An Accurate Estimate of Monthly Mean Land Surface Temperatures from MODIS Clear-Sky Retrievals
MODIS thermal sensors can provide us with global land surface temperature (LST) several times each day, but have difficulty in obtaining information from the land surface in cloudy situations. As a result, the monthly day or night LST products [Terra monthly day LST (TMD), Terra monthly night LST (T...
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| Tác giả chính: | |
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| Định dạng: | BB |
| Ngôn ngữ: | eng |
| Thông tin xuất bản: |
2020
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| Chủ đề: | |
| Truy cập trực tuyến: | http://tailieuso.tlu.edu.vn/handle/DHTL/4417 |
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| Tóm tắt: | MODIS thermal sensors can provide us with global land surface temperature (LST) several times each day, but have difficulty in obtaining information from the land surface in cloudy situations. As a result, the monthly day or night LST products [Terra monthly day LST (TMD), Terra monthly night LST (TMN), Aqua monthly day LST (AMD), and Aqua monthly night LST (AMN)] are the average LST values calculated over a variable number of clear-sky days in a month. Is it possible to derive an accurate estimate of monthly mean LST based on averaging of the multidaily overpasses of MODIS sensors? In situ ground measurements and ERA-Interim reanalyses data, both of which provide continuous information in either clear or cloudy conditions, have been used to validate the approach. Using LST measurements from 156 ground flux towers, it was found that the three mean values LST(AMD,AMN), LST(TMD,TMN), and LST(AMD,AMN,TMD,TMN) (mean biases of 0.19, 0.59, and 0.40 K, respectively) can all provide a reliable estimate of all-sky monthly mean LST. Of the three means, we recommend the use of LST(AMD,AMN,TMD,TMN) for monthly mean LST in climate studies as it provides the most complete coverage. When retrievals from either Terra or Aqua are not available, then either LST(AMD,AMN) or LST(TMD,TMN) may be used to fill the gaps. The intrinsic error in the MODIS monthly mean LST cannot be explained from monthly mean view time, view angle, and clear-sky ratio. MODIS monthly LST calculated using this approach LST(AMD,AMN,TMD,TMN) (RMSE 5 2.65, mean bias , 61 K) will have wide applicability for climate studies and numerical model evaluation. |
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