Skip all navigation and jump to content Jump to site navigation
NASA Logo - Goddard Space Flight Center

+ NASA Homepage

    
Goddard Space Flight Center
About MODIS News Data Tools /images2 Science Team Science Team Science Team

   + Home
ABOUT MODIS
MODIS Publications Link
MODIS Presentations Link
MODIS Biographies Link
MODIS Science Team Meetings Link
 

 

 

Le, CF; Hu, CM; Cannizzaro, J; English, D; Muller-Karger, F; Lee, Z (2013). Evaluation of chlorophyll-a remote sensing algorithms for an optically complex estuary. REMOTE SENSING OF ENVIRONMENT, 129, 75-89.

Abstract
Estimation of chlorophyll-a concentrations (Chla in mg m(-3)) in optically complex waters using remote sensing is difficult because traditional empirical (e.g., OCx blue/green band ratios) or semi-analytical algorithms (e.g., GSM or QAA) rely on blue-green wavelengths where water constituents other than phytoplankton often dominate the optical properties and satellite-derived reflectance in these wavelengths often contains substantial uncertainties. Some recent attempts have been made to derive Chla based on red and near-infrared wavelengths to avoid such problems. Using in situ data collected from 9 cruises between 1998 and 2010 in Tampa Bay, a large estuary (similar to 1000 km(2)) located in Florida (U.S.A.), we first tested three Chla algorithms, where each algorithm derived Chla as a function of independent variable x = Rrs(lambda(2))/Rrs(lambda(1)), x = [Rrs(lambda(1))(-1) - Rrs(lambda(2))(-1)] * Rrs(lambda(3)), and x = [Rrs(lambda(1))(-1) - Rrs(lambda(2))(-1)]/[Rrs(lambda(4))(-1) - Rrs(lambda(3))(-1)], respectively. Here Rrs(lambda) is the remote sensing reflectance, lambda is the wavelength from 660 to 760 nm, and the positions of the these wavelengths were determined using an objective method while the functional forms of Chla=f(x) were determined through regression between Chla and x. A synthetic chlorophyll index (SCI) using 4 green-red MERIS wavelengths was also tested. Using field measured Rrs(lambda) data, all these algorithms showed the potentials to retrieve Chla between similar to 2 and similar to 80 mg m(-3), with 2-band algorithms showing the best performance. However, when satellite-derived Rrs(lambda) data were used, none of the four algorithms was applicable to MODIS in this estuary, and only a 2-band algorithm in the form of Rrs(709)/Rrs(665) was applicable to MERIS, with other algorithms significantly degraded. The coefficients of the 2-band algorithm for MERIS were determined through regression between in situ Chla and MERIS-derived Rrs(709)/Rrs(665), with the final form of Chla = 10.55x(1.51). The algorithm yielded mean relative errors of 35.3% for Chla between 1.0 and 30.0 mg m(-3) when an in situ dataset from the Environmental Protection Commission of Hillsborough County (EPCHC) was used to tune the algorithm coefficients, which was further validated using another independent dataset. Spatial Chla patterns derived from MERIS for both low and high concentrations also showed reasonable agreement with in situ measurements. In contrast, 3-band and 4-band algorithms yielded significant patchiness in spatial patterns that differed from in situ measurements. Tests in Chesapeake Bay (located on the east coast of the U.S.A.) showed reasonable spatial patterns of Chla using the Rrs(709)/Rrs(665) approach, with a mean relative error of 34.7% for Chla ranging between 1.6 and 21.5 mg m(-3), suggesting that the approach may be generally applicable to other turbid estuaries once algorithm coefficients are adjusted using local data. (C) 2012 Elsevier Inc. All rights reserved.

DOI:

ISSN:
0034-4257

FirstGov logo Privacy Policy and Important Notices NASA logo

Curator: Brandon Maccherone
NASA Official: Shannell Frazier

NASA Home Page Goddard Space Flight Center Home Page