顾及地面传感器观测数据的遥感影像地面温度反演算法

针对遥感数据对地面温度反演精度低及地面温度传感器数据为点数据的特点,构建了融合地面温度传感器实时监测数据与遥感反演地面温度数据的协同反演方法体系。以HJ-1遥感影像的地表温度反演为例,提出了四种融合策略,通过分析比较得到四种结果。四种方案的融合结果的均方根误差分别从0.8848℃下降为0.6562℃、0.4288℃、0.4535℃、0.4261℃;相关系数分别从初始的0.6195提高到0.6343、0.8629、0.8507、0.8648。其中,方案④在增加地面点间隔的情况下,均方根误差能够保持在0.45℃以下,相关系数在0.85以上,并采用不同影像和实测数据进行相对验证。最后探讨了不同方案的特点,得出最优的融合方案,以达到对地表温度进行实时动态监测的目的。

Algorithm for Retrieving Surface Temperature Considering HJ-1 Images and Ground Sensor Network Data

Current methods for retrieving surface temperature using remote sensing data and point data from ground temperature sensor networks yield low temperature inversion precision. To solve this problem, collaborative inversion methods with ground temperature sensor network(GSN) data and remote sensing inversion data fusion were explored four solutions for combination ground sensor network technology and remote sensing based on HJ-1 data, which were proposed to retrieve ground temperature. Experimental results shown that root mean square error of four solutions respectively decreased from 0.8848℃ to 0.6562℃, 0.4288℃, 0.4535℃ and 0.4261℃, and the correlation coefficients increased from the initial 0.6195 to 0.6343, 0.8629, 0.8507 and 0.8629. Moreover, the temperature error of solution four was below 0.45℃ and correlation coefficients were above 0.85 in the case of increasing pixel intervals. The results were validated using different images and GSN data. A comparison of the results and analysis of the models shown that the new model combining brightness temperature with classification results increased the accuracy of the initial retrieved results.