Abstract

We have presented an improved short-wave infrared (SWIR)-based iterative algorithm for the atmospheric correction (AC) of Moderate Resolution Imaging Spectroradiometer (MODIS) data over Lake Taihu, China. The algorithm was validated by means of matchup comparison between MODIS-retrieved and in situ remote sensing reflectances (R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rs</sub> ). Four examples of the matchup comparison were first carried out for the observation stations within a ±5-min time window of MODIS overpass and field measurements. It is shown in the examples that the retrieved R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rs</sub> spectra compare reasonably well with the in situ measurements not only over relatively clear waters (with R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rs</sub> (859) about 0.0014 sr <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> ) but also over turbid waters (with R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rs</sub> (859) about 0.013 sr <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> ). The matchup comparison was further carried out for a total of 54 observation stations within a ±2-h time window, indicating that the AC algorithm has good performance for producing water spectra from MODIS data over Lake Taihu. The development of an algal bloom event has been monitored using MODIS-measured R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rs</sub> (443) and R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rs</sub> (859), showing that MODIS data, combined with the AC algorithm, can be a useful tool for monitoring the water quality of Lake Taihu. The SWIR iterative algorithm, along with the chlorophyll-a concentration (Chl-a) retrieval model using red to near-infrared bands, has the potential of monitoring Chl-a quantitatively and providing useful information for decision makers to manage the water environment and to prepare for events as algal blooms.

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