Detection and Classification of Non-Photosynthetic Vegetation from PRISMA Hyperspectral Data in Croplands

• Published in: Remote sensing (Basel, Switzerland) Vol. 12; no. 23; p. 3903
• Main Authors: Pepe, Monica, Pompilio, Loredana, Gioli, Beniamino, Busetto, Lorenzo, Boschetti, Mirco
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-12-2020
• Subjects: Absorption spectra; Agricultural land; Carbon; Cellulose; Classification; Crop residues; Diagnostic systems; Environmental monitoring; hyperspectral remote sensing; Lignin; non-photosynthetic vegetation; Optimization; Photosynthesis; PRISMA; Remote sensing; Residues; Satellites; Sensors; Sustainable agriculture; Vegetation
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs12233903

This study introduces a first assessment of the capabilities of PRISMA (PRecursore IperSpettrale della Missione Applicativa)—the new hyperspectral satellite sensor of the Italian Space Agency (ASI)—for Non-Photosynthetic Vegetation (NPV) monitoring, a topic which is becoming very relevant in the field of sustainable agriculture, being an indicator of crop residue (CR) presence in the field. Data-sets collected during the mission validation phase in croplands are used for mapping the NPV presence and for modelling the diagnostic absorption band of cellulose around 2.1 μm with an Exponential Gaussian Optimization approach, in the perspective of the prediction of the abundance of crop residues. Results proved that PRISMA data are suitable for these tasks, and call for further investigation to achieve quantitative estimates of specific biophysical variables, also in the framework of other hyperspectral missions.