全球暖化加劇了如乾旱、熱浪、野火、洪水等極端天氣事件，導致土壤快速流失和退化(Shukla et al. 2019)。乾旱導致農作物產量減少，長時間的乾旱造成飢荒。為了監測乾旱，使用乾旱指數來評估乾旱強度。乾旱指數主要是透過蒸發散量(Evapotranspiration, ET)和降水量來進行計算的(Ganatsas et al., 2011)，因此持續觀測全球ET並提供早期乾旱警報已成為應對這一毀滅性威脅最重要的措施之一。考慮到直接測量ET的困難，通常從氣象數據（如溫度和濕度）估算參考ET，使用作物係數和壓力係數轉換為實際ET。ET的估算中，地表溫度將透過衛星影像資料去估算取得。Landsat-8/9衛星上的熱紅外儀器(Thermal Infrared Sensor , TIRS)已提供地表溫度(Land Surface Temperature, LST)產品。前人研究提出了各種方法來從熱紅外波段中估算LST 。然而，要評估和改進Landsat-8/9標準LST產品以及各種方法的LST估算，需要地面實測LST的真實數據。
本研究利用臺灣共40個農業氣象站(Agrometeorological Stations, AS)每小時的土壤溫度測量數據。將收集的Landsat-8影像中檢索的LST與AS的LST進行比較，平均誤差為8.97°C，均方根誤差(Root Mean Square Error, RMSE)為10.34°C。這樣明顯的誤差在後續估算ET的過程中會進一步增加。因此，本研究採取了多種方法來改進從Landsat-8/9影像中提取LST的方法。利用大氣校正模型校正大氣干擾。使用紅外和近紅外波段（波段4、5）計算常態化差異植生指標和發射率。採用輻射傳輸模型計算向上和向下輻射和大氣透射率，進而估計亮溫和去除熱紅外波段的大氣干擾，從而提取LST。將研究的結果與從Landsat-8的LST進行比較，平均誤差為3.10°C，均方根誤差(RMSE)為3.84°C。針對大氣水氣含量較高的區域，本研究的修正能夠讓地表溫度估算表現得更好。

Taiwan often experiences high levels of water vapor in the atmosphere, which can result in significant errors in satellite image products. In order to improve the accuracy of estimating land surface temperature using satellite imagery data during periods of high cloud cover, it is necessary to correct for the presence of water vapor in the atmosphere.
This study utilized data from 48 agricultural meteorological stations in Taiwan to perform corrections and validation of land surface temperature estimation. The data were divided into two subsets: all data and cloud-free data. Regression models were separately constructed and used for the corrections.
The research results were compared with the measured land surface temperature from agricultural meteorological stations. The original root mean square error (RMSE)for all data was 18.049℃, which was reduced to 5.759℃ after correction. For the cloud-free data, the original RMSE was 6.262℃, which was reduced to 3.114℃ after correction.
When estimating land surface temperature, the correction for water vapor in the atmosphere is crucial, especially in regions with high water vapor. Water vapor in the atmosphere has a significant impact on the estimation of land surface temperature, and therefore, higher water vapor leads to larger errors in land surface temperature estimation.

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