Impact of Monsoon-Transported Anthropogenic Aerosols and Sun-Glint on the Satellite-Derived Spectral Remote Sensing Reflectance in the Indian Ocean

Rongjie Liu,Haocheng Yu,Tingwei Cui,Jie Zhang

doi:10.3390/rs13020184

Abstract

Spectral remote sensing reflectance (Rrs(λ), sr−1) is one of the most important products of ocean color satellite missions, where accuracy is essential for retrieval of in-water, bio-optical, and biogeochemical properties. For the Indian Ocean (IO), where Rrs(λ) accuracy has not been well documented, the quality of Rrs(λ) products from Moderate Resolution Imaging Spectroradiometer onboard both Terra (MODIS-Terra) and Aqua (MODIS-Aqua), and Visible Infrared Imaging Radiometer Suite onboard the Suomi National Polar-Orbiting Partnership spacecraft (VIIRS-NPP), is evaluated and inter-compared based on a quality assurance (QA) system, which can objectively grade each individual Rrs(λ) spectrum, with 1 for a perfect spectrum and 0 for an unusable spectrum. Taking the whole year of 2016 as an example, spatiotemporal pattern of Rrs(λ) quality in the Indian Ocean is characterized for the first time, and the underlying factors are elucidated. Specifically, QA analysis of the monthly Rrs(λ) over the IO indicates good quality with the average scores of 0.93 ± 0.02, 0.92 ± 0.02 and 0.92 ± 0.02 for VIIRS-NPP, MODIS-Aqua, and MODIS-Terra, respectively. Low-quality (~0.7) data are mainly found in the Bengal Bay (BB) from January to March, which can be attributed to the imperfect atmospheric correction due to anthropogenic absorptive aerosols transported by the northeasterly winter monsoon. Moreover, low-quality (~0.74) data are also found in the clear oligotrophic gyre zone (OZ) of the south IO in the second half of the year, possibly due to residual sun-glint contributions. These findings highlight the effects of monsoon-transported anthropogenic aerosols, and imperfect sun-glint removal on the Rrs(λ) quality. Further studies are advocated to improve the sun-glint correction in the oligotrophic gyre zone and aerosol correction in the complex ocean–atmosphere environment.

Highlights

Satellite ocean color remote sensing plays an unique and important role in the aquatic science researches of ocean biogeochemical cycles [1,2,3,4], water quality variability [5,6,7], and climate change [8,9,10], through providing long term, high-quality, and consistent data records in a large scale
The lower scores are mainly found in the Bengal Bay (BB)
The satellite-observed radiance is a combination of aerosol radiance contribution and ocean radiance contribution

Summary

Introduction

Satellite ocean color remote sensing plays an unique and important role in the aquatic science researches of ocean biogeochemical cycles [1,2,3,4], water quality variability [5,6,7], and climate change [8,9,10], through providing long term, high-quality, and consistent data records in a large scale (globally or regionally). As the most important ocean optical parameter derived from satellite ocean color observations, spectral remote sensing reflectance (Rrs (λ), sr−1 ) is key for retrieval of the inwater bio-optical and biogeochemical properties [11,12]. Rrs (λ) is of particular importance in terms of the spatial, temporal, and spectral distributions [13,14,15,16], which are usually derived through comparison with concurrent in situ. Bailey and Werdell [13] found that SeaWiFS data approached the target uncertainties of ±5% for clear water as defined prior to launch

Objectives

Methods

Results

Conclusion