determination is square of the Pearson correlation product. The formula for the Pearson correlation coefficient can be seen below:
2 2
y y
x x
y y
x x
r
13
where, r = the coefficient of correlation
x = the value of model output y = the value of published data
The error of prediction will be used to measure differences between model output and published data. The big differences will be caused the big error. The complete
calculation for error of prediction can be seen below:
| |
14 where,
E = error of prediction M = the value of model output
D = the value of measured data.
IV. RESULTS
4.1. MODIS LAI Product in West Java Province
The annual pattern of MODIS LAI in paddy field area was shown clearly in Figure 3. Although in several data MODIS LAI fluctuated, but the cycle seen
during 5 years. Fluctuated of MODIS LAI was caused by cloud cover. The higher cloud covers made the canopy radiation transfer model fail to estimate LAI.
Consequently, a backup model was applied mainly during rainy season Kim et.al. 2005. Several factor influences performance of MODIS LAI are coarse
resolution, effect of water background on the spectral reflectance, and the criteria of which the algorithm retrieves information according to a predefined biome
Suarez, 2010.
Figure 3 Annual pattern of MODIS LAI for year 2004 until 2008.
4.2. Weather Condition in the Study Area
NCEP-NOAA and TRMM data were promising to get daily and timely temperature, RH, solar radiation, wind and rainfall variables. Figure 4 shows the
yearly rainfall from 2004 until 2008. The yearly precipitation in the study area varied from 2143 mm to 2864 mm per years. During 2004, 2005 and 2007, the
yearly rainfall was stable where the value was more than 2600 mmyear, but fell slightly in 2006 and 2008 with 2143 mmyear and 2374 mmyear respectively.
Figure 4 The yearly rainfall from year 2004 year until 2008. The temperature was corrected using elevation data which were derived from
DEM data. According to Braak formula 1929 in Ritung et.al. 2007, the temperature in Indonesia will decrease by 6.1
C regularly every increasing 1000 m above sea level. Figure 5 shows the yearly average of temperature for 5 years in
West Java province. It varied from 10 C until 26
C. The minimum temperature associated with high elevation which it located on top of mountain while the
maximum temperature founded near of low-land area.
Figure 5 Yearly average temperature from year 2004 until year 2004. Daily relative humidity, solar radiation, and wind speed were directly used
as an input of the model. The statistical downscaling was used to change the spatial resolution of those data into 1 x 1 km. Even though coarse resolution
