Enhanced Statistical Estimation of Air Temperature Incorporating Nighttime Light Data

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 (R 2 ) 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 R 2 ).

Evaluation of the Effect of Urban Redevelopment on Surface Urban Heat Islands

“…Previous studies have mainly focused on studying differences in ground temperatures as a function of land-use land cover and its evolution at the city level [16,16,20,[33][34][35], temporal trends in urban SUHI in urban areas [18,36,37], the refreshing impact of parks on their surroundings [15,38,39], the evolution of SUHI as a function of day and night [9,34,40,41], the impact of vegetation on LST at the urban scale [12,36,37,42], comparison of surface temperatures and air temperature [8][9][10]20,43,44], the impact of surface temperatures on health [30,41] and transversely at surface temperatures at moderate resolutions (MODIS 1 km) [16,20,[34][35][36]42,45] but, to our knowledge, there are no similar studies such as ours that analyze the thermal monitoring of site redevelopment at such a detailed spatial and temporal resolution.…”

Land Surface Temperature Variation Due to Changes in Elevation in Northwest Vietnam

“…A few studies have investigated the direct effect of elevation on LST; however, elevation was found to have an impact on most studies using MODIS LST, particularly those conducted over a large area where the terrain is variable. For example, in Ta estimation using MODIS LST data, along with LST, elevation was considered one of the most impactful variables effecting the results of Ta estimation [14,26,27].…”

Estimating High Resolution Daily Air Temperature Based on Remote Sensing Products and Climate Reanalysis Datasets over Glacierized Basins: A Case Study in the Langtang Valley, Nepal

“…The DDL is calculated from local latitude (ϕ) and day of year (DOY) following the method of Chen et al [59]:…”

“…As MODIS LST is only available under clear sky in the land products (MOD11 and MYD11), many previous studies [25,27,35,36,59] estimated Ta by integrating MODIS LST and station observations under clear sky. To estimate Ta under both clear and cloudy sky conditions, Zhu et al [16] used the clear sky LST from the MODIS cloud product (MOD06) and Ta from the MODIS atmosphere profile products (MOD07) to build the relationship between LST and Ta at 5-km scale, and then applied this relationship under cloudy sky.…”