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 data set of coastal waters of eastern Arabian Sea. The algorithm was derived using CHL index of the form, x = (Rrs(λ1)−1−Rrs(λ2)−1) × Rrs(λ3). The first wavelength (λ1) represents the secondary peak of CHL, while the second wavelength (λ2) and third wavelength (λ3) 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 = (Rrs(λ1)−1−Rrs(λ2)−1) × Rrs(λ2). This was further regressed with CHL data initially used for three wavelength index. The final form of algorithm, Goa University Case II (GUC2), cMCHL=113.112x3−58.408x2+8.669x − 0.0384, was validated with in situ CHL ranging between 0.11 and 25.56 μg/L, resulted in a strong correlation r2 = 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 and Oceanography Research data set 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.