寇晓康等: Detection of land surface freeze-thaw status on the Tibetan Plateau using passive microwave and thermal infrared remote sensing data
被阅读 156 次
2017-10-19
Detection of land surface freeze-thaw status on the Tibetan Plateau using passive microwave and thermal infrared remote sensing data
作者:Kou, XK (Kou, Xiaokang)[ 1,2 ] ; Jiang, LM (Jiang, Lingmei)[ 1,2 ] ; Yan, S (Yan, Shuang)[ 3 ] ; Zhao, TJ (Zhao, Tianjie)[ 4 ] ; Lu, H (Lu, Hui)[ 5 ] ; Cui, HZ (Cui, Huizhen)[ 1,2 ]
 
REMOTE SENSING OF ENVIRONMENT
卷: 199  页: 291-301
DOI: 10.1016/j.rse.2017.06.035
出版年: SEP 15 2017
 
摘要
The freeze/thaw (F/T) cycle plays an important role in climate change and ecology research. Currently, soil F/T monitoring is restricted by low satellite spatial resolution or a relative long revisit cycle, which is one of the main problems in improving F/T monitoring resolution using available satellite data. Because temperature is a key parameter in determining soil FIT status, in this study, relatively high-resolution merged land surface temperature data were obtained using the Bayesian Maximum Entropy (BME) method by blending LSTs retrieved from passive microwave and infrared remotely sensed data. The merged temperature data were then used to downscale the passive microwave brightness temperature from 0.25 degrees to 0.05 degrees. Finally, the merged temperature and downscaled brightness temperature data were applied to discriminate the surface freeze/thaw status. A comparison with in situ data turned out that the downscaled brightness temperature could be used to determine soil F/T status with a total classification accuracy higher than 80%. The total freeze/thaw classification accuracy using merged temperature data was only 59.7%, which can be attributed to the temperature difference between the land surface and soil. After the adjustment with a relationship between soil temperature and land surface temperature, the classification accuracy reached 89.7%. (C) 2017 Elsevier Inc. All rights reserved.
 
通讯作者地址: Jiang, LM (通讯作者)
Beijing Normal Univ, Fac Geog Sci, Inst Remote Sensing Sci & Engn, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China.
通讯作者地址: Jiang, LM (通讯作者)
Joint Ctr Global Change Studies, Beijing 100875, Peoples R China.
地址:
[ 1 ] Beijing Normal Univ, Fac Geog Sci, Inst Remote Sensing Sci & Engn, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China
[ 2 ] Joint Ctr Global Change Studies, Beijing 100875, Peoples R China
[ 3 ] Hebei Acad Sci, Inst Geog Sci, Shijiazhuang 050011, Hebei, Peoples R China
[ 4 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100101, Peoples R China
[ 5 ] Tsinghua Univ, Ctr Earth Syst Sci, Beijing, Peoples R China