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Mapping spatial distribution of crop residues using PRISMA satellite imaging spectroscopy
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Journal Article
Mapping spatial distribution of crop residues using PRISMA satellite imaging spectroscopy
2023
Overview
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.
Publisher
Taylor & Francis,Taylor & Francis Ltd,Taylor & Francis Group
Subject
