This study investigates the scale effect of the relationship between the normalized difference vegetation index (NDVI) and land surface temperature (T) and improves a thermal sharpening method called TsHARP. The study finds that the slope of the NDVI-T relationship increases more significantly with spatial extent than with spatial resolution alone. An improved TsHARP method is developed that establishes the NDVI-T regression relationship based on the spatial extent of individual thermal pixels, rather than the entire image extent. Testing shows the improved method produces a sharper and more accurate thermal sharpening result compared to the original TsHARP method.

1. Scale Effect of Vegetation Index Based Thermal Sharpening: A Simulation Study Based on ASTER Data X.H. Chena, Y. Yamaguchia, J. Chenb, Y.S. Shia a Graduate School of Environmental Studies, Nagoya University, Nagoya, 464-8601, Japan b State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, 100875, China

2. Outlines 1 3 Introduction Scale Effect of NDVI-T Relationship 2 TsHARP 4 Improved TsHARP Method 5 Discussion and Conclusion 6

3. 1. INTRODUCTION Thermal infrared (TIR) band imagery has been widely applied in many studies (e.g. evapotranspiration esitimation; urban heat island; drought monitoring, etc.) Unfortunately, the spatial resolution of TIR bands is usually coarser than that of visble-near infrared (VNIR) bands Several thermal sharpening methods have been developed for sharpening spatial resolution of TIR band by using VNIR band

4. Vegetation Index Based Thermal Sharpening TsHARP(Kutas et al, 2003) was intensively studied Negative correlation between NDVI and surface temperature (T) NDVI-T Relationship established on coarse resolution is applied on fine resolution. Previous studies found that the spatial resolution does not affect NDVI-T relationship largely; However, another factor, spatial extent, was largely neglected in the previous studies. Our study aims to: Investigate the scale effect of NDVI-T Improve TsHARP by considering the effect of spatial extent

6. The regression relationship is applied to the NDVI at their finer resolution (NDVIhigh).

7. Then, the divergence of the retrieved temperatures from the observed temperature field is due to spatial variability in T driven by factors other than vegetation cover, and can be assessed at the coarse resolution

8. This coarse-resolution residual field is added back into the sharpened mapThe slope is key parameter for sharpening result

10. A subset image (256×256 pixels) with 90m resolution of ASTER captured in the grassland in Inner Mongolia, China (44.6ºN, 116.0ºE), on the date of July 16th, 2010, was used for study.

11. A subset image (256)VNIR band NDVI Surface Temperature

13. Spatial Resolution (size of a pixel)

14. Spatial Extent (size of study area)90m 720m 1440m

16. Slope (a) of NDVI-T on different resolutions were investigatedThe regressed slope increases slightly with increasing of spatial resolution

17. Spatial Extent of m pixels Original image is divided into N/(m×m) windows. Average the values of the pixels in each window Local difference image is derived by subtracting the original image with the averaged image Regression is conducted on the local difference images of NDVI and T Local Difference Image NDVI-T Relationship on Different Extents

18. Regressed slope (a) increases with the increasing of spatial extent following a power function Compared with spatial resolution, spatial extent affects regressed slope more largely. (b) (a) Spatial extent (m)

19. 4. IMPROVED TsHARP Sharpening T image is equal to retrieving the local difference image of T on extent of a thermal pixel. The regression relationship should be established on the spatial extent of one thermal pixel Slope on extent of whole image (a) We use the power function of (spatial extent -regressed slope) to estimate the slope (alocal) on the extent of one thermal pixel； Improved TsHARP replaces a with alocal Slope Slope on extent of one thermal pixel (alocal):Unkown without high resolution T image Slope on extent of 2×2 thermal pixels Spatial Extent

20. Algorithm Test T image with 900m resolution was generated. The coarse T image was sharpened to 90m using TsHARP and improved TsHARP respectively TsHARP (a) (23040m, 38.1) Improved TsHARP (alocal) Spatial extent (m)

21. Sharpened Result Coarse T image TsHARP Image sharpened by Improved TsHARP is smoother than that by original TsHARP Improved TsHARP True T image (c) ℃

22. Accuracy Assessment Actual T image with 90m is used for accuracy assessment Improved TsHARP Best slope TsHARP The best value of slope is around 15.9 Improved method acquired higher sharpening accuracy Original TsHARP over-sharpens the T image

23. 5. DISCUSSION and CONCLUSION Why spatial extent affects the NDVI-T relationship? Other than NDVI, soil moisture also affects surface temperature. Assuming that Since NDVI is somehow positively correlated with soil moisture, when T is regressed with only NDVI, the regressed slope becomes (for convenience, we assume the data is standardized) As spatial pattern of moisture is smoother than NDVI, when spatial extent increases, the correlation between NDVI and Moisture increases, consequently the regressed slope also increases. 15

24. Conclusion Spatial extent is an important factor affecting the NDVI-T relationship, and should not be neglected in the related studies Improved TsHARP considers the effect of spatial extent and can acquire better sharpening result in this case of study.

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