Climate change impact on soil erosion in the Mandakini River Basin, North India. Appl. Water Sci., 7, 2373- 2383.

Kouli M; Soupios P; Vallianatos F. 2009. Soil erosion prediction using the Revised Universal Soil Loss Equation (RUSLE) in a GIS framework, Chania, Northwestern Crete. Greece Environ. Geol., 57(3), 483- 497.

Mandal D; Chandrakala M; Alam N M; Roy T; Mandal U. 2021. Assessment of soil quality and productivity in different phases of soil erosion with the focus on land degradation neutrality in tropical humid region of India. CATENA, 204, 105440.

Maji A K; Reddy G P Obi; Sarkar Dipak. 2010. Degraded and Wastelands of India – Status and Spatial Distribution. Indian Council of Agricultural Research, New Delhi, pp: 169.

Maury S; Gholkar M; Jadhav A; Rane N. 2019. Geophysical evaluation of soils and soil loss estimation in a semiarid region of Maharashtra using revised universal soil loss equation (RUSLE) and GIS methods. Environ. Earth Sci., 78, 144.

Pandey A; Chowdary V M; Mal B C. 2007. Identification of critical erosion prone areas in the small agricultural watershed using USLE, GIS and remote sensing. Water Resour. Manage., 21(4), 729-746.

Patil R J; Sharma S K; Tignath S. 2015. Remote Sensing and GIS based soil erosion assessment from an agricultural watershed. Arab J. Geosci., 8(9), 6967- 6984.

Patil R J; Sharma S K; Tignath S; Sharma A P M. 2017. Use of remote sensing, GIS, and C++ for soil erosion assessment in the Shakkar River basin, India. Hydrol. Sci. J., 62(2), 217-231. 667.2016.1217413

Phadtare M; Nandgude S; Salunkhe S; Mahale D. 2020. Soil erosion and crop productivity loss for Raigad district of Kokan region. Int. J. Curr. Microbiol. Appl. Sci., 9(2), 1655-1666.

Raj R; Saharia M; Chakma S; Rafieinasab A. 2022. Mapping rainfall erosivity over India using multiple precipitation datasets. CATENA, 214,106-256.

Raj R; Saharia M; Chakma S. 2023. Mapping soil erodibility over India. CATENA, 230,107-271.

Renard K G; Freimund J R. 1994. Using monthly precipitation data to estimate the R-factor in the revised USLE. J. Hydrol., 157, 287-306.

Renard K; Foster GR; Weesies G A; Porter J P. 1991. RUSLE Revised Universal Soil Loss Equation. J. Soil Water Conserv., 46, 30-33.

Roy S; Das S; Sengupta S. Mistry S; Chatterjee J. 2022. Monitoring the temporal dimension of soil erosion in Mayurakshi Basin, India: A novel approach integrating RUSLE, Shannon’s entropy, and landscape ecological metrics. J. Earth Syst. Sci., 131, 249.

Scholes M C; Scholes R J. 2013. Dust Unto Dust. Sci., 342 (6158), 565-566.

Sharma N; Kaushal A; Yousuf A. 2022. Geospatial technology for assessment of soil erosion and prioritization of watersheds using RUSLE model for lower Sutlej sub-basin of Punjab, India. Environ. Sci. Pollut. Res., 30, 515-531.

Sharda V N; Dogra P; Prakash C. 2010. Assessment of production losses due to water erosion in rainfed areas of India. J. Soil Water Conserv., 65 (2), 79-91.

Sharda V N; Mandal D; Ojasvi P. R. 2013. Identification of soil erosion risk areas for conservation planning in different states of India. J. Environ. Biol., 34, 219-226. Sharda V N; Ojasvi P R. 2016. A revised soil erosion budget for India: role of reservoir sedimentation and land-use protection measures. Earth Surf. Process. Landforms, 41 (14), 2007-2023.

Suryawanshi A; Nema A K; Jaiswal R K; Jain S; Kar S K. 2021. Identification of soil erosion prone areas of Madhya Pradesh using USLE/RUSLE. J. Agric. Eng., 58(2), 177-191.

Thapa P. 2020. Spatial estimation of soil erosion using RUSLE modelling: A case study of Dolakha district, Nepal. Environ. Syst. Res., 9-15.

Tirkey A S; Pandey A C; Nathawat M S. 2013. Use of satellite data, GIS, and RUSLE for estimation of average annual soil loss in Dalton Ganj Watershed of Jharkhand (India). J. Remote Sens. Technol., 1 (1), 20-30.

Wischmeier W H; Johnson C; Cross B. 1971. A Soil erodibility nomograph for farmland and construction sites. J Soil Water Conserv., 26, 189-192.

Wischmeier W H; Smith D. 1978. Predicting Rainfall Erosion Losses. USDA Agricultural Research Service Handbook, pp: 537.





Regular Issue

How to Cite

Pranjali D. Patil, Nitin G. Patil, & Atul A. Atre. (2023). Estimation of Soil Loss using RUSLE, GIS, and Remote Sensing: A Case Study of Sangli District, Maharashtra. Journal of Agricultural Engineering (India), 60(3), 297-310.