摘要:
高时间/高空间分辨率遥感数据的应用具有极为广泛的前景.为此,利用中等分辨率成像光谱仪(moderate-resolution imaging spectroradiometer,MODIS)和高级热量散射和反射辐射仪(advanced spaceborne thermal emission and reflection radiometer,ASTER)数据,基于一种灵活的时空数据融合(flexible spatio–temporal data fusion,FSDAF)方法生成高时间/高空间分辨率的地表温度(land surface temperature, LST),对融合结果用ASTER温度产品(7 d)及自动气象站(automatic weather station,AWS)站点的地表辐射红外温度数据(4 d)进行验证,结果表明:基于FSDAF的数据融合方法生成的LST影像清晰度较高;融合影像与ASTER LST产品的决定系数R2≥0.91,均方根误差≤2.44 K,平均绝对误差≤1.84 K;融合影像与AWS LST数据的决定系数R2≥0.64.
Abstract:
The application of the high spatio - temporal resolution data possesses very extensive foreground. Consequently,based on a flexible spatio -temporal data fusion(FSDAF)method and using MODIS and ASTER data,the authors generate the land surface temperature(LST) with high spatial and temporal resolution. FSDAF is a method based on spectral unmixing and thin plate spline interpolation function. Compared with the existing spatio-temporal data fusion method, its advantages lie in less input data,suitableness for heterogeneous surface and capability of predicting the gradient of land cover types and so on. The fusion results were verified by using the ASTER temperature products(7 days) and the surface radiation infrared temperature data(4 days)of the automatic weather station(AWS) sites. The results show that the LST images generated by the data fusion method based on FSDAF have higher clarity,the correlation coefficient of the fusion images and the ASTER LST products is higher than 0.91(September 28),the room mean square error (RMSE) is less than 2.44 k(September 19), the mean absolute error (MAE) is less than 1.84 k (September 19)and the correlation coefficient of the fusion images and the AWS LST data R2 is higher than 0.64(August 18).
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