卫星遥感地表温度降尺度的光谱归一化指数法
发布时间：2018-12-18 02:46
【摘要】：针对卫星遥感技术监测地表温度(land surface temperature,LST)存在时空分辨率矛盾这一难题,以TsHARP温度降尺度算法为基础,根据地表覆盖类型的不同,分别选择与LST相关性更好的光谱指数(归一化植被指数,NDVI;归一化建造指数,NDBI;改进的归一化水体指数,MNDWI;增强型裸土指数,EBSI)提出了新的转换模型,并从定性和定量两个角度评价了TsHARP法和新模型的降尺度精度。结果表明:两种模型在提高LST空间分辨率的同时又能较好地保持MODIS LST影像热特征的空间分布格局,消除了原始1km影像中的马赛克效应,两种模型均能够达到较好的降尺度效果;全局尺度分析表明,不管是在降尺度结果的空间变异性还是精度方面,本文提出的模型(RMSE:1.635℃)均要优于TsHARP法(RMSE:2.736℃);TsHARP法在水体、裸地和建筑用地这些低植被覆盖区表现出较差的降尺度结果,尤其对于裸地和建筑用地更为明显(|MBE|3℃),新模型提高了低植被覆盖区地物的降尺度精度;不同季节的降尺度结果表明,两种模型都是夏、秋季的降尺度结果优于春、冬季,新模型的降尺度结果四季均好于TsHARP法,其中春、冬季的降尺度精度提升效果要优于夏、秋季。
[Abstract]:In view of the difficult problem of spatial and temporal resolution contradiction in monitoring surface temperature (land surface temperature,LST) by satellite remote sensing technology, based on the TsHARP temperature downscaling algorithm, according to the different types of land cover, The spectral index (normalized vegetation index, NDVI;) with better correlation with LST was selected respectively. The normalized construction index, NDBI; improved normalized water index and MNDWI; enhanced bare soil index, EBSI) proposed a new transformation model, and evaluated the downscaling accuracy of TsHARP method and new model from both qualitative and quantitative aspects. The results show that the two models can not only improve the spatial resolution of LST but also maintain the spatial distribution pattern of thermal characteristics of MODIS LST images, and eliminate the mosaic effect in the original 1km images. Both models can achieve a better downscaling effect. The global scale analysis shows that the proposed model (RMSE:1.635 鈩，
本文编号：2385194
[Abstract]:In view of the difficult problem of spatial and temporal resolution contradiction in monitoring surface temperature (land surface temperature,LST) by satellite remote sensing technology, based on the TsHARP temperature downscaling algorithm, according to the different types of land cover, The spectral index (normalized vegetation index, NDVI;) with better correlation with LST was selected respectively. The normalized construction index, NDBI; improved normalized water index and MNDWI; enhanced bare soil index, EBSI) proposed a new transformation model, and evaluated the downscaling accuracy of TsHARP method and new model from both qualitative and quantitative aspects. The results show that the two models can not only improve the spatial resolution of LST but also maintain the spatial distribution pattern of thermal characteristics of MODIS LST images, and eliminate the mosaic effect in the original 1km images. Both models can achieve a better downscaling effect. The global scale analysis shows that the proposed model (RMSE:1.635 鈩，
本文编号：2385194
本文链接：https://www.wllwen.com/kejilunwen/dizhicehuilunwen/2385194.html
