×

You are using an outdated browser that does not fully support the intranda viewer.
As a result, some pages may not be displayed correctly.

We recommend you use one of the following browsers:

Full text

Title
Technical Commission VII


] four input
established.
©)
and NDSI,
> models of
: R°=0 774,
T-value are
1 addition,
sessing the
8.651%, it
PE and high
ased on situ
to calculate
ure 4 shows
dy area. As
) 0.30. The
vith ranging
land had a
1e area was
ell with the
n model for
nfirmed that
'e.
ve to TIN of
has a better
implying the
tter in sea
improve the
is because
information


REFERENCES
Dugdale, R. C., Morel, A., Bricaud, A., Wilkerson, F. P., 1989.
Modeling new production in upwelling centers - a case-study of
modeling new production from remotely sensed temperature
and color. Journal of Geophysical Research-Oceans, 94(C12),
pp. 18119-18132.
Goes, J. I., Saino, T., Oaku, H., Ishizaka, J., Wong, C. S., Nojiri,
Y., 2000. Basin scale estimates of sea surface nitrate and new
production from remotely sensed sea surface temperature and
chlorophyll. Geophysical Research Letters, 27(9), pp.
1263-1266.
Goes, J. I., Saino, T., Oaku, H., Jiang, D. L., 1999. A method
for estimating sea surface nitrate concentrations from remotely
sensed SST and chlorophyll a - A case study for the north
Pacific Ocean using OCTS ADEOS data. [EEE Transactions
on Geoscience and Remote Sensing, 37(3), pp. 1633-1644.
Kamykowski, D., Zentara, S. J., 2003. Can phytoplankton
community structure be inferred from satellite-derived sea
surface temperature anomalies calculated relative to nitrate
depletion temperatures. Remote Sensing of Environment, 86(4),
pp. 444-457.
Kamykowski, D., Zentara, S. J., 2006. Changes in world
ocean nitrate availability through the 20th century (vol 52, pg
1719, 2005). Deep-Sea Research Part I-Oceanographic
Research Papers, 53(9), pp. 1578-1579.
Silio-Calzada, A., Bricaud, A., Gentili, B., 2008. Estimates of
sea surface nitrate concentrations from sea surface temperature
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
and chlorophyll concentration in upwelling areas: A case study
for the Benguela system. Remote Sensing of Environment,
112(6), pp. 3173-3180.
Singh, K. P., Basant, A., Malik, A., Jain, G, 2009. Artificial
neural network modeling of the river water quality-A case study.
Ecological Modelling, 220(6), pp. 888-895.
Traganza, E. D., Silva, V. M., Austin, D. M., Hanson, W. L.,
Bronsink, S. H., 1983. Nutrient mapping and recurrence of
coastal upwelling centers by satellite remote sensing: Its
implication to primary production and the sediment record.
In: E. Suess and J. Thiede(Eds.), Coastal Upwelling: Its
Sediment Record, Plenum Press, New York, USA, Part A,
pp.61-83.
Wang, Y.-M., Elhag, T. M. S., 2007. A comparison of neural
network, evidential reasoning and multiple regression analysis
in modelling bridge risks. Expert Systems with Applications,
32(2), pp. 336-348.
ACKNOWLEDGEMENTS
This research was supported by the National Natural Science
Foundation of China (No. U0933005). The authors wish to
thank the anonymous reviewers for their constructive
comments that helped improve the scholarly quality of the
paper.