Water Quality Properties Derived from VIIRS Measurements in the Great Lakes

Seunghyun Son,Menghua Wang

doi:10.3390/rs12101605

Abstract

Refined empirical algorithms for chlorophyll-a (Chl-a) concentration, using the maximum ratio of normalized water-leaving radiance nLw(λ) at the blue and green bands, and Secchi depth (SD) from nLw(λ) at 551 nm, nLw(551), are proposed for the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite in the Great Lakes. We demonstrated that water quality properties and phytoplankton production can be successfully monitored and assessed using the new regional Chl-a and SD algorithms, with reasonably accurate estimates of Chl-a and SD from the VIIRS-SNPP ocean color data in the Great Lakes. VIIRS-derived Chl-a and SD products using the proposed algorithms provide the temporal and spatial variabilities in the Great Lakes. Overall, Chl-a concentrations are generally low in lakes Michigan and Huron, while Chl-a data are highest in Lake Erie. The seasonal pattern shows that overall low Chl-a concentrations appear in winter and high values in June to September in the lakes. The distribution of SD in the Great Lakes is spatially and temporally different from that of Chl-a. The SD data are generally lower in summer and higher in winter in most of the Great Lakes. However, the highest SD in Lake Erie appears in summer, and lower values in winter. Significantly high values in Chl-a, and lower values in SD, in the nearshore regions, such as Thunder Bay, Saginaw Bay, and Whitefish Bay, can be related to the very shallow bathymetry and freshwater inputs from the land. The time series of VIIRS-derived Chl-a and SD data provide strong interannual variability in most of the Great Lakes.

Highlights

ThhigehimCahgl-eas csohnocwenthtraatttihoenshiignhtehset Cnehal-rashcoornecewnatrtaetrisoanrseaspepeenarininspLraiknegEtroiefainll amllomnothnst.hsS,euansolinkaelthdeisotrtihbeurtliaokneso.f Chl-a in Lake Erie is different from the other lakes, showing that relatively lower Chl-a appear in June and December, and significantly high Chl-a appears in August and September
An empirical Chl-a algorithm with the maximum band ratio (MBR) for Visible Infrared Imaging Radiometer Suite (VIIRS)-Suomi National Polar-orbiting Partnership (SNPP) ocean color applications is refined for the Great Lakes
A regional Secchi depth (SD) algorithm is derived using a strong relationship between VIIRS-SNPP-derived nLw(551) and in-situ-measured SD data in the Great Lakes

Summary

Introduction

There are increasing interests in satellite algorithms to estimate water quality and biogeochemical parameters in the Great Lakes [1,2,3,4,5,6,7,8,9] for satellite ocean color sensors, e.g., the Coastal Zone Color Scanner (CZCS) [10,11], the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) [12], the Moderate Resolution Imaging Spectroradiometer (MODIS) [13] on the Terra and Aqua satellites, and the Visible Infrared Imaging Radiometer Suite (VIIRS) [14] on the Suomi National Polar-orbiting Partnership (SNPP) and National Oceanic and Atmospheric Administration (NOAA)-20. From the satellite ocean color sensor at blue to green wavelengths, is generally used to derive chlorophyll-a (Chl-a) data in the global ocean waters, as the standard in NASA retrieval algorithms [15] (e.g., OC2 and OC4 for SeaWiFS, OC3M for MODIS, and OC3V for VIIRS). Lesht et al [4] have developed tuned OCx-based Chl-a algorithms for the entire Great Lakes, using the SeaWiFS and MODIS ocean color data with in-situ Chl-a measurements, and shown improved results. Binding et al [2] developed an empirical SD algorithm for the entire Great Lakes for the various satellite ocean color sensors (e.g., CZCS, SeaWiFS, and MODIS-Aqua). Regional Chl-a and SD algorithms for the VIIRS-SNPP ocean color data in the entire Great Lakes are developed, using in-situ Chl-a and SD data and the VIIRS-SNPP-derived ocean color products.

In-Situ Measurements

SD Algorithm for VIIRS-SNPP in the Great Lakes

Results

Conclusions