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.

The cotton boll opening rate (BOR) is an important indicator for evaluating the physiological maturation process of cotton and the critical stage of yield formation, and it provides essential guidance for subsequent defoliant application and mechanical harvesting. The investigation of cotton BOR usually relies on manual field surveys, which are time-consuming and destructive, making it difficult to achieve large-scale and efficient monitoring. UAV remote sensing technology has been widely used in crop growth monitoring due to its operational flexibility and high image resolution. However, because of the dense growth of the cotton canopy in UAV remote sensing imagery, the boll opening condition in the lower parts of the canopy cannot be completely observed. In contrast, UAV imagery can effectively monitor cotton leaf chlorophyll content (SPAD) and leaf area index (LAI), both of which undergo continuous changes during the boll opening process. Therefore, this study proposes using SPAD and LAI retrieved from UAV multispectral imagery as physiological intermediary variables to construct an empirical statistical equation and compare it with end-to-end machine learning baselines. Multispectral and ground synchronous data (n = 360) were collected in Baibi Town, Anyang, Henan Province, across four dates (8/28, 9/6, 9/13, 9/24). Twenty-eight commonly used vegetation indices were calculated from multispectral imagery, and Pearson’s correlation analysis was conducted to select indices sensitive to cotton SPAD, LAI, and BOR. Prediction models were constructed using the Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), and Partial Least Squares (PLS) models. The results showed that GBDT achieved the best prediction performance for SPAD (R² = 0.86, RMSE = 1.19), while SVM performed best for LAI (R² = 0.77, RMSE = 0.38). The quadratic polynomial equation constructed using SPAD and LAI achieved R² = 0.807 and RMSE = 0.109 in BOR testing, which was significantly better than the baseline model using vegetation indices to directly regress BOR. The method demonstrated stable performance in spatial mapping of BOR during the boll opening period and showed promising potential for guiding defoliant application and harvest timing.