Badaruddin, Syamani D. Ali

Remote Sensing Remote Sensing-Based Soil Erosion Rate Estimation Using the E30 Model and Sentinel-2 Imagery

Introduction

Remote sensing remote sensing-based soil erosion rate estimation using the e30 model and sentinel-2 imagery . Estimate soil erosion rates using remote sensing, E30 model, and Sentinel-2 imagery. KHDTK ULM Mandiangin shows very high erosion (480 tons/ha/year) and a serious hazard, needing urgent conservation.

Abstract

Estimating the rate of soil erosion generally takes time, money, and energy. There are many parameters that must be accommodated, such as the physical properties of the soil, land cover, rainfall, topography, and so on. One alternative method for estimating erosion rates is to use a remote sensing approach. The aim of this research is to estimate the rate of soil erosion in the Special Purpose Forest Area of the University of Lambung Mangkurat (KHDTK ULM) Mandiangin, using the E30 model and Sentinel-2 imagery. The erosion rate are measured directly in the field with a number of sample points. According to the E30 model concept, field erosion samples are only measured on land that has a slope of 300. The topographic data itself is extracted from DEMNAS data. Meanwhile, soil bulk density data was obtained from https://soilgrids.org/, and solum data was taken from https://daac.ornl.gov/. From the Sentinel-2 imagery, Normalized Difference Vegetation Index (NDVI) data was extracted, which is one of the parameters in the E30 model. The estimated results of the erosion rate at KHDTK ULM Mandiangin show that, in general, the highest erosion rate at KHDTK ULM Mandiangin is around 480 tons/ha/year. Additionally, almost 80% of the KHDTK ULM Mandiangin area has a very serious erosion hazard level. Of course, the fastest rate of erosion is located on hill slopes with steep topography. Apart from having steep topography, one of the factors causing the high rate of erosion at KHDTK ULM is the thin soil layer and the lack of dense forest cover. This finding indicates the need to conserve vegetation cover on steep lands.

Review

This paper presents a timely and relevant investigation into estimating soil erosion rates using remote sensing techniques, aiming to provide a more efficient alternative to traditional field-intensive methods. Focusing on the Special Purpose Forest Area of the University of Lambung Mangkurat (KHDTK ULM) Mandiangin, the study utilizes the E30 model in conjunction with Sentinel-2 imagery, DEMNAS topographic data, and global soil datasets to assess erosion hazards. The core objective of leveraging widely available remote sensing products and existing models to address a critical environmental issue like soil erosion is commendable and aligns well with current trends in environmental monitoring. From a methodological standpoint, the integration of multi-source data including Sentinel-2 for NDVI, DEMNAS for topography, and global databases like SoilGrids and ORNL DAAC for soil properties, offers a practical approach to parameterizing an erosion model. However, the abstract's description of "field erosion samples [being] only measured on land that has a slope of 300" within the E30 model concept requires further clarification. In a full paper, it would be crucial to detail how these specific field measurements relate to and validate the broader remote sensing-based E30 model estimates across the entire study area, especially given that the remote sensing approach is meant to extrapolate beyond direct field sampling. A clearer exposition of the E30 model's structure, its specific input parameters beyond NDVI, and the validation process for the remote sensing-derived outputs would significantly strengthen the paper. The findings underscore the severity of soil erosion in the study area, identifying alarmingly high rates of up to 480 tons/ha/year and indicating that nearly 80% of KHDTK ULM Mandiangin faces a "very serious erosion hazard." The identification of steep topography, thin soil layers, and sparse forest cover as primary drivers of this erosion is a critical insight, reinforcing established ecological principles. The paper's conclusion advocating for vegetation cover conservation on steep lands is a direct and impactful policy recommendation. For future work, quantifying the uncertainties associated with using global datasets for local parameters and exploring the potential for finer-scale validation of the E30 model's performance would further enhance the robustness and applicability of these important findings.

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