A Study On The Contrast Between The Thermal Infrared Data From Satellite In The Northwest Part Of Beijing & The Practical Measured Ground Surface Temperature

Land surface temperature(LST) is an important parameter of energy balance of land surface and plays an important role in the interaction between land surface and atmosphere, and has important significance for energy balance on land surface, widely applied in many fields especially aerography, geology, hydrology, ecology and so on. It need to know the parameters of atmosphere and information of earth's ground with the traditional way that get LST from MODIS and operation is complex. This paper took the northwest part of Beijing as the test field, made use of the MODIS thermal infrared remote sensed data and relevant practical measured ground surface data to establish the land surface temperature retrieval model. The theoretical sense and the practical value were obvious. The detailed task included the following:Firstly, the system was established to get practical measured ground surface data. The ground surface data which was long period & real time with the way of telemetry was recorded. In order to transform the digital signals to temperature data with the precision is 0.2 K, the sensors were calibrated. All the ground control points of the test field were analysed, and found that the average temperature of the GCPs with mean square error is fewer than 5.8K can replace the temperature of the test field. So the temperature was distributed of the whole test field.Secondly, the temperature heat exchange model of low earth surface was established with practical measured ground surface data, and got the way of temperature heat exchange by depth in vertical section.The components of practical measured ground surface data were analysed by FFT, and found the cycle change by year and day with filter.Thirdly, the system was established to get the MODIS thermal infrared remote sensed data. This paper got long period data of our test field from this system and picked up the interested areas. In this paper radiate temperature of band 31 &band 32 was got from the pixels which the sensors located. Fourthly, the difference in estimated temperature by watched emissivity increment which resulted by the difference in practical measured ground surface temperature in mix pixel were calculated. In this paper, If there is 10K difference in practical measured resulted in 0-2K difference in estimated temperature. In order to get estimated temperature with high precision, the location of GCPs is important.Lastly, LST with different ways was got by estimating the MODIS thermal infrared remote sensed data. The estimated LST was got with high precision by split-window algorithms and compared with off-the-peg algorithms. This paper got another result from the thermal infrared remote sensed data and relevant practical measured ground surface data by the way of least squares linear regression.From comparing with the result by split-window algorithm, It found that the distributing of difference followed the seasons, that the summe(r3K) is larger than winte(r1K).So, It suggested that the LST could be got with high precision by using synchronized practical measured ground surface data in winter simply and steadily.