Validation of Two Operative Google Earth Engine Applications to Generate 10 m Land Surface Temperature Maps at Daily to Weekly Temporal Resolutions

Current land surface temperature (LST) products, estimated by sensors on board satellites,
show a trade-off between their spatial and temporal resolution. If the spatial resolution
is high (i.e., around 100 m), the LST product is delivered every 2 weeks, and for those
LST products estimated daily, its spatial resolution is 1 km. Current spatial and temporal
resolutions are not adequate for disciplines such as high-precision agriculture, urban
decision making, and planning how to mitigate the overheating of cities, for which LST
maps at 50–100 m resolution every few days are desirable. This situation has led to the
development of disaggregation techniques in order to enhance the spatial resolution of
daily LST products. Unfortunately, disaggregation techniques are usually complex since
they rely on a number of external inputs and computer resources and are difficult to apply
in practice. To our knowledge, there are only two operative downscaled 10 m LST products
available to the end user, which are implemented in the Google Earth Engine (GEE) tool.
They are the Daily Ten-ST-GEE and LST-downscaling-GEE systems. This study provides
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a critical benchmark by performing the first direct intercomparison and rigorous in situ
validation of these two operative GEE systems. The validation, conducted with reference
temperature data from dedicated field campaigns over contrasting agricultural sites in
Spain, showed a good correlation of both methods with a R2 of 0.74 for Daily Ten-ST-GEE
and 0.94 for LST-downscaling-GEE, but the poor results of the first method in a highly
heterogeneous site (RMSE of 5.8 K) make the second method the most suitable (RMSE of
3.6 K) for obtaining high-spatiotemporal-resolution LST maps.