Soil erosion monitoring based on cloud computing platform

¹ Agroecotechnology, Agriculture Faculty, University of Trunojoyo Madura, Bangkalan, Indonesia
² Geography education, Faculty of Teaching and Pedagogy, Universit of Muhammadiyah prof. Dr Hamka, Jakarta, Indonesia

^* Corresponding author: fahmi.rahman@trunojoyo.ac.id

Abstract

Soil erosion is one of important causes of land degradation. Soil erosion can be estimated by an empirical model (RUSLE) based on cloud computing platform (GEE). This platform has several advantages including free access, availability of spatial big data, and effective and efficient spatial data analysis. The objective of this study was to estimate the rate of soil erosion in Blega Watershed, Bangkalan, Madura by means of RUSLE based on Cloud Computing Platform. The data obtained from several satellites’ imageries, were processed and analysed by employing GEE platform. The data collected were CHIRPS for rainfall erosivity (R), Open Land Map Soil Texture Class for soil erodibility (K), MODIS Terra vegetation index for land cover management (C), NASA DEM SRTM for Slope length and steepness (LS), and MODIS Land Cover Type Yearly and NASA DEM SRTM for Support practice factor (P). The result showed that the rates of soil erosion in Blega watershed from 2018 to 2022 respectively were 1.1490, 1.1320, 1.1388, 1.1491, and 1.1595 ton/ha/yr and therefore categorized as very low. The fluctuation of soil erosion in the study area was mainly caused by changes in R and C factor.