Sentinel-2 estimation of CNC and LAI in rice cropping system through hybrid approach modelling

ABSTRACT

Earth observation techniques represent a reliable and faster alternative to in-situ measurements by providing spatio-temporal information on crop status. In this framework, a study was conducted to assess the performance of hybrid approaches, either standard (HYB) or exploiting an active learning optimisation strategy (HYB-AL), to estimate leaf area index (LAI) and canopy nitrogen content (CNC) from Sentinel–2 (S2) data, in rice crops. To achieve this, the PROSAIL-PRO Radiative Transfer Model (RTM) was tested. Results demonstrate that a wide range of rice spectra, simulated according to realistic crop parameters, are reliable when appropriate field background conditions are considered. Simulations were used to train a Gaussian Process Regression (GPR) algorithm. Both cross-validation and validation results showed that HYB-AL approach resulted the best performing retrieval schema. LAI estimation achieved good performance (R²=0.86; RMSE=0.54) and resulted very promising for model application in operational monitoring systems. CNC estimations showed moderate performance (R²=0.63; RMSE=0.89) due to a saturation behaviour limiting the retrieval accuracy for moderate/high CNC values, approximately above 4 [g m⁻²]. S2 maps of LAI and CNC provided spatio-temporal information in agreement with crop growth, nutritional status and agro-practices applied to the study area, resulting in an important contribution to precision farming applications.

KEYWORDS:

• Rice crop monitoring
• hybrid modelling
• precision agriculture
• Sentinel-2
• canopy nitrogen content
• leaf area index

Acknowledgements

The research activities have been framed in the CNR- DIPARTIMENTO DI INGEGNERIA, ICT E TECNOLOGIE PER L’ENERGIA E I TRASPORTI project “DIT.AD022.180 Transizione industriale e resilienza delle Società post-Covid19 (FOE 2020)”, sub task activity “Agro-Sensing”.

Disclosure statement

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

Data availability statement

The data that support the findings of this study are available from the corresponding author, M.R., upon reasonable request.