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A Simple Atmospheric Correction Algorithm for MODIS in Shallow Turbid Waters: A Case Study in Taihu Lake

Figure 1

Figure 1
Field measurements. (a) Calibration dataset collected from turbid waters of Changjiang River Estuary, Yellow River Estuary, and Jiaozhou Bay, China; (b) valibration dataset collected from turbid waters of Taihu Lake, in China.

Figure 2

Figure 2
Spectral response function of MODIS sensor.

Figure 3

Figure 3
Spectral characterizes in a spectral band centered of 531, 551, 667, and 678 nm.(a) Spectral characteristics in a spectral band centered at 531 and 551 nm; (b) spectral characterizes in a spectral band centered at 667 and 678 nm.

Figure 4

Figure 4
Empirical spectra relationship between water-leaving reflectance. (a) Formula$\rho_{\rm w}(531)$ vs. Formula$\rho_{\rm w}(551)$ and Formula$\rho_{\rm w}(667)$ vs. Formula$\rho_{\rm w}(678)$ regressed by average measurements at each station; (b) Formula$\rho_{\rm w}(531)$ vs. Formula$\rho_{\rm w}(551)$ and Formula$\rho_{\rm w}(667)$ vs. Formula$\rho_{\rm w}(678)$ regressed by repeated various measurements at each station.

Figure 5

Figure 5
Aerosol contribution reflectance considering multiple scattering.

Figure 6

Figure 6
MODIS-derived water-leaving reflectance plotted against field measurements.

Figure 7

Figure 7
Uncertainty of SACA and SWIR algorithms, respectively, in predicting water-leaving reflectance from MODIS data.

Figure 8

Figure 8
MODIS-derived Formula$\rho_{\rm w}(412)$ products using SACA algorithm in Taihu Lake.