Estimation of Wheat Grain Protein Content at Multiple Growth Stages Based on Space–Air–Ground Collaborative Observation Data

Wheat grain protein content (GPC), quantified as the mass proportion of protein relative to the total dry grain weight, is a key indicator for wheat quality evaluation. Based on space–air–ground collaborative observations, this study screened GPC-sensitive spectral parameters and multi-stage and multi-scale remote-sensing GPC estimation models. Results showed that the optimal feature set derived from unmanned aerial vehicle (UAV) imagery consisted of four vegetation indices (VIs) and three optimized texture indices (TIs). The Random Forest-based Soil–Plant Analysis Development (SPAD) model for field plots achieved the highest accuracy at the anthesis stage, with a coefficient of determination (R2) of 0.91 and a root mean square error (RMSE) of 1.77. Cross-year validation gave a correlation coefficient of 0.80 and RMSE of 3.83. Data analysis indicated that GPC of mature wheat had an extremely significant correlation with SPAD values across various growth stages, higher than leaf area index (LAI). We thus established a quantitative GPC estimation framework with UAV-derived SPAD as an intermediate variable, which kept stable R2 (0.60) and RMSE (1.1%) except during grain-filling. On this basis, UAV-retrieved GPC data were scaled up to 10 m. Eight optimal parameters were selected from Sentinel-2 satellite data, and the regional-scale GPC estimation model was developed via the partial least squares regression (PLSR) algorithm. Compared with the model established directly using ground measured data without scale conversion, the scale-up model increased R2 by 8.9% and reduced RMSE by 40%, improving the model inversion accuracy. The proposed regional-scale GPC remote-sensing estimation model effectively solves the scale mismatch between ground observation and satellite data, and provides technical support for field and regional wheat quality remote-sensing estimation.