The Effect of Mineral Sediments on Satellite Chlorophyll-a Retrievals from Line-Height Algorithms Using Red and Near-Infrared Bands

Chuiqing Zeng,Caren Binding

doi:10.3390/rs11192306

Abstract

Red and near-infrared line-height algorithms such as the maximum chlorophyll index (MCI) are often considered optimal for remote sensing of chlorophyll-a (Chl-a) in turbid eutrophic waters, under the assumption of minimal influence from mineral sediments. This study investigated the impact of mineral turbidity on line-height algorithms using MCI as a primary example. Inherent optical properties from two turbid eutrophic lakes were used to simulate reflectance spectra. The simulated results: (1) confirmed a non-linear relationship between Chl-a and MCI; (2) suggested optimal use of the MCI at Chl-a < ~100 mg/m3 and saturation of the index at Chl-a ~300 mg/m3; (3) suggested significant variability in the MCI:Chl-a relationship due to mineral scattering, resulting in an RMSE in predicted Chl-a of ~23 mg/m3; and (4) revealed elevated Chl a retrievals and potential false positive algal bloom reports for sediment concentrations > 20 g/m3. A novel approach combining both MCI and its baseline slope, MCIslope reduced the RMSE to ~5 mg/m3. A quality flag based on MCIslope was proposed to mask erroneously high Chl-a retrievals and reduce the risk of false positive bloom reports in highly turbid waters. Observations suggest the approach may be valuable for all line-height-based Chl-a algorithms.

Highlights

Remote sensing is increasingly being used as a complementary approach to conventional ground-based water quality monitoring of inland waters [1,2,3], with applications supporting water resource management, protecting ecosystem services, and furthering our understanding of lake biogeochemical processes [4]
It is possible that the a*mineral suspended particulate matter (MSPM) adopted here may result in overestimations in our simulated reflectance, the effect on simulated maximum chlorophyll index (MCI) is anticipated to be small
This study investigated the potential impact of MSPM on chlorophyll-a concentrations as derived from red/near-infrared line-height algorithms, such as the MCI and cyanobacteria index (CI)—approaches widely used in the detection of algal blooms in turbid coastal and inland waters

Summary

Introduction

Remote sensing is increasingly being used as a complementary approach to conventional ground-based water quality monitoring of inland waters [1,2,3], with applications supporting water resource management, protecting ecosystem services, and furthering our understanding of lake biogeochemical processes [4]. Numerous algorithms have been developed for the estimation of chlorophyll-a (Chl-a) concentrations from aquatic colour remote sensing (see references [9,10] for reviews), allowing the production of synoptic mapped algal bloom conditions for water quality managers and other water resource stakeholders. The blue/green (B/G) reflectance ratio [10,11,12] is considered optimal for Case 1 or oligotrophic waters, whereas algorithms targeting spectral features in the red–near-infrared (R-NIR) are frequently applied to turbid eutrophic waters [2,13,14]. Band-ratio algorithms taking the form of two-band [15], three-band [13], and four-band [16] models have been applied to a range of turbid eutrophic waters

Methods

Results

Conclusion