Environmental applications require accurate air temperature (Tair) datasets with different temporal and spatial resolutions. Existing methods generally improve the estimation accuracy of Tair using environmental variables as auxiliary data to overcome problems related to sparse metrological stations. However, these data are always fixed and do not comprehensively explain the variations in Tair values at all temporal and spatial scales. Moreover, these methods seldom consider the spatial heterogeneity of relationships between Tair and auxiliary data. This heterogeneity is often caused by several factors, such as land type, topography, and climate. This study proposes an estimation method to produce maximum, minimum, and mean Tair (Tmax, Tmin, and Tmean) datasets at different temporal and spatial resolutions using satellite‐derived digital elevation model data and both nighttime and daytime land surface temperature data as auxiliary data. The method is based on the assumption that the relationships between Tair and the chosen auxiliary data vary spatially. These relationships were further explored using geographically weighted regression with adaptive bi‐square kernel function. The derived relationships were used to construct a Tair estimation model. Monthly Tair data with 5‐km resolution and 8‐day Tair data with 1‐km resolution were produced for 2010. The results show that the proposed method can accurately represent the variations in Tair; the R2 values were in the range of 0.95–0.99 for the monthly Tair data and 0.93–0.99 for the 8‐day Tair data. The root mean square errors (RMSEs) for the monthly and 8‐day Tmax, Tmin, and Tmean data of the year 2010 were 1.29 and 1.45 °C, 1.24 and 1.29 °C, and 0.8 and 1.2 °C, respectively. These results were compared with those from other estimation methods, specifically the estimation of Tair based on multiple linear regression (EATMLR) and regression kriging (EATRK). The proposed method was found to produce RMSEs that were 25–26% smaller than EATMLR and 34–42% smaller than EATRK.

Spatially downscaling satellite precipitation products have been performed on annual and monthly precipitation. Accurate downscaling on daily precipitation remains a challenge due to the limitation of the downscaling assumption, the large spatial discontinuity of daily precipitation, and the relatively poor quality of satellite‐derived daily precipitation product. In this study, an integrated downscaling‐fusion framework was proposed and used to downscale satellite‐derived daily precipitation. First, a spatio‐temporal downscaling scheme is applied to produce preliminary downscaled daily precipitation. The accuracy of the derived preliminary results is then boosted by merging with daily gauge observations using an ensemble fusion method. The performance of the proposed framework was tested and evaluated by downscaling the Integrated Multi‐satellite Retrievals for Global Precipitation Measurement (IMERG) daily precipitation data from 0.1 to 0.01° over eastern and central China for the period of 2015–2016. The results showed that (a) the downscaling scheme accurately mapped the spatio‐temporal variation in daily precipitation, and the preliminary downscaled results perfectly maintained the accuracy of the original IMERG data; (b) the fused results were much more accurate than the original IMERG data, decreasing the root‐mean‐square errors (RMSEs) by 22, 10, and 18% at daily, monthly, and annual timescales, respectively, for the whole period; and (c) the fused daily precipitation data considerably strengthened the detection of rain/no rain area compared with the original IMERG daily precipitation data, with a 17% reduction in the inconsistency index.

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has special geomorphology features of mountains and basins, where the primary water resources (snow, glaciers, etc.) are distributed in the mountainous areas, which are called as the "water tower of Central Asia" (Sorg et al., 2012). Rivers like Syr Darya and Amu Darya, which are formed by the melting water of mountains snow-glaciers, flow into low-altitude areas and provide important water sources for the croplands widely distributed in arid regions of Central Asia. The precipitation in Central Asia is mainly concentrated in mountainous areas and is scarce in the low-lying deserts and oases (Bohner, 2006). Therefore, the croplands of Central Asia must be irrigated to meet the normal growth of crops. Irrigation not only increases the water vapor content in boundary layer, but also affects the partitioning of surface energy, which produces important impacts on local and regional climate (

As tea is an important economic crop in many regions, efficient and accurate methods for remotely identifying tea plantations are essential for the implementation of sustainable tea practices and for periodic monitoring. In this study, we developed and tested a method for tea plantation identification based on multi-temporal Sentinel-2 images and a multi-feature Random Forest (RF) algorithm. We used phenological patterns of tea cultivation in China’s Shihe District (such as the multiple annual growing, harvest, and pruning stages) to extracted multi-temporal Sentinel-2 MSI bands, their derived first spectral derivative, NDVI and textures, and topographic features. We then assessed feature importance using RF analysis; the optimal combination of features was used as the input variable for RF classification to extract tea plantations in the study area. A comparison of our results with those achieved using the Support Vector Machine method and statistical data from local government departments showed that our method had a higher producer’s accuracy (96.57%) and user’s accuracy (96.02%). These results demonstrate that: (1) multi-temporal and multi-feature classification can improve the accuracy of tea plantation recognition, (2) RF classification feature importance analysis can effectively reduce feature dimensions and improve classification efficiency, and (3) the combination of multi-temporal Sentinel-2 images and the RF algorithm improves our ability to identify and monitor tea plantations.