基于Cubist的中国植被区域叶绿素荧光数据重建

Reconstruction of SIF Remote Sensing Data of Vegetation in China based on Cubist

日光诱导叶绿素荧光(Solar-Induced chlorophyll Fluorescence,SIF)是植物在太阳光照条件下,在光合作用过程中发射出的光谱信号(650~800 nm),SIF相比于植被指数等参数更能直接地反映植被光合作用的相关信息,为大尺度GPP估算带来了新的途径。但目前卫星SIF数据或存在分辨率较低的不足,或存在数据空间不连续的局限,对于应用到大尺度中连续GPP的估算中有一定难度。OCO-2 SIF数据拥有较高的空间分辨率,但却是空间离散数据。针对上述问题,着重研究对离散的OCO-2 SIF数据进行连续预测的方法,生成中国—蒙古草地生态系统的较高精度连续SIF数据集。结果如下:通过Cubist回归树算法,结合MODIS反射率数据,气象数据及土地利用类型,建立了每8 d的0.05°分辨率的连续SIF数据集,预测精度为R^(2)=0.65,RMSE=0.114。其中,对作物类SIF预测的精度最高,为R^(2)=0.71,RMSE=0.117;其次为对森林与草地的预测,两者的R^(2)和RMSE分别为0.64/0.123,0.60/0.112。

Solar-Induced Chlorophyll Fluorescence(SIF)is the spectral signal(650~800 nm)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,it brings a new way for large-scale Gross Primary Productivity(GPP)estimation.However,the current satellite SIF data may have insufficient resolution or discontinuity in the data space,which is difficult to apply to the estimation of continuous GPP on a large scale.OCO-2 SIF data has high spatial resolution,but it is spatially discrete data.In response to the above problems,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:Through the Cubist regression tree algorithm,combined with MODIS reflectance data,meteorologi-cal data and land use types,a continuous SIF data set with a resolution of 0.05°every 8 days is established,and the prediction accuracy is R^(2)=0.65 and RMSE=0.114.Among them,the accuracy of crop SIF prediction is the highest,with R^(2)=0.71 and RMSE=0.117;the second is the prediction of forest and grassland,with R^(2) and RMSE of 0.64/0.123 and 0.60/0.112 respectively.