Quantitative monitoring of salt stress in rice with solar-induced chlorophyll fluorescence

Under salt stress, rice leaves usually accumulate sodium and chloride ions, causing water loss. The consequent nutrition imbalance and damage to photosynthetic systems ultimately affects growth and development. Therefore, early identification of rice salt stress is important for timely amelioration to reduce rice yield loss. Solar-induced chlorophyll fluorescence (SIF) shows substantial effectiveness for its quick and sensitive response to early-stage stress, suggesting its potential for early crop stress detection. To further assess the capacity of SIF for salt stress detection, this study conducted field plot experiments with a single rice cultivar across two growth stages – jointing and booting. The leaf reflectance spectrum of rice was obtained using an ASD FieldSpec Pro FR portable field spectrometer (Analytical Spectral Devices, Boulder, Colorado, USA) and a leaf clip holder. The leaf solar-induced chlorophyll fluorescence (SIF) spectrum was obtained from an ASD coupling filter and FluoWat, which filters out incident sunlight beyond 650 nm. We calculated nine SIF yield indices (FYs), including ↑FY687 (upward fluorescence yield at 687 nm), ↑FY739 (upward fluorescence yield at 739 nm), ↓FY739 (downward fluorescence yield at 739 nm), totFY687 (total of upward and downward fluorescence yield at 687 nm), totFY739 (total upward and downward fluorescence yield at 739 nm). We also explored the adaptive regulation of three biological rice parameters under salt stress: the net photosynthetic rate (Photo), chlorophyll content (Chl), and maximum photochemical efficiency of photosystem II (Fv/Fm). Photo was the most sensitive to salt stress, and showed significant differences on the first day under salt stress. A salt stress response index (SSRI), which combines physiological and biochemical parameters, was constructed to describe the salt stress level. SSRI showed a significant difference on the first day of salt stress, and the dynamics of SSRI were consistent with that of Photo. In addition, we clarified the change rule of SIF for rice leaves under the salt stress. The gray correlation analysis identified five SIF fluorescence yield indices (FYs) that are highly correlated with SSRI: totFY739, ↓FY739, ↑FY739, totFY687, and ↑FY687. Based on the results of gray correlation analysis, a support vector regression model of SSRI was established, which gave an excellent result as follow: R2 of the calibration for the all dataset, booting dataset, and jointing dataset were 0.74, 0.74, and 0.70, respectively, and R2 of the validation data were 0.71, 0.73, and 0.68. In summary, the proposed method to quantitatively detect rice leaf salt stress based on SIF technology allows the early monitoring of rice leaf salt stress.