TROPICAL GEOGRAPHY ›› 2017, Vol. 37 ›› Issue (3): 434-442.doi: 10.13284/j.cnki.rddl.002954

Previous Articles    

AMSR-E Passive Microwave Remote Sensing Surface Temperature Inversion Experimental Model Focused on Differences between Dry Season and Rainy Season

LIU Liyang1,3,SU Yongxian2,CHEN Xiuzhi3,SHAO Huaiyong1   

  1. (1.a.College of Earth Science;b.Key Laboratory of Geoscience Spatial Information Technology of Ministry of Land and Resources, Chengdu University of Technology,Chengdu 610059,China;2.Guangzhou Institute of Geography,Guangzhou 510070,China; 3.Guangdong Provincial Key Laboratory of Applied Botany,South China Botanical Garden,Guangzhou 510650,China)
  • Online:2017-05-05 Published:2017-05-05

Abstract: Based on the AMSR-E/Aqua L2A brightness temperature data and MODIS MYD11A2 Land Surface Temperature (LST) product in 2009, we retrieved LST in Central and Southern China. Firstly, the study area was divided into the southern (humid) and northern (semi-humid) regions along Qinling Mountain and Huaihe River based on local humidity. Next, according to the temperature difference between the dry season (October to March) and the rainy season (April to September), the study area was further divided into four types: northern rainy season, northern dry season, southern rainy season, and southern dry season. Finally, using the stepwise regression method, LST inversion models were built for each type based on AMSR-E 4-channel brightness temperatures. The decision coefficients (R2) of the regression models are 0.437 (southern rainy season), 0.663 (southern dry season), 0.701 (northern rainy season), and 0.682 (northern dry season), and the corresponding root mean square errors (RMSE) are 3.052 K, 2.637 K, 3.510 K, and 2.931 K, respectively. We selected the MODIS MYD11A2 LST product and the AMSR-E brightness temperature data in 2010 to validate the model. Firstly, the LST of different regions and different periods were simulated, and then the simulation errors of each period were calculated. Finally, the average errors and their spatial distributions in different regions and different periods were obtained. The validation results showed that, for the areas with an error ranging from 2.5 K to 5 K, their distribution in the northern region is relatively of disorder, with no prominent features, while their distribution in the southern region is mainly concentrated in the southwest of the Sichuan-Chongqing-Yunnan area. And the areas with an error greater than 4 K, such as the western Sichuan Plateau and the Hengduan Mountains, are mainly distributed at the edge of the Qinghai-Tibet Plateau. From the statistical results, the average error of the model is less than 2.5 K in about 80% of our study area, and the average error is less than 2.5 K in 88.64% of Southern China in the dry season. This study mainly investigated the impact of water vapor on microwave. An empirical model of surface temperature was constructed by taking into count the difference in regional humidity and the difference between dry season and rainy season, which provided an improvement on previous empirical methods.

Key words: passive microwave remote sensing, AMSR-E, land surface temperature, MODIS, regression analysis