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

Abstract

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 R2 = 0.76 to 0.83 and rRMSE = 13.62% to 21.33%. Furthermore, the estimation accuracy for the entire growth season is R2 =0.84 and rRMSE =21.07%. However, there is no consensus regarding the selection of the worst TFs computation direction; (3) Correlation (Cor), Mean, and Homogeneity (Hom) from the first principal component image reflecting internal information of rice plant and Contrast (Con), Dissimilarity (Dis), and Second Moment (SM) from the second principal component image expressing edge texture are more important to estimate rice AGB among the whole growth stages; and (4) Considering the optimal parameters, the accuracy of texture-based AGB estimation slightly outperforms the estimation accuracy based on spectral reflectance alone. In summary, the present study can help researchers confident use of GLCM-based TFs to enhance the estimation accuracy of physiological and biochemical parameters of crops.

Urban Above-Ground Biomass Estimation Using GEDI Laser Data and Optical Remote Sensing Images

“…Topography Slope Slope in degrees / Elevation Elevation above sea level in meters / P ij represents the probability of each pixel pair (i, j) value and i, j are the gray tones in the windows, which are also the coordinates of the co-occurrence matrix space; N represents the number of distinct grey levels in the quantized image, which has a gray value range of the original image [54].…”

Improving Wheat Leaf Nitrogen Concentration (LNC) Estimation across Multiple Growth Stages Using Feature Combination Indices (FCIs) from UAV Multispectral Imagery

“…Guo et al [60] discovered that Con had the highest accuracy in extracting maize heading date. Liu et al [23] demonstrated that Con outperformed other Tm in estimating rice above-ground biomass (AGB). The study's optimal TFCI D and TFCI T treatments primarily consisted of Mea, Cor, Con, and Dis.…”

“…They show the parts of the plant growing quickly and consistently, documenting the developmental variations in wheat at various growth stages. Con and Dis focus on high-frequency information, demonstrated by Liu et al [23] to help estimate crop information, such as the degree of gray-level fluctuations and the relationship between pixel distances and diagonal. Sensitive Tm enhanced the response to LNC across multiple growth stages by employing feature combination formulas, which reflected the changes in LNC over these times.…”

“…High-resolution images acquired by UAV multispectral sensors capture the spectral characteristics of wheat canopies and the texture features that reflect crop structure changes. These texture features can reflect variations in wheat leaf color across various varieties and fertilizer treatments and respond to changes in canopy structure during different growth stages [23]. Texture metrics and crop biochemistry are closely related, but developing trustworthy crop information prediction models based only on texture metrics is difficult due to the absence of a formulaic method to enhance their response to vegetation physiology [24].…”

Improving the estimation of rice above-ground biomass based on spatio-temporal UAV imagery and phenological stages

“…These dimensionless indices integrate spectral data from different narrow-band wavelengths and can reflect characteristics including coverage, chlorophyll content, moisture, and health status ( Bannari et al., 1995 ). Along with VIs that make use of spectral features, texture indices that reflect subtle variations in canopy structure are also commonly used to enhance the estimation of AGB ( Wang et al., 2022b ; Liu et al., 2023a ). Many studies predominantly focus on establishing empirical relationships directly between these features and ground-measured AGB values using statistical analysis and machine learning algorithms.…”