Evaluation of MODIS Land Surface Temperature Data to Estimate Near-Surface Air Temperature in Northeast China

Air temperature (Tair) near the ground surface is a fundamental descriptor of terrestrial environment conditions and one of the most widely used climatic variables in global change studies. The main objective of this study was to explore the possibility of retrieving high-resolution Tair from the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) products, covering complex terrain in Northeast China. The All Subsets Regression (ASR) method was adopted to select the predictors and build optimal multiple linear regression models for estimating maximum (Tmax), minimum (Tmin), and mean (Tmean) air temperatures. The relative importance of predictors in these models was evaluated via the Standardized Regression Coefficients (SRCs) method. The results indicated that the optimal models could estimate the Tmax, Tmin, and Tmean with relatively high accuracies (Model Efficiency ≥ 0.90). Both LST and day length (DL) predictors were important in estimating Tmax (SRCs: daytime LST = 0.53, DL = 0.35), Tmin (SRCs: nighttime LST = 0.74, DL = 0.23), and Tmean (SRCs: nighttime LST = 0.72, DL = 0.28). Models predicting Tmin and Tmean had better performance than the one predicting Tmax. Nighttime LST was better at predicting Tmin and Tmean than daytime LST data at predicting Tmax. Land covers had noticeable influences on estimating Tair, and even seasonal vegetation greening could result in temporal variations of model performance. Air temperature could be accurately estimated using remote sensing, but the model performance was varied across different spatial and temporal scales. More predictors should be incorporated for the purpose of improving the estimation of near surface Tair from the MODIS LST production

Air temperature in Barcelona metropolitan region from MODIS satellite and GIS data

The metropolitan region of Barcelona (BMR) is one of the most densely populated areas in the Mediterranean countries. The estimation of air temperature at a short scale from satellite measurements would contribute to a better understanding of the varied and complex spatial distribution of temperatures in BMR. This estimation would be a first step to study several patterns of the thermometric regime affecting population life quality and health. Taking advantage of MODIS data, air temperature measurements at 48 thermometric stations along the year 2015, together with their geographic and topographic data, multiple regression analyses have permitted to obtain fine spatial distributions (pixels of 1 km2) of minimum, mean and maximum daily air temperatures. Previous to the multiple regression, Pearson coefficients and principal component analysis offer a first overview of the relevance of the variables on the empiric temperatures. The most relevant variables on the multiple regression process at annual and seasonal scale are land surface temperatures, latitude, longitude and calendar day. At a monthly scale, altitude (maximum temperature) and continentality (cold months for minimum and hot months for maximum temperatures) are also relevant. The best fits between empiric temperatures and those derived from the multiple regression processes have square regression coefficients within the range (0.92–0.96) for the annual case, (0.70–0.92) at seasonal scale and (0.52–0.87) at monthly scale. The root mean square error varies from 1.5 to 2.0 °C (annual case), from 1.3 to 2.0 °C (seasonal scale) and from 1.2 to 2.1 °C (monthly scale). In agreement with these regression coefficients and mean square errors, the obtained spatial distribution of temperatures is of notable quality. As an outstanding application, the detection of several urban heat islands on different conurbations within BMR along the Mediterranean coast becomes possible.Postprint (author's final draft

Empirical estimation of near-surface air temperature in China from MODIS LST data by considering physiographic features

Spatially and temporally resolved observations of near-surface air temperatures (Ta, 1.5–2 m above ground) are essential for understanding hydrothermal circulation at the land–atmosphere interface. However, the uneven spatial distribution of meteorological stations may not effectively capture the true nature of the overall climate pattern. Several studies have attempted to retrieve spatially continuous Ta from remotely sensed and continuously monitored Land Surface Temperature (LST). However, the topographical control of the relationship between LST and Ta in regions with complex topographies and highly variable weather station densities is poorly understood. The aim of this study is to improve the accuracy of Ta estimations from the Moderate Resolution Imaging Spectroradiometer (MODIS) LST via parameterization of the physiographic variables according to the terrain relief. The performances of both Terra and Aqua MODIS LST in estimating Ta have been explored in China. The results indicated that the best agreement was found between Terra nighttime LST (LSTmodn) and the observed Ta in China. In flat terrain areas, the LSTmodn product is significantly linearly correlated with Ta (R2 > 0.80), while, in mountainous areas, the LSTmodn-Ta relationship differed significantly from simple linear correlation. By taking the physiographic features into account, including the seasonal vegetation cover (NDVI), the altitudinal gradient (RDLS), and the ambient absolute humidity (AH), the accuracy of the estimation was substantially improved. The study results indicated that the relevant environmental factors must be considered when interpreting the spatiotemporal variation of the surface energy flux over complex topography

Empirical Estimation of Near-Surface Air Temperature in China from MODIS LST Data by Considering Physiographic Features

Spatially and temporally resolved observations of near-surface air temperatures (Ta, 1.5-2 m above ground) are essential for understanding hydrothermal circulation at the land-atmosphere interface. However, the uneven spatial distribution of meteorological stations may not effectively capture the true nature of the overall climate pattern. Several studies have attempted to retrieve spatially continuous Ta from remotely sensed and continuously monitored Land Surface Temperature (LST). However, the topographical control of the relationship between LST and Ta in regions with complex topographies and highly variable weather station densities is poorly understood. The aim of this study is to improve the accuracy of Ta estimations from the Moderate Resolution Imaging Spectroradiometer (MODIS) LST via parameterization of the physiographic variables according to the terrain relief. The performances of both Terra and Aqua MODIS LST in estimating Ta have been explored in China. The results indicated that the best agreement was found between Terra nighttime LST (LSTmodn) and the observed Ta in China. In flat terrain areas, the LSTmodn product is significantly linearly correlated with Ta (R-2 > 0.80), while, in mountainous areas, the LSTmodn-Ta relationship differed significantly from simple linear correlation. By taking the physiographic features into account, including the seasonal vegetation cover (NDVI), the altitudinal gradient (RDLS), and the ambient absolute humidity (AH), the accuracy of the estimation was substantially improved. The study results indicated that the relevant environmental factors must be considered when interpreting the spatiotemporal variation of the surface energy flux over complex topography