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.