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Non-photosynthetic vegetation (NPV) plays a key role in soil conservation, which in turn is important in sustainable agriculture and carbon farming. For mapping NPV image spectroscopy proved to outperform multispectral sensors. PRISMA (PRecursore IperSpettrale della Missione Applicativa) is the forerunner of a new era of hyperspectral satellite missions, providing the proper spectral resolution for NPV mapping. This study takes advantage from both spectroscopy and machine-learning techniques. Exponential Gaussian Optimization was used for modelling known absorption bands (cellulose-lignin, pigments, water content and clays), resulting in a reduced feature space, which is split by a decision tree (DT) for mapping different field conditions (emerging, green and standing dead vegetation, crop residue and bare soil). DT training and validation exploited reference 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 time are derived by processing 12 images over 2020 and 2021. Results proved that PRISMA data are suitable for mapping field conditions at parcel scale with high confidence level. This is important in the perspective of other hyperspectral missions and is a premise toward quantitative estimates of NPV biophysical variable. [ABSTRACT FROM AUTHOR]

The present investigation aims at providing a reliable evaluation of the geometric and radiometric accuracy of the airborne Daedalus-Coastal Zone Color Scanner (CZCS), through cal/val techniques. To this aim, on November 2014, we accomplished a remote sensing campaign over the Peligna Valley (Italy). Together with the personnel of the III Nucleo Aereo of the Italian Coast Guard, based at the Pescara Airport, we deployed the Daedalus AA1268EM1 CZCS scanner on board the fixed-wing aircraft type ATR42MP and a ASD FieldSpec spectroradiometer for a simultaneous field survey. We used vicarious calibration and secondary non-parametric geometric correction to achieve absolute atmospheric correction and geometric calibration, respectively. Although the validation and calibration targets used in this study were similar in nature, correlation coefficients of the prediction equations between at-sensor radiance and ground reflectance were > 0.90 for each of the 10 CZCS wavebands and the independent error assessment demonstrate that the empirical line method can be applied to correct CZCS imagery with satisfactory results. [ABSTRACT FROM AUTHOR]