Integrating RUSLE Model with Cloud-Based Geospatial Analysis: A Google Earth Engine Approach for Soil Erosion Assessment in the SatlujWatershed

In Water
Volume (Issue): 16 (8)
Peer-reviewed Article
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This study employed an advanced geospatial methodology using the Google Earth Engine
(GEE) platform to assess soil erosion in the Satluj Watershed thoroughly. To achieve this, the Revised
Universal Soil Loss Equation (RUSLE) model was integrated into the study, which was revealed
through several analytical tiers, each with a unique function. The study commenced with estimating
the R factor, which was carried out using annual precipitation data from the Climate Hazards Group
Infra-Red Precipitation with Station (CHIRPS). The erodibility of the soil, which the K factor describes,
was then calculated using the USDA soil texture classifications taken from the Open Land Map. The
third layer emphasizes the LS factor, which analyzes slope data and how they affect soil erosion
rates, using digital elevation models. To understand the impact of vegetation on soil conservation,
the fourth layer presents the C factor, which evaluates changes in land cover, and the Normalized
Difference Vegetation Index (NDVI) derived from Sentinel-2 data. The P factor incorporates MODIS
data to assess the types of land cover and slope conditions. Combining these layers with the RUSLE
model produces a thorough soil loss map, revealing different levels of soil erosion throughout the
SatlujWatershed. The preliminary findings indicate that 3.3% of the watershed had slight soil loss,
0.2% had moderate loss, and 1.2% had high soil erosion rates. And 92% had severe rates of soil erosion.
After a thorough investigation, the detected regions were divided into risk classifications, providing
vital information for the watershed’s land management and conservation plans. The mean soil loss
throughout the watershed was determined to be 10,740 tons/ha/year. This novel method creates a
strong foundation for evaluating soil erosion, while also highlighting the value of the cloud-based
geospatial analysis and the RUSLE model in comprehending intricate environmental processes.

Author:
Anshul
Sud
Bhartendu
Sajan
Shruti
Kanga
Suraj Kumar
Singh
Saurabh
Singh
Bojan
Durin
Gowhar
Meraj
Dhrubajyoti
Sahariah
Jatan
Debnath
Kesar
Chand
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