This research contributes to the application of hyperspectral image analysis techniques to soil texture classes retrieval considering the USDA (United Stated Agriculture Department) model. It explores the value added by existing hyperspectral data of similar characteristics to CHIME, namely AVIRIS-NG and PRISMA, for detecting topsoil texture properties exploiting the linear spectral mixture concepts.
Hyperspectral Mixture Models in the CHIME Mission Implementation for Topsoil Texture Retrieval
Valentini E.
Primo
Conceptualization
;
2023
Abstract
This research contributes to the application of hyperspectral image analysis techniques to soil texture classes retrieval considering the USDA (United Stated Agriculture Department) model. It explores the value added by existing hyperspectral data of similar characteristics to CHIME, namely AVIRIS-NG and PRISMA, for detecting topsoil texture properties exploiting the linear spectral mixture concepts.File in questo prodotto:
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