本文利用MODIS数据反演大气透射率,利用HJ-1B/CCD进行分类,并反演地表比辐射率.在此基础上,借鉴单窗算法,利用HJ-1B/IRS数据反演得到地表温度,并利用MODIS温度产品对反演结果进行了初步验证.最后利用热场变异指数进一步分析重庆的热岛空间分布特征,并对NDVI与NDBI对热岛效应的影响进行了分析.其结论如下:(1)重庆城市热岛大致基于中梁山、铜锣山走势,呈东北、西南走向分布;(2)主城区"热岛效应"的中心并不在建筑物密集的市中心区域——解放碑,而是集中在大渡口的工业园区、江北机场这些能耗大、人口密集区域,热岛强度7.7℃,热岛效应较为明显;(3)接近长江、嘉陵江水域的密集建筑用地区域,典型区域如渝中区,其热岛效应并不明显;(4)NDVI与地表温度呈负相关关系,NDBI与地表温度呈现较为明显的正相关关系,NDVI与NDBI对地表温度都有重要影响,而NDBI,即建筑用地比例与建筑密度的影响更大.

Urban heat island effect in Chongqing is in rising trend with the gradual expansion of urban construction land. And this trend increases hot weather of Chongqing, known as a 'stove’, so analyzing and understanding the spatial distribution of Chongqing urban heat island has great significance. In this paper, we referenced single-window algorithm to inverse surface temperature using Environmental Satellite image (HJ-1B) as the main data source, made a preliminary validation on retrieval result using MODIS temperature products, and used thermal field variability index to further analyze Chongqing spatial distribution feature of urban heat island. First, atmospheric water vapor content and atmospheric transmissivity was inversed from MODIS second band and 19th band, and land surface emissivity was obtained after classification by HJ-1B/CCD. On this basis, land surface temperature was inversed based on single-window algorithm, further, spatial distribution of Chongqings heat islands and their relationship with NDVI and NDBI and the heat island effect were analyzed. The results showed that: (1) Chongqings urban heat islands are roughly northeast and southwest distribution along Liangshan and Tongluoshan; (2) The center of Chongqings urban heat island is not in the downtown area, i.e. Jiefangbei, of which buildings are dense, but in the Dadukou industrial park and Jiangbei airport, of which energy is consumed largely and population is dense, the heat island intensity is between 7.7℃; (3) The heat island effect is not clear in dense building land area, the typical region such as the Yuzhong District is close to the Yangtze River and Jialing River waters; And (4) NDVI and surface temperature are negatively correlated, NDBI and the surface temperature show a more obvious positive correlation, NDVI and NDBI have a significant impact on surface temperature, and NDBI, i.e. the proportion of building land and building density, has a greater impact.