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, Japanb State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, 100875, China

Outlines

Introduction1

TsHARP2

Scale Effect of NDVI-T Relationship3

Improved TsHARP Method4

6

Discussion and Conclusion5

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

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

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

low lowˆ( )T NDVI a NDVI b

high highˆ( )T NDVI a NDVI b

low low lowT̂ T a NDVI b

high high low

low high low

ˆ ˆ

( )

T a NDVI b T

T a NDVI NDVI

The slope is key parameter for

sharpening result

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

high low high lowˆ ( )T T a NDVI NDVI

SCALE EFFECT OF NDVI-T

Two aspects of “Scale” Spatial Resolution (size of a pixel) Spatial Extent (size of study area)

90m 720m 1440m

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

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

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.

y = 2.6881x0.2599

R2 = 0.9941

0

10

20

30

40

50

0 5000 10000 15000 20000 25000

Spatial size (m)

abs

(a)

0

10

20

30

40

50

0 500 1000 1500 2000 2500 3000

Spatial resolution (m)

abs

(a)

(a) (b)

Spatial extent (m)

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

high low local high lowˆ ( )T T a NDVI NDVI

Spatial Extent

Slo

pe

Slope on extent of whole image (a)

Slope on extent of one thermal pixel (alocal):

Unkown without high resolution T image

Slope on extent of 2×2 thermal pixels

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

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)

Improved TsHARP (alocal)

Spatial extent (m)

(23040m, 38.1)

Sharpened Result

Image sharpened by Improved TsHARP is smoother than that by original TsHARP

0 5 10km

N

0 5 10km0 5 100 5 10km

NN

℃

(c)

TsHARP

Improved TsHARP

True T image

Coarse T image

Accuracy Assessment

0

0.4

0.8

1.2

1.6

2

0 10 20 30 40 50abs (a )

RM

SE

( ℃)

0.76

0.80

0.84

0.88

0.92

R s

qu

are

RMSE

R squareImproved TsHARP

TsHARPBest slope

The best value of slope is around 15.9 Improved method acquired higher sharpening accuracy Original TsHARP over-sharpens the T image

Actual T image with 90m is used for accuracy assessment

15

5. DISCUSSION and CONCLUSIONWhy 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.

mT a NDVI a Moisture

mˆ * ( , )a a a r NDVI Moisture

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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