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

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).

[1]  Yoram J. Kaufman,et al.  Water vapor retrievals using Moderate Resolution Imaging Spectroradiometer (MODIS) near‐infrared channels , 2003 .

[2]  Thomas R. Loveland,et al.  A review of large area monitoring of land cover change using Landsat data , 2012 .

[3]  Darrel L. Williams,et al.  Landsat sensor performance: history and current status , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Gang Li,et al.  The HITRAN 2008 molecular spectroscopic database , 2005 .

[5]  Martha C. Anderson,et al.  Free Access to Landsat Imagery , 2008, Science.

[6]  Ferran Gascon,et al.  Atmospheric Correction Inter-comparison Exercise (ACIX) , 2017 .

[7]  Eric Vermote,et al.  Atmospheric correction for the monitoring of land surfaces , 2008 .

[8]  Zhiqiang Yang,et al.  Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr — Temporal segmentation algorithms , 2010 .

[9]  Mathew R. Schwaller,et al.  On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Shilong Piao,et al.  Regional differences of lake evolution across China during 1960s–2015 and its natural and anthropogenic causes , 2019, Remote Sensing of Environment.

[11]  Daniel Schläpfer,et al.  Atmospheric Precorrected Differential Absorption Technique to Retrieve Columnar Water Vapor , 1998 .

[12]  Steven A. Ackerman,et al.  Cloud Detection with MODIS. Part II: Validation , 2008 .

[13]  Peter Scarth,et al.  Remote sensing of tree-grass systems: The Eastern Australian woodlands , 2010 .

[14]  David P. Roy,et al.  Analysis Ready Data: Enabling Analysis of the Landsat Archive , 2018, Remote. Sens..

[15]  M. Claverie,et al.  Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. , 2016, Remote sensing of environment.

[16]  Beatriz de la Iglesia,et al.  Clustering Rules: A Comparison of Partitioning and Hierarchical Clustering Algorithms , 2006, J. Math. Model. Algorithms.

[17]  M. Claverie,et al.  Evaluation of the Landsat-5 TM and Landsat-7 ETM+ surface reflectance products , 2015 .

[18]  Julia A. Barsi,et al.  The next Landsat satellite: The Landsat Data Continuity Mission , 2012 .

[19]  J. Pekel,et al.  High-resolution mapping of global surface water and its long-term changes , 2016, Nature.

[20]  Zhe Zhu,et al.  Perspectives on monitoring gradual change across the continuity of Landsat sensors using time-series data , 2016 .

[21]  Joachim Hill,et al.  An Operational Radiometric Landsat Preprocessing Framework for Large-Area Time Series Applications , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[22]  Hurtado Abril,et al.  Generación de un índice espectro-temporal para la identificación de zonas afectadas por deforestación usando imágenes Landsat. , 2020 .

[23]  Xiaoxiong Xiong,et al.  Absolute Radiometric Calibration of Landsat Using a Pseudo Invariant Calibration Site , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[24]  Brian L. Markham,et al.  Radiometric Cross Calibration of Landsat 8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) , 2014, Remote. Sens..

[25]  E. R. Polovtseva,et al.  The HITRAN2012 molecular spectroscopic database , 2013 .

[26]  E. Vermote,et al.  Operational Atmospheric Correction of MODIS Visible to Middle Infrared Land Surface Data in the Case of an Infinite Lambertian Target , 2006 .

[27]  David Frantz,et al.  Water vapor database for atmospheric correction of Landsat imagery , 2018 .

[28]  A. Smirnov,et al.  AERONET-a federated instrument network and data archive for aerosol Characterization , 1998 .

[29]  Alan H. Strahler,et al.  The Moderate Resolution Imaging Spectroradiometer (MODIS): land remote sensing for global change research , 1998, IEEE Trans. Geosci. Remote. Sens..

[30]  P. Deschamps,et al.  Atmospheric modeling for space measurements of ground reflectances, including bidirectional properties. , 1979, Applied optics.