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ARS Home » Pacific West Area » Maricopa, Arizona » U.S. Arid Land Agricultural Research Center » Water Management and Conservation Research » Research » Publications at this Location » Publication #222402
Title: DISAGGREGATION OF GOES-LAND SURFACE TEMPERATURES USING MODIS OBSERVATIONS

Author
item Inamdar, Anand
item French, Andrew

Submitted to: American Meteorological Society Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 1/14/2008
Publication Date: 1/20/2008
Citation: Inamdar, A.K., French, A.N. 2008. Disaggregation of goes-land surface temperatures using modis observations. Proceedings of the AMS 88th Annual Meeting, New Orleans, LA, Jan. 20-24, 2008. pp.1-9

Interpretive Summary: Accurate knowledge of temporal and spatial distribution of land surface temperature (LST) is important for modeling the water cycle at field to global scales, because LSTs can improve estimates of soil moisture and evapotranspiration and aid in crop water demands, etc. Using remote sensing satellites, accurate LSTs could be routine, but unfortunately the only instruments available to provide diurnal cycle observations have coarse spatial resolution (4 km). Examples are the Geostationary Environmental Satellites (GOES). These provide invaluable ½ hourly observations but have limited capacity to distinguish significantly different land surface types. Disaggregation of the low spatial resolution LST to 1 km rely upon correlations between land cover types and LST. In the present study, we make use of the data from the polar-orbiting MODIS (Moderate Resolution Imaging Spectroradiometer) instrument on board the NASA EOS spacecraft. MODIS measurements yield surface properties like LST, surface emissivity and vegetation indices at 1 km spatial scale once or at most twice a day. The present study describes how the single time of day, high spatial resolution MODIS measurements of surface emissivity and LST can be combined with low spatial resolution, half-hourly GOES data in conjunction with a diurnal model to produce half-hourly LST on a 1 km scale over the southwest US region. Accuracy of the 1 km LST product is validated using high quality ground measurements at specific sites. This will be very valuable to researchers and crop modelers in implementing water management decisions.

Technical Abstract: Accurate temporal and spatial estimation of land surface temperatures (LST) is important for modeling the hydrological cycle at field to global scales because LSTs can improve estimates of soil moisture and evapotranspiration. Using remote sensing satellites, accurate LSTs could be routine, but unfortunately the only instruments available to provide diurnal cycle observations have coarse spatial resolution (4 km). Examples are the Geostationary Environmental Satellites (GOES). These provide invaluable ½ hourly observations but have limited capacity to distinguish significantly different land surface types. This inability greatly constrains their utility since hydrological models respect differences in cover that the satellite data cannot provide. A technique that may help overcome the spatial resolution constraint is to disaggregate geostationary LST data using single time of day MODIS 1 km observations along with a diurnal-scale model. The resultant data are 1 km, hourly LSTs. Disaggregation procedures rely upon correlations between land cover types and LST. In studies using data for the U.S. Southwest, the most consistent and stable correlative estimators were obtained from 1 km MODIS emissivity data. An alternative estimator, MODIS Normalized Difference Vegetation Indices (NDVI) was more inconsistent and less correlative. Accuracies of LST estimates, investigated using 2002-3 ground observations at Southern Great Plains sites will be discussed.