Daily NDVI relationship to clouds

TANG ， Qiuhong

The University of Tokyo

IIS, OKI’s Lab.

PUB

Prediction in Ungauged Basins

Relatively reliable Prediction in Un-ground-gauged Basins

UngaugedBasin A

UngausedBasin B

PUB and observation

If there isn’t information of the basins, we cannot estimate hydrological response.

“UNGAUGED” (Un-ground-gauged)

Indirectly ‘gauge’ (observation/information)

Remote sensing Similar climate zone data

What’s gauged? What’s ungauged? What’s PUB?

PUB started with the geophysical science.

PUB model forcing data“UNGAUGED”

Remote sensing Similar climate zone data

Available for RS

Describe Land Surface

DEM, Land Cover, …

Unavailable for RS

Temperature, Wind speed

Vapor pressure…

DEM: geomorphologic factor to hydrological response

Land Cover: vegetation phonology to hydrological response

Wind speed, vapor pressure etc. : regional climate forcing

RS

Interpolate

RS: Unreliable, distributed data | Ground observation: Reliable, point data

Land Cover: NDVI data

NDVI = (NIR-Red) / (NIR+Red) NDVI (normalized difference vegetation index)

cloud

Satellite NDVI

‘True’ NDVI

Cloud Index (NCI)

-0.2

0.0

0.2

0.4

0.6 NDVIr (Real time NDVI) NDVIc (Corrected NDVI)

ND

VI =

(N

IR-R

ed)/

(NIR

+R

ed)

Time (day)

NDVImin

'Drop of NDVI' caused by Cloud&Aerosols etc.

NCAI = (NDVIr -NDVImin) / (NDVIc -NDVImin) [0, 1]

Cloud Index from Ground Observation

Cloud Amount Sunshine Index = n/N

Where, n is duration of sunshine and N is maximum possible sunshine

Comparison (Satellite & Ground Based Cloud Index)

R-squared value between daily NCI values and the observed cloud amount (Left) and Sunshine time index (right). (1995-2000, 120 stations)

0 20 40 60 80 100 1200.0

0.1

0.2

0.3

0.4

0.5

0.6

Mean R2 = 0.40741SD = 0.06817

R2

Clo

ud A

moun

t - N

CA

I (R

2)

Stations

0 20 40 60 80 100 1200.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

R2

Suns

hin

e -

NC

AI R

2Stations

Mean R2 = 0.50377SD = 0.07033

Comparison (Satellite & Ground Based Cloud Index)

RMSE value between daily NCI values and the observed cloud amount (Left) and Sunshine time index (right). (1995-2000, 120 stations)

0 20 40 60 80 100 1200.00

0.05

0.10

0.15

0.20

0.25

0.30

Mean RMSE = 0.25754SD = 0.01379

RMSE

Clo

ud A

mount -

NC

AI (

RM

SE

)

Stations

0 20 40 60 80 100 1200.00

0.05

0.10

0.15

0.20

0.25

Mean RMSE = 0.20653SD = 0.01338

RMSE

Sunsh

ine -

NC

AI (

RM

SE

)

Stations

Comparison (Satellite & Ground Based Cloud Index)

R-squared value between daily NCI values and the observed cloud amount (Left) and Sunshine time index (right). (1995-2000)

The poorest relationship in the Tibet Plateau (red circle) might due to the distortion of satellite scans.

.00-.09

.09-.25

.25-.36

.36-.49

.49-.64

.00-.09

.09-.25

.25-.49

.49-.64

.64-.81

Satellite data vs. Interpolate to all over the River Basin

Comparison (R2)

Distribution of R-squared values over the study area. NCI and Cloud Amount Index (left), NCAI and Sunshine Index (right).

>

80%

>

90%

Comparison (RMSE)

The root-mean-square error (RMSE) associated with NCI and cloud amount ranges from 0.21 to 0.30 with an averaged value of 0.25.

For Sunshine time, RMSE ranges from 0.16 to 0.26 with an averaged value of 0.20.

ComparisonUSGS Land use

The daily NCI versus observed cloud amount and SCAI relationships are strong and land cover independent expect for water body

Conclusion The NCI and observed cloud index retain a high

correlation coefficient suggesting that NCI may be useful for estimating clouds influence to solar radiation.

Satellite data

(NDVI)

Solar radiation

Ground based observation

(cloud amount, sunshine)

Thank you for your attention.Thank you for your attention.

Solar and Scan Geometry

Sunshine – NCI a Sunshine – NCI b