Most current statistical models for downscaling the remotely sensed land surface temperature (LST) are based on the assumption of the scale-invariant LST-descriptors relationship, which is being debated and requires an in-depth examination. Additionally, research on downscaling LST to high or very high resolutions (~10 m) is still rare. Here, a simple analytical model was developed to quantify the scale effect in downscaling the LST from a medium resolution (~100 m) to high resolutions. The model was verified in the Zhangye oasis and Beijing city. Examinations of the simulation datasets that were generated based on airborne and space station LSTs demonstrate that the developed model can predict the scale effect in LST downscaling; the scale effect exists in both of these two study areas. The model was further applied to 12 ASTER images in the Zhangye oasis during a complete crop growing season and one Landsat-8 TIRS image in Beijing city in the summer. The results demonstrate that the scale effect is intrinsically caused by the varying probability distribution of the LST and its descriptors at the native and target resolutions. The scale effect depends on the values of the descriptors, the phenology, and the ratio of the native resolution to the target resolution. Removing the scale effect would not necessarily improve the accuracy of the downscaled LST.

Validation and performance evaluations are beneficial for developing methods that estimate the remotely sensed land surface temperature (LST). However, such evaluations for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data are rare. By selecting the middle reach of the Heihe River basin (HRB), China, as the study area, the atmospheric correction (AC), mono-window (MW), single-channel (SC), and split-window (SW) methods were evaluated based on in situ measured LSTs. Results demonstrate that the influences of surface heterogeneity on the validation are significant in the study area. For the AC, MW, and SC methods, the LSTs estimated from channel 13 are more accurate than those from channel 14 in general cases. When the in situ measured atmospheric profiles are available, the AC method has the highest accuracy, with a root-mean squared error (RMSE) of about 1.4–1.5 K at the homogenous oasis sites. In actual application without sufficient in situ measured inputs, the MW method is highly accurate; the RMSE is around 1.5–1.6 K. The SC method systematically overestimates LSTs and it is sensitive to error in the water vapor content. The two SW methods are simple to use but their performances are limited by accuracies, revealed by the simulation dataset. Therefore, when the in situ atmospheric profiles are available, the AC method is recommended to generate reliable ASTER LSTs for modeling the eco-hydrological processes in the middle reach of the HRB. When sufficient in situ measured inputs are not available, the MW method can be used instead.

Acquiring the satellite land surface temperature (LST) with high spatiotemporal resolutions is pressing in the land surface biophysical process. However, most current LST products hardly satisfy this requirement. LST downscaling provides an effective way to solve this issue by introducing driving factors, but existing methods usually ignore the weights of descriptors. In this letter, based on the geographically weighted regression (GWR) and random forest (RF), a new downscaling method [i.e., weighted GWR (WGWR)] considering the weights of LST descriptors is proposed. To examine the performance of WGWR, the 100-m Landsat−8 thermal infrared sensor (TIRS) and Terra advanced spaceborne thermal emission and reflection radiometer (ASTER) LSTs are aggregated to 1000 m as the simulated coarse LSTs, and then the coarse LSTs are downscaled to 100 m using WGWR, RF, and GWR. Meanwhile, the original 100-m LSTs are used as validation references. Results indicate that the proposed WGWR outperforms RF and GWR: for RF (GWR), the root mean square error (RMSE) can be reduced by 0.34 K (0.26 K) in Zhangye (ZY) and 0.22 K (0.1 K) in Beijing (BJ). Compared to RF and GWR, WGWR also yields better image quality: the downscaled LST images have neither obvious smoothing effect nor boundary effect and maintain the details of the image at high spatial resolution. Validation based on in situ LST indicates that the downscaled LST based on WGWR has better agreement with the in situ LST, and the RMSE is reduced by 0.57 K. The proposed WGWR contributes to obtain high spatio-temporal resolution LSTs and promote hydrological, meteorological, and ecological studies.

Crops are greatly affected by the temperature of farmland surface during their growing period. It is feasible to investigate the growth status of crops based on temperature information. For serving the research of crop growth status, the component temperature (e.g. temperature of vegetation and temperature of soil) are in need to be obtained. In this study, an unmanned aerial vehicle (UAV) temperature measurement system with a thermal infrared (TIR) imager and a charge-coupled device (CCD) camera is assembled and applied to measure the brightness temperatures of farmland surface. The target areas were photographed by the UAV temperature measurement system according to a pre-set route, and obtain TIR and visible images. The component temperatures are obtained from the TIR image as following processes: (1) When shaded components are negligible at noon, two components, i.e. vegetation and soil, are divided by the OTSU algorithm; and (2) When shaded components cannot be ignored in the morning and afternoon, various components, i.e. vegetation, soil and concrete, the TIR image is divided into soil, vegetation and concrete by the corresponding classified visible images; Then, each of the components is divided into light and shaded components by the OTSU algorithm; thus, four components are obtained, including sunlit vegetation, shaded vegetation, sunlit soil, and shaded soil. The derived component temperatures can serve as inputs to agricultural and water resource models.

Lunar exploration has put further demands on high spatial resolution of the lunar surface temperature (LuST) data. However, available LuST data with coarse resolution provided by current remote sensing platforms (e.g., LRO Diviner) are unable to meet the requirement. Therefore, it is necessary to develop new methods to obtain LuST data with high spatial resolution. Inspired by the study of land surface temperature (LST) downscaling, here, we perform the exploratory study of the LuST downscaling. The core of the exploration work consists of two parts: 1) the 16 pixels per degree (ppd) Diviner Gridded Data Records (GDRs) images are downscaled to 128 ppd using a downscaling model based on geographically weighted regression (GWR), in order to explore the feasibility of downscaling LuST; and 2) two sets of experiments with scales of 4 and 8 are conducted and the growth characteristics of the errors are observed to determine whether it is feasible to obtain higher-spatial-resolution data. The evaluation metrics for the experiments in two specific craters with scale of 8 are 4.03/5.42 K for mean absolute error, 5.89/8.05 K for root mean square error and 0.037/0.048 for normalized root mean square error, respectively. The LuST downscaling study is feasible, and it provides an effective way for obtaining high-spatial-resolution LuST data, which has positive implications for lunar exploration.