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Exploring Patterns and Effects of Aerosol Quantity Flag Anomalies in MODIS Surface Reflectance Products in the Tropics

K. Grogan,R. Fensholt

Published 2013 in Remote Sensing

ABSTRACT

The Moderate Resolution Imaging Spectroradiometer (MODIS) has been supplying a continuous data stream since 2000, lending to detailed time series analysis of the global terrestrial environment. This paper explores a quality anomaly present in the tropics relating to the aerosol quantity flag in the daily MODIS surface reflectance products (MOD09 series) and the 16-day Vegetation Index (VI) composite products (MOD13 series) derived from the daily observations. While the anomaly is to some extent a known issue reported by the MODIS Land Quality Assessment group, very little is known about the scale of the issue, the nature and patterns of its occurrence, and potential consequences for data analysis, which explains why it is not adequately recognized throughout the literature. Two tropical regions were used to explore the anomaly and demonstrate the effects it has on the quality of selected MODIS products—one in the South American Amazon, the other in mainland Southeast Asia. The origins of the anomaly are described qualitatively in detail, and quantitative estimates of affected evergreen forest area in the MOD13A1 time series are made using blue band thresholding. The anomaly originates in the 1 km State dataset, whereby, under certain conditions, high aerosol quantity pixels are given a low aerosol quantity label, resulting in poor quality pixels with "good" quality labels. MODIS users are advised to investigate whether this anomaly has significant implications for their respective analysis and to consider the effects it may have on past studies.

PUBLICATION RECORD

  • Publication year

    2013

  • Venue

    Remote Sensing

  • Publication date

    2013-07-19

  • Fields of study

    Computer Science, Environmental Science

  • Identifiers
    DOI 10.3390/rs5073495
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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