Furthering the precision of RUSLE soil erosion with PSInSAR data: an innovative model

Soil erosion is a severe environmental problem worldwide, espedaily in tropical regions. The Revised Universal Soil Loss Equation (RUSLE), one of the universally accepted empirical soil erosion models, is quite commonly used in tropical climatic conditions to estimate the magnitude and severity of soil erosion. This study, apart from identifying the role of individual parameters in influencing the results of the RUSLE, also aims at refining the RUSLE results by incorporating the state-of-the-art technique Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) in a GIS environment by utilizing its ability to measure minute surface changes in millimetre levels. Apart from this novel approach of prioritising soil erosion classes using PSInSAR, the eroding surface conditions were also studied using low coherence value (<0.75 in this study). The spatially and temporally averaged annual soil loss and net soil erosion (2015-2019), derived through RUSLE and transport limited sediment delivery (TLSD) approach, respectively, was improved by spatially integrating the PSInSAR velocity map. The integrated methodological framework is demonstrated for a tropical river basin in South India (Muvattupuzha River Basin (MRBD, which shows a mean rate of net soil loss of 6.8 ton/ha/yr, and nearly 8% of the area experiences deposition. Our approach to improve the accuracy of RUSLE-based soil erosion classes using PSInSAR techniques clearly demarcated the areas that call for utmost priority in implementing management practices. The corollary results show that the very severe soil erosion class is characterized by PSI velocity with higher negative values, followed by the successively lower classes. Results strongly suggest that RUSLE output can be improved as well as validated using a velocity map derived from radar data.

Automated summary

This study aims to improve the estimation model of soil erosion in tropical regions using the Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) technique and enhance the accuracy of the model by integrating spatial data. The results highlight the importance of prioritizing areas for soil management practices based on the PSInSAR technique.