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

ABSTRACT

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 (R² = 0.82 and R² = 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.

KEYWORDS:

• Precision farming
• radiative transfer models
• machine learning regression algorithms
• plant nitrogen uptake estimation

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research was funded by the CHIME Mission Requirement Consolidation Study (RCS) (ESA Contract 4000125506/18/NL/IA) funded by the European Space Agency (ESA). J. Verrelst is funded by the European Research Council (ERC) under ERC-2017-STG SENTIFLEX (\#755617). Project carried out using ORIGINAL PRISMA Products - © Italian Space Agency (ASI); the Products have been delivered under an ASI License to Use.