Abstract: 【Objective】To study the non-destructive estimation of spring wheat SPAD value by UAV remote sensing technology,and establish a model for spring wheat SPAD value estimation on a small field scale.【Methods】A multispectral UAV was used to acquire spring wheat canopy multispectral data in 2022 in Hetao region of Inner Mongolia,and 5 vegetation indices were extracted. The correlation between vegetation indices and plant SPAD values was analyzed,and support vector regression（SVR）,partial least squares regression（PLSR）,and random forest regression（RFR） were combined to construct SPAD value estimation models for key growth periods of spring wheat. The optimal SPAD value estimation models for each period were selected.【Results】The optimal model was determined by pairing with 5 vegetation indices,and all 15 combination methods were able to estimate SPAD values during the jointing,heading,flowering,and filling stages of spring wheat. Comparing the optimal models of 3 modeling methods,it was found that the NDVI+RFR model had the highest accuracy and good robustness in estimating the SPAD value of spring wheat,with R²=0.96 and RMSE=0.19.【Conclusion】It is feasible to use UAV to estimate the SPAD value of spring wheat, and the optimal model is the NDVI+RFR model.

Key words: UAV monitoring, Multispectral imaging, Spring wheat, SPAD value