This study investigated whether surface soil organic carbon (SOC) content could be estimated using hyperspectral data provided by the Italian Space Agency PRISMA satellite. We collected 100 representative topsoil samples in an area of 30x30 Km2 in the province of Ferrara (Northern Italy), estimated their SOC content and other soil properties through thermo-gravimetric analysis, and matched these to the spectra of the sampled areas that were measured by PRISMA on 7 April 2020. A tentative model was created for SOC estimation using ordinary least-squares (OLS) regression and an artificial neural network (ANN). Repeated k-fold cross-validation of the OLS and ANN models yielded R2 values of 0.64 and 0.49, respectively. The performance of the models was inferior to that obtained from the literature using similar modeling techniques in relatively small areas (up to 3 3 Km2) and characterized by restricted SOC variability (0.2–2.1 wt%). However, our data were collected over a wider area with high SOC content variability (0.7–9.3 wt%); consequently, significant variations were observed over a spatial scale of just a few meters. Therefore, this work shows the importance of testing remote sensing techniques for SOC measurements in more complex areas than those reported in the existing literature. Furthermore, our study sheds light on the geolocation errors and missing data of PRISMA.
Soil Organic Carbon Estimation in Ferrara (Northern Italy) Combining In Situ Geochemical Analyses and Hyperspectral Remote Sensing
Salani G. M.;Bianchini G.;Brombin V.
;Natali S.;Natali C.
2023
Abstract
This study investigated whether surface soil organic carbon (SOC) content could be estimated using hyperspectral data provided by the Italian Space Agency PRISMA satellite. We collected 100 representative topsoil samples in an area of 30x30 Km2 in the province of Ferrara (Northern Italy), estimated their SOC content and other soil properties through thermo-gravimetric analysis, and matched these to the spectra of the sampled areas that were measured by PRISMA on 7 April 2020. A tentative model was created for SOC estimation using ordinary least-squares (OLS) regression and an artificial neural network (ANN). Repeated k-fold cross-validation of the OLS and ANN models yielded R2 values of 0.64 and 0.49, respectively. The performance of the models was inferior to that obtained from the literature using similar modeling techniques in relatively small areas (up to 3 3 Km2) and characterized by restricted SOC variability (0.2–2.1 wt%). However, our data were collected over a wider area with high SOC content variability (0.7–9.3 wt%); consequently, significant variations were observed over a spatial scale of just a few meters. Therefore, this work shows the importance of testing remote sensing techniques for SOC measurements in more complex areas than those reported in the existing literature. Furthermore, our study sheds light on the geolocation errors and missing data of PRISMA.I documenti in SFERA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
