Estimation of Rice Aboveground Biomass by Combining Canopy Spectral Reflectance and Unmanned Aerial Vehicle-Based Red Green Blue Imagery Data

Abstract

Estimating the aboveground biomass (AGB) of rice using remotely sensed data is critical for reflecting growth status, predicting grain yield, and indicating carbon stocks in agroecosystems. A combination of multisource remotely sensed data has great potential for providing complementary datasets, improving estimation accuracy, and strengthening precision agricultural insights. Here, we explored the potential to estimate rice AGB by using a combination of spectral vegetation indices and wavelet features (spectral parameters) derived from canopy spectral reflectance and texture features and texture indices (texture parameters) derived from unmanned aerial vehicle (UAV) RGB imagery. This study aimed to evaluate the performance of the combined spectral and texture parameters and improve rice AGB estimation. Correlation analysis was performed to select the potential variables to establish the linear and quadratic regression models. Multivariate analysis (multiple stepwise regression, MSR; partial least square, PLS) and machine learning (random forest, RF) were used to evaluate the estimation performance of spectral parameters, texture parameters, and their combination for rice AGB. The results showed that spectral parameters had better linear and quadratic relationships with AGB than texture parameters. For the multivariate analysis and machine learning algorithm, the MSR, PLS, and RF regression models fitted with spectral parameters (R2 values of 0.793, 0.795, and 0.808 for MSR, PLS, and RF, respectively) were more accurate than those fitted with texture parameters (R2 values of 0.540, 0.555, and 0.485 for MSR, PLS, and RF, respectively). The MSR, PLS, and RF regression models fitted with a combination of spectral and texture parameters (R2 values of 0.809, 0.810, and 0.805, respectively) slightly improved the estimation accuracy of AGB over the use of spectral parameters or texture parameters alone. Additionally, the bior1.3 of wavelet features at 947 nm and scale 2 was used to predict the grain yield and had good accuracy for the quadratic regression model. Therefore, the combined use of canopy spectral reflectance and texture information has great potential for improving the estimation accuracy of rice AGB, which is helpful for rice productivity prediction. Combining multisource remotely sensed data from the ground and UAV technology provides new solutions and ideas for rice biomass acquisition.

Biomass Estimation of Subtropical Arboreal Forest at Single Tree Scale Based on Feature Fusion of Airborne LiDAR Data and Aerial Images

“…The visible light color channel contains li etation information. However, many previous studies have shown that some com rial operation of different color channels [14][15][16] can better reflect vegetation infor Therefore, in this paper, 25 commonly used visible light vegetation indexes and channels were selected to form 28 vegetation index variables. The selected vegeta dex variables are shown in Table 5.…”

“…Vegetation spectral information, such as the vegetation index and image texture, can fully express leaf color, vegetation richness, and health status [11,12]. In recent years, some studies have applied the visible light spectrum [13], image texture [14,15], and visible vegetation index [16][17][18] to estimate vegetation biomass by using the practical and inexpensive measurement of UAVs equipped with cameras. Both Wang Li et al (2016) [19] and Bo Li et al (2020) [20] have used vegetation index (VI) features and vegetation heightrelated variables obtained using aerial photography to estimate aboveground biomass and forecast the yield of maize and potato, respectively.…”

Retrieving rice (Oryza sativa L.) net photosynthetic rate from UAV multispectral images based on machine learning methods

“…As a new near-ground remote sensing approach, unmanned aerial vehicles (UAVs) ( de Castro et al., 2021 ) can flexibly provide higher-resolution and bigger-scale images by carrying different sensors (e.g., multispectral, hyperspectral, and thermal infrared cameras). It has already been widely used in the inversion of physiological and biochemical parameters such as plant height (PH) ( Che et al., 2020 ), leaf area index (LAI) ( Chen et al., 2022b ), nutrient states ( Xu et al., 2021 ), and aboveground biomass ( Wang et al., 2022 ). Equipped with hyperspectral imaging (HSI) sensors, Liu and Peng.…”

Remote Sensing Extraction Method of Illicium verum Based on Functional Characteristics of Vegetation Canopy

“…The vegetation functional datasets include FPAR, canopy chlorophyll content, and ecosystem functional characteristics such as carbon storage and evapotranspiration. At present, some common methods have been used to estimate biomass [4,[37][38][39][40].…”