An EOF-Based Algorithm to Estimate Chlorophyll a Concentrations in Taihu Lake from MODIS Land-Band Measurements: Implications for Near Real-Time Applications and Forecasting Models

Lin Qi,Ronghua Ma,Hongtao Duan,Brian Barnes,Chuanmin Hu

doi:10.3390/rs61110694

Abstract

For near real-time water applications, the Moderate Resolution Imaging Spectroradiometers (MODIS) on Terra and Aqua are currently the only satellite instruments that can provide well-calibrated top-of-atmosphere (TOA) radiance data over the global aquatic environments. However, TOA radiance data in the MODIS ocean bands over turbid atmosphere in east China often saturate, leaving only four land bands to use. In this study, an approach based on Empirical Orthogonal Function (EOF) analysis has been developed and validated to estimate chlorophyll a concentrations (Chla, μg/L) in surface waters of Taihu Lake, the third largest freshwater lake in China. The EOF approach analyzed the spectral variance of normalized Rayleigh-corrected reflectance (Rrc) data at 469, 555, 645, and 859 nm, and subsequently related that variance to Chla using 28 concurrent MODIS and field measurements. This empirical algorithm was then validated using another 30 independent concurrent MODIS and field measurements. Image analysis and radiative transfer simulations indicated that the algorithm appeared to be tolerant to aerosol perturbations, with unbiased RMS uncertainties of <80% for Chla ranging between 3 and 100 μg/L. Application of the algorithm to a total of 853 MODIS images between 2000 and 2013 under cloud-free conditions revealed spatial distribution patterns and seasonal changes that are consistent to previous findings based on floating algae mats. The current study can provide additional quantitative estimates of Chla that can be assimilated in an existing forecast model, which showed improved performance over the use of a previous Chla algorithm. However, the empirical nature, relatively large uncertainties, and limited number of spectral bands all point to the need of further improvement in data availability and accuracy with future satellite sensors.

Highlights

A number of studies have shown increasing trends of phytoplankton blooms in coastal and inland waters around the world (e.g., [1]), often caused by excessive nutrients and other pollutants derived from agriculture, urbanization, and industries
The performance of the algorithm may be acceptable, especially when considering that only four land bands were used in the Empirical Orthogonal Function (EOF)-based algorithm and only a partial atmospheric correction was performed
The model initiation did not use any Moderate Resolution Imaging Spectroradiometers (MODIS) data, but used water quality data obtained from fixed monitoring stations during the previous month (July 2010) [33]

Summary

Introduction

A number of studies have shown increasing trends of phytoplankton blooms in coastal and inland waters around the world (e.g., [1]), often caused by excessive nutrients and other pollutants derived from agriculture, urbanization, and industries. On the west Florida shelf, increased nutrient inputs from coastal runoff were thought to be at least one reason leading to the increased occurrences of blooms of the toxic dinoflagellate Karenia brevis between the 1950s and the. In coastal waters of the Bohai Sea, Yellow Sea, and East China Sea, the number and size of toxic algae blooms have increased since 1998 [5], likely due to coastal eutrophication. The increased frequency and severity of macroalgae blooms of Ulva prolifera in these waters were linked to the increased coastal aquaculture [6,7].

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Conclusion