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A simple correction method for the MODIS surface reflectance product over typical inland waters in China

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The Moderate Resolution Imaging Spectroradiometer (MODIS) has the advantage of providing continuous, global, near-daily spatial measurements, and has greatly aided in understanding physical, optical, and biological processes in the global ocean biosphere. However, little research has been implemented for the remote-sensing monitoring of global inland waters. One important factor is that there is no operational atmospheric correction method designed for global inland waters. The MODIS surface reflectance product (MOD09) provides surface reflectance data for land at the global scale, but it does not offer accurate atmospheric correction over inland waters because of the constraints of its primary correction algorithm. The purpose of this article is to provide a simple and operational correction method for the MOD09 product to retrieve the water-leaving reflectance for large inland waters larger than 25 km2. The correction method is based on an analysis of additive noises in MOD09 data over inland waters and on the adoption of two assumptions. Field-measured data collected in three typical inland waters in China were used to assess the performance of the correction method to ensure its applicability for waters in different conditions. The results show acceptable agreement with field data over the three inland waterbodies, with a mean relative error of 17.1% in visible bands. Our study demonstrates that the MOD09 correction method is moderately accurate when compared with the optimal method for specific waterbodies, but it has the potential for use in operational data-processing systems to derive water-leaving reflectance data from MOD09 data over inland waters in a variety of conditions and large regions.
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Document Type: Research Article

Affiliations: Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences, Beijing, China

Publication date: December 16, 2016

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