Mapping spatial distribution of crop residues using PRISMA satellite imaging spectroscopy

Non-photosynthetic vegetation (NPV) plays a key role in soil conservation, which in turn is important in sustainable agriculture and carbonfarming. For mapping NPV image spectroscopy proved to outperform multispectralsensors. PRISMA (PRecursore IperSpettrale della Missione Applicativa) is theforerunner of a new era of hyperspectral satellite missions, providing theproper spectral resolution for NPV mapping. This study takes advantage fromboth spectroscopy and machine-learning techniques. Exponential GaussianOptimization was used for modelling known absorption bands (cellulose-lignin, pigments, water content and clays), resulting in a reduced feature space, whichis split by a decision tree (DT) for mapping different field conditions (emerging,green and standing dead vegetation, crop residue and bare soil). DT trainingand validation exploited refer- ence data, collected during PRISMA overpasses on a large farmland. Mapping results are accurate both at pixel and parcel level (O.A.> 90%; K > 0.9). Field status and crop rotation trajectories through timeare derived by processing 12 images over 2020 and 2021. Results proved thatPRISMA data are suitable for mapping field conditions at parcel scale with highconfidence level. This is important in the perspective of other hyperspec- tralmissions and is a premise toward quantitative estimates of NPV biophysicalvariable.