Remote sensing of chlorophyll-a in case II waters: A novel approach with improved accuracy over widely implemented turbid water indices

Abstract

A new semianalytical algorithm was formulated to retrieve chlorophyll-a (CHL) in optically complex waters using in situ dataset of coastal waters of eastern Arabian Sea. The algorithm was derived using CHL index of the form, x=(R sub(rs)(lambda sub(1)) sup(-1)-R sub(rs)(lambda sub(2)) sup(-1)) x R sub(rs)(lambda sub(3)). The first wavelength (lambda sub(1)) represents the secondary peak of CHL, while the second wavelength (lambda sub(2)) and third wavelength (lambda3) were delineated using a radiative transfer model and partial derivative analysis of hyperspectral remote sensing reflectance, respectively. Further iteration of three wavelengths between 600 and 700 nm resulted in a two-wavelength index, x = (R sub(rs)(lambda sub(1)) sup(-1)-R sub(rs)(lambda sub(2)) sup(-1)) x R sub(rs)(lambda sub(2)). This was further regressed with CHL data initially used for three wavelength index. The final form of algorithm, Goa University Case II (GUC2), c sub(MCHL)=113.112x sup(3)-58.408x sup(2)+?8.669x-0.0384, was validated with in situ CHL ranging between 0.11 and 25.56 Mu g L sup(-1), resulted in a strong correlation r sup(2) = 0.99, RMSE = 0.30, and bias = 0.03. A comparison with NIR-Red two-band, three-band, four-band, synthetic chlorophyll index, and normalized difference chlorophyll index pointed to the nonsuitability of turbid water indices in different water types of the study area. For the first time, a CHL algorithm has been tested successfully in water types outside the region of its formulation. A pixel-to-pixel validation of GUC2 derived MERIS CHL with NASA bio-Optical Marine Algorithm Dataset and Satellite Coastal & Oceanography Research dataset resulted in correlation, bias, and RMSE of 0.90, -0.0013, and 1.2499, respectively. Furthermore, GUC2 was successfully tested in Chesapeake Bay for accurate retrieval of CHL from stations with varying turbidity levels.