Total suspended matter concentration (TSM) algorithms for ocean color sensors use empirical relationship between
satellite-retrieved remote sensing reflectances and TSM. However the estimated-TSM has no enough accuracy because
the reflectance at visible bands has error after atmospheric correction in high turbid area. The purpose of this study is to
estimate simultaneously total suspended matter concentration, aerosol optical thickness and Angstrom exponent using
three bands at near infrared from MODIS/Aqua and SeaWiFS data. We applied this scheme to MODIS/Aqua and
SeaWiFS data, and satellite-derived TSM were compared with ship-observed TSM dataset in Yellow Sea and East China
Sea. RMSE of TSM was 0.338 in log-log coordinates and correlation coefficient was 0.850. The scheme was better than
Clark’s or Tassan’s TSM algorithm.
It has been shown that the laboratory calibration coefficient of charge-coupled device camera (CCD camera) on China-Brazil Earth Resources Satellite-02B (CBERS-02B) is not fit for the water component retrieval. As a result, the CCD was cross-calibrated by Aqua MODIS with higher calibration accuracy, which was based on the total radiance at the top of atmosphere calculated from normalized water-leaving radiance and aerosol parameters of Aqua MODIS. The analysis about the error related to the parameters used in the cross-calibration showed that the error of the calibration coefficients can be less than 5%, which was mostly determined by the accuracy of aerosol scattering of CCD band 830nm. Using the calibration coefficients, chlorophyll concentration was retrieved from CCD imagery and was compared with that from Aqua MODIS. The comparison showed that the calibration result worked better than the laboratory coefficient.
Application of MODIS in ocean color is mainly based on bands 8-16, whose spatial resolution is 1km. This spatial
resolution can't meet the demand of inland waters with small area. Then, taking TaiHu lake in China as an example, we
put forward an atmospheric correction algorithm for bands 1 and 2 whose spatial resolution is 250m. Firstly, we choose
one pixel whose digital number of band 16 is the smallest in Taihu lake as the clear pixel. The aerosol parameters of the
clear pixel are calculated by the standard atmospheric correction algorithm for Case 1 waters. Secondly, we can calculate
the aerosol scattering radiance of bands 1, 2 of other pixels with assumption that the aerosol type and optical thickness
keep the same over Taihu lake. This algorithm combines the advantage of bands 8-16 in ocean color atmospheric
correction with the high spatial resolution of bands 1 and 2. In order to test the precision of this algorithm, we choose an
MODIS-Aqua image which are covering Taihu lake and are acquired in the time of 2004 Taihu autumn cruise. We use
our atmospheric correction algorithm to process the selected image and compare the retrieved remote sensing reflectance
(Rrs) with measured Rrs. The average relative of bands 1 and 2 are respectively 24.85% and 41.44%, demonstrating that
this algorithm has the potential of application in the atmospheric correction of inland waters.
This study employs SeaWiFS data over the waters off the southeastern China to evaluate a semi-analytical algorithm for
euphotic zone depth (Ze). This algorithm is based on water's inherent optical properties (IOPs), which can be
near-analytically calculated from spectral remote-sensing reflectance, where remote-sensing reflectance can be derived
from the normalized water-leaving radiance provided by SeaWiFS. In the Taiwan Strait, compared with in situ Ze (±3
hour within SeaWiFS collection), average error (ε) is 15.0 % and root mean square error (RMSE) is 0.074, with Ze in a
range of 14-34 m from field measurements. In the South China Sea, compared with in situ Ze (±48 hour within SeaWiFS
collection),ε is 5.1 % in summer and 22.6 in winter, while RMSE is 0.032 in summer and 0.129 in winter, with Ze in a
range of 10-82 m from field measurements. For comparison, we also evaluate the performance of the empirical Ze
algorithm that is based on chlorophyll concentration. It is found that the IOP-centered approach has higher accuracy
compared to the chlorophyll-a centered approach (e.g. in the South China Sea in winter, ε is 55.3 % and RMSE is 0.219).
The new algorithm is thus found not only worked well with waters of the Gulf of Mexico, Monterey Bay and the Arabian
Sea, but also worked well with waters of the China Sea.
The bidirectionality of water light field is one of the error sources of the ocean color models that now commonly used to retrieve pigments' concentrations. As the launching of new generation ocean color sensors, e.g. SeaWiFS, or the coming MODIS, the goal of accuracy of ocean color sensing is now much higher than that of CZCS. The original minor factors, such as the bidirectionality of water-leaving radiance, become more important for retrieving algorithms. This paper is to give some results based on 3D Monte Carlo simulations and proposes some aspects that should be considered for in situ optical data collection and related remote sensor's calibration and validation activities.
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