Disaggregation of GOES land surface temperatures using surface emissivity

Abstract

[1] 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). One approach that may help overcome the spatial resolution constraint is to disaggregate geostationary LST data using visible to thermal infrared information provided by single time of day MODIS 1 km observations. These higher-resolution observations are correlative with observations at 4-km scales, and thus can be used to estimate 1-km LST values throughout a day. Inamdar et al 2008, for example, showed how GOES 10 imager and MODIS data could be combined to produce accurate half-hourly, 1-km LST values. However, the method disaggregated coarse LST values using Normalized Difference Vegetation Index (NDVI) data and was sometimes highly inaccurate when considering heterogeneous terrain. This problem can be greatly reduced with an alternative approach, whereby MODIS land cover emissivity data sets supply the needed 1-km information. In a study of LST estimation over the US Southwest, diurnal disaggregation models using emissivity data were significantly more accurate than a comparable NDVI-based model. This alternative approach which directly employs 8-day composites of MODIS 1 km emissivity is a simple and fast method. Citation: Inamdar, A. K., and A. French (2009), Disaggregation of GOES land surface temperatures using surface emissivity, Geophys. Res. Lett., 36, L02408,

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

“…The authors found that the emissivity-based approach was more 3 accurate than a similar one based on NDVI. Note that the approach of Inamdar and French (2009) is not applicable to the disaggregation of MODIS type data over agricultural areas. The rationale is that agricultural covers evolve quickly and surface changes drastically between two successive ASTER emissivity products separated by a minimum of 16 days and more often (clouds, programming requests) by several months.…”

“…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. The authors found that the emissivity-based approach was more 3 accurate than a similar one based on NDVI.…”

Improving the Downscaling of Diurnal Land Surface Temperatures Using the Annual Cycle Parameters as Disaggregation Kernels

“…The employed empirical model can be linear or nonlinear [12,13] depending mostly on the type and number of LST predictors employed (for downscaling TIR DN or radiances, the nonlinear factors of the atmospheric and emissivity effects should also be taken into consideration during this selection [12]). Zhan et al [11] discuss that simple tools such as linear and quadratic tools are effective when the predictors' number is low (e.g., [9,10,16,17]), while complex tools such as support vector regression machines (SVM) are better suited when multiple LST predictors are employed (e.g., [18][19][20]). In principle, the LST is determined by numerous factors, including topography, vegetation abundance and vigor, soil moisture, land cover and meteorological conditions [16]; and usually the relationship between the LST data and the LST predictors is nonlinear [13].…”

“…For instance, Kustas et al [9] utilized the NDVI with a quadratic regression tool (this method is referred in literature as disTrad: disaggregation procedure for radiometric surface temperature), whereas Agam et al [10] used the fractional vegetation cover with a linear tool and also other variants of disTrad. Inamdar et al [16] employed the emissivity for downscaling GOES (Geostationary Environmental Satellite) LST data, while Essa et al [26] expanded the disTrad methodology and tested 15 remote sensing based indices (individually) as LST predictors (including soil, vegetation and built-up indices). Stathopoulou and Cartalis [8] enhanced the spatial resolution of AVHHR (Advanced Very High Resolution Radiometer) LST data using as LST predictors the effective emissivity and a LST map retrieved from Landsat 5 data.…”

“…In principle, the LST is determined by numerous factors, including topography, vegetation abundance and vigor, soil moisture, land cover and meteorological conditions [16]; and usually the relationship between the LST data and the LST predictors is nonlinear [13]. However, this nonlinearity is so complex [12,16] that the derivation of explicit global models is not an easy task (a localization strategy can be carried out as in [21,22] and a linear relationship can be individually constructed for each group of adjacent pixels) and hence even to this date no strong evidence, that support whether the linearity or nonlinearity performs better, exist [12].…”