UAV-Based Multispectral Inversion of Integrated Cotton Growth - Haozheng Gu; Chen Xue; Guobin Wang; Yubin Lan; Huizheng Wang; Cancan Song (Agronomy)
Cotton growth monitoring can provide macroscopic information for cotton field management. In this paper, we utilised UAV multispectral remote sensing to monitor cotton growth and constructed a Comprehensive Growth Index (CGI) by combining cotton multi-period Leaf Area Index (LAI) and relative chlorophyll content (Soil and plant analyser development, SPAD) through the coefficient of variation method and constructed 28 types of vegetation cover indexes by combining multispectral data. CGI combined with multispectral data to construct 28 vegetation indices and a digital surface model, selected the indexes with higher correlation through correlation analysis, and constructed a single growth model (LAI and SPAD) and CGI by using the support vector machine (SVM), random forest (RF), and gradient boosted decision tree (GBDT) model. In the GBDT model, the comprehensive growth model was better than the single growth index model, with an R2 of 0.85 and an RMSE of 0.044, and in the RF model, the comprehensive growth model was better than the single growth index model, with an R2 of 0.86 and an RMSE of 0.037. In the comprehensive analysis, the comprehensive growth index can improve the accuracy of the inversion model of the cotton growth and provide a new method for the monitoring of the cotton growth by remote sensing.