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This study aims to evaluate crop water status by fusing multiple features from the unmanned aerial vehicle (UAV)-based canopy images with model updating strategy. A UAV platform carrying multispectral and thermal infrared cameras was used to collect high spatial resolution images of winter wheat and summer maize under different water treatments over two years. The plant water content (PWC) and above-ground biomass (AGB), which represent crop water status, were collected simultaneously. The vegetation indices (VIs), texture features, and canopy thermal indicators were extracted from UAV-based images to estimate PWC and AGB based on CNN-LSTM-Attention (CLA) model. The results showed that combining spectral, textural, and thermal features with the CLA model significantly improved estimation accuracy. Specifically, multi-feature fusion achieved the best performance in winter wheat, with MAE of 1.80 % and 1.23 %, and RMSE of 2.13 % and 1.57 % for PWC in 2022 and 2023, respectively. For AGB, the corresponding MAE values were 1.12 t/hm² and 1.04 t/hm², and RMSE values were 1.41 t/hm² and 1.31 t/hm². In addition, the model updating strategy successfully verified the robustness of the estimation model for winter wheat across different years, and the application of the CLA model to summer maize demonstrated its effective transferability. In summary, this method can improve the estimation accuracy of PWC and AGB, thereby achieving efficient evaluation of crop water status.
Yang et al. (Thu,) studied this question.