Assessing a scheme of spatial-temporal thermal remote-sensing sharpening for estimating regional evapotranspiration

ABSTRACT

A high temporal frequency of high-resolution thermal data is required in regional evapotranspiration (ET) studies. In this article, a spatial-temporal thermal remote-sensing sharpening scheme, which can be used to perform temporally stable land surface temperature (LST) mapping with high spatial resolution and further facilitate the estimation of ET, is discussed in the context of the Soil Moisture Experiment of 2002. To demonstrate this scheme, relationships between LST and three remote-sensing parameters (normalized difference vegetation index (NDVI), fractional vegetation cover (FVC), and Bowen ratio) were first used in a thermal disaggregation procedure for retrieving LSTs at a 250-m scale. Then, the spatial and temporal adaptive reflectance fusion (STARFM) model was applied to the 250-m LSTs, producing LST data at a fine resolution of 60 m and a fine temporal resolution of 1 day. Two remote-sensing-based energy balance models were then used to retrieve the ET at the Moderate Resolution Imaging Spectroradiometer overpass time respectively using 250- and 60-m LSTs. The results showed that the Bowen ratio-based LSTs were matched field observations better than did the LSTs obtained with the other two approaches (NDVI- and FVC-based) at the 250-m scale, and consequently produced 250-m ET mapping that better matched the observed tower-based values. When combined with the STARFM fusion model, the 250-m Bowen ratio-based LSTs produced more accurate time-series LSTs and ET at the 60-m scale. The Bowen ratio, which is more related to surface energy principles and the soil moisture variation, was effective in disaggregating LSTs and promoting the estimation of ET. Overall, sharpened LSTs using the combination of thermal disaggregation procedure and the STARFM fusion model could substantially improve remote-sensing-based ET estimates. Moreover, the STARFM model that can fuse LST from 250 to ~100 m should be given more attention as long as the thermal disaggregation procedure that can disaggregate LST from 1000 to 250 m, provided that it contributed approximately 10.1% to further improving ET retrieval performance.

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Acknowledgment

The authors thank the researchers in SMACEX data acquisition and sharing.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [41571356]; the National Key Research and Development Program of China [2016YFA0602500]; the National Natural Science Foundation of China [41671354] and the China Postdoctoral Science Foundation [2016M600120].