鍏抽敭璇�: 鏃ュ厜璇卞鍙剁豢绱犺崸鍏�, Cubist妯″瀷, 鏁版嵁閲嶅缓

Abstract:

Solar-Induced Chlorophyll Fluorescence 锛圫IF锛� is the spectral signal 锛�650~800 nm锛� emitted by plants in the process of photo-synthesis under sunlight conditions. SIF is more direct than vegetation index and other parameters. Reflecting the relevant infor-mation of vegetation photosynthesis锛� it brings a new way for large-scale Gross Primary Productivity锛圙PP锛塭stimation. However锛� the current satellite SIF data may have insufficient resolution or discontinuity in the data space锛� which is difficult to apply to the estimation of continuous GPP on a large scale. OCO-2 SIF data has high spatial resolution锛� but it is spatially discrete data. In response to the above problems锛� this paper focuses on the method of con-tinuous prediction of discrete OCO-2 SIF data to generate a high-precision continuous SIF data set of the China-Mongolia grassland ecosy-stem. The results are as follows锛� Through the Cubist regression tree algorithm锛� combined with MODIS reflectance data锛� meteorologi-cal data and land use types锛� a continuous SIF data set with a resolution of 0.05掳 every 8 days is established锛� and the prediction accuracy is R2= 0.65 and RMSE = 0.114. Among them锛� the accuracy of crop SIF prediction is the highest锛� with R2= 0.71 and RMSE= 0.117锛� the second is the prediction of forest and grassland锛� with R2 and RMSE of 0.64/0.123 and 0.60/0.112 respectively.

Key words: Solar-Induced chlorophyll fluorescence, Cubist model, Data reconstruction