ABSTRACT
BACKGROUND: Leaf water content (LWC) significantly affects rice growth and development. Real-time monitoring of rice leaf water status is essential to obtain high yield and water use efficiency of rice plants with precise irrigation regimes in rice fields. Hyperspectral remote sensing technology is widely used in monitoring crop water status because of its rapid, nondestructive, and real-time characteristics. Recently, multi-source data have been attempted to integrate into a monitored model of crop water status based on spectral indices. However, there are fewer studies using spectral index model coupled with multi-source data for monitoring LWC in rice plants. Therefore, 2-year field experiments were conducted with three irrigation regimes using four rice cultivars in this study. The multi-source data, including canopy ecological factors and physiological parameters, were incorporated into the vegetation index to accurately predict LWC in rice plants. RESULTS: The results presented that the model accuracy of rice LWC estimation after combining data from multiple sources improved by 6-44% compared to the accuracy of a single spectral index normalized difference index (ND). Additionally, the optimal prediction accuracy of rice LWC was produced using a machine algorithm of gradient boosted decision tree (GBDT) based on the combination of ND(1287,1673) and crop water stress index (CWSI) (R2 = 0.86, RMSE = 0.01). CONCLUSIONS: The machine learning estimation model constructed based on multi-source data fully utilizes the spectral information and considers the environmental changes in the crop canopy after introducing multi-source data parameters, thus improving the performance of spectral technology for monitoring rice LWC. The findings may be helpful to the water status diagnosis and accurate irrigation management of rice plants.
ABSTRACT
Water-saving and drought-resistant rice (WDR) has high a yield potential in drought. However, the photosynthetic adaptation mechanisms of WDR to drought and rehydration have yet to be conclusively determined. Hanyou 73 (HY73, WDR) and Huanghuazhan (HHZ, drought-sensitive cultivar) rice cultivars were subjected to drought stress and rewatering when the soil water potential was −180 KPa in the booting stage. The leaf physiological characteristics were dynamically determined at 0 KPa, −30 KPa, −70 KPa, −180 KPa, the first, the fifth, and the tenth day after rewatering. It was found that the maximum net photosynthetic rate (Amax) and light saturation point were decreased under drought conditions in both cultivars. The change in dark respiration rate (Rd) in HY73 was not significant, but was markedly different in HHZ. After rewatering, the photosynthetic parameters of HY73 completely returned to the initial state, while the indices in HHZ did not recover. The antioxidant enzyme activities and osmoregulatory substance levels increased with worsening drought conditions and decreased with rewatering duration. HY73 had higher peroxidase (POD) activity as well as proline levels, and lower catalase (CAT) activity, ascorbate peroxidase (APX) activity, malondialdehyde (MDA) level, and soluble protein (SP) content during all of the assessment periods compared with HHZ. In addition, Amax was markedly negatively correlated with superoxide dismutase (SOD), POD, CAT, and SP in HY73 (p < 0.001), while in HHZ, it was negatively correlated with SOD, CAT, APX, MDA, Pro, and SP, and positively correlated with Rd (p < 0.001). These results suggest that WDR has a more simplified adaptation mechanism to protect photosynthetic apparatus from damage in drought and rehydration compared with drought-sensitive cultivars. The high POD activity and great SP content would be considered as important physiological bases to maintain high photosynthetic production potential in WDR.
