Above ground biomass (AGB) is an important indicator of evaluating crop growth and guiding agricultural production and management. Therefore, AGB information was obtained timely, accurately and efficiently to provide a strong basis for predicting yields and securing grain trade. The conventional way to obtain AGB is to use destructive sampling methods that require manual harvesting of crops, weighing, and recording, making large-area and long-term measurements difficult. However, UAV remote sensing technology is considered the most effective way to estimate AGB of large area crops with the rapid development of precision agriculture. In this study, the multispectral images of the tuber formation period, tuber growth period and starch accumulation period were obtained by the UAV platform equipped with multispectral sensors. The measured plant height, AGB and latitude, longitude and altitude of ground control point (GCP) were measured on the ground. Firstly, using UAV multispectral images combined GCP location information basing structure from motion (SFM) algorithm to generate the digital surface model (DSM) of the potato experimental field, and DSM extracted the plant height (Hdsm) of each growth period. Then, four original single band vegetation indices, 9 multiband vegetation indices,high-frequency information (HFI) in the red edge band and Hdsm were selected with AGB for correlation analysis. Finally, based on single-band vegetation indices (x(1)), multiband vegetation indices (x(2)), vegetation indicescombined Hdsm (x(3)),vegetation indices combined HFI (x(4)) and their integration (x(5)) as input parameters were used to estimate AGB of each growth period by partial least squares regression (PLSR) and ridge regression (RR). The results showed that: (1) The R-2 of extracted Hdsm and measured plant height was 0.87 and NRMSE was 14.34%. (2) All model parameters reached highly significant levels with the AGB, and correlations increased and then decreased from the tuber formation period to the starch accumulation period. (3) Using the same method to estimate potato AGB with five variables at different growth periods, it starts to get better and then it gets worse for the effect of potato AGB from tuber formation period to starch accumulation period with the estimation accuracy from high to low was x(5)>x(4)>x(3)>x(2)>x(1). (4) The results showed that PLSR was better than RR in estimating AGB for different growth stages and basing x(5) combined PLSR method was the best in estimating AGB at tuber growth period with R-2 of 0.73 and NRMSE of 15.22%. Therefore, this study combined the selected multispectral vegetation indices combined HFI and Hdsm with the PLSR method can significantly improve the estimation accuracy of AGB, which provides new technical support for the monitoring of AGB in large areas of potato crops.
