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Land Surface Temperature (LST) is a key variable in the energy and water balance between the surface and the atmosphere. LST is typically retrieved from remote sensing data because of the lack of sufficient flux towers for meteorological data collection, which makes local availability of LST scarce. Remote sensing can provide data for large areas, but the spatial resolution of the data may be coarser than the temporal resolution (e.g. a daily revisit interval for satellite-based Moderate-Resolution Imaging Spectroradiometer (MODIS) sensors), or conversely, the spatial resolution may be coarser than the temporal resolution (e.g. Landsat satellite images obtained every 16 days). This tradeoff between spatial and temporal resolutions has motivated researchers to develop methods and algorithms to create image collections with both high spatial and temporal resolutions. In this study, we used a cloud-based downscaling algorithm to create LST images with high temporal and spatial resolution for the southern region of Brazil using Google Earth Engine (GEE). A comparison of the downscaling estimates with thermal Landsat images showed that 65% of the root mean squared errors (RMSE) were lower than 2 K, and 77% of the correlation coefficients (R) were greater than 0.70, while 84% of the bias values were between −2 and 2 K. The downscaling estimates were also validated using in situ measurements. Overall, a comparison between in situ measurements and different LST retrieval methods differed slightly in accuracy, with average RMSE values between 1.55 and 4.32 K, bias between −1.07 and 3.96 K, and correlation coefficients of nearly 0.90. These results demonstrate that cloud computing can be used to retrieve LST with high spatiotemporal resolution.

A MODIS–Landsat cloud-based spatiotemporal downscaling algorithm to estimate land surface temperature