Abstract
Land surface temperature (LST) images retrieved from the thermal infrared (TIR) band data of Moderate Resolution Imaging Spectroradiometer (MODIS) have much lower spatial resolution than the MODIS visible and near-infrared (VNIR) band data. The coarse pixel scale of MODIS LST images (1000 m under nadir) have limited their capability in applying to many studies required high spatial resolution in comparison of the MODIS VNIR band data with pixel scale of 250–500 m. In this paper we intend to develop an efficient approach for pixel decomposition to increase the spatial resolution of MODIS LST image using the VNIR band data as assistance. The unique feature of this approach is to maintain the thermal radiance of parent pixels in the MODIS LST image unchanged after they are decomposed into the sub-pixels in the resulted image. There are two important steps in the decomposition: initial temperature estimation and final temperature determination. Therefore the approach can be termed double-step pixel decomposition (DSPD). Both steps involve a series of procedures to achieve the final result of decomposed LST image, including classification of the surface patterns, establishment of LST change with normalized difference of vegetation index (NDVI) and building index (NDBI), reversion of LST into thermal radiance through Planck equation, and computation of weights for the sub-pixels of the resulted image. Since the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) with much higher spatial resolution than MODIS data was on-board the same platform (Terra) as MODIS for Earth observation, an experiment had been done in the study to validate the accuracy and efficiency of our approach for pixel decomposition. The ASTER LST image was used as the reference to compare with the decomposed LST image. The result showed that the spatial distribution of the decomposed LST image was very similar to that of the ASTER LST image with a root mean square error (RMSE) of 2.7 K for entire image. Comparison with the evaluation DisTrad (E-DisTrad) and re-sampling methods for pixel decomposition also indicate that our DSPD has the lowest RMSE in all cases, including urban region, water bodies, and natural terrain. The obvious increase in spatial resolution remarkably uplifts the capability of the coarse MODIS LST images in highlighting the details of LST variation. Therefore it can be concluded that, in spite of complicated procedures, the proposed DSPD approach provides an alternative to improve the spatial resolution of MODIS LST image hence expand its applicability to the real world.
Highlights
Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared (TIR) band data are mainly used to retrieve land surface temperature with such techniques as split window algorithms for the study of land surface energy budgets [1,2], water resource management [3,4], agricultural drought [5,6], and environmental biogeochemistry processes [7].spatial resolution of the data is 1000 m under nadir, which is very low in comparison with that of visible and near-infrared band data
We applied the procedures of our DSDP approach to MODIS Land surface temperature (LST) images for pixel decomposition with its visible and near-infrared (VNIR) data
This is done on the basis of the relationships respectively between LST and NDVI for natural terrain, LST and normalized difference of building index (NDBI) for urban surface, LST and water color index (WCI) for water surface
Summary
MODIS thermal infrared (TIR) band data are mainly used to retrieve land surface temperature with such techniques as split window algorithms for the study of land surface energy budgets [1,2], water resource management [3,4], agricultural drought [5,6], and environmental biogeochemistry processes [7].spatial resolution of the data is 1000 m under nadir, which is very low in comparison with that of visible and near-infrared band data (for example, 250 m for bands 1 and 2). The retrieved LST images from MODIS TIR data are with much low spatial resolution (1000 m under nadir), which has limited their applications in many studies requiring high spatial resolution to identify detailed variation of thermal heat flux over the region under study [8]. To increase the spatial resolution of an image means to decompose its pixels into smaller ones. To increase the spatial resolution of a MODIS LST image means to decompose its pixels into smaller ones with relative high spectral multispectral image as assistance. Since ASTER was on board the same Terra platform as MODIS for Earth observation, the spatial resolution of MODIS LST can be decomposed from 1000 m into 250 m with its VNIR data or 90 m with ASTER visible/infrared (VNIR) data. The pixel scale of the MODIS image is 1000 m while the scale of ASTER image is 90 m
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