基于MODIS数据的吉林省中部地表温度反演及空间分布研究

利用MODIS数据,采用劈窗算法,定量反演吉林省中部陆地表面温度;利用TM数据,通过目视解译获得研究区的土地利用状况,并结合地势图,研究吉林省中部农业区的地表温度空间分布特征以及地表温度与土地利用类型之间的关系。结果表明,研究区陆地表面温度南高北低,西高东低,符合该区域气候特征;由于地表土地类型热力性质的不同,不同土地利用类型的地表温度存在差异,地表温度最高的裸岩要比最低的湖泊高出26.6K;地表温度与海拔呈负相关关系,即随着海拔的升高,地表温度呈下降趋势,下降率为23.6°C/km;在植被覆盖区,地表温度与NDVI之间存在负相关性,且城市地表温度对植被覆盖度的敏感性要高于农田和林地;对于整个研究区土地利用空间结构,地表温度与NDVI表现为LST-NDVI梯形关系。

Retrieval and Spatial Distribution of Land Surface Temperature in the Middle Part of Jilin Province Based on MODIS Data

Land surface temperature (LST) is a very important parameter to impact the energy and water exchange between the atmosphere and the territorial system. The technology of thermal infrared (TIR) remote sensing provides a chance to retrieve LST over large portions of the earth surface at different spatial resolutions and periodicities. In this study, the middle part of Province is chosen as the study area. It is the most important crop production area in Jilin Province, and a commodity grain base in China. The land surface temperature is retrieved from Moderate-resolution Imaging Spectroradiometer (MODIS) by split-window algorithm. The land use/cover is acquired from TM data by human-computer interactive interpretation. Combined with land use/cover and DEM data, the spatial distribution of LST is identified and the relation between NDVI and LST is analyzed in the middle part of Jilin Province. The result shows that: ① the temperature in the north and the west is higher than the south and the east respectively in the study area. It coincides with the local climate. The main reason to result in higher temperature is from landform in the west, and from difference of land use types in the north because the south with forest has much more transpiration than the north with cropland; ② With influence of human activities, difference of land use types result in differences of LST. The quantitative difference is 26.6K between the highest land surface temperature of bare rock and the lowest temperature of lake. There is a negative relation between altitude and LST. The rate of temperature change could be 23.6°C/km as elevation increases; ③ The LST has a negative relation with NDVI in vegetation covered region. The relation between LST and NDVI in urban areas is much more sensitive than that in forest and cropland. For the whole configuration of land use in the study area, the relation between LST and NDVI displays 'trapeziform’. The forest with higher NDVI and lower LST is located in right-down corner of the trapezoid, while the cropland with high NDVI and low LST is distributed in the middle of the trapezoid. Because of different heat capacities, cities covered by impermeable layers lay to the top of trapezoid with lower NDVI and higher LST. The NDVI-LST feature space contains a lot of geo-information, especially in soil moisture monitoring, and drought detection, and more research could be done in the future.