Inference of leaf nitrogen concentration using machine learning on data resampled to the spectral resolution of Sentinel-2
Abstract
Nitrogen (N) is among the main nutrients widely used in agriculture worldwide; however, its administration and management can be challenging. Excess nitrogen is harmful to plant health and the environment, requiring effective monitoring of leaf nitrogen concentration (LNC) in field crops. Remote sensing stands out as a valuable tool in this context. This study contributed to the monitoring of LNC by implementing a machine learning algorithm based on the processing of reflectance data from Sentinel-2 (S2) satellites obtained via spectral resampling. For this purpose, five independent datasets containing leaf reflectance measurements collected by spectroradiometers were resampled to the spectral resolution of the sensors onboard the S2 satellites. LNC prediction models were developed from the resampled datasets, using Support Vector Regression (SVR) and Random Forest Regression (RFR), with 75% of the data from each set used to train a model and the remaining 25% for validation. The models demonstrated good predictive power, with the Root Mean Squared Error (RMSE) ranging from 0.39 to 0.94%. Furthermore, this study investigated the transferability of the models' predictive power by using 100% of the data from each set for training and validating predictions on the other sets. To improve transferability, the Transfer Component Analysis (TCA) technique was applied to adapt domains between the sets. This analysis revealed favorable results, especially with the TCA-SVR and TCA-RFR combinations, highlighting a greater capacity to extract transferable spectral features between different leaf reflectance datasets. It was concluded that spectral resampling does not hinder the development of effective LNC prediction models. Aligning this resampling with the resolution of Sentinel-2 sensors, resulted in more efficient monitoring of LNC, eliminating the need to individually reference each sampling point. This approach simplified the monitoring process, reduced both time and costs, and was directly beneficial to producers.
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Copyright (c) 2026 Maria Clara Rodrigues Simão, Francisco Assis da Silva, Carlos Henrique dos Santos, Leandro Luiz de Almeida, Almir Olivette Artero (Autor)
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