Integrating the Textural and Spectral Information of UAV Hyperspectral Images for the Improved Estimation of Rice Aboveground Biomass

Abstract

The accurate and rapid estimation of the aboveground biomass (AGB) of rice is crucial to food security. Unmanned aerial vehicles (UAVs) mounted with hyperspectral sensors can obtain images of high spectral and spatial resolution in a quick and effective manner. Integrating UAV-based spatial and spectral information has substantial potential for improving crop AGB estimation. Hyperspectral remote-sensing data with more continuous reflectance information on ground objects provide more possibilities for band selection. The use of band selection for the spectral vegetation index (VI) has been discussed in many studies, but few studies have paid attention to the band selection of texture features in rice AGB estimation. In this study, UAV-based hyperspectral images of four rice varieties in five nitrogen treatments (N0, N1, N2, N3, and N4) were obtained. First, multiple spectral bands were used to identify the optimal bands of the spectral vegetation indices, as well as the texture features; next, the vegetation index model (VI model), the vegetation index combined with the corresponding-band textures model (VI+CBT model), and the vegetation index combined with the full-band textures model (VI+FBT model) were established to compare their respective rice AGB estimation abilities. The results showed that the optimal bands of the spectral and textural information for AGB monitoring were inconsistent. The red-edge and near-infrared bands demonstrated a strong correlation with the rice AGB in the spectral dimension, while the green and red bands exhibited a high correlation with the rice AGB in the spatial dimension. The ranking of the monitoring accuracies of the three models, from highest to lowest, was: the VI+FBT model, then the VI+CBT model, and then the VI model. Compared with the VI model, the R2 of the VI+FBT model and the VI+CBT model increased by 1.319% and 9.763%, respectively. The RMSE decreased by 2.070% and 16.718%, respectively, while the rRMSE decreased by 2.166% and 16.606%, respectively. The results indicated that the integration of vegetation indices and textures can significantly improve the accuracy of rice AGB estimation. The full-band textures contained richer information that was highly related to rice AGB. The VI model at the tillering stage presented the greatest sensitivity to the integration of textures, and the models in the N3 treatment (1.5 times the normal nitrogen level) gave the best AGB estimation compared with the other nitrogen treatments. This research proposes a reliable modeling framework for monitoring rice AGB and provides scientific support for rice-field management.

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

“…These factors impose constraints on further advancements in monitoring accuracy. To address the issue of low accuracy in monitoring AGB based on spectral information, an increasing number of researchers are inclined toward utilizing texture features (TFs) to improve the precision of crop AGB monitoring ( Liu Y. et al., 2019 ; Yue et al., 2019 ; Wang F. et al., 2022 ; Xu T. et al., 2022 ). TFs describe the frequency of variations in attribute values among adjacent pixel pairs within a specific window ( Liu Y. et al., 2019 ; Yue et al., 2019 ; Zhang et al., 2021 ).…”

Estimation of the rice aboveground biomass based on the first derivative spectrum and Boruta algorithm

“…This occurs because this band serves as the transition region between the infrared and near-infrared bands, where spectral reflectance transitions rapidly from a low negative correlation to a high positive correlation. This shift is attributed to strong absorption and reflection ( Kanke et al., 2016 ; Xu et al., 2022 ). The correlation between FD and AGB was generally greater than that between OS and SD at the different fertility stages ( Figure 4 ).…”

Improving Efficiency of Ground-Truth Data Collection for UAV-based Rice Growth Estimation Models: Investigating the Effect of Sampling Size on Model Accuracy

“…In conclusion, previous studies have typically employed multi-hill harvesting to acquire a single point of data when constructing the datasets required for the development of UAV-based growth estimation models 12,16,18,25 . This study has demonstrated the potential of utilizing single-hill datasets, which is an important finding for the labor-saving collection of ground-truth data in model development.…”

“…When we quantify the plant growth of a specific plot in a field experiment within the realm of crop science research, multiple hills are harvested within the plot and their average value is employed to obtain a representative value for the plot to account for growth variation occurring among hills within the plot 14 . In line with this, studies on growth estimation using UAV aerial imagery have conventionally collected ground measurements in a similar manner, for example, Zheng et al 15 collected AGB data based on the mean value of 20 randomly selected hills; Xu et al 16 obtained AGB data by harvesting four adjacent hills for each data point; Buscon et al 17 conducted AGB and LAI measurements by surveying four contiguous hills collectively; Yamaguchi et al 18 sampled eight neighboring hills to measure LAI and their average value was used to prepare datasets. However, when we focus on developing a growth estimation model using UAV photogrammetry, we can consider using data based on fewer harvested hills to save labor in ground measurement.…”