|HAIN, C. - Collaborator|
Submitted to: Geophysical Research Letters
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/10/2017
Publication Date: 8/16/2017
Citation: Hain, C., Anderson, M.C. 2017. Estimating morning changes in land surface temperature from MODIS day/night land surface temperature: Applications for surface energy balance modeling. Geophysical Research Letters. https://doi.org/10.1002/2017GL074952.
Interpretive Summary: Time-changes in land surface temperature, as measured between sunrise and noon from geostationary satellite orbits, provide valuable information for deducing the surface moisture status. Wetter surfaces, evaporating more water, warm more slowly during the morning hours. A model based on principles of surface energy balance has been developed to use the morning surface temperature rise geostationary satellites to robustly map crop water use and availability over large regions. However, it is difficult to apply this model routinely at global scales, because this requires assembly of data from a number of international satellites with different calibrations, data formats, and periods of record. In this paper we described a simplified method for obtaining these temperature-change inputs from a single polar orbiting satellite sensor (the Moderate Resolution Imaging Spectroradiometer, or MODIS). A method is presented to infer the morning temperature rise from MODIS-derived day-night temperature differences. This new technique will enable global, routine monitoring of evapotranspiration (ET, or crop water use) and crop stress at spatial resolutions down to 1km and daily timesteps. A follow-on paper will describe a prototype global ET modeling system based on this temperature-change retrieval technique.
Technical Abstract: Observations of land surface temperature (LST) are crucial for the monitoring of surface energy fluxes from satellite. Methods that require high temporal resolution LST observations (e.g., from geostationary orbit) can be difficult to apply globally because several geostationary sensors are required to attain near-global coverage (60°N to 60ºS). While these LST observations are available from polar-orbiting sensors, providing global coverage at higher spatial resolutions, the temporal sampling (twice daily observations) can pose significant limitations. For example, the Atmosphere Land Exchange Inverse (ALEXI) surface energy balance model, used for monitoring evapotranspiration and drought, requires an observation of the morning change in LST - a quantity not directly observable from polar-orbiting sensors. Therefore, we have developed and evaluated a data-mining approach to estimate the mid-morning rise in LST from twice-daily observations of LST from the Moderate Resolution Imaging Spectroradiometer (MODIS). In general, the data-mining approach produced estimates with low relative error (5 to 10%) and statistically significant correlations when compared against geostationary observations. This approach will facilitate global, near real-time applications of ALEXI at considerably higher spatial resolution than currently achievable with geostationary LST datasets.