Predicting grain yield of maize using a new multispectral-based canopy volumetric vegetation index

作者：Yahui Guo, Yongshuo H. Fu, Shouzhi Chen, Fanghua Hao, Xuan Zhang, Kirsten M. de Beurs, Yuhong He · 发表于：Ecological Indicators · 年份：2024 · DOI：10.1016/j.ecolind.2024.112295 · 被引用次数：22 · 研究领域：Remote Sensing in Agriculture、Remote Sensing and Land Use、Land Use and Ecosystem Services

• A new volumetric vegetation index (MSCVI) was proposed for predicting maize grain yields. • MSCVI built at reproductive growth stages were more correlated with maize yields. • MSCVI with machine learning using transfer strategy was robust and stable. Accurately predicting agricultural yields is crucial for developing adaptative strategies to ensure food security. Unmanned aerial vehicle (UAV) remote sensing equipped with portable multispectral sensors are commonly applied to acquire high temporal and spatial resolutions of remote sensing data. The vegetation indices (VIs) extracted from multispectral images are conducted for agricultural yield prediction. However, existing VIs often suffered from saturation problems when the canopy coverage is high. Integrating UAV-derived canopy height data with spectral indices holds the potential to solve saturation problem. However, this method is still at the infant stage and requires further validation. Here, we have newly proposed a multispectral-based canopy volumetric vegetation index (MSCVI) that integrates RGB-based volumetric index (VCI) and multispectral images derived VIs from UAV platform for predicting irrigated maize yields for three years (2019, 2020, and 2021). To test the stability of the proposed method, the maize was well managed and different levels of fertilizers were applied in each plot. The results using regression analysis showed the MSCVI outperformed the single adoption of VIs and VCI, and the MSCVI at reproduc...