{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T15:31:40Z","timestamp":1760369500666,"version":"build-2065373602"},"reference-count":33,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2019,1,28]],"date-time":"2019-01-28T00:00:00Z","timestamp":1548633600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Geo.X: Research Network for Geosciences in Berlin and Potsdam","award":["SO_087_GeoX"],"award-info":[{"award-number":["SO_087_GeoX"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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).<\/jats:p>","DOI":"10.3390\/rs11030257","type":"journal-article","created":{"date-parts":[[2019,1,29]],"date-time":"2019-01-29T03:40:55Z","timestamp":1548733255000},"page":"257","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A Global MODIS Water Vapor Database for the Operational Atmospheric Correction of Historic and Recent Landsat Imagery"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9292-3931","authenticated-orcid":false,"given":"David","family":"Frantz","sequence":"first","affiliation":[{"name":"Geomatics Lab, Geography Department, Humboldt-Universit\u00e4t zu Berlin, Unter den Linden 6, 10099 Berlin, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7325-6152","authenticated-orcid":false,"given":"Marion","family":"Stellmes","sequence":"additional","affiliation":[{"name":"Remote Sensing and Geoinformatics, Freie Universit\u00e4t Berlin, Malteserstr. 74-100, 12249 Berlin, Germany"}]},{"given":"Patrick","family":"Hostert","sequence":"additional","affiliation":[{"name":"Geomatics Lab, Geography Department, Humboldt-Universit\u00e4t zu Berlin, Unter den Linden 6, 10099 Berlin, Germany"},{"name":"Integrated Research Institute on Transformations of Human Environment Systems (IRI THESys), Humboldt-Universit\u00e4t zu Berlin, Unter den Linden 6, 10099 Berlin, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.rse.2016.02.060","article-title":"Perspectives on monitoring gradual change across the continuity of Landsat sensors using time-series data","volume":"185","author":"Vogelmann","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1038\/nature20584","article-title":"High-resolution mapping of global surface water and its long-term changes","volume":"540","author":"Pekel","year":"2016","journal-title":"Nature"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1016\/j.rse.2018.11.038","article-title":"Regional differences of lake evolution across China during 1960s\u20132015 and its natural and anthropogenic causes","volume":"221","author":"Zhang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.rse.2011.08.026","article-title":"The next Landsat satellite: The Landsat Data Continuity Mission","volume":"122","author":"Irons","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"12619","DOI":"10.3390\/rs61212619","article-title":"Radiometric Cross Calibration of Landsat 8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+)","volume":"6","author":"Mishra","year":"2014","journal-title":"Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1360","DOI":"10.1109\/TGRS.2013.2243738","article-title":"Absolute Radiometric Calibration of Landsat Using a Pseudo Invariant Calibration Site","volume":"51","author":"Helder","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1011a","DOI":"10.1126\/science.320.5879.1011a","article-title":"Free Access to Landsat Imagery","volume":"320","author":"Woodcock","year":"2008","journal-title":"Science"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Hill, M.J., and Hanan, N.P. (2010). Remote Sensing of Tree-Grass Systems: The Eastern Australian Woodlands. Ecosystem Function in Savannas: Measurement and Modeling at Landscape to Global Scales, CRC Press.","DOI":"10.1201\/b10275"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.rse.2011.08.024","article-title":"A Review of Large Area Monitoring of Land Cover Change using Landsat Data","volume":"122","author":"Hansen","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2897","DOI":"10.1016\/j.rse.2010.07.008","article-title":"Detecting Trends in Forest Disturbance and Recovery using Yearly Landsat Time Series: 1. LandTrendr\u2014Temporal Segmentation Algorithms","volume":"114","author":"Kennedy","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Dwyer, J., Roy, D., Sauer, B., Jenkerson, C., Zhang, H., and Lymburner, L. (2018). Analysis Ready Data: Enabling Analysis of the Landsat Archive. Remote Sens., 10.","DOI":"10.20944\/preprints201808.0029.v1"},{"key":"ref_12","unstructured":"Frantz, D. (2018, January 28). Available online: https:\/\/www.researchgate.net\/publication\/328094593_FORCE_v_20_-_Technical_User_Guide."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3587","DOI":"10.1364\/AO.18.003587","article-title":"Atmospheric Modeling for Space Measurements of Ground Reflectances, Including Bidirectional Properties","volume":"18","author":"Herman","year":"1979","journal-title":"Appl. Opt."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3928","DOI":"10.1109\/TGRS.2016.2530856","article-title":"An Operational Radiometric Landsat Preprocessing Framework for Large-Area Time Series Applications","volume":"54","author":"Frantz","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Doxani, G., Vermote, E., Roger, J.-C., Gascon, F., Adriaensen, S., Frantz, D., Hagolle, O., Hollstein, A., Kirches, G., and Li, F. (2018). Atmospheric Correction Inter-Comparison Exercise. Remote Sens., 10.","DOI":"10.3390\/rs10020352"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1016\/S0034-4257(98)00044-3","article-title":"Atmospheric Precorrected Differential Absorption Technique to Retrieve Columnar Water Vapor","volume":"65","author":"Borel","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Gao, B.-C., and Kaufman, Y.J. (2003). Water Vapor Retrievals using Moderate Resolution Imaging Spectroradiometer (MODIS) Near-Infrared Channels. J. Geophys. Res. Atmos., 108.","DOI":"10.1029\/2002JD003023"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2691","DOI":"10.1109\/TGRS.2004.840720","article-title":"Landsat Sensor Performance: History and Current Status","volume":"42","author":"Markham","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.1175\/2007JTECHA1053.1","article-title":"Cloud Detection with MODIS. Part II: Validation","volume":"25","author":"Ackerman","year":"2008","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_20","unstructured":"Frantz, D., and Stellmes, M. (2018). Water vapor database for atmospheric correction of Landsat imagery. PANGAEA."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1007\/s10852-005-9022-1","article-title":"Clustering Rules: A Comparison of Partitioning and Hierarchical Clustering Algorithms","volume":"5","author":"Reynolds","year":"2006","journal-title":"J. Math. Model. Algorithms"},{"key":"ref_22","unstructured":"Maechler, M., Rousseeuw, P., Struyf, A., Hubert, M., Hornik, K., Studer, M., Roudier, P., Gonzalez, J., and Kozlowski, K. (2018, January 28). Available online: https:\/\/cran.r-project.org\/web\/packages\/cluster\/cluster.pdf."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.rse.2016.04.008","article-title":"Preliminary Analysis of the Performance of the Landsat 8\/OLI Land Surface Reflectance Product","volume":"185","author":"Vermote","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1228","DOI":"10.1109\/36.701075","article-title":"The Moderate Resolution Imaging Spectroradiometer (MODIS): Land Remote Sensing for Global Change Research","volume":"36","author":"Justice","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0034-4257(98)00031-5","article-title":"AERONET\u2014A Federated Instrument Network and Data Archive for Aerosol Characterization","volume":"66","author":"Holben","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Vermote, E.F., and Kotchenova, S. (2008). Atmospheric correction for the monitoring of land surfaces. J. Geophys. Res. Atmos., 113.","DOI":"10.1029\/2007JD009662"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1016\/j.rse.2015.08.030","article-title":"Evaluation of the Landsat-5 TM and Landsat-7 ETM+ surface reflectance products","volume":"169","author":"Claverie","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_28","unstructured":"NASA (2018, October 05). MODIS\/Terra Data Outages, Available online: https:\/\/modaps.modaps.eosdis.nasa.gov\/services\/production\/outages_terra.html."},{"key":"ref_29","unstructured":"NASA (2018, October 05). MODIS\/Aqua Data Outages, Available online: https:\/\/modaps.modaps.eosdis.nasa.gov\/services\/production\/outages_aqua.html."},{"key":"ref_30","unstructured":"USGS (2018, October 05). Spectral Characteristics Viewer, Available online: https:\/\/landsat.usgs.gov\/spectral-characteristics-viewer."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.jqsrt.2017.06.038","article-title":"The HITRAN2016 molecular spectroscopic database","volume":"203","author":"Gordon","year":"2017","journal-title":"J. Quant. Spectrosc. Radiat. Transf."},{"key":"ref_32","unstructured":"Qu, J.J., Gao, W., Kafatos, M., Murphy, R.E., and Salomonson, V.V. (2006). Operational Atmospheric Correction of MODIS Visible to Middle Infrared Land Surface Data in the Case of an Infinite Lambertian Target. Earth Science Satellite Remote Sensing: Vol. 1: Science and Instruments, Springer."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1109\/TGRS.2006.872081","article-title":"On the Blending of the Landsat and MODIS Surface Reflectance: Predicting Daily Landsat Surface Reflectance","volume":"44","author":"Gao","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/3\/257\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:29:05Z","timestamp":1760185745000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/3\/257"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,28]]},"references-count":33,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2019,2]]}},"alternative-id":["rs11030257"],"URL":"https:\/\/doi.org\/10.3390\/rs11030257","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2019,1,28]]}}}