right 
ages 
50m, 
t and 
)04). 
‚28% 
from 
)0m) 
used 
and 
ASH 
and 
tions 
the 
used 
ticle 
ty or 
le of 
were 
band 
R2; 
ad a 
ands 
ation 
t for 
rical 
)DIS 
shed 
atial 
the 
008) 
nong 
d as 
y the 
udy, 
atial 
an et 
(1) 
of h 
  
Nugget (v0), Still (yO + y), change range (a) are three important 
parameters of semi-variance function. Chla concentration is a 
region random variable. Nugget represents: when sampling 
interval h equals 0, chla concentration is variation; nugget effect 
and the observed scale are closely linked. Still represents: the 
steady variograms value with distance enough. Change range 
represents: the reflection from the scale of chla concentration 
spatial autocorrelation. Under the change range, the closer 
points have lager correlation (Zhang, 2008). 
3.3 Modelling Spatial Scale Error 
According to the definitions of Distributed chla (chlaD) and 
Lumped chla (chlaL) (Bao et al., 2011), chlaD is the true value 
of the low resolution image while chlaL is the value which is 
estimated from low resolution image. In this study, chlaD and 
chlaL are given as following: 
chlaD EIN fp) 2) 
ni. 
dabo [OS a3 3) 
hj 
And the estimation error between high resolution image and 
low resolution image is as below: 
1 n n 
Error » 5X. f(9)- fC-3, p) (4) 
where n= Ratio of high resolution and low resolution 
p = Difference reflectance of high resolution 
fp) = Estimation equation of chla concentration 
Then the equation of estimation error is simplified by Taylor 
Formula as follows (Bao et al., 2011; Zhu et al., 2010): 
Ix 
f (e s Pi) 
Error = D (3) 
2 p 
RelativeError = Error (6) 
chlaD 
where f"(p)= Second order derivative of f(p) 
D Variance of chla concentration in the window of 
4. RESULTS AND DISCUSSION 
4.1 Seasonal Spatial Distribution in Different Scales 
According to the scale error formula, the chla concentration 
estimate equation need to have the second order derivative. 
Through the comparison of the chla concentration estimate 
model, cubic polynomial function was used to calculate chla. 
And inversion of chla concentration in different seasons and 
different scales were shown as below (see Figure 2.- Figure 4.). 
Figure 2, 3, 4 represented the spatial distribution maps about 
chla concentration on Mach 28th, 2011(spring), September 4th, 
2011(summer) and October 31st, 2010(autumnr) in different 
scales of Lake Taihu. Firstly, the picture showed that chla 
concentration of Lake Taihu had uneven spatial distribution in 
different season from the figure. Generally speaking, the chla 
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
concentration was higher in the northwestward of Lake Taihu 
and shore, and lower in the south. Secondly, the chla 
concentration of Lake Taihu was lower in spring. The values 
centred on 0-35ug/L. The phenomenon of large area 
cyanobacteria agglomeration occurred in the summer and 
autumn. On September 4*. 2011 (summer), chla concentration 
varied in a wide range occurred in the west bank side of Lake 
Taihu , the outlet of Meiliang Bay and Gonghu Bay. The values 
centred on 1-120ug/L. Finally, by contrast the estimates of chla 
concentration under different data sources, the variation trend 
of spatial distribution map of chla concentration was similarity. 
But the inversion results, which were estimated from HJ 
satellite, had high spatial resolution and can better reflect the 
meticulous changes of the chla concentration, as shown in the 
red box of the figure. 
  
  
gus MP - 35 
5 ag 555 
  
  
  
  
Figure 2. Comparison of chla concentration estimated from 
different scales on Mach 28^, 2011 (spring). (a)Chla 
concentration estimated from HJ-1 CCD (b) Chla concentration 
estimated from MODIS 250m (c) Chla concentration estimated 
from MODIS 500m 
  
    
   
ETN RTE 
and 
771 Loin Banat y 
uirga E 
£u 
EG 
UM aum 
   
DEM LI aa ia 
if. Ais EE To. 
Lx. 60 EER Ve 128 
" 
* Se