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Open AccessArticle

A Global MODIS Water Vapor Database for the Operational Atmospheric Correction of Historic and Recent Landsat Imagery

1
Geomatics Lab, Geography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany
2
Remote Sensing and Geoinformatics, Freie Universität Berlin, Malteserstr. 74-100, 12249 Berlin, Germany
3
Integrated Research Institute on Transformations of Human Environment Systems (IRI THESys), Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(3), 257; https://doi.org/10.3390/rs11030257
Received: 7 January 2019 / Revised: 23 January 2019 / Accepted: 26 January 2019 / Published: 28 January 2019
(This article belongs to the Section Remote Sensing Image Processing)
Analysis Ready Data (ARD) have undergone the most relevant pre-processing steps to satisfy most user demands. The freely available software FORCE (Framework for Operational Radiometric Correction for Environmental monitoring) is capable of generating Landsat ARD. An essential step of generating ARD is atmospheric correction, which requires water vapor data. FORCE relies on a water vapor database obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). However, two major drawbacks arise from this strategy: (1) The database has to be compiled for each study area prior to generating ARD; and (2) MODIS and Landsat commissioning dates are not well aligned. We have therefore compiled an application-ready global water vapor database to significantly increase the operational readiness of ARD production. The free dataset comprises daily water vapor data for February 2000 to July 2018 as well as a monthly climatology that is used if no daily value is available. We systematically assessed the impact of using this climatology on surface reflectance outputs. A global random sample of Landsat 5/7/8 imagery was processed twice (i) using daily water vapor (reference) and (ii) using the climatology (estimate), followed by computing accuracy, precision, and uncertainty (APU) metrics. All APU measures were well below specification, thus the fallback usage of the climatology is generally a sound strategy. Still, the tests revealed that some considerations need to be taken into account to help quantify which sensor, band, climate, and season are most or least affected by using a fallback climatology. The highest uncertainty and bias is found for Landsat 5, with progressive improvements towards newer sensors. The bias increases from dry to humid climates, whereas uncertainty increases from dry and tropic to temperate climates. Uncertainty is smallest during seasons with low variability, and is highest when atmospheric conditions progress from a dry to a wet season (and vice versa). View Full-Text
Keywords: atmospheric correction; analysis ready data; global; Landsat; water vapor atmospheric correction; analysis ready data; global; Landsat; water vapor
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MDPI and ACS Style

Frantz, D.; Stellmes, M.; Hostert, P. A Global MODIS Water Vapor Database for the Operational Atmospheric Correction of Historic and Recent Landsat Imagery. Remote Sens. 2019, 11, 257.

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  • Supplementary File 1:

    ZIP-Document (ZIP, 3581 KB)

  • Externally hosted supplementary file 1
    Doi: https://doi.org/10.1594/PANGAEA.893109
    Link: https://doi.pangaea.de/10.1594/PANGAEA.893109
    Description: Video S1: Long-term mean water vapor per month. Video S2: Long-term variability of water vapor per month. Video S3: Long-term trend of monthly averages.
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