Mapping spatial distribution of crop residues using PRISMA satellite imaging spectroscopy

ABSTRACT

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.

KEYWORDS:

• Hyperspectral remote sensing
• non-photosynthetic vegetation
• sustainable agriculture
• machine learning
• spectroscopy

Acknowledgments

The Authors would like to thank Lorenzo Genesio and Franco Miglietta (CNR-IBE), and Roberta Falone and Patrizio Tempesta (Telespazio) for their support for satellite data procurement. We are also very grateful to Giorgia Verza (CNR-IREA) for collecting data about phenological stages and crop practices. The authors also thank three anonymous reviewers for their helpful comments which contributed to manuscript improvement. Project carried out using ORIGINAL PRISMA Products - © Italian Space Agency (ASI); the Products have been delivered under an ASI License to Use.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Italian Space Agency [2019-5-HH.0].CNR-IREA co-founded these research activities in preparation of PRIS4VEG – ASI SCIENZA project [2022-5-U.0].