Nian Ying, Su Xiangxiang, Yue Hu, Zhu Yongji, Li Jun, Wang Weiqiang, Sheng Yali, Ma Qiang, Liu Jikai, Li Xinwei
College of Resource and Environment, Anhui Science and Technology University, Chuzhou, China.
Anhui Province Crop Intelligent Planting and Processing Technology Engineering Research Center, Anhui Science and Technology University, Chuzhou, Anhui, China.
Front Plant Sci. 2024 Apr 25;15:1396183. doi: 10.3389/fpls.2024.1396183. eCollection 2024.
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.
地上生物量（AGB）被视为监测作物生长和产量的关键变量。高光谱遥感的应用已成为快速精确监测AGB的可行方法。由于高光谱数据的维度广泛且数据量庞大，有效降低数据维度并选择敏感光谱特征对于提高水稻AGB估算模型的准确性至关重要。目前，导数变换和特征选择算法已成为解决这一问题的重要手段。然而，很少有研究系统地评估导数光谱结合特征选择算法对水稻AGB估算的影响。为此，本研究于2020年在中国滁州市小岗村实验基地，使用美国科罗拉多州博尔德市分析光谱设备公司的ASD FieldSpec手持式2型地面光谱仪，获取水稻关键生长阶段（分蘖期、拔节期、孕穗期、抽穗期和成熟期）的冠层光谱数据，并通过偏最小二乘回归（PLSR）、主成分回归（PCR）、支持向量机（SVM）和岭回归（RR）评估递归特征消除（RFE）和Boruta特征选择算法的性能。此外，我们分析了最优导数光谱的重要性。研究结果表明：（1）随着生长阶段的推进，水稻冠层光谱与AGB之间的相关性呈现从高到低的趋势，其中一阶导数光谱（FD）与AGB的相关性最强。（2）Boruta算法选择的特征波段数量为1935个，具有良好的降维效果。（3）FD-Boruta-PCR（FB-PCR）组合在估算水稻AGB方面表现最佳，R²增加约10%20%，均方根误差（RMSE）降低约0.08%~14%。（4）最佳估算阶段是孕穗期，R值在0.60至0.74之间，RMSE值在1288.23至1554.82 kg/hm之间。本研究证实了高光谱遥感在估算植被生物量方面的准确性，并进一步探索了监测水稻生长动态的理论基础和未来方向。
