Near-surface air temperature (T_a) is a key variable in global climate studies. A global gridded dataset of daily maximum and minimum T_a (T_max⁡ and T_min⁡) is particularly valuable and critically needed in the scientific and policy communities but is still not available. In this paper, we developed a global dataset of daily T_max⁡ and T_min⁡ at 1 km resolution over land across 50^∘ S–79^∘ N from 2003 to 2020 through the combined use of ground-station-based T_a measurements and satellite observations (i.e., digital elevation model and land surface temperature) via a state-of-the-art statistical method named Spatially Varying Coefficient Models with Sign Preservation (SVCM-SP). The root mean square errors in our estimates ranged from 1.20 to 2.44 ^∘C for T_max⁡ and 1.69 to 2.39 ^∘C for T_min⁡. We found that the accuracies were affected primarily by land cover types, elevation ranges, and climate backgrounds. Our dataset correctly represents a negative relationship between T_a and elevation and a positive relationship between T_a and land surface temperature; it captured spatial and temporal patterns of T_a realistically. This global 1 km gridded daily T_max⁡ and T_min⁡ dataset is the first of its kind, and we expect it to be of great value to global studies such as the urban heat island phenomenon, hydrological modeling, and epidemic forecasting. The data have been published by Iowa State University at https://doi.org/10.25380/iastate.c.6005185 (Zhang and Zhou, 2022).

The concept of local climate zone (LCZ) has standardized the calculation of urban heat island (UHI) intensity (UHII) and established the connection among urban morphology, surface property, and UHI. In addition, LCZ has provided a new insight into the studies on urban thermal environment. This study selected Nanjing, China, as the study area and utilized a combined method that comprised remote sensing based and geographic information system based methods based on random forest classifier for LCZ classification. Overall accuracy reached 92%, and kappa coefficient was 0.911. In addition, the seasonal and diurnal differences of land surface temperature (LST) were analyzed via LST retrieval from Landsat data and computational fluid dynamics model simulation, respectively. Results confirmed the warmest and coldest zones in four seasons. The LST distribution characteristics of land cover and built types were basically the same during the four seasons. Moreover, the UHII difference of an LCZ class in various seasons and the UHII difference of a season in various LCZs were investigated. The daily LSTs of the simulated LCZs (1 to 6) within 24 h showed the same variation law but different variation extents in a day. The LST variations of built types were related to building elements, such as building height, building density, building layout, and green ratio. This study identified an existing relation between LST and LCZ and analyzed LST on the basis of LCZs from seasonal and diurnal scales, which provides guidance for future researchers.

Integration of different models may improve the performance of landslide susceptibility assessment, but few studies have tested it. The present study aims at exploring the way to integrating different models and comparing the results among integrated and individual models. Our objective is to answer this question: Will the integrated model have higher accuracy compared with individual model? The Lvliang mountains area, a landslide-prone area in China, was taken as the study area, and ten factors were considered in the influencing factors system. Three basic machine learning models (the back propagation (BP), support vector machine (SVM), and random forest (RF) models) were integrated by an objective function where the weight coefficients among different models were computed by the gray wolf optimization (GWO) algorithm. 80 and 20% of the landslide data were randomly selected as the training and testing samples, respectively, and different landslide susceptibility maps were generated based on the GIS platform. The results illustrated that the accuracy expressed by the area under the receiver operating characteristic curve (AUC) of the BP-SVM-RF integrated model was the highest (0.7898), which was better than that of the BP (0.6929), SVM (0.6582), RF (0.7258), BP-SVM (0.7360), BP-RF (0.7569), and SVM-RF models (0.7298). The experimental results authenticated the effectiveness of the BP-SVM-RF method, which can be a reliable model for the regional landslide susceptibility assessment of the study area. Moreover, the proposed procedure can be a good option to integrate different models to seek an “optimal” result.