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Soil Erosion Assessment by RUSLE, Google Earth Engine, and Geospatial Techniques over Rel River Watershed, Gujarat, India

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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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Data Availability

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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Authors and Affiliations

  1. Department of Civil Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat, 382007, India

    Keval H. Jodhani & Dhruvesh Patel

  2. Department of Civil Engineering, Institute of Technology, Nirma University, Ahmedabad, Gujarat, 382481, India

    Keval H. Jodhani

  3. Petroleum Engineering, School of Petroleum Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat, 382007, India

    N. Madhavan

  4. K. Banerjee Centre of Atmospheric & Ocean Studies (KBCAOS), IIDS, Nehru Science Centre, University of Allahabad, Prayagraj, Uttar Pradesh, 211002, India

    Sudhir Kumar Singh

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Contributions

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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Correspondence to Dhruvesh Patel.

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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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