Accurate estimation of winter-wheat leaf water content using continuous wavelet transform-based hyperspectral combined with thermal infrared on a UAV platform

Abstract

Timely and accurate assessment of crop water status using unmanned aerial vehicle (UAV) imagery is helpful for precision irrigation and field management. The aim of this study is to investigate the application potential of continuous wavelet transform (CWT)-based hyperspectral combined with thermal infrared image data for the estimation of leaf water content (LWC) in winter wheat. This study evaluates the performance of convolutional neural networks (CNN) for feature extraction and long short-term memory (LSTM) networks for sequential data processing in LWC estimation. A UAV platform carrying hyperspectral and thermal infrared sensors was used to collect high spatial resolution images of winter wheat under different water treatments over two years. The LWC was collected simultaneously. The original (OR) and CWT-transformed canopy spectral and textural features, as well as canopy temperature indicators, were extracted from the UAV-based images. On this basis, the LWC estimation model was established using the CNN and LSTM model. The results showed that the combination of thermal features with spectral and texture features significantly improves model performance compared to models built on a single data. The CWT-transformed spectral features improved LWC estimation compared to the original spectrum, with the third scale (CWT3) yielding the best results. Moreover, the CWT-transformed texture at multi-decomposition scales proved to be effective for estimating LWC. Compared to other models, the LSTM model (T-ST_CWT3-LSTM), built by thermal feature fusion with CWT3-based spectral and texture features, achieved the best LWC estimation results, with R² of 0.827 and 0.836, RMSE of 2.575 % and 1.822 %, and MAE of 2.041 % and 1.434 % for 2022 and 2023, respectively. In addition, the robustness of the T-ST_CWT3-LSTM model was successfully verified at different growth stages. Overall, the CWT technique and multi-feature fusion approach provide a valuable technical reference for real-time crop water status monitoring, supporting improved precision irrigation practices and sustainable crop management.