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

• Published in: Frontiers in plant science Vol. 15; p. 1396183
• Main Authors: Nian, Ying, Su, Xiangxiang, Yue, Hu, Zhu, Yongji, Li, Jun, Wang, Weiqiang, Sheng, Yali, Ma, Qiang, Liu, Jikai, Li, Xinwei
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 25.04.2024
• Subjects: AGB; derivative transform; hyperspectral data; machine learning algorithm; Plant Science; remote sensing; rice; hyperspectral data; remote sensing; derivative transform; AGB; rice; machine learning algorithm
• ISSN: 1664-462X; 1664-462X
• DOI: 10.3389/fpls.2024.1396183

Aboveground biomass (AGB) is regarded as a critical variable in monitoring crop growth and yield. The use of hyperspectral remote sensing has emerged as a viable method for the rapid and precise monitoring of AGB. Due to the extensive dimensionality and volume of hyperspectral data, it is crucial to effectively reduce data dimensionality and select sensitive spectral features to enhance the accuracy of rice AGB estimation models. At present, derivative transform and feature selection algorithms have become important means to solve this problem. However, few studies have systematically evaluated the impact of derivative spectrum combined with feature selection algorithm on rice AGB estimation. To this end, at the Xiaogang Village (Chuzhou City, China) Experimental Base in 2020, this study used an ASD FieldSpec handheld 2 ground spectrometer (Analytical Spectroscopy Devices, Boulder, Colorado, USA) to obtain canopy spectral data at the critical growth stage (tillering, jointing, booting, heading, and maturity stages) of rice, and evaluated the performance of the recursive feature elimination (RFE) and Boruta feature selection algorithm through partial least squares regression (PLSR), principal component regression (PCR), support vector machine (SVM) and ridge regression (RR). Moreover, we analyzed the importance of the optimal derivative spectrum. The findings indicate that (1) as the growth stage progresses, the correlation between rice canopy spectrum and AGB shows a trend from high to low, among which the first derivative spectrum (FD) has the strongest correlation with AGB. (2) The number of feature bands selected by the Boruta algorithm is 19~35, which has a good dimensionality reduction effect. (3) The combination of FD-Boruta-PCR (FB-PCR) demonstrated the best performance in estimating rice AGB, with an increase in R² of approximately 10% ~ 20% and a decrease in RMSE of approximately 0.08% ~ 14%. (4) The best estimation stage is the booting stage, with R values between 0.60 and 0.74 and RMSE values between 1288.23 and 1554.82 kg/hm . This study confirms the accuracy of hyperspectral remote sensing in estimating vegetation biomass and further explores the theoretical foundation and future direction for monitoring rice growth dynamics.