Band Depth Analysis and Partial Least Square Regression Based Winter Wheat Biomass Estimation Using Hyperspectral Measurements
FU Yuan-yuan1, 2，3, WANG Ji-hua1, 2，3*, YANG Gui-jun2, 3, SONG Xiao-yu2, 3, XU Xin-gang2, 3, FENG Hai-kuan2, 3
1. Institute of Applied Remote Sensing & Information Technology, Zhejiang University, Hangzhou 310029, China 2. Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China 3. Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China
Abstract：The major limitation of using existing vegetation indices for crop biomass estimation is that it approaches a saturation level asymptotically for a certain range of biomass. In order to resolve this problem, band depth analysis and partial least square regression (PLSR) were combined to establish winter wheat biomass estimation model in the present study. The models based on the combination of band depth analysis and PLSR were compared with the models based on common vegetation indexes from the point of view of estimation accuracy, subsequently. Band depth analysis was conducted in the visible spectral domain (550~750 nm). Band depth, band depth ratio (BDR), normalized band depth index, and band depth normalized to area were utilized to represent band depth information. Among the calibrated estimation models, the models based on the combination of band depth analysis and PLSR reached higher accuracy than those based on the vegetation indices. Among them, the combination of BDR and PLSR got the highest accuracy (R2=0.792, RMSE=0.164 kg·m-2). The results indicated that the combination of band depth analysis and PLSR could well overcome the saturation problem and improve the biomass estimation accuracy when winter wheat biomass is large.
Key words：Hyperspectral remote sensing;Winter wheat;biomass;Band depth analysis;Partial least square regression
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