Quantification of the Scale Effect in Downscaling Remotely Sensed Land Surface Temperature

Abstract

Abstract:Most current statistical models for downscaling the remotely sensed land surface temperature (LST) are based on the assumption of the scale-invariant LST-descriptors relationship, which is being debated and requires an in-depth examination. Additionally, research on downscaling LST to high or very high resolutions (~10 m) is still rare. Here, a simple analytical model was developed to quantify the scale effect in downscaling the LST from a medium resolution (~100 m) to high resolutions. The model was verified in the Zhangye oasis and Beijing city. Examinations of the simulation datasets that were generated based on airborne and space station LSTs demonstrate that the developed model can predict the scale effect in LST downscaling; the scale effect exists in both of these two study areas. The model was further applied to 12 ASTER images in the Zhangye oasis during a complete crop growing season and one Landsat-8 TIRS image in Beijing city in the summer. The results demonstrate that the scale effect is intrinsically caused by the varying probability distribution of the LST and its descriptors at the native and target resolutions. The scale effect depends on the values of the descriptors, the phenology, and the ratio of the native resolution to the target resolution. Removing the scale effect would not necessarily improve the accuracy of the downscaled LST.

Estimating Subpixel Surface Heat Fluxes through Applying Temperature-Sharpening Methods to MODIS Data

“…Irrigation increases soil moisture and cools the land surface. Because albedo is mostly negatively related with soil moisture within barren agricultural areas [19], it displays a larger influence over the results. To determine the reasons for the above phenomenon, we calculated the contributions of the different independent variables to the LSTs on each date separately by calculating the normalized regression coefficients [19].…”

“…The main objective of the GWR-based downscaling algorithm is to address the spatially heterogeneous relationships between LST and the other land surface parameters. According to the surface characteristics of the HRB and previous studies of this area [19,23], NDVI, albedo and the digital elevation model (DEM) data were selected as auxiliary variables for use in LST downscaling. DEM is recognized as an important factor in characterizing variations in LST [23].…”

“…DEM is recognized as an important factor in characterizing variations in LST [23]. The surface albedo was selected because it influences the surface radiation balance and is mostly negatively related to soil moisture over barren agricultural areas [19]. Because longitude and latitude were the input parameters in the GWR model, these two parameters were not explicitly selected as auxiliary variables in the LST downscaling process.…”

“…The performance of these algorithms has been evaluated over different land cover types, including agricultural areas [15], urban areas [16], and other areas, some of which display high heterogeneity [17,18]. Recently, many improvements have been made to DisTrad and TsHARP so that they include a greater number of independent variables (e.g., the normalized difference built-up index (NDBI), the surface albedo, and the surface reflectance) [19][20][21]. Moreover, many studies have questioned the hypothesis that the relationship between LST and NDVI is stationary in space [19,22,23].…”

“…Recently, many improvements have been made to DisTrad and TsHARP so that they include a greater number of independent variables (e.g., the normalized difference built-up index (NDBI), the surface albedo, and the surface reflectance) [19][20][21]. Moreover, many studies have questioned the hypothesis that the relationship between LST and NDVI is stationary in space [19,22,23]. In fact, the relationship between LST and other land surface parameters is geographically variable.…”