@article{Luo2026, 
author = {Shanjun Luo and Qian Li and Wu Zhaocong},
title = {Spatiotemporal stability estimation of physicochemical parameters of rice based on dynamic process orientation and refined abundance modification},
year = {2026},
journal = {Geo-Spatial Information Science},
volume = {29},
number = {1},
pages = {319-338},
keywords = {Leaf area index, chlorophyll content, precision agriculture, unmanned aerial vehicle (UAV), refined spectral mixture analysis},
url = {https://www.sciopen.com/article/10.1080/10095020.2025.2498049},
doi = {10.1080/10095020.2025.2498049},
abstract = {Accurate assessment of the physicochemical parameters of rice is critical for increasing agricultural production and ensuring food security. The leaf area index (LAI), leaf chlorophyll content (expressed as the soil plant analysis development (SPAD)), and canopy chlorophyll content (CCC) of the rice at single and multiple periods were estimated using abundance correction indicators by integrating the normalized difference red edge index (NDRE) and refined abundance information, with the goal of developing a high-precision and unified estimation model (AC-NDRE) of the physicochemical parameters of rice that can be adapted to various temporal and spatial scales. The results showed that while not significant, the accuracy of predicting the LAI, SPAD, and CCC of rice using the NDREgreen with the soil background removed was better than that of the NDRE model. The NDREgreen yielded the highest coefficients of determination (R2) of 0.69, 0.71, and 0.70, root mean square errors (RMSEs) of 2.35, 2.19, and 97.61, relative RMSEs (RRMSEs) of 31.87%, 5.61%, and 33.06%, respectively. Furthermore, severe instability was observed in the accuracy of the NDREgreen model on both the spatial and temporal scales. The AC-NDRE-Ⅰ, which is based on strong light, and the AC-NDRE-Ⅱ, which is based on moderate light, exhibited evident advantages in estimating the LAI/CCC and SPAD, respectively. The optimal LAI, SPAD, and CCC estimation accuracies based on the AC-NDRE were R2 values of 0.83, 0.74, and 0.82, RMSEs of 1.73, 2.06, and 76.41, RRMSEs of 23.49%, 5.29%, and 25.88%, respectively. The AC-NDRE approach achieved a stable performance under complicated circumstances. The conclusions of this study indicated that the AC-NDRE-based method for estimating the LAI, SPAD, and CCC of rice could effectively address the issues of a limited model estimation accuracy caused by soil background, NDRE saturation during the middle to late growth stages of rice, and shaded leaves.}
}