Publications

Menon, HB; Adhikari, A (2018). Remote Sensing of Chlorophyll-A in Case II Waters: A Novel Approach With Improved Accuracy Over Widely Implemented Turbid Water Indices. JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS, 123(11), 8138-8158.

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 = (R-rs((1))(-1)-R-rs((2))(-1)) x R-rs((3)). The first wavelength ((1)) represents the secondary peak of CHL, while the second wavelength ((2)) and third wavelength ((3)) 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-rs((1))(-1)-R-rs((2))(-1)) x R-rs((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(MCHL)=113.112x(3)-58.408x(2)+8.669x - 0.0384, was validated with in situ CHL ranging between 0.11 and 25.56 g/L, resulted in a strong correlation r(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 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. Plain Language Summary Chlorophyll-a (CHL) in coastal and inland water bodies is an index of productivity and has huge implications for identifying fishing zones, eutrophication, hypoxia, and climate change studies. Although, spatiotemporal coverage of Earth via optical remote sensors have long been available, a method to accurately generate CHL in a wide variety of coastal and inland water types (areas prone to human interaction) for a synoptic analysis was desiderated. To address this concern, a new method (an optical algorithm) is developed that when applied to remotely sensed data produced accurate results when compared with widely used indices. The accurate monitoring of CHL is a boost to local fisherman in identifying region of high fishery resources, which enable better catch with less economy and efforts. Thus, this paper depicts the application of space technology to the livelihood of common man.

DOI:
10.1029/2018JC014052

ISSN:
2169-9275