Relationship between remote sensing data and field-observed interril erosion

Abstract

-The objective of this work was to evaluate the relationship between different remote sensing data, derived from satellite images, and interrill soil losses obtained in the field by using a portable rainfall simulator. The study was carried out in an area of a hydrographic basin, located in Médio Paraíba do Sul, in the state of Rio de Janeiro -one of the regions most affected by water erosion in Brazil. Evaluations were performed for different vegetation indices (NDVI, Savi, EVI, and EVI2) and fraction images (FI), derived from linear spectral mixture analysis (LSMA), obtained from RapidEye, Sentinel2A, and Landsat 8 OLI images. Vegetation indices are more adequate to predict soil loss than FI, highlighting EVI2, whose exponential model showed R 2 of 0.74. The best prediction models are generated from the RapidEye image, which shows the highest spatial resolution among the sensors evaluated.Index terms: linear spectral mixing analysis, rainfall simulator, vegetation indices. Relação entre dados de sensoriamento remoto e perdas de solo em entressulcos observadas em campoResumo -O objetivo deste trabalho foi avaliar a relação entre diferentes dados de sensoriamento remoto, derivados de imagens de satélite, e perdas de solo em entressulcos obtidas em campo por meio de um simulador de chuvas portátil. O estudo foi realizado em uma microbacia hidrográfica localizada no Médio Paraíba do Sul, no Estado do Rio de Janeiro -uma das regiões mais afetadas por erosão hídrica no Brasil. Foram avaliados diferentes índices de vegetação (NDVI, Savi, EVI e EVI2) e frações de componentes puros (FCP), derivados de modelos lineares de mistura espectral (MLME), obtidos de imagens RapidEye, Sentinel2A e Landsat 8 OLI. Os índices de vegetação são mais adequados à predição da perda de solo do que as FCP, com destaque para o EVI2, cujo modelo exponencial apresentou R 2 de 0,74. Os melhores modelos de predição são gerados a partir da imagem RapidEye, que apresenta maior resolução espacial entre os sensores avaliados.Termos para indexação: modelos lineares de mistura espectral, simulador de chuva, índices de vegetação.

Development of a Rainfall and Runoff Simulator for Performing Hydrological and Geotechnical Tests

“…Several studies have presented the development, calibration, and use of RSs for a wide range of purposes, such as agricultural research [17][18][19][20][21][22]; biomass studies [23,24]; infil-Sustainability 2021, 13, 3060. https://doi.org/10.3390/su13063060 https://www.mdpi.com/journal/sustainability Sustainability 2021, 13, 3060 2 of 22 tration analysis [14,15,25,26]; urban hydrology simulation, including permeable pavement performance [27], evaluation of processes of accumulation and transport of pollutants on pavements and roof surfaces [28][29][30][31][32]; and rainwater quality research. RSs are frequently used in geotechnical evaluations of soil loss and erosion [1,16,22,[33][34][35][36][37][38], slope stability [13,39,40] and infiltration, and percolation in unsaturated soils [12,14,15,41,42]. The performance of RSs must be evaluated considering various initial conditions, boundary conditions, simulated rainfall characteristics, and the scale factor (i.e., the evaluation of the specimen geometry and dimensions).…”