Utilizing UAV-based hyperspectral remote sensing combined with various agronomic traits to monitor potato growth and estimate yield

• The crop growth monitoring indicator (CGMI) was constructed. • The relationship between CGMI and crop traits was analyzed. • CGMI could be used to monitor potato crop growth and estimate yields. • Spatial distribution maps of estimated yields and errors were mapped and analyzed. Timely and accurate monitoring of potato crop growth and estimating yields are essential to improve agricultural production. Unmanned aerial vehicle (UAV)-based hyperspectral remote sensing is a non-destructive method for crop growth monitoring (CGM) and yield estimation, which plays a vital role in the agricultural application. However, CGM and yield estimation are typically achieved through quantitative inversion of specific crop traits, which lacks consideration for the interactive impacts among traits. Thus, this study aimed to integrate multiple agronomic traits using a fuzzy comprehensive evaluation (FCE) method to construct a new crop growth monitoring indicator (CGMI) for CGM and yield estimation. In 2018 and 2019, UAV hyperspectral images and ground parameters were acquired during three growth stages of potatoes. Compared to single agronomic traits, CGMI could be better described by vegetation indices (VIs). The accuracy and stability of the CGMI estimation model were effectively validated, while the single trait estimation model performed poorly on the validation set. The coefficient of determination (R 2 ) values of CGMI estimation for three stages were in the range of 0.56–0.72 and 0.56–0.66 for calibration and validation sets. The CGMI at different stages was closely correlated with potato yield, reaching a highly significant level. The VIs selected based on CGMI and Akaike information criterion (AIC) were input into the PLSR model to estimate potato yields. The R 2 values of yield estimation for three stages were in the range of 0.63–0.69 and 0.54–0.60 for calibration and validation sets. The study demonstrated that integrating multiple crop traits could enhance the relationship with yield and provided a comprehensive reflection of crop growth. The CGMI constructed in this study can provide decision-making services for crop production management in the field.