Remote Sensing, Vol. 15, Pages 2701: Estimation of Instantaneous Air Temperature under All-Weather Conditions Based on MODIS Products in North and Southwest China
Remote Sensing doi: 10.3390/rs15112701
Air temperature (Ta) is a common meteorological element involved in many fields, such as surface energy exchange and water circulation. Consequently, accurate Ta estimation is essential for the establishment of hydrological, climate, and environmental models. Unlike most studies concerned with the estimation of daily Ta from land surface temperature, this study focused on the estimation of instantaneous Ta from Moderate Resolution Imaging Spectroradiometer (MODIS) atmospheric profile products aboard the Terra and Aqua satellites. The applicability of various estimation methods was examined in two regions with different geomorphological and climate conditions, North and Southwest China. Specifically, the spatiotemporal trend of Ta under clear sky conditions can be reflected by the atmospheric profile extrapolation and average methods. However, the accuracy of Ta estimation was poor, with root mean square error (RMSE) ranging from 3.5 to 5.2 &deg;C for North China and from 4.0 to 7.7 &deg;C for Southwest China. The multiple linear regression model significantly improved the accuracy of Ta estimation by introducing auxiliary data, resulting in RMSE of 1.6 and 1.5 &deg;C in North China and RMSE of 2.2 and 2.3 &deg;C in Southwest China for the Terra and Aqua datasets, respectively. Since atmospheric profile products only provide information under clear sky conditions, a new multiple linear regression model was established to estimate the instantaneous Ta under cloudy sky conditions independently from atmospheric profile products, resulting in RMSE of 1.9 and 1.9 &deg;C in North China and RMSE of 2.5 and 2.8 &deg;C in Southwest China, for the Terra and Aqua datasets, respectively. Finally, instantaneous Ta products with high accuracy were generated for all-weather conditions in the study regions to analyze their Ta spatial patterns. The accuracy of Ta estimation varies depending on MODIS datasets, regions, elevation, and land cover types.
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