Optimizing window size and directional parameters of GLCM texture features for estimating rice AGB based on UAVs multispectral imagery

作者：Jikai Liu, Yongji Zhu, Lijuan Song, Xiangxiang Su, Jun Li, Jing Zheng, Xueqing Zhu, Lantian Ren, Wenhui Wang, Xinwei Li · 发表于：Frontiers in Plant Science · 年份：2023 · DOI：10.3389/fpls.2023.1284235 · 被引用次数：49 · 研究领域：Remote Sensing in Agriculture、Remote Sensing and LiDAR Applications、Remote Sensing and Land Use

Aboveground biomass (AGB) is a crucial physiological parameter for monitoring crop growth, assessing nutrient status, and predicting yield. Texture features (TFs) derived from remote sensing images have been proven to be crucial for estimating crops AGB, which can effectively address the issue of low accuracy in AGB estimation solely based on spectral information. TFs exhibit sensitivity to the size of the moving window and directional parameters, resulting in a substantial impact on AGB estimation. However, few studies systematically assessed the effects of moving window and directional parameters for TFs extraction on rice AGB estimation. To this end, this study used Unmanned aerial vehicles (UAVs) to acquire multispectral imagery during crucial growth stages of rice and evaluated the performance of TFs derived with different grey level co-occurrence matrix (GLCM) parameters by random forest (RF) regression model. Meanwhile, we analyzed the importance of TFs under the optimal parameter settings. The results indicated that: (1) the appropriate window size for extracting TFs varies with the growth stages of rice plant, wherein a small-scale window demonstrates advantages during the early growth stages, while the opposite holds during the later growth stages; (2) TFs derived from 45° direction represent the optimal choice for estimating rice AGB. During the four crucial growth stages, this selection improved performance in AGB estimation with R 2 = 0.76 to 0.83 and rRMSE = 13....