Abstract
Near surface air temperature (Ta) is one of the most critical variables in climatology, hydrology, epidemiology, and environmental health. In situ measurements are not efficient for characterizing spatially heterogeneous Ta, while remote sensing is a powerful tool to break this limitation. This study proposes a mapping framework for daily mean Ta using an enhanced empirical regression method based on remote sensing data. It differs from previous studies in three aspects. First, nighttime light data is introduced as a predictor (besides land surface temperature, normalized difference vegetation index, impervious surface area, black sky albedo, normalized difference water index, elevation, and duration of daylight) considering the urbanization-induced Ta increase over a large area. Second, independent components are extracted using principal component analysis considering the correlations among the above predictors. Third, a composite sinusoidal coefficient regression is developed considering the dynamic Ta-predictor relationship. This method was performed at 333 weather stations in China during 2001–2012. Evaluation shows overall mean error of −0.01 K, root mean square error (RMSE) of 2.53 K, correlation coefficient (R2) of 0.96, and average uncertainty of 0.21 K. Model inter-comparison shows that this method outperforms six additional empirical regressions that have not incorporated nighttime light data or considered predictor independence or coefficient dynamics (by 0.18–2.60 K in RMSE and 0.00–0.15 in R2).
Highlights
Near-surface air temperature (Ta), defined as the air temperature 2 m above the land surface [1], is one of the most critical variables in climatology [2], hydrology [3], epidemiology [4], and environmental health [2]
Even though duration of daylight (DDL) is unable to reflect the effects of clouds and aerosols on the solar radiation reaching the surface, the biases can be limited because we only focused on estimating Ta under clear skies (Section 3.3)
This study proposed a method to estimate daily mean Ta based on a time-varying coefficient regression of independent components against collocated weather station Ta
Summary
Near-surface air temperature (Ta), defined as the air temperature 2 m above the land surface [1], is one of the most critical variables in climatology [2], hydrology [3], epidemiology [4], and environmental health [2]. Ta is traditionally measured at weather stations. They provide highly frequent measurements with long-term records but are not efficient for characterizing spatial heterogeneities due to their low sampling densities [5,6], over urban and mountainous areas, where climatic effects can be fairly strong. Provides a greatly important and valuable source of information that can be used to estimate spatially continuous Ta. The previous studies can be grouped into two categories: atmosphere-based and land-based. The previous studies can be grouped into two categories: atmosphere-based and land-based The former retrieves air temperature profiles over a Remote Sens. The former retrieves air temperature profiles over a Remote Sens. 2016, 8, 656; doi:10.3390/rs8080656 www.mdpi.com/journal/remotesensing
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