Abstract
The assessment of soil erosion holds paramount significance in sustainable land management and environmental conservation. In this context, the integration of advanced technologies such as the Revised Universal Soil Loss Equation (RUSLE), Google Earth Engine (GEE), and geospatial techniques presents a formidable approach for evaluating soil erosion dynamics. This integrated methodology proves particularly valuable when applied to the Rel River watershed, where factors such as terrain, land use, and precipitation patterns intricately influence erosion processes. The collective use of two methods, the quantitative method focused on RUSLE to assess soil under various circumstances of erosion and sediment yield, whereas the qualitative approach focused on spectral indices of soil erosion in GEE to generate degradation map. This study was aimed at evaluating soil erosion and land degradation across the Rel River watershed in the western region of Gujarat, India. Soil loss has been estimated using soil loss models, i.e., RUSLE and geoinformation datasets, which were extracted from GEE. The degraded area was prepared using GEE and mapped using geographical information system (GIS). The results demonstrate that estimated value for erosion due to rainfall is 37 to 40 MJ mm h−1 ha−1 year−1, soil erodibility is 0.01 to 0.05 ton h MJ−1 mm−1, topographic variables lies in a range from 0 to 20, and crop management factor is 0.001 to 1. The findings also demonstrate that the total annual soil loss for flood events in 2017 is 35.36 t/ha/year, which is categorized into the severe zone of degradation. According to the soil degradation map created using GEE, the majority of the study region falls into the low and medium degradation zones, while the northeastern part and river fall into the high degradation zone. The findings will be helpful in implementing soil management and conservation techniques to arrest soil erosion in the Rel River watershed.
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There is no associated data with this manuscript. Raw data will be made available as per request.
Abbreviations
- BI:
-
Brightness index
- C factor:
-
Cover factor
- CHG:
-
Climate Hazard Group
- CHRIPS:
-
Climate Hazards Group InfraRed Precipitation with Station data
- CI:
-
Color index
- CN:
-
Curve number
- DEM:
-
Digital elevation model
- FAO:
-
Food and Agriculture Organization
- FCC:
-
False color composite
- FI:
-
Form index
- GDP:
-
Gross domestic product
- GEE:
-
Google Earth Engine
- GHG:
-
Greenhouse gases
- GIS:
-
Geographical information system
- ENVI:
-
Environment for visualizing images
- ESA:
-
European Space Agency
- IPCC:
-
Intergovernmental Panel on Climate Change
- JPL:
-
Jet Propulsion Laboratory
- K factor:
-
Soil erodibility factor
- LP DAAC:
-
Land Processes Distributed Active Archive Centre
- LS factor:
-
Slope length and slope steepness (topographic factor)
- MODIS:
-
Moderate Resolution Imaging Spectroradiometer
- NASA:
-
National Aeronautics and Space Administration
- NDVI:
-
Normalized difference vegetation index
- NIR:
-
Near infrared
- OLI:
-
Operational Land Imager
- P factor:
-
Support practice factor
- R factor:
-
Rainfall erosivity factor
- RGB:
-
Red green blue
- RS:
-
Remote sensing
- RUSLE:
-
Revised Universal Soil Loss Equation
- SRTM:
-
Shuttle Radar Topography Mission
- SWDC:
-
State Water Data Centre
- UCSB:
-
University of California Santa Barbara
- USDA:
-
United States Development of Agriculture
- USGS:
-
United States Geological Survey
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Acknowledgements
The authors acknowledge the support of Pandit Deendayal Energy University and Nirma University. The author Sudhir Kumar Singh expresses sincere thanks to the Coordinator of KBCAOS and DST-FIST for providing infrastructural facilities to the Centre.
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Conceptualization: Dhruvesh Patel (DP), Keval H. Jodhani (KHJ), N. Madhavan (NM), and Sudhir Kumar Singh (SKS); methodology: DP, KHJ, NM, and SKS; formal analysis: DP, KHJ, and SKS; investigation: DP, KHJ, and SKS; data curation: KHJ and DP; visualization: KHJ and DP; writing—original draft preparation: DP, KHJ, NM, and SKS; writing, review, and editing: DP, KHJ, NM, and SKS. All authors have read and agreed to publish the manuscript.
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Jodhani, K.H., Patel, D., Madhavan, N. et al. Soil Erosion Assessment by RUSLE, Google Earth Engine, and Geospatial Techniques over Rel River Watershed, Gujarat, India. Water Conserv Sci Eng 8, 49 (2023). https://doi.org/10.1007/s41101-023-00223-x
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DOI: https://doi.org/10.1007/s41101-023-00223-x