Journal of New Approaches in Water Engineering and Environment
Scientific Journal

Spatial assessment of soil erosion using the Revised Universal Soil Loss Equation (RUSLE) in the Google Earth Engine platform

Document Type : Original Article

Authors

Omid khorashadizadeh 1
elham ghochanian haghverdi 2

1 South Khorasan Regional Water Company, Iran Water Resources Management Company, Birjand, Iran.

2 Department of Water Science and Engineering, Faculty of Agriculture, University of Birjand, Birjand, Iran, Soil and Water Research Expert, South Khorasan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Birjand, Iran.

10.22034/nawee.2026.575555.1196
Abstract
Objective: Soil erosion is one of the most critical environmental challenges in arid and semi-arid regions, threatening the sustainability of water and soil resources and increasing sedimentation in reservoir systems. The aim of this study was to quantitatively estimate and spatially map soil erosion risk in the Siahoo Dam watershed, located in South Khorasan Province, eastern Iran.
Methods: In this research, the Revised Universal Soil Loss Equation (RUSLE) model was implemented within the Google Earth Engine (GEE) cloud computing platform. The use of this platform enabled the processing of large volumes of satellite data without the need for local computational resources. Five main RUSLE factors were derived and analyzed, including rainfall erosivity (R), soil erodibility (K), topographic factor (LS), land use/land cover (C), and conservation practice factor (P). The R factor was calculated using 20 years of CHIRPS precipitation data. The K factor was derived by integrating soil texture maps and geological information. The LS factor was generated using the 30-meter SRTM digital elevation model. The C factor was extracted from ESA land cover products and Sentinel imagery, while the P factor was assigned based on slope classes and land use patterns.
Results: The results showed that the soil erodibility factor (K) was 0.029 (t·ha·h / ha·MJ·mm), indicating a relatively high susceptibility of the regional soils to water erosion. Soil erosion was unevenly distributed across the watershed. Based on model outputs and the FRWMO classification criteria, approximately 65.8% of the watershed was classified as very low erosion (0–10 t ha⁻¹ yr⁻¹), 20% as low to moderate erosion (10–40 t ha⁻¹ yr⁻¹), and nearly 14% as high to very high erosion (>40 t ha⁻¹ yr⁻¹). The mean annual soil loss was estimated at 15–20 t ha⁻¹ yr⁻¹, demonstrating the effectiveness of the proposed approach for spatial erosion assessment.
Conclusions: The findings of this study demonstrate that integrating remote sensing with hydrological modeling in the GEE environment offers a rapid, scalable, and high-precision approach for identifying erosion-prone areas.

Keywords

Soil erosion risk mapping
RUSLE model
Google Earth Engine (GEE)
Siahoo Dam Watershed
Arid Regions
 
Abkar, A., Saeedian, H., & Haj Seyed Alikhani, N., 2023. Investigating the role of socio-economic issues in the development of the desert Watersheds (Case study: Yazdanabad watershed, Kerman Province). Jornal of New Approaches in Water Engineering and Environment, 3(1),pp. 33–46. https://doi.org/10.22034/nawee.2023.418861.1051
Alkharabsheh, M. M. Alexandridis, T. K. Bilas, G. Misopolinos, N., Silleos, N., 2013. Impact of land cover change on soil erosion hazard in Northern Jordan using remote sensing and GIS. Procedia Environmental Sciences, 19, pp.912-921. https://doi.org/10.1016/j.proenv.2013.06.101
Ang, M. L. E.  Arts, D.  Crawford, D.  Labatos Jr, B. V. Ngo, K. D. Owen, J. R. Gibbins, C., Lechner, A. M., 2021. Socioenvironmental land cover time-series analysis of mining landscapes using Google Earth Engine and web-based mapping. Remote Sensing Applications- Society and Environment, 21, p.100458. https://doi.org/10.1016/j.rsase.2020.100458
Bouguerra, H., 2018. Quantification and modelling of solid transport, mapping of areas at risk of water erosion by using a GIS: Application to the Bouhamdane and Ressoul basins (North-East Algeria). Ph.D. Thesis, University of Tlemcen, Algeria.
Dutta, D. Das, S. Kundu, A., Taj, A., 2015. Soil erosion risk assessment in Sanjal watershed, Jharkhand (India) using geo- informatics, RUSLE model and TRMM data. Modeling Earth Systems and Environment, 1(4), pp.1–9. https://doi.org/10.1007/s40808-015-0034-1
Elnashar, A. Zeng, H. Wu, B. Fenta, A. A. Nabil, M., Duerler, R., 2021. Soil erosion assessment in the Blue Nile Basin driven by a novel RUSLE-GEE framework. Science of the Total Environment, 793, p.148466. https://doi.org/10.1016/j.scitotenv.2021.148466
El Jazouli, A. Barakat, A. Khellouk, R. Rais, J., Baghdadi, E. M., 2019. Remote sensing and GIS techniques for prediction of land use land cover change effects on soil erosion in the high basin of the Oum Er Rbia River (Morocco). Remote Sensing Applications- Society and Environment, 13, pp.361–374. https://doi.org/10.1016/j.rsase.2018.12.004
FAO., 2015a. Status of the world’s soil resources. Food and Agriculture Organization of the United Nations. https://www.fao.org/3/i5199e/i5199e.pdf
FAO., 2015b. Global soil partnership: Towards a global agenda for sustainable soil management. Food and Agriculture Organization of the United Nations. https://www.fao.org/3/ai3794e.pdf
Fathizad, H. Karimi, H., Alibakhshi S. M., 2014. The estimation of erosion and sediment by using the RUSLE model and RS and GIS techniques (Case study: Arid and semi-arid regions of Doviraj, Ilam province, Iran). International Journal of Agriculture and Crop Sciences, 7(6), pp.304-314.
Ganasri, B. P.,  Ramesh, H., 2016. Assessment of soil erosion by RUSLE model using remote sensing and GIS—A case study of Nethravathi Basin. Geomatics, Natural Hazards and Risk, 7(1), pp.589–602. https://doi.org/10.1080/19475705.2014.994007
Gong, W. Liu, T. Duan, X. Sun, Y. Zhang, Y.  Tong, X., Qiu, Z., 2022. Estimating the Soil Erosion Response to Land-Use Land-Cover Change Using GIS-Based RUSLE and Remote Sensing: A Case Study of Miyun, North China. Water, 14(5), p.742. https://doi.org/10.3390/w14050742
Goshu, G. P. Koide, D., Gessesse, G. D., 2024. Soil erosion risk assessment and monitoring using the RUSLE model in a Google Earth Engine environment. Scientific Reports, 14(1), pp.1-15. https://doi.org/10.1038/s41598-023-50965-w
Gelagay, H. & Minale, A., 2016 Soil loss estimation using GIS and remote sensing techniques: A case of Koga watershed, Northwestern Ethiopia. International Soil and Water Conservation Research, 4, pp.126–136. https://doi.org/10.1016/j.iswcr.2016.01.002
Gorelick, N. Hancher, M. Dixon, M. Ilyashenko, S.  Thau, D.  & Moore, R., 2017. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, pp.18-27. https:// doi.org/10.1016/j.rse.2017.06.03
Issaka, S. & Ashraf, M. A., 2017. Impact of soil erosion and degradation on water quality: A review. Geology, Ecology, and Landscapes, 1(1), pp.1–11. https://doi.org/10.1080/24749508.2017.1301050
Kamuju, N., 2016. Soil erosion and sediment yield analysis using prototype & enhanced SATEEC GIS system models. International Journal of Advanced Remote Sensing and GIS, 5(1), pp.1471–1482. https://doi.org/10.23953/cloud.ijarsg.39
Khemiri, K., Jebari, S., 2021. Assessment of water erosion in watersheds of the Tunisian semi-arid zone with RUSLE and MUSLE models coupled to a Geographic Information System. Cahiers Agricultures, 30(7), pp.1–13. https://doi.org/10.1051/cagri/2020048
Kondolf, G. M.  Gao, Y. Annandale, G. W. Morris, G. L. Jiang, E., Zhang, J., 2014. Sustainable sediment management in reservoirs and regulated rivers: Experiences from five continents. Earth’s Future, 2(5), pp.256–280. https://doi.org/10.1002/2013EF000184
Kushwaha, N. L., Yousuf, A., 2017. Soil erosion risk mapping of watersheds using RUSLE, remote sensing, and GIS: A review. Research Journal of Agricultural Sciences, 8(2), pp.269–277.
Kamari-Yekdangi, F.  Khalidi-Darvishan, A., Aqabigi Amin, S., 2024. Impact of corrective points in soil erodibility map on soil erosion estimation in G2 model. Jornal of New Approaches in Water Engineering and Environment.3(1).pp.158-169.  https://doi.org/10.22034/nawee.2024.436707.1062.(In Persian).
Lal, R., 1998. Soil erosion impact on agronomic productivity and environmental quality. Critical Reviews in Plant Sciences,17(4), pp.319–464. https://doi.org/10.1080/07352689891304249
Lee, S., 2004. Soil erosion assessment and its verification using the universal soil loss equation and geographic information system: A case study at Boun, Korea. Environmental Geology, 45(4), pp.457–465. https://doi.org/10.1007/s00254-003-0897-8
Mengie, M. A. Hagos, Y. G. Malede, D. A., Andualem, T. G., 2022. Assessment of soil loss rate using GIS–RUSLE interface in Tashat Watershed, Northwestern Ethiopia. Journal of Sedimentary Environments, 7(4), pp.617–631. https://doi.org/10.1007/s43217-022-00112-8
Morgan, R. P. C., 1995. Soil erosion and conservation (Secend edition). Longman Group:Har-low.
Mohammadi, Sh. Karimzadeh, H. R. & Alizadeh, M., 2018. Spatial estimation of soil erosion in Iran using RUSLE model. Iran Journal of Eco Hydorolgy, 5(2), pp.551-569. (In Persian)
Mohsenifar, K. Halafi, A., Panahpour, A., 2024. Effect of Hazar Daryeh erosion on loss of organic matter and changes in some soil properties in arid regions of Khuzestan province (Dareh Khazineh, Shushtar basin). Jornal of New Approaches in Water Engineering and Environment, 4 (1). https://doi.org/10.22034/nawee.2024.440441.1065.(In Persian)
Niknammi, D., Mehdian, M., 2015 . Rainfall erosivity mapping in Iran, Journal of Watershed Engineering and Management, 6(4), pp.364-376. (In Persian)
Nghia, B. P. Q. Pal, I. Chollacoop, N., Mukhopadhyay, A., 2022. Applying Google Earth Engine for flood mapping and monitoring in the downstream provinces of the Mekong River. Progress in Disaster Science, 15, p.100235. https://doi.org/10.1016/j.pdisas.2022.100235
Patel, D. P. & Pandey, A., 2016. Estimating soil erosion risk in lower Himalayan watershed using USLE, remote sensing, and GIS. Arabian Journal of Geosciences, 9(2), 131–142. https://doi.org/10.1007/s12517-015-2224-1
Petito, M. Cantalamessa, S. Pagnani, G. Degiorgio, F. Parisse, B. & Pisante, M., 2022. Impact of conservation agriculture on soil erosion in the annual cropland of the Apulia Region (Southern Italy) based on the RUSLE-GIS-GEE framework. Agronomy, 12(2). https://doi.org/10.3390/agronomy12020281
Pimentel, D. & Kounang, N., 1998. Ecology of soil erosion in ecosystems. Ecosystems, 1(5), pp.416–426. https://doi.org/10.1007/s100219900035
Pratiksha, J., Raaj, R., 2018. GIS-based modelling of soil erosion processes using the modified-MMF model in a large watershed having vast agro-climatological differences. Earth Surface Processes and Landforms, 43(10), pp.2064–2076. https://doi.org/10.1002/esp.14437
Rao, K. V., 1981. Evaluation of cover management factor (C) for soil erosion studies. Journal of Soil and Water Conservation in India, 29(2), pp.50–55.
Renard, K. G. Foster, G. R.  Weesies, G. A., Porter, J. P., 1991. RUSLE: Revised universal soil loss equation. Journal of Soil and Water Conservation, 46(1), pp.30–33.
Roose, E., Sarrailh, J. M., 1990. Erodibility of some tropical soils: Twenty years of measurement in erosion plots under natural rains. ORSTOM Notebooks, Pedological Series, 25(12).
Saha, S., Mandal, D., 2022. Application of RUSLE and GIS for soil erosion risk assessment in Indian agricultural landscapes. Environmental Monitoring and Assessment, 194(1), pp.21–34. https://doi.org/10.1007/s10661-021-09490-7
Salazar, J. García, M., Sánchez, P., 2024. Assessment of soil erosion by RUSLE in the Ecuadorian basins (2001−2020) based on GIS and high-resolution satellite data. Geomorphology, 419, p.109515. https://doi.org/10.1016/j.geomorph.2024.109515
Sharma, R., Sharma, S., 2022. Comparative study on morphometric analysis and RUSLE-based approaches for microwatershed prioritization using remote sensing and GIS. Arabian Journal of Geosciences, 15(4), p.111. https://doi.org/10.1007/s12517-022-09837-2
Sarouni, F. Khalidi-Darvishan, A., Mousavi, V., 2024. Monthly variability of soil erosion in the Kasilian watershed using the RUSLE model. Watershed Management Research, 37 (3), pp.54-75.
Talchabhadel, R. Prajapati, R. Aryal, A., Maharjan, M., 2020. Assessment of rainfall erosivity (R-factor) during 1986–2015 across Nepal: A step towards soil loss estimation. Environmental Monitoring and Assessment, 192 (5). https://doi.org/10.1007/s10661-020-8239-9.
Uddin, K. Abdul Matin, M., Maharjan, S ., 2018. Assessment of land cover change and its impact on changes in soil erosion risk in Nepal. Sustainability 10(12), p.4715. https://doi.org/10.3390/su10124715
Wang, H., Zhao, H., 2020. Dynamic changes of soil erosion in the taohe river basin using the RUSLE model and google earth engine. Water 12(5), p.1293. https://doi.org/10.3390/w12051293
Wischmeier, W. H. Johnson, C. B., Cross, B. V., 1971. A soil erodibility nomograph for farmland and construction sites. Journal of Soil and Water Conservation, 26(5), pp.189 193. Retrieved from https://www.nrcs.usda.gov/Internet/FSE_D OCUMENTS/stelprdb1044266
Wilson, J. P. Hession, W. C., O’Donnell, T., 2000. A new method for modeling erosion and deposition in small watersheds: The USPED model. Geomorphology, 34(3–4), pp.273–286. https://doi.org/10.1016/S0169- 555X(00)00070-2.
Yusron, S., Eko, P., 2023. Soil erosion prediction and risk assessment using RUSLE model and GIS techniques in the nangka watershed. Journal of Water and Land Development 55 (184),p. 191. https://doi.org/10.24425/jwld.2022.142320
Zare, M. Samani, A. N. Mohammady, M. Salmani, H., Bazrafshan, J., 2017. Investigating effects of land use change scenarios on soil erosion using CLUE-s and RUSLE models. International Journal of Environmental Science and Technology, 14(9), pp.1905-1918.
 
Journal of New Approaches in Water Engineering and Environment
Volume 5, Issue 3
Summer 2026
Pages 127-144

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