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Modeling soil organic matter and texture from satellite data in areas affected by wildfires and cropland abandonment in Aragón, Northern Spain

Abstract:

Land use/land cover (LULC) changes create the need for regular monitoring of soil properties. Modern technology and data sources offer possibilities to perform it. In this context, the objective of the study is to explore the possibility of pbkp_redicting soil organic matter (SOM) content and texture from the spectral information of the three last generation satellites [Landsat-8, Sentinel-2, and the Environmental Mapping and Analysis Program (EnMAP)]. Soil samples (113) were collected in areas affected by wildfires and cropland abandonment in Aragón, Northern Spain. Reflectance spectra of soils were obtained in controlled laboratory conditions using the analytical spectral device Fieldspec4 spectroradiometer (spectral range 350 to 2500 nm). Reflectances simulated for Landsat-8, Sentinel-2, and EnMAP bands were used as pbkp_redictors in multivariate models developed using partial least squares regression (PLSR) and step-down variable selection algorithm (SD). Modeling of all soil variables was performed simultaneously. The EnMAP models employed few pbkp_redictors (10 to 58 of 244) and demonstrated good fit (Rcal2>0.8 and Rval2∼0.8), especially for SOM (Rcal2 and Rval2=0.9; RMSEP ∼1.6). Landsat models showed the least reliable estimates (Rcal2 0.54 to 0.77 and Rval2 0.42 to 0.80), whereas Sentinel-2 models showed Rcal2 of 0.70 to 0.81 and Rval2 between 0.67 (clay) and 0.79 (SOM). The results confirm high potential of spectral data from multispectral and hyperspectral satellites for soil monitoring. Application of PLSR combined with SD results in sparser and better-fit models. SOM and soil texture can be estimated with an acceptable accuracy from EnMAP and Sentinel-2 data enabling soil status monitoring in areas of wildfire burns and cropland abandonment.