1State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, 430072, Hubei, China
2Institute of Crop Sciences, Chinese Academy of Agricultural Sciences/Key Laboratory of Crop Physiology and Ecology, Ministry of Agriculture, Beijing, 100081, China
| Received 05 Aug 2024 |
Accepted 18 Jan 2025 |
Published 28 Feb 2025 |
Water stress is a crucial environmental factor that impacts the growth and yield of rice. Complex field microclimates and fluctuating water conditions pose a considerable challenge in accurately evaluating water stress. Measurement of a particular crop trait is not sufficient for accurate evaluation of the effects of complex water stress. Four comprehensive indicators were introduced in this research, including canopy chlorophyll content (CCC) and canopy equivalent water (CEW). The response of the canopy-specific traits to different types of water stress was identified through individual plant experiments. A hybrid method integrating the PROSAIL radiative transfer model and multidimensional imaging data to retrieve these traits. The synthetic dataset generated by PROSAIL was utilized as prior knowledge for developing a pre-trained machine learning model. Subsequently, reflectance separated from hyperspectral images and phenotypic indicators extracted from front-view images were innovatively united to retrieve water stress-related traits. The results demonstrated that the hybrid method exhibited improved stability and accuracy of CCC (R = 0.7920, RMSE = 24.971 μg cm−2) and CEW (R = 0.8250, RMSE = 0.0075 cm) compared to both data-driven and physical inversion modeling methods. Overall, a robust and accurate method is proposed for assessing water stress in rice using a combination of radiative transfer modeling and multidimensional image-based data.
