Clear and cloudy daytime comparisons of land surface temperature (LST) and air temperature (Tair) were made for 14 stations included in the U.S. Climate Reference Network (USCRN) of stations from observations made from 2003 through 2008. Generally, LST was greater than Tair for both the clear and cloudy conditions; however, the differences between LST and Tair were significantly less for the cloudy-sky conditions. In addition, the relationships between LST and Tair displayed less variability under the cloudy-sky conditions than under clear-sky conditions. Wind speed, time of the observation of Tair and LST, season, the occurrence of precipitation at the time of observation, and normalized difference vegetation index values were all considered in the evaluation of the relationship between Tair and LST. Mean differences between LST and Tair of less than 2°C were observed under cloudy conditions for the stations, as compared with a minimum difference of greater than 2°C (and as great as 7+°C) for the clear-sky conditions. Under cloudy conditions, Tair alone explained over 94%—and as great as 98%—of the variance observed in LST for the stations included in this analysis, as compared with a range of 81%–93% for clear-sky conditions. Because of the relatively homogeneous land surface characteristics encouraged in the immediate vicinity of USCRN stations, and potential regional differences in surface features that might influence the observed relationships, additional analyses of the relationships between LST and Tair for additional regions and land surface conditions are recommended.

Monthly and seasonal relationships between urban–rural differences in minimum, maximum, and average temperatures measured at surface-based observation stations were compared to satellite-derived Advanced Very High Resolution Radiometer estimates of a normalized difference vegetation index (NDVI) and surface radiant temperature (Tsfc). The relationships between surface- and satellite-derived variables were developed during 1989–91 and tested on data acquired during 1992–93. The urban–rural differences in air temperature were linearly related to urban–rural differences in the NDVI and Tsfc. A statistically significant but relatively small (less than 40%) amount of the variation in these urban–rural differences in air temperature [the urban heat island (UHI) bias] was associated with variation in the urban–rural differences in NDVI and Tsfc. A comparison of the satellite-based estimates of the UHI bias with population-based estimates of the UHI bias indicated similar levels of error. The use of sate... Abstract Monthly and seasonal relationships between urban–rural differences in minimum, maximum, and average temperatures measured at surface-based observation stations were compared to satellite-derived Advanced Very High Resolution Radiometer estimates of a normalized difference vegetation index (NDVI) and surface radiant temperature (Tsfc). The relationships between surface- and satellite-derived variables were developed during 1989–91 and tested on data acquired during 1992–93. The urban–rural differences in air temperature were linearly related to urban–rural differences in the NDVI and Tsfc. A statistically significant but relatively small (less than 40%) amount of the variation in these urban–rural differences in air temperature [the urban heat island (UHI) bias] was associated with variation in the urban–rural differences in NDVI and Tsfc. A comparison of the satellite-based estimates of the UHI bias with population-based estimates of the UHI bias indicated similar levels of error. The use of sate...

Many novel research algorithms have been developed to analyze urban heat island (UHI) and UHI regional impacts (UHIRIP) with remotely sensed thermal data tables. We present a comprehensive review of some important aspects of UHI and UHIRIP studies that use remotely sensed thermal data, including concepts, datasets, methodologies, and applications. We focus on reviewing progress on multi-sensor image selection, preprocessing, computing, gap filling, image fusion, deep learning, and developing new metrics. This literature review shows that new satellite sensors and valuable methods have been developed for calculating land surface temperature (LST) and UHI intensity, and for assessing UHIRIP. Additionally, some of the limitations of using remotely sensed data to analyze the LST, UHI, and UHI intensity are discussed. Finally, we review a variety of applications in UHI and UHIRIP analyses. The assimilation of time-series remotely sensed data with the application of data fusion, gap filling models, and deep learning using the Google Cloud platform and Google Earth Engine platform also has the potential to improve the estimation accuracy of change patterns of UHI and UHIRIP over long time periods.

Several storms produced extensive hail damage over Iowa on 9 August 2009. The hail associated with these supercells was observed with radar data, reported by surface observers, and the resulting hail swaths were identified within satellite data. This study includes an initial assessment of cross validation of several radar-derived products and surface observations with satellite data for this storm event. Satellite-derived vegetation index data appear to be a useful product for cross validation of surface-based reports and radar-derived products associated with severe hail damage events. Satellite imagery acquired after the storm event indicated that decreased vegetation index values corresponded to locations of surface reported damage. The areal extent of decreased vegetation index values also corresponded to the spatial extent of the storms as characterized by analysis of radar data. While additional analyses are required and encouraged, these initial results suggest that satellite data of vegetated land surfaces are useful for cross validation of surface and radar-based observations of hail swaths and associated severe weather.

Two datasets that depict the night-time light emitted from the conterminous USA during 1992/1993 and 2000 were compared for changes in light emission. The locations of observed differences in night-time light during this interval were examined for differences observed in a time-integrated vegetation index associated with net primary production. Just over 13% of the land area within the study region exhibited greater night-time light emitted in 2000 compared to 1992/1993. The locations of greater emitted light were found to have decreased values of the time-integrated vegetation index compared to locations that did not exhibit significant increases in emitted light. The observed decrease in the time-integrated vegetation index within the regions of greater emitted light is likely to be due to the change in land cover (increased urbanization) during this interval. The results suggest that the emitted light data were more useful for assessment of urban growth than the integrated vegetation index data.