Spatio-temporal monitoring of plant water status using optical remote sensing data and in situ measurements

Water content as an important physiological status variable in plants, is closely linked to transpiration, photosynthesis, water stress, and biomass productivity. Obtaining plant water information that provides sufficient spatial and temporal resolution for such applications remains a challenge. In this study, the data distribution space of fractional vegetation cover (FVC) and shortwave infrared transformed reflectance (STR) with linear and nonlinear edges was used to monitor plant water content. Four indicators of plant water status, equivalent water thickness (EWT), leaf water content (LWC), relative water content (RWC), and leaf water potential (LWP), were measured in a 10-ha corn field at the Karkaj Agricultural Research Station of Tabriz University, Tabriz, Iran. Furthermore, the Sentinel-2 Level 2 prototype processor (SL2P) was utilized to estimate EWT and compare the results with FVC-STR space. The FVC-STR space with nonlinear edges (FSNLE) provided better estimation accuracy of plant water status than the FVC-STR space with linear edges (FSLE). The root mean square errors of the EWT, LWC, RWC and LWP estimates for FSLE were 0.00306 g/cm2, 4.03 %, 6.56 %, and 0.38 bar, respectively, while those for FSNLE were 0.00303 g/cm2, 3.75 %, 5.57 %, and 0.37 bar, respectively. In addition, the R2 value for FSNLE was higher than that for FSLE (0.43-0.70 vs. 0.39-0.64). The RMSE and R2 for SL2P were 0.0041 g/cm2 and 0.401, respectively. Among the four measured indicators, the highest and lowest estimation accuracies in both FSLE and FSNLE were obtained with EWT and RWC, respectively. It can be concluded that FVC-STR space model based on Sentinel-2 imagery data provide acceptable accuracy for estimating plant water content. The FVC-STR space with nonlinear edges provided a better estimation accuracy for plant water indicators than the FVC-STR space with linear edges. (c) 2024 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.