Assessment of maize nitrogen uptake from PRISMA hyperspectral data through hybrid modelling

The spaceborne imaging spectroscopy mission PRecursore IperSpettrale della Missione Applicativa (PRISMA), launched on 22 March 2019 by the Italian Space Agency, opens new opportunities in many scientific domains, including precision farming and sustainable agriculture. This new Earth Observation (EO...

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Vydáno v:European journal of remote sensing Ročník ahead-of-print; číslo ahead-of-print; s. 1 - 17
Hlavní autoři: Ranghetti, Marina, Boschetti, Mirco, Ranghetti, Luigi, Tagliabue, Giulia, Panigada, Cinzia, Gianinetto, Marco, Verrelst, Jochem, Candiani, Gabriele
Médium: Journal Article
Jazyk:angličtina
Vydáno: Italy Taylor & Francis 31.12.2023
Taylor & Francis Ltd
Taylor & Francis Group
Témata:
Algorithms
Canopies
Chlorophyll
Data transmission
Image acquisition
Machine learning
machine learning regression algorithms
Nitrogen
Nitrogen content
plant nitrogen uptake estimation
Plant phenology
Precision agriculture
Precision farming
Radiative transfer
radiative transfer models
Spectroscopy
Sustainable agriculture
radiative transfer models
Precision farming
plant nitrogen uptake estimation
machine learning regression algorithms
ISSN:2279-7254, 2279-7254
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Shrnutí:The spaceborne imaging spectroscopy mission PRecursore IperSpettrale della Missione Applicativa (PRISMA), launched on 22 March 2019 by the Italian Space Agency, opens new opportunities in many scientific domains, including precision farming and sustainable agriculture. This new Earth Observation (EO) data stream requires new-generation approaches for the estimation of important biophysical crop variables (BVs). In this framework, this study evaluated a hybrid approach, combining the radiative transfer model PROSAIL-PRO and several machine learning (ML) regression algorithms, for the retrieval of canopy chlorophyll content (CCC) and canopy nitrogen content (CNC) from synthetic PRISMA data. PRISMA-like data were simulated from two images acquired by the airborne sensor HyPlant, during a campaign performed in Grosseto (Italy) in 2018. CCC and CNC estimations, assessed from the best performing ML algorithms, were used to define two relations with plant nitrogen uptake (PNU). CNC proved to be slightly more correlated to PNU than CCC (R 2  = 0.82 and R 2  = 0.80, respectively). The CNC-PNU model was then applied to actual PRISMA images acquired in 2020. The results showed that the estimated PNU values are within the expected ranges, and the temporal trends are compatible with plant phenology stages.
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ISSN:2279-7254
2279-7254
DOI:10.1080/22797254.2022.2117650