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Ground-Based Hyperspectral Estimation of Maize Leaf Chlorophyll Content Considering Phenological Characteristics

高光谱成像 单变量 物候学 特征选择 数学 阶段(地层学) 环境科学 计算机科学 多元统计 遥感 统计 农学 人工智能 生物 地理 古生物学
作者
Yiming Guo,Shiyu Jiang,Huiling Miao,Zhenghua Song,Junru Yu,Song Guo,Qingrui Chang
出处
期刊:Remote Sensing [Multidisciplinary Digital Publishing Institute]
卷期号:16 (12): 2133-2133 被引量:2
标识
DOI:10.3390/rs16122133
摘要

Accurately measuring leaf chlorophyll content (LCC) is crucial for monitoring maize growth. This study aims to rapidly and non-destructively estimate the maize LCC during four critical growth stages and investigate the ability of phenological parameters (PPs) to estimate the LCC. First, four spectra were obtained by spectral denoising followed by spectral transformation. Next, sensitive bands (Rλ), spectral indices (SIs), and PPs were extracted from all four spectra at each growth stage. Then, univariate models were constructed to determine their potential for independent LCC estimation. The multivariate regression models for the LCC (LCC-MR) were built based on SIs, SIs + Rλ, and SIs + Rλ + PPs after feature variable selection. The results indicate that our machine-learning-based LCC-MR models demonstrated high overall accuracy. Notably, 83.33% and 58.33% of these models showed improved accuracy when the Rλ and PPs were successively introduced to the SIs. Additionally, the model accuracies of the milk-ripe and tasseling stages outperformed those of the flare–opening and jointing stages under identical conditions. The optimal model was created using XGBoost, incorporating the SI, Rλ, and PP variables at the R3 stage. These findings will provide guidance and support for maize growth monitoring and management.
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