Assessing wheat genotype response under combined nitrogen and water stress scenarios coupling high-resolution optical and thermal sensors with radiative transfer models

Remote sensing (RS) offers great possibilities to acquire data for early determination of crop performance, which is essential for water and nitrogen (N) management. The objective of this study was to assess the nutritional and water status of different wheat genotypes (Triticum aestivum L.) by means of vegetation indices (VIs) derived from ground-level hyperspectral measurements, plant traits retrieved by coupling a radiative transfer (RT) model with a 3-layer neural network, and thermal data. To this end, two field experiments were conducted in central Spain during 2018–2021, using two wheat genotypes (Cellule and Nogal) under three N and two irrigation doses. Pigment and polyphenolic compound measurements were carried out with a Dualex® device, canopy reflectance (400–900 nm) was measured with a handheld spectroradiometer, and thermal indicators were assessed using a thermal camera at different wheat growth stages. The nitrogen nutrition index (NNI) at flowering and the grain yield (GY) and grain N concentration (GNC) at harvest were determined. The spectral reflectance measurements were used to calculate VIs, and the PROSAIL-PRO radiative transfer model was used to retrieve chlorophyll (Chl) content and leaf area index (LAI). In addition, the water deficit index (WDI) and canopy–air temperature difference (T_c-T_a) were used to assess the water status of wheat. Our results show that predicted Chl and VIs built with red-edge bands obtained the best agreement with NNI (R² ≥ 0.60 at flowering in Cellule and R² = 0.60 at medium milk in Nogal) and GNC (R² ≥ 0.45 and 0.38 in Cellule and Nogal respectively). The GY was best assessed with VIs combining near infrared (NIR) / visible bands and NIR / red-edge bands (R² ≥ 0.74 and RMSE ≤ 903 kg ha^-1 at watery ripe in Cellule; R² ≥ 0.56 and RMSE ≤ 791 kg ha^-1 at the end of stem elongation in Nogal) than by the predicted plant traits. The best differentiation between genotype performance was achieved by predicted Chl and LAI retrieved through PROSAIL-PRO and the canopy Chl content index, showing that the Cellule genotype had a stronger response than Nogal to N application. Similarly, the WDI and T_c-T_a proved that the more drought-resistant genotype (Cellule) suffered lower water stress than Nogal. This study highlights that the combination of optical and thermal data improved the assessment of the agronomic parameters studied (R² increased by 23 % and RMSE decreased by 8 %), suggesting its potential for conducting irrigation and N fertilization management.