Abstract

Numerous algorithms have been proposed to retrieve chlorophyll-a concentrations in Case 2 waters; however, the retrieval accuracy is far from satisfactory. In this research, seven algorithms are assessed with different band combinations of multispectral and hyperspectral bands using linear (LN), quadratic polynomial (QP) and power (PW) regression approaches, resulting in altogether 43 algorithmic combinations. These algorithms are evaluated by using simulated and measured datasets to understand the strengths and limitations of these algorithms. Two simulated datasets comprising 500,000 reflectance spectra each, both based on wide ranges of inherent optical properties (IOPs), are generated for the calibration and validation stages. Results reveal that the regression approach (i.e., LN, QP, and PW) has more influence on the simulated dataset than on the measured one. The algorithms that incorporated linear regression provide the highest retrieval accuracy for the simulated dataset. Results from simulated datasets reveal that the 3-band (3b) algorithm that incorporate 665-nm and 680-nm bands and band tuning selection approach outperformed other algorithms with root mean square error (RMSE) of 15.87 mg·m−3, 16.25 mg·m−3, and 19.05 mg·m−3, respectively. The spatial distribution of the best performing algorithms, for various combinations of chlorophyll-a (Chla) and non-algal particles (NAP) concentrations, show that the 3b_tuning_QP and 3b_680_QP outperform other algorithms in terms of minimum RMSE frequency of 33.19% and 60.52%, respectively. However, the two algorithms failed to accurately retrieve Chla for many combinations of Chla and NAP, particularly for low Chla and NAP concentrations. In addition, the spatial distribution emphasizes that no single algorithm can provide outstanding accuracy for Chla retrieval and that multi-algorithms should be included to reduce the error. Comparing the results of the measured and simulated datasets reveal that the algorithms that incorporate the 665-nm band outperform other algorithms for measured dataset (RMSE = 36.84 mg·m−3), while algorithms that incorporate the band tuning approach provide the highest retrieval accuracy for the simulated dataset (RMSE = 25.05 mg·m−3).

Highlights

  • Monitoring the water quality of water bodies is an essential tool for determining and controlling pollution-prone areas in order to conserve our planet

  • Inland lakes and coastal areas should be continuously and accurately monitored. The former contains approximately 90% of the global freshwater that supports human activities [1]. The latter provides the majority of the dissolved organic carbon (DOC), which is a vital link in the global carbon cycle [2]

  • Remote sensing reflectance spectra were generated based on a bio-optical model

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Summary

Introduction

Monitoring the water quality of water bodies is an essential tool for determining and controlling pollution-prone areas in order to conserve our planet. Inland lakes and coastal areas should be continuously and accurately monitored. The former contains approximately 90% of the global freshwater that supports human activities [1]. The latter provides the majority of the dissolved organic carbon (DOC), which is a vital link in the global carbon cycle [2]. Concentrations are used as indicators to assess water quality These constituents include chlorophyll-a (Chla), total suspended solids (TSS), non-algal particles (NAP), colored dissolved organic matter (CDOM) and nutrients [3,4]. On the other hand, provide the spatial relationship for whole water bodies with repetitive coverage and historical records [6]

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