Scale Effect of Vegetation Index Based Thermal Sharpening: A Simulation Study Based on ASTER Data

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. 1 3 Scale Effect of NDVI-T Relationship Introduction Outlines 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

5. 2. TsHARP • Establish relationship between T and NDVI on the coarse resolution • The regression relationship is applied to the NDVI at their finer resolution (NDVIhigh). • 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 • This coarse-resolution residual field is added back into the sharpened map The slope is key parameter for sharpening result

6. 3.SCALE EFFECT OF NDVI-T • 3.1 Data • 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. • A subset image (256) VNIR band NDVI Surface Temperature

7. SCALE EFFECT OF NDVI-T • Two aspects of “Scale” • Spatial Resolution (size of a pixel) • Spatial Extent (size of study area) 90m 720m 1440m

8. NDVI-T Relationship on Different Resolutions • NDVI and T images were resampled to different spatial resolutions (90m to 2880m) by linear aggregation. • Slope (a) of NDVI-T on different resolutions were investigated The regressed slope increases slightly with increasing of spatial resolution

9. NDVI-T Relationship on Different Extents • 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

10. 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)

11. 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

12. 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)

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

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

15. 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.

16. 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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