Land surface temperature retrieval at high spatial and temporal resolutions over the southwestern United States

Abstract

[1] Land surface temperature (LST) and its diurnal variation are important when evaluating climate change, land-atmosphere energy exchange, and the global hydrological cycle. These characteristics are observable from satellites using thermal infrared measurements, but doing so at both high spatial and temporal resolutions has been difficult. Accurate temporal and spatial knowledge of LST is critical in global-scale hydrological assimilation to improve estimates of soil moisture and evapotranspiration. Historically, satellite retrieval of global LST at high spatial resolutions (1 km) has relied on NOAA polar-orbiting satellites recently augmented by Moderate Resolution Imaging Spectroradiometer (MODIS) data on board the Earth Observing System (EOS). Each satellite instrument in a polar orbit typically provides one to two observations per day. High temporal sampling of LST is achievable with geostationary satellites but at spatial resolutions is too coarse to distinguish different land surface types (4-5 km) and with lower accuracy. We describe an approach which employs MODIS data as a calibration source for Geostationary Environmental Satellite (GOES) data, then uses both data sets to yield half-hourly LST values, at 1 km spatial resolution, and returns LST with an accuracy better than 2°C. The approach requires good cloud clearing, atmospheric correction, and an underlying LST model to propagate values between observations. Retrieved LST against ground truth data indicate the approach is accurate to about 2°C.

Disaggregation of MODIS surface temperature over an agricultural area using a time series of Formosat-2 images

“…To bridge the gap between the lowspatial resolution of available thermal data and the high-spatial resolution required over agricultural areas, one may disaggregate low-spatial-resolution thermal images at high-temporal frequency. To date, most disaggregation approaches of remotely sensed surface temperature have been based on the Normalized Difference Vegetation Index (NDVI) available from shortwave data at a spatial resolution finer than that of thermal data Agam et al, 2007;Inamdar et al, 2008). Although the NDVI-based approach has been successfully tested over agricultural areas, Agam et al (2007) and Inamdar et al (2008) emphasized the limitation that the variability of surface temperature is not explained entirely by NDVI.…”

“…To date, most disaggregation approaches of remotely sensed surface temperature have been based on the Normalized Difference Vegetation Index (NDVI) available from shortwave data at a spatial resolution finer than that of thermal data Agam et al, 2007;Inamdar et al, 2008). Although the NDVI-based approach has been successfully tested over agricultural areas, Agam et al (2007) and Inamdar et al (2008) emphasized the limitation that the variability of surface temperature is not explained entirely by NDVI. Recently, Inamdar and French (2009) developed a new disaggregation methodology of 5 km resolution GOES data using 1 km resolution MODIS-derived surface emissivity.…”

Simulation of land surface temperatures: comparison of two climate models and satellite retrievals

“…It could be argued that if the model is to represent the surface flux budget correctly it is more important that its upward longwave flux should match the average observed flux than that its land surface temperature should match the observed mean over the area of the gridbox. Inamdar et al (2008) discuss the variation of their 1-km data within a region 100×50 km, giving a standard deviation of the land surface temperature of 5.85 K for a mean temperature of 326.2 K. In this case, the difference between the mean land surface temperature and a mean inferred by averaging the upward longwave flux from each 1-km pixel, then extracting an equivalent temperature is less than 0.2 K. This suggests that variations on scales unresolved within the models will not significantly affect the following comparisons.…”

“…Sun and Pinker, 2003;Inamdar et al, 2008), particularly with a view to characterising the diurnal range of temperature. Satellite data offer a number of potential advantages for this Correspondence to: J. M. Edwards (john.m.edwards@metoffice.gov.uk) purpose including homogeneous spatial coverage and good temporal resolution of the diurnal cycle.…”

“…Sun and Pinker (2003) and Sun et al (2006b) produced retrievals of land surface temperatures from geostationary satellites over the contiguous United States under clear-sky conditions: they estimated that the bias of their retrievals was less than 1 K with RMS errors of about 1-2 K. Sun et al (2006b) used these retrievals to characterize the seasonal variation of the diurnal temperature range across the United States. Inamdar et al (2008) proposed an algorithm using data from geostationary and polar-orbiting satellites to retrieve land surface temperatures at a resolution of 1 km. They validated their retrievals against surface observations at two sites in the United States and demonstrated an accuracy generally better than 2 K throughout the diurnal cycle.…”