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According to the equation of estimation error, retrieval
algorithm of MODIS 250m should exist second order derivative.
Then cubic polynomial model was chosen for estimating chla
concentration after comparing correlation coefficient. And the
scale relative error of low resolution images (MODIS 500m) in
different seasons can be calculated by formula (6) and cubic
polynomial formulas. As field measured data were distributed
in the north and central of Taihu, the paper only discussed
relative errors in these places (see Figure 5.).
Figure 5. Comparison of relative errors in different season
(MODIS 250m and MODIS 500m). (a) Relative error on Mach
28". 2011 (spring). (b) Relative error on September 4^, 2011
(summer). (c) Relative error on October 31%, 2010 (autumn)
Figure 5. showed the distribution of scale relative error of chla
concentration in different seasons. In this paper effect of lake’s
boundary was ignored, and the results were as follows. In
spring, the error was relatively lower and more homogeneous.
The error concentrated between 0 and 4%. The high values of
relative errors were primarily concentrated in Meiliang Bay,
Gonghu Bay and Western Lakeshore. The errors were higher
and more unevenly distributed in summer. The scale relative
error was high in Western Lakeshore and the area water
between Meiliang Bay and Gonghu Bay, and the highest error
reached to 23%. The values were between 0-12% in other
regions. Scale relative error was also high in autumn. The range
of the relative error was 10% -17% in north of Meiliang Bay
and central of Taihu. And it was 0-10% in other regions.
According to the paper above, the relative error of chla
concentration varies with the seasons. Scale relative errors were
lower and more evenly distributed in spring. Because of the low
value, uniform spatial distribution of chla concentration and low
variance of chla concentration, the relative scale errors were
lower than that in summer and qutumn. For summer terms, the
chla concentration was high and the spatial distribution was
uneven, especially for the regions where accumulation of algal
blooms (eg Meiliang Bay, Western Lakeshore). It also leaded to
high variances, resulting high scale relative errors in these
regions. In addition, it is noted that the scale relative error also
closely related to the inversion model of chla concentration, and
the results could affected by different inversion models.
5. CONCLUSIONS
Firstly, the paper established estimation models of chla
concentration using HJ-1 CCD, MODIS 250m and MODIS
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
500m and field data in October 2010, March 2011, and
September 2011. Secondly, spatial heterogeneity was studied
using geostatistics. Thirdly, according to the spatial scale
relative error model, scale errors of chla concentration which
were inverted by MODIS 500m were calculated in different
seasons.
Chla concentration was variation in different seasons. The
values were low and evenly distributed in spring. In summer,
chla concentration was high in the west shore of Lake Taihu,
export of Meiliang Bay and Gonghu Bay. And the high values
mainly distributed in Meiliang Bay, Zhushan Bay and Gonghu
Bay in autumn. In addition, the trends of chla concentration
estimated from different scales were similar to each other in the
same season. But details of low-resolution remote sensing
images (especially blue-green cyanobacteria agglomeration area)
couldn't be well express. Through the seasonal analysis of
semi-variance function on different scales, it is known that
structural factors caused by chla concentration were mainly due
to spatial heterogeneity. Chla concentration at different scales
showed similar structure in the same season, while the chla
concentration in different seasons had different structural..
Lake Taihu is typical inland case II water. Higher spatial
heterogeneity of chla concentration caused higher scale error.
And the error changed with seasons. Scale errors were low and
evenly distributed in spring. Because of exploding and floating
together of cyanobacteria, scale errors increased up to 23%.
Moreover, great uncertainties and errors existed in the inversion
of chla concentration using MODIS 500m data.
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