Inversion of Leaf Chlorophyll Content in Different Growth Periods of Maize Based on Multi-Source Data from “Sky–Space–Ground”

Abstract

Leaf chlorophyll content (LCC) is a key indicator of crop growth condition. Real-time, non-destructive, rapid, and accurate LCC monitoring is of paramount importance for precision agriculture management. This study proposes an improved method based on multi-source data, combining the Sentinel-2A spectral response function (SRF) and computer algorithms, to overcome the limitations of traditional methods. First, the equivalent remote sensing reflectance of Sentinel-2A was simulated by combining UAV hyperspectral images with ground experimental data. Then, using grey relational analysis (GRA) and the maximum information coefficient (MIC) algorithm, we explored the complex relationship between the vegetation indices (VIs) and LCC, and further selected feature variables. Meanwhile, we utilized three spectral indices (DSI, NDSI, RSI) to identify sensitive band combinations for LCC and further analyzed the response relationship of the original bands to LCC. On this basis, we selected three nonlinear machine learning models (XGBoost, RFR, SVR) and one multiple linear regression model (PLSR) to construct the LCC inversion model, and we chose the optimal model to generate spatial distribution maps of maize LCC at the regional scale. The results indicate that there is a significant nonlinear correlation between the VIs and LCC, with the XGBoost, RFR, and SVR models outperforming the PLSR model. Among them, the XGBoost_MIC model achieved the best LCC inversion results during the tasseling stage (VT) of maize growth. In the UAV hyperspectral data, the model achieved an R2 = 0.962 and an RMSE = 5.590 mg/m2 in the training set, and an R2 = 0.582 and an RMSE = 6.019 mg/m2 in the test set. For the Sentinel-2A-simulated spectral data, the training set had an R2 = 0.923 and an RMSE = 8.097 mg/m2, while the test set showed an R2 = 0.837 and an RMSE = 3.250 mg/m2, which indicates an improvement in test set accuracy. On a regional scale, the LCC inversion model also yielded good results (train R2 = 0.76, test R2 = 0.88, RMSE = 18.83 mg/m2). In conclusion, the method proposed in this study not only significantly improves the accuracy of traditional methods but also, with its outstanding versatility, can achieve rapid, non-destructive, and precise crop growth monitoring in different regions and for various crop types, demonstrating broad application prospects and significant practical value in precision agriculture.