Disaggregation of MODIS Land Surface Temperature Using Stepwise Regression:A Case Study over Sichuan Basin

  • School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China

Received date: 2013-04-02

  Revised date: 2013-05-20

  Online published: 2014-01-05


Accurate temporal and spatial estimation of land surface temperature (LST) is important for evaluating climate change, global hydrological cycle and monitoring urban heat islands (UHI). LSTs with high quality can be routine by using satellite remote sensing. However, characters of both high spatial and temporal resolutions have been difficult. Cloud cover further reduces the useable observations of surface conditions. Monthly LST product (MOD11C3) composited and averaged temperature values at 0.05 degree latitude/longitude grids (CMG) have coarse spatial resolution (~5.5 km). An alternative to the lack of high-resolution observations is to disaggregate LST data using other products of MODIS of 1 km observations. Historically, disaggregation of LST at high resolutions (1 km) has relied on vegetation index, e.g. NDVI (Normalized Difference Vegetation Index). However, this downscaling method is not adequate for areas encompass basin and upland. We applied Digital Elevation Model (DEM), NDVI, Enhanced Vegetation Index (EVI), Albedo, and slope to resolve this drawback by utilizing stepwise regression method with a moving window. The following is the algorithm. Land surface parameter (LSP) data are sampled to the coarser thermal resolution. A stepwise regression is performed between the monthly temperature product and sampled land surface parameters, then a function f (LSP) framed. The parameters of the regression function are applied to LSP data at high, target resolution. Coarse-scale residual field represent variability in temperature driven by other factors other than vegetation and DEM is added back into the high-resolution base map. So, we utilize LSP to sharpen original images. A reasonable rectangle box that making certain pixel be center is outlined for stepwise regression. Function is obtained by stepwise between LST and LSP. Loop and downscale the other pixels until image processed. Coefficients and intercept are saved as images. The disaggregation LST is achieved by substituting images at target resolution to function. The size of the box flowed over the image in this paper is 19 by 19. Stepwise disaggregation algorithm is applied to the resample MOD13A1 and DEM data. The fitting parameters vary with different window scenes. In contrast, the number of DEM entered function is much larger than NDVI. That indicated DEM is more significant than NDVI, EVI, albedo and slope in most fields of the study area, especially in mountain area. The RMSE of downscaling LST is 4.93K. Image sharpening is therefore not a replacement for high-resolution thermal imaging sensors. Nevertheless, in the absence of thermal imagery because of cloud in Sichuan, DisTrad seems to be able to enhance the resolution of MOD11C3 product.

Cite this article

PANG Qingfei, QUAN Ling . Disaggregation of MODIS Land Surface Temperature Using Stepwise Regression:A Case Study over Sichuan Basin[J]. Journal of Geo-information Science, 2014 , 16(1) : 45 -53 . DOI: 10.3724/SP.J.1047.2014.00045


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