Vermote E. et al. (Co-PI), “Maintenance and refinement of the MODIS Aqua/Terra surface reflectance product suite”, NASA, 2014-2018
The primary objective of this proposal is the maintenance and refinement of a well calibrated, high quality, multi-satellite, daily, multispectral land surface reflectance dataset from 2000 to present, providing a critical data set of global land observations needed to advance Earth System Science. Such a high quality global multi-year dataset is fundamental for interdisciplinary studies of the earth system and can be used to study a wide range of land and climate related questions, quantify land use and land cover change, characterize processes and examine functions within the Earth’s land surface over time. The objectives of this research proposal are directly relevant to those specified in ROSES 2013 A.46, Terra and Aqua – Algorithms – Existing Data Products.
This maintenance proposal covers the Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product suite, M[O,Y]D09 (level 2 surface reflectance), M[Y,O]D09GA and M[Y,O]D09GQ (gridded daily product at 500m and 250m), M[Y,O]D09Q1 and M[Y,O]D09A1 (gridded 8 day composite product at 500m and 250m), M[Y,O]D09CMG (the daily global product in the climate modeling grid at 0.05deg).
The focus of this proposed work will be:
a) The close monitoring of the calibration and instrument characteristics (polarization, spectral response, band to band registration, noisy or dead detectors, instrument point spread function) and their impact on the surface reflectance product. This will provide critical input to downstream products (e.g. how the changes in the instrument impact the reflectance and cascade into Vegetation Indices, and Albedo).
b) The continuous and systematic close-to-real-time evaluation of the surface reflectance product over the AERONET sites through the use of rigorous performance metrics (Accuracy, Precision and Uncertainties)
c) The in-house quality assessment of the global performance of the product as well as the quality flags (cloud mask, exception conditions) mainly by using the Climate Modeling Product and inter-comparison with others dataset of opportunity (Terra/Aqua consistency, CALIPSO data for clouds)
d) Algorithm minor refinements as needed (polarization correction, aerosol models improvements based on AERONET).
We request to join the Land Measurements Team, and play the role of liaison with the calibration team and will focus on calibration and validation of land measurements, including cross-calibration and inter-comparison of systematic measurements and data products from the different sensors used to produce a time series. These activities will also directly benefit from our involvement in the Visible Infrared Imager Radiometer Suite (VIIRS) and Landsat-8 science teams, providing a common approach to atmospheric correction.
Vermote E. et al. (Co-PI),➢"A Terrestrial Surface Climate Data Record for Global Change Studies :AVHRR Surface Reflectance FCDR and the Normalized Difference Vegetation Index TCDR”, NOAA, 10/1/2009-9/30/2015
In this phase of the project we will continue to improve and validation the AVHRR product and transition the CDR’s to NOAA NCDC: (1) AVHRR Surface Reflectance FCDR and the Normalized Difference Vegetation Index TCDR, in aggregate, for the period from 1982 – present), (2) LAI and fPAR ancillary data, in aggregate, for the period from 1982 – present. Develop near real time capability.
Vermote E. et al. (Co-PI), “Prototype Atmospheric Correction Algorithm for Sentinel-2 Data Stream”, NASA, 10/1/2013-9/30/2016
This project is aimed at producing a merged surface reflectance product from the Landsat and Sentinel-2 missions to ultimately achieve high temporal coverage (2 to 4 days repeat cycle, depending on the latitude) at high spatial resolution (20-60m). The goal is to achieve a seamless/consistent stream of surface reflectance data from the different sensors.
After having established the basic requirements for such a product and the necessary processing steps: mainly calibration, atmospheric corrections, BRDF effect corrections, spectral band pass adjustments and gridding. We will demonstrate the performance of those different corrections by using MODIS and VIIRS (Climate Modeling Grid at 0.05deg) data globally as well as Formosat-2 (8m spatial resolution) data (one crop site in South of France where 105 scenes were acquired during 2006-2010).
We will also work on the simulation of the merged Landsat and Sentinel-2 mission by using Landsat-7, Landsat-8 and the SPOT-4 Take 5 dataset. SPOT-4 Take 5 dataset is a collection of 42 sites distributed globally and systematically acquired by SPOT-4 HRV-1 and HRV-2 every 5 days during the decommissioning phase of the SPOT-4 mission (February-June 2013).
Vermote E. (Co-PI), “NPP/VIIRS Land Product Validation Research and Algorithm Refinement: Surface Reflectance”, NOAA, (2011-present).
This research support an evaluation of the accuracy of select operational algorithms for NPP/VIIRS land products. The work includes analysis of the land surface reflectance product, analysis and impact evaluation of the VIIRS SDR and VIIRS cloud mask.
We have developed VIIRS vicarious cross/calibration with Aqua over desert/bright sites. This method has been routinely applied to the data stream in order to provide near-real time monitoring of the VIIRS calibration focusing in the reflective domain. We focused in particular on the red and near-infrared bands (I1,I2 M5 and M7) which are critical for downstream products such as Vegetation indices which are used in near time for the monitoring of vegetation and agriculture area, for detecting climate induced anomaly (e.g. drought) and assessing impact on production and forecasting yields.
We have performed preliminary inter-comparison of MODIS Aqua and VIIRS surface reflectance, aerosol and cloud mask products using the Climate Modeling Grid (CMG) dataset developed for MODIS and VIIRS by our science computing facility. This activity has enabled to diagnose very rapidly issues with the VIIRS aerosol, cloud mask and reflectance algorithm on a global and continuous basis which is complementary with the detailed analysis performed on a limited amount of instrument sites (see next paragraph on the validation subset).
We have performed preliminary evaluation of VIIRS aerosol and surface reflectance products on validation subsets. This evaluation will be done by the use of the AERONET data collected at those sites which provide with the 6S radiative transfer code an independent basis to generate surface product and assess the properties of the aerosols.
Vermote E. (Co-PI), “Development of the surface reflectance Fundamental Climate Data Record from the Landsat archive, the LDCM mission and future Landsats”,NASA/USGS, (2013-2017).
The Surface Reflectance standard product developed for MODIS provides the basis for a number of higher order land products for global change and applications research. Unfortunately there was no equivalent suite of standard geophysical products provided for Landsat 7 beyond Level 1b. This has placed the burden of standard processing of Landsat data on the user community. In the LDCM era of large volume, regional to global data Landsat processing and operational use of the data, there is an urgent need for product standardization. Through this proposal and building on our MODIS and Landsat experience, we will provide a standard surface reflectance product for LDCM including cloud and cloud shadow screening. The algorithm will be robust, globally applicable and fully automated for integration into the LDCM processing chain and the code made openly available for others to use (e.g. LDCM ground stations). The approach is to extend our current research on Landsat, carried out as part of the NASA LEDAPS project for N. America and test the approach globally. Prior to the launch of LDCM the project will apply the approach to the Mid Decadal Global Land Survey Data (Phase 2), with the aim of extending data product continuity. Experience of the team with AVHRR and MODIS vicarious calibration will be applied to LDCM and close attention will be given to pre and post-launch instrument testing and performance. The team will continue to participate in a number of strategic national and international programs (e.g. LCLUC, GLAM, IGOL and GOFC-GOLD), bringing the new capabilities of LDCM into the international land observation arena and looking for synergy with other observing systems (e.g. Sentinel 2).
Vermote E. (Co-PI), “Enhancing Compatibility of Sentinel 2 and Landsat data for improved monitoring of the Earth System”, NASA, (2011-2016)
The overarching objective of the proposed research is to ensure coordination between the Landsat and Sentinel-2 programs such that the products forthcoming from these similar missions are as compatible as possible in support of their combined use for science.
More specifically, the objectives of the proposed research are to ensure:
1. careful cross calibration between Landsat and Sentinel 2
2. consistency in methods for atmospheric correction
3. effective cloud and shadow screening
4. compatibility of datasets in support of a whole suite of science applications,
including monitoring of land cover and land use change.
5. Sharing of data and methods in support of product validation
In addition, the proposed research supports a number of international science programs and objectives. For example, Land Cover (including land cover change) is one of the Global Climate Observing System (GCOS) Essential Climate Variables (ECVs). The proposed research may make possible the production of a Land Cover ECV based on datasets from both the Landsat and Sentinel programs. Further, near real-time monitoring of forest change would greatly enhance the REDD Program (Reduced Emissions from Deforestation and Degradation) and the GEO Forest Carbon Tracking Task.
Vermote E. (Co-I), “Sentinel 3 Science Products: A US contribution”, NASA Earth Science U.S. Participating Investigator (2012-2017).
This proposal is to provide a US investigator team with support to participate in the development of land products from European satellite systems, with an emphasis on harmonization between US and ESA products from AVHRR, MODIS, VIIRS, ATSR, MERIS and Sentinel 3. Currently there are a number of similar products being generated by NASA (MODIS) and ESA (Glob- series) from the coarse-resolution instruments and there is considerable interest in cooperation between the various parties with respect to product inter-comparison, algorithm standardization, processing procedures, validation protocols and data sharing. With the major new instruments (i.e. VIIRS, Sentinel 3) in development there is an opportunity to improve cooperation on understanding instrument performance and developing products. Similarly, with new requirements for Essential Climate Variables (ECV’s) being set by the Global Climate Observing System (GCOS), there is an opportunity for improved space agency coordination at the technical level on product generation. This proposed cooperation will focus on Level 1b (calibrated and geo-located radiances), cloud mask, surface reflectance, albedo, vegetation index, active fire and burned area products. The US team has considerable experience with the AVHRR and MODIS data processing and products and the VIIRS Land EDR’s and has a number of on-going interactions with their European algorithm counterparts. Through support from this proposal the US PI team will participate in the Sentinel 3 product-team activities coordinated by ESA. Our European counterparts will be invited to participate in the NASA MODIS and VIIRS Science Team Meetings. The cooperation will be implemented in the framework of the GTOS GOFC- GOLD program and the CEOS Land Product Validation (LPV) working group, engaging a broader community in the various activities.
Vermote E. (Co-I), “Agricultural Monitoring Applications of VIIRS Data and Land Discipline Lead ”, NASA, 2014-2017, ($49,000 excluding CS salary)
Vermote E. et al. (PI), “Development of the VIIRS Climate quality Surface Reflectance Product Suite”, NASA, 2014-2017.
The primary objective of this proposal is the development of a well-calibrated, high quality, daily, multispectral-land surface reflectance product from the VIIRS sensor, providing global land observations to meet the needs of NASA Earth System Science and Applied Sciences and continuity with the EOS-MODIS surface reflectance product. Such a high quality, global, multi-year dataset is fundamental for interdisciplinary studies of the earth system and can be used to study a wide range of land and climate related questions, quantify land use and land cover change, providing the basis for time-series analysis to characterize processes and examine functions within the earth’s land surface. A science quality surface reflectance product compatible with MODIS, will be a fundamental data record and a necessary input to several other VIIRS Land CDR’s. It is recognized that the IDPS system is producing an intermediate surface reflectance product (SR-IP), however there is no plan of producing a consistent and reprocessed highly accurate surface reflectance from the beginning of the mission that will ensure the continuity with EOS-MODIS. It is recognized that the IDPS system is producing an intermediate surface reflectance product (SR-IP) and will make incremental improvements to known problems, however there is no plan for the IDPS to produce a consistent, reprocessed and highly accurate surface reflectance from the beginning of the mission that will ensure the continuity with EOS-MODIS and allow higher order CDR’s to be developed.
This proposal covers the VIIRS surface reflectance product suite to continue the MODIS record: level 2 surface reflectance, gridded daily product, gridded 8-day composite product.
The focus areas will be:
a) The close monitoring of the calibration and instrument characteristics and their impact on the surface reflectance product as well as upstream product (VIIRS Cloud Mask (VCM) and Aerosol), we will act as the land liaison with the calibration, VCM and atmosphere group. We will also provide those critical inputs to downstream products (e.g. Vegetation indices, Albedo).
b) The continuous and systematic near real time Validation of the surface reflectance CDR product over the AERONET sites through the use of rigorous performance metrics (Accuracy, Precision and Uncertainties).
c) The in-house quality assessment of the global performance of the product as well as the quality flags (cloud mask, exception conditions) mainly by using the global daily product and cross-comparison with others dataset of opportunity (consistency with Aqua MODIS, CALIPSO data for clouds).
d) MODIS algorithm refinements from Collection 6 will be integrated into the VIIRS algorithm and shared with the NOAA JPSS project for possible inclusion in future versions of the operational product.
Research Physical Scientist
NASA - Goddard Space Flight Center, Code 619
December 2012 - Present
University of Maryland - Dpt of Geographical Sciences
2009 - 2012
Senior Research Scientist
University of Maryland - Dpt of Geographical Sciences
2007 - 2009
Associate Research Scientist
University of Maryland - Dpt of Geographical Sciences
1997 - 2007
Assistant Research Scientist
University of Maryland - Dpt of Geographical Sciences
1991 - 1997
University of Lille, Lille, France - Dpt of Physics
1990 - 1991
Air Force Scientist
French Air Force - CERT-DERO, Toulouse, France
1989 - 1990
University of Lille, Lille, France - Dpt of Physics
1987 - 1989
2006, Fall Main Instructor, GEOG-788 seminar course: “Advances in remote sensing of terrestrial global change: past, present & future”, group teaching. University of Maryland College Park.
2004, Fall Coordinator, GEOG-788 seminar course: “Advances in remote sensing of terrestrial global change: past, present & future”, coordinated by Vermote E. and Roy D.P. University of Maryland College Park.
2002, Aug Lecturer, International Summer School, l’Aquila, Italy: “Atmospheric and Oceanic Science”.
2002, Jan Lecturer, MIT/Goddard Short Course: “Remote Sensing of the Earth’s Environment from Terra”. NASA Goddard Space Flight Center.
2001, Oct Lecturer, Master program of physics at University of Bari, “Advanced Remote Sensing”, three lectures.
2001, Sep Guest lecturer, GEOG-609: “Global Terrestrial Satellite Observing Systems course”. University of Maryland College Park.
2001, Jul Lecturer, NASA Summer Internship Program: “MODIS data use for land atmosphere remote sensing”. NASA Goddard Space Flight Center.
2001, Jun Lecturer, George Mason University workshop: “Earth Science Remote Sensing Data Processing, Analysis & Applications”.
1998, Fall Instructor, GEOG-480: “Advanced Remote Sensing”. University of Maryland College Park.
1998, Apr 4 Presenter, “NOAA-AVHRR-PM channel 1 and 2 calibration results from 1982 to present”, (GSFC), University of Maryland GEOG 789 course: The Impacts of El Nino taught by John Townshend, Jim Tucker and Bruce Douglas.
1998, Jan 29 Lecturer, MIT, Independent Activity Period (IAP) Course 12.265, 12.565, Techniques in Remote Sensing, “AVHRR and SeaWiFS Global Product”.
1996, Apr 10 Presenter/ Lab instructor, “6S: Second Simulation of the Satellite Signal in the Solar Spectrum, an overview”, UMD/Dept of Geography for Physical Fundamentals of Remote Sensing course.
1993, Dec 6-12 Lecturer, Commission of the European Communities on Remote Sensing, Joint Research Centre Ispra, Italy: “Remote Sensing and Radiative transfer modelling, Advances in the use of NOAA AVHRR for Land Applications”, EUROCOURSES.
1987-1988, Sep Lecturer, Undergraduate level engineering, HEI, Lille: “Introduction of numerical analyses and programming”.
1987, Spring Lecturer, Undergraduate level School of Pharmacy, Lille, France: “Spread Sheet and Relational Database software”.
1987 M.S. Computer Science, Hautes Etudes Industrielles, Lille, France
IEEE Geoscience and Remote Sensing Society
1996 - Present
Present NASA/VIIRS Science Team Member
Present Member of the Committee on Space Research (COSPAR)
Present Review Editor for Frontiers in Atmospheric Science
Present Landsat 8 Science Team Member
Present MODIS Science Team Member (Land representative for calibration group)
Present NPOESS/VIIRS Science Team Member (Land Representative for calibration and cloud mask group and surface reflectance lead)
Present Associate Editor for the Canadian Journal of Remote Sensing
2015 NASA review panel
2015 Member of the International Scientific Committee for the 36th International Geoscience and Remote Sensing Symposium (IGARSS)
2015 Judge at the Electronic STEM fair competition (projects in French), Robert Goddard French Immersion School, PGPS, MD.
2014 Member of the International Scientific Committee for the 4th International Symposium on Recent Advances in Quantitative Remote Sensing (RAQRS’14).
2013 NASA/GSFC Code 600 Publication award committee
2013 Member of Goddard Diversity Dialogue Project team
2013-2014 Member of the International Scientific Committee for the 4th International Symposium on Recent Advances in Quantitative Remote Sensing (RAQRS’14).
2013 Member of the Scientific Committee for the ESA Living planet symposium.
2012-Present Adjunct Professor at the University of Maryland
2012-2013 Co-Chair of the Non Tenure Track Faculty Task Force
2010 Member of the Scientific Committee for the ESA Living planet symposium.
2009-present Associate Editor for the Canadian Journal of Remote Sensing
2008-present Member of the GCOS (Global Climate Observing System) AOPC/TOPC Joint Working Group on Land Surface and Atmospheric issues
2007 Reviewer for NOAA Research Program/ Member of the review panel
2007 Reviewer for NASA Research Program/ Member of the review panel
2005-2007 Chair of Research Faculty Advisory Committee
2003 Reviewer for NASA Research Program/ Member of the 2 review panels
2003 Reviewer for NOAA Research Program/ Member of the review panel
2001 Reviewer for NASA Research Program/ Member of the review panels
2001 Reviewer NSF Research Program
1999 Reviewer for the Centre for Earth Observation (CEO) European program
1995-1996 Chairman of IEEE Geoscience and Remote Sensing Society, Washington DC/ Virginia Chapter
1995 Reviewer for NOAA Climate Research / Member of the review panel
2014 PECORA Group Award as part of the Landsat 8 Team
2014 Certificate of appreciation for outstanding support to NASA Advanced Information Systems Technology Program
2013 Midwest Archives Conference, Presidents’ Award presented to the 2006-2011 Landsat Science Team, “For outstanding support in improving the preservation and accessibilty of historically valuable documents and for contributing to public appreciation of the archival profession”, April 2013.
2012 NASA Group Achievement award to MODIS NDVI Anomaly Team
2012 Hydrospheric and Biospheric Sciences (HOBI) Annual Awards – Scientific Achievement MODIS characterization Support Team & MODIS sensor working group
2012 Certificate of recognition for SUOMI National Polar orbiting Partnership Satellite System mission.
2011 University of Maryland “Research Leader” designation for level of research funding won
2010 Honored at the 3rd Annual University Wide Celebration of Scholarship and Research nominated by the Dean of BSOS.
2004-2008 University of Maryland “Rainmaker” designation for level of research funding won
2003 NASA group achievement award (signed by the NASA administrator), Group Achievement Award Aqua Mission Team, August 27 2003, In recognition of the talent, care, energy and devotion your outstanding group of civil servants, members of academia, and contractor personnel put into the Aqua Mission.
2003 NASA Goddard Space Flight Center (GSFC) group achievement award (signed by the NASA GSFC director), Outstanding Teamwork Earth Observing (EOS) Aqua Mission Team, May 16 2003, In recognition of the talent, care, energy and devotion this outstanding team of civil servants, contractors, scientists, and academia put into the Aqua Mission.
2003 Customer Service Excellence Award, Moderate Resolution Imaging Spectroradiometer (MODIS) Support Team, April 2003, In recognition of your support for the calibration/characterization of the MODIS instrument on the Terra spacecraft.
1996 Award for service as the Chairman of IEEE Geoscience and Remote Sensing Society, Washington DC/ Virginia Chapter.
Other Professional Information
Enrique Montano (2009-present) PhD, (Co-Advisor)
Corinne Carter (2012-present),Phd,(Committee Member)
Catherine Nakalembe (2012-present)),Phd,(Committee Member)
Yolande Munzimi (2012-present),Phd,(Committee Member)
Jie Zhang, (2011-present),Phd,(Committee Member)
Kim Do-Hyung, (2011-present), Phd, (Committee Member)
Alyssa Whitcraft, (2011-2014),Phd,(Committee Member)
Belen Franch Gras (2010-2013) PhD in Physics, University of Valencia, (Co-Advisor)
Inbal Becker-Reshef (2007-2012) PhD, (Co-Advisor)
Jyoteshwar Nagol (2006-2011) PhD, (Co-Advisor)
Angira Baruah (2007-2012) PhD, (Committee Member)
Evan Ellicott (2006-2009) PhD, (Co-Advisor)
Timothy Clark (2007-2008) Master, (Co-Advisor)
David Béal (2006) PhD, Université d’Avignon, France (External Examiner)
Marc Mallet (2003) PhD, Université de Toulon et du Var, France (External Examiner)
Catherine Schmetig (2000) PhD, Université du Littoral, France (External Examiner)
Misrak Gezmu (1998), Faculty, University of West Virginia (Advisor for NASA Summer Faculty Fellowship).
Isabelle Mao-Che, (1998) Master, University of Paris (Supervisor for Summer Internship)
Laura Dempere Marco (1997) Master, University of Valencia (Supervisor for Summer Internship)
Misrak Gezmu (1997) Faculty, University of West Virginia (Advisor for NASA Summer Faculty Fellowship)
Crawford, C. J., D. P. Roy, S. Arab, et al. C. Barnes, E. Vermote, G. Hulley, A. Gerace, M. Choate, C. Engebretson, E. Micijevic, G. Schmidt, C. Anderson, M. Anderson, M. Bouchard, B. Cook, R. Dittmeier, D. Howard, C. Jenkerson, M. Kim, T. Kleyians, T. Maiersperger, C. Mueller, C. Neigh, L. Owen, B. Page, N. Pahlevan, R. Rengarajan, J.-C. Roger, K. Sayler, P. Scaramuzza, S. Skakun, L. Yan, H. K. Zhang, Z. Zhu, and S. Zahn. 2023. The 50-year Landsat collection 2 archive Science of Remote Sensing 8 100103 [10.1016/j.srs.2023.100103]
Maciel, D. A., N. Pahlevan, C. C. Barbosa, et al. E. M. de Novo, R. S. Paulino, V. S. Martins, E. Vermote, and C. J. Crawford. 2023. Validity of the Landsat surface reflectance archive for aquatic science: Implications for cloud‐based analysis Limnology and Oceanography Letters 8 (5): [10.1002/lol2.10344]
Colin, J., O. Hagolle, L. Landier, et al. S. Coustance, P. Kettig, A. Meygret, J. Osman, and E. Vermote. 2023. Assessment of the Performance of the Atmospheric Correction Algorithm MAJA for Sentinel-2 Surface Reflectance Estimates Remote Sensing 15 (10): 2665 [10.3390/rs15102665]
Doxani, G., E. F. Vermote, J.-C. Roger, et al. S. Skakun, F. Gascon, A. Collison, L. De Keukelaere, C. Desjardins, D. Frantz, O. Hagolle, M. Kim, J. Louis, F. Pacifici, B. Pflug, H. Poilvé, D. Ramon, R. Richter, and F. Yin. 2023. Atmospheric Correction Inter-comparison eXercise, ACIX-II Land: An assessment of atmospheric correction processors for Landsat 8 and Sentinel-2 over land Remote Sensing of Environment 285 113412 [10.1016/j.rse.2022.113412]
Skakun, S., J. Wevers, C. Brockmann, et al. G. Doxani, M. Aleksandrov, M. Batič, D. Frantz, F. Gascon, L. Gómez-Chova, O. Hagolle, D. López-Puigdollers, J. Louis, M. Lubej, G. Mateo-García, J. Osman, D. Peressutti, B. Pflug, J. Puc, R. Richter, J.-C. Roger, P. Scaramuzza, E. Vermote, N. Vesel, A. Zupanc, and L. Žust. 2022. Cloud Mask Intercomparison eXercise (CMIX): An evaluation of cloud masking algorithms for Landsat 8 and Sentinel-2 Remote Sensing of Environment 274 112990 [10.1016/j.rse.2022.112990]
Roger, J.-C., E. Vermote, S. Skakun, et al. E. Murphy, O. Dubovik, N. Kalecinski, B. Korgo, and B. Holben. 2022. Aerosol models from the AERONET database: application to surface reflectance validation Atmospheric Measurement Techniques 15 (5): 1123-1144 [10.5194/amt-15-1123-2022]
Franch, B., E. Vermote, S. Skakun, et al. A. Santamaria-Artigas, N. Kalecinski, J.-C. Roger, I. Becker-Reshef, B. Barker, C. Justice, and J. Sobrino. 2021. The ARYA crop yield forecasting algorithm: Application to the main wheat exporting countries International Journal of Applied Earth Observation and Geoinformation 104 102552 [10.1016/j.jag.2021.102552]
Franch, B., A. S. Bautista, D. Fita, et al. C. Rubio, D. Tarrazó-Serrano, A. Sánchez, S. Skakun, E. Vermote, I. Becker-Reshef, and A. Uris. 2021. Within-Field Rice Yield Estimation Based on Sentinel-2 Satellite Data Remote Sensing 13 (20): 4095 [10.3390/rs13204095]
Santamaria-Artigas, A., E. F. Vermote, B. Franch, J.-C. Roger, and S. Skakun. 2021. Evaluation of the AVHRR surface reflectance long term data record between 1984 and 2011 International Journal of Applied Earth Observation and Geoinformation 98 102317 [10.1016/j.jag.2021.102317]
Skakun, S., E. F. Vermote, A. E. Artigas, W. H. Rountree, and J.-C. Roger. 2021. An experimental sky-image-derived cloud validation dataset for Sentinel-2 and Landsat 8 satellites over NASA GSFC International Journal of Applied Earth Observation and Geoinformation 95 102253 [10.1016/j.jag.2020.102253]
Skakun, S., N. I. Kalecinski, M. G. Brown, et al. D. M. Johnson, E. F. Vermote, J.-C. Roger, and B. Franch. 2021. Assessing within-Field Corn and Soybean Yield Variability from WorldView-3, Planet, Sentinel-2, and Landsat 8 Satellite Imagery Remote Sensing 13 (5): 872 [10.3390/rs13050872]
Villaescusa-Nadal, J. L., E. Vermote, B. Franch, et al. A. E. Santamaria-Artigas, J.-C. Roger, and S. Skakun. 2021. MODIS-Based AVHRR Cloud and Snow Separation Algorithm IEEE Transactions on Geoscience and Remote Sensing 1-13 [10.1109/tgrs.2021.3059428]
Vermote, E. F., S. Skakun, I. Becker-Reshef, and K. Saito. 2020. Remote Sensing of Coconut Trees in Tonga Using Very High Spatial Resolution WorldView-3 Data Remote Sensing 12 (19): 3113 [10.3390/rs12193113]
Kuhn, C., M. Bogard, S. E. Johnston, et al. A. John, E. Vermote, R. Spencer, M. Dornblaser, K. Wickland, R. Striegl, and D. Butman. 2020. Satellite and airborne remote sensing of gross primary productivity in boreal Alaskan lakes Environmental Research Letters 15 (10): 105001 [10.1088/1748-9326/aba46f]
Wulder, M. A., T. R. Loveland, D. P. Roy, et al. C. J. Crawford, J. G. Masek, C. E. Woodcock, R. G. Allen, M. C. Anderson, A. S. Belward, W. B. Cohen, J. Dwyer, A. Erb, F. Gao, P. Griffiths, D. Helder, T. Hermosilla, J. D. Hipple, P. Hostert, M. J. Hughes, J. Huntington, D. M. Johnson, R. Kennedy, A. Kilic, Z. Li, L. Lymburner, J. McCorkel, N. Pahlevan, T. A. Scambos, C. Schaaf, J. R. Schott, Y. Sheng, J. Storey, E. Vermote, J. Vogelmann, J. C. White, R. H. Wynne, and Z. Zhu. 2019. Current status of Landsat program, science, and applications Remote Sensing of Environment 225 127-147 [10.1016/j.rse.2019.02.015]
Kuhn, C., A. de Matos Valerio, N. Ward, et al. L. Loken, H. O. Sawakuchi, M. Kampel, J. Richey, P. Stadler, J. Crawford, R. Striegl, E. Vermote, N. Pahlevan, and D. Butman. 2019. Performance of Landsat-8 and Sentinel-2 surface reflectance products for river remote sensing retrievals of chlorophyll-a and turbidity Remote Sensing of Environment 224 104-118 [10.1016/j.rse.2019.01.023]
Doxani, G., E. Vermote, J.-C. Roger, et al. F. Gascon, S. Adriaensen, D. Frantz, O. Hagolle, A. Hollstein, G. Kirches, F. Li, J. Louis, A. Mangin, N. Pahlevan, B. Pflug, and Q. Vanhellemont. 2018. Atmospheric Correction Inter-Comparison Exercise Remote Sensing 10 (3): 352 [10.3390/rs10020352]
Franch, B., E. Vermote, J.-C. Roger, et al. E. Murphy, I. Becker-Reshef, C. Justice, M. Claverie, J. Nagol, I. Csiszar, D. Meyer, F. Baret, E. Masuoka, R. Wolfe, and S. Devadiga. 2017. A 30+ Year AVHRR Land Surface Reflectance Climate Data Record and Its Application to Wheat Yield Monitoring Remote Sensing 9 (3): 296 [10.3390/rs9030296]
Pahlevan, N., S. Sarkar, S. Devadiga, et al. R. E. Wolfe, M. Roman, E. Vermote, G. Lin, and X. Xiong. 2017. Impact of Spatial Sampling on Continuity of MODIS–VIIRS Land Surface Reflectance Products: A Simulation Approach IEEE Transactions on Geoscience and Remote Sensing 55 (1): 183-196 [10.1109/tgrs.2016.2604214]
Roy, D., V. Kovalskyy, H. Zhang, et al. E. Vermote, L. Yan, S. Kumar, and A. Egorov. 2016. Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity Remote Sensing of Environment 185 57-70 [10.1016/j.rse.2015.12.024]
Vermote, E., C. Justice, M. Claverie, and B. Franch. 2016. Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product Remote Sensing of Environment 185 46-56 [10.1016/j.rse.2016.04.008]
Skakun, S., B. Franch, J.-C. Roger, et al. E. Vermote, I. Becker-Reshef, C. Justice, and A. Santamaria-Artigas. 2016. Incorporating yearly derived winter wheat maps into winter wheat yield forecasting model 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) [10.1109/igarss.2016.7730869]
Morton, D. C., J. Nagol, C. C. Carabajal, et al. J. Rosette, M. Palace, B. D. Cook, E. F. Vermote, D. J. Harding, and P. R. North. 2016. Dry-season greening of Amazon forests Reply Nature 531 (7594): E6-E6 [10.1038/nature16458]
Claverie, M., E. F. Vermote, B. Franch, and J. G. Masek. 2015. Evaluation of the Landsat-5 TM and Landsat-7 ETM + surface reflectance products Remote Sensing of Environment 169 390–403 [10.1016/j.rse.2015.08.030]
Claverie, M., E. F. Vermote, B. Franch, et al. T. He, O. Hagolle, M. Kadiri, and J. G. Masek. 2015. Evaluation of Medium Spatial Resolution BRDF-Adjustment Techniques Using Multi-Angular SPOT4 (Take5) Acquisitions Remote Sens. 7 (9): 12057-12075 [10.3390/rs70912057]
Franch, B., E. F. Vermote, I. Becker-Reshef, et al. M. Claverie, J. Huang, J. Zhang, C. Justice, and J. A. Sobrino. 2015. Improving the timeliness of winter wheat production forecast in the United States of America, Ukraine and China using MODIS data and NCAR Growing Degree Day information Remote Sensing of Environment 161 131-148 [10.1016/j.rse.2015.02.014]
Nagol, J. R., J. O. Sexton, D. H. Kim, et al. A. Anand, E. F. Vermote, D. C. Morton, and J. R. Townshend. 2015. Bidirectional effects in Landsat reflectance estimates: Is there a problem to solve? ISPRS Journal of Photogrammetry and Remote Sensing 103 129-135 [10.1016/j.isprsjprs.2014.09.006]
Breon, F., E. F. Vermote, E. Murphy, and B. Franch. 2015. Measuring the Directional Variations of Land Surface Reflectance From MODIS Geoscience and Remote Sensing, IEEE Transactions on 53 (8): 4638 - 4649 [10.1109/TGRS.2015.2405344]
Whitcraft, A. K., E. Vermote, I. Becker-Reshef, and C. Justice. 2015. Cloud cover throughout the agricultural growing season: Impacts on passive optical earth observations Remote Sensing of Environment 156 438-447 [10.1016/j.rse.2014.10.009]
Franch, B., E. F. Vermote, and M. Claverie. 2014. Intercomparison of Landsat albedo retrieval techniques and evaluation against in situ measurements across the US SURFRAD network Remote Sensing of Environment 152 627-637 [doi:10.1016/j.rse.2014.07.019]
Zhou, L., Y. Tian, R. B. Myneni, et al. P. Ciais, S. Saatchi, Y. Y. Liu, S. Piao, H. Chen, E. Vermote, C. Song, and T. Hwang. 2014. Widespread decline of Congo rainforest greenness in the past decade Nature 509 86-90 [10.1038/nature13265]
Morton, D. C., J. Nagol, C. C. Carabajal, et al. J. Rosette, M. Palace, D. Cook, E. F. Vermote, D. Harding, and P. North. 2014. Amazon forests maintain consistent canopy structure and greenness during the dry season Nature 506 (7487): 221-224 [10.1038/nature13006]
Franch, B., E. F. Vermote, J. A. Sobrino, and Y. Julien. 2014. Retrieval of surface albedo on a daily basis: Application to MODIS data IEEE Transaction on Geoscience and Remote Sensing 52 (12): 7549-7558 [10.1109/TGRS.2014.2313842]
Vermote, E., C. Justice, and I. Csiszar. 2014. Early evaluation of the VIIRS calibration, cloud mask and surface reflectance Earth data records Remote Sensing of Environment 148 134-145 [10.1016/j.rse.2014.03.028]
Roy, D., M. A. Wulder, T. R. Loveland, et al. C. E. Woodcock, R. G. Allen, M. C. Anderson, D. Helder, J. Irons, D. M. Johnson, R. Kennedy, T. A. Scambos, C. B. Schaaf, J. R. Schott, Y. Sheng, E. Vermote, A. S. Belward, R. Bindschadler, W. B. Cohen, F. Gao, J. D. Hipple, P. Hostert, J. Huntington, C. O. Justice, A. Kilic, V. Kovalskyy, Z. P. Lee, L. Lymburner, J. Masek, J. McCorkel, Y. Shuai, R. Trezza, J. Vogelmann, R. H. Wynne, and Z. Zhu. 2014. Landsat-8: Science and product vision for terrestrial global change research Remote Sensing of Environment 145 154-172 [10.1016/j.rse.2014.02.001]
Roy, D. P., Y. Qin, V. Kovalskyy, et al. E. F. Vermote, J. Ju, A. Egorov, M. Hansen, I. Kommareddy, and L. Yan. 2014. Conterminous United States demonstration and characterization of MODIS-based Landsat ETM+ atmospheric correction Remote Sensing of Environment 140 433-449 [10.1016/j.rse.2013.09.012]
Yeo, I.-Y., M. Lang, and E. Vermote. 2014. Improved Understanding of Suspended Sediment Transport Process Using Multi-Temporal Landsat Data: A Case Study From the Old Woman Creek Estuary (Ohio) IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing 7 (2): 636-647 [10.1109/JSTARS.2013.2265191]
Vadrevu, K., I. Csiszar, E. Ellicott, et al. L. Giglio, K. S. Badarinath, E. Vermote, and C. Justice. 2013. Hotspot Analysis of Vegetation Fires and Intensity in the Indian Region IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing 6 (1): 224-238 [10.1109/JSTARS.2012.2210699]
O'Connell, J., J. Connolly, E. Vermote, and N. M. Holden. 2013. Radiometric normalization for change detection in peatlands: a modified temporal invariant cluster approach International Journal of Remote Sensing 34 (8): 2905-2924 [10.1080/01431161.2012.752886]
Feng, M., J. O. Huang, C. Huang, et al. J. Masek, E. Vermote, F. Gao, R. Narasimhan, S. Channan, R. Wolfe, and J. R. Townshend. 2013. Global surface reflectance products from Landsat: Assessment using coincident MODIS observations Remote Sensing of Environment 134 276-293 [10.1016/j.rse.2013.02.031]
Sobrino, J. A., B. Franch, R. Oltra-Carrio, E. Vermote, and E. Fedele. 2013. Evaluation of the MODIS Albedo product over a heterogeneous agricultural area International Journal of Remote Sensing 34 (15): 5530-5540 [10.1080/01431161.2013.792968]
Verger, A., F. Baret, M. Weiss, S. Kandasamy, and E. Vermote. 2013. The CACAO Method for Smoothing, Gap Filling, and Characterizing Seasonal Anomalies in Satellite Time Series IEEE Transactions on Geoscience and Remote Sensing 51 (4): 1963-1972 [10.1109/TGRS.2012.2228653]
Claverie, M., E. Vermote, M. Weiss, et al. F. Baret, O. Hagolle, and V. Demarez. 2013. Validation of coarse spatial resolution LAI and FAPAR time series over cropland in southwest France Remote Sensing of Environment 139 216-230 [10.1016/j.rse.2013.07.027]
Justice, C., M. O. Roman, I. Csiszar, et al. E. Vermote, R. E. Wolfe, S. J. Hook, M. Friedl, Z. Wang, C. B. Schaaf, T. Miura, M. Tschudi, G. Riggs, D. K. Hall, A. I. Lyapustin, S. Devadiga, C. C. Davidson, and E. J. Masuoka. 2013. Land and cryosphere products from Suomi NPP VIIRS: Overview and status J. Geophys. Res. Atmos. 118 (17): 9753-9765 [10.1002/jgrd.50771]
Tan, B., R. E. Wolfe, F. Gao, et al. E. Vermote, J. O. Sexton, and G. A. Ederer. 2013. Improved forest change detection with terrain illumination corrected Landsat images Remote Sensing of Environment 136 469–483 [10.1016/j.rse.2013.05.013]
Ju, J., D. P. Roy, E. Vermote, J. Masek, and V. Kovalskyy. 2012. Continental-scale validation of MODIS-based and LEDAPS Landsat ETM+ atmospheric correction methods Remote Sensing of Environment 122 175-184 [10.1016/j.rse.2011.12.025]
Ganguly, S., R. R. Nemani, Z. Gong, et al. H. Hashimoto, C. Milesi, A. Michaelis, W. Wang, P. Votava, A. Samanta, F. Melton, J. Dungan, E. Vermote, F. Gao, Y. Knyazikhin, and R. Myneni. 2012. Generating global Leaf Area Index from Landsat: Algorithm formulation and demonstration Remote Sensing of Environment 122 185-205 [10.1016/j.rse.2011.10.032]
Vadrevu, K., E. Ellicott, L. Giglio, et al. K. V. Badarinath, E. Vermote, C. Justice, and W. K. Lau. 2012. Vegetation fires in the himalayan region – Aerosol load, black carbon emissions and smoke plume heights Atmospheric Environment 47 241–251 [10.1016/j.atmosenv.2011.11.009]
Samanta, A., S. Ganguly, E. Vermote, R. Nemani, and R. Myneni. 2012. Interpretation of variations in MODIS-measured greenness levels of Amazon forests during 2000 to 2009 Environ. Res. Lett. 7 (2): 024018 [10.1088/1748-9326/7/2/024018]
Wang, D., D. Morton, J. Masek, et al. A. Wu, J. Nagol, J. Xiong, R. Levy, E. Vermote, and R. Wolfe. 2012. Impact of sensor degradation on the MODIS NDVI time series Remote Sensing of Environment 119 55-61 [10.1016/j.rse.2011.12.001]
Fenga, M., C. Huang, S. Channan, et al. E. Vermote, J. Masek, and J. Townshend. 2012. Quality assessment of Landsat surface reflectance products using MODIS data Computers & Geosciences 38 (1): 9-22 [10.1016/j.cageo.2011.04.011]
Townshend, J., J. G. Masek, C. Q. Huang, et al. E. F. Vermote, F. Gao, S. Channan, J. O. Sexton, M. Feng, R. Narasimhan, D. Kim, K. Song, D. X. Song, X. P. Song, P. Noojipady, B. Tan, M. C. Hansen, M. X. Li, and R. E. Wolfe. 2012. Global characterization and monitoring of forest cover using Landsat data: opportunities and challenges International Journal of Digital Earth 5 (5): 373-397 [10.1080/17538947.2012.713190]
Xiong, J., R. E. Wolfe, W. Barnes, et al. B. Guenther, E. Vermote, and V. V. Salomonson. 2011. Terra and Aqua MODIS Design, Radiometry, and Geometry in Support of Land Remote Sensing Land Remote Sensing and Global Environmental Change 11 133-164 [10.1007/978-1-4419-6749-7_7]
Becker-Reshef, I., C. Justice, M. Sullivan, et al. E. Vermote, C. Tucker, A. Anyamba, J. Small, E. Pak, E. Masuoka, J. Schmaltz, M. Hansen, K. Pittman, C. M. Birkett, D. Williams, C. Reynolds, and B. Doorn. 2010. Monitoring Global Croplands with Coarse Resolution Earth Observations: The Global Agriculture Monitoring (GLAM) Project Remote Sensing 2 (6): 1589-1609 [10.3390/rs2061589]
Samanta, A., S. Ganguly, H. Hashimoto, et al. S. Devadiga, E. F. Vermote, Y. Knyazikhin, R. Nemani, and R. Myneni. 2010. Amazon forests did not green-up during the 2005 drought Geophys Res Lett 37 (5): L05401 [10.1029/2009GL042154]
Huang, C., S. Goward, J. Masek, et al. F. Gao, E. F. Vermote, N. Thomas, K. Schleeweis, R. Kennedy, Z. Zhu, J. Eidenshink, and J. R. Townshend. 2009. Development of time series stacks of Landsat images for reconstructing forest disturbance history International Journal of Digital Earth 2 (3): 195-218 [10.1080/17538940902801614]
Ellicott, E., E. F. Vermote, F. Petitcolin, and S. Hook. 2009. Validation of a New Parametric Model for Atmospheric Correction of Thermal Infrared Data IEEE Trans. Geosci. Remote Sensing 47 (1): 295-311 [10.1109/TGRS.2008.2006182]
Vermote, E. F., C. Justice, and F. Breon. 2009. Towards a Generalized Approach for Correction of the BRDF Effect in MODIS Directional Reflectances IEEE Trans. Geosci. Remote Sensing 47 (3): 898-908 [10.1109/TGRS.2008.2005977]
Eck, T. F., B. N. Holben, J. Reid, et al. A. Siniuk, E. Hyer, N. O'Neil, G. Shaw, J. R. Vande Castle, F. Chapin, O. Dubovik, A. Smirnov, E. F. Vermote, J. S. Schafer, D. M. Giles, I. Slutsker, M. Sorokin, and W. Newcomb. 2009. Optical properties of boreal region biomass burning aerosols in central Alaska and seasonal variation of aerosol optical depth at an Arctic coastal site J. Geophys. Res. 114 (D11): D11201 [10.1029/2008JD010870]
Vermote, E., E. Ellicott, O. Dubovik, et al. T. Lapyonok, M. Chin, L. Giglio, and G. Roberts. 2009. An approach to estimate global biomass burning emissions of organic and black carbon from MODIS fire radiative power J. Geophys. Res. 114 (D18): D18205 [10.1029/2008JD011188]
Woodcock, C. E., R. Allen, M. Anderson, et al. A. Belward, R. Bindschadler, W. Cohen, F. Gao, S. N. Goward, D. Helder, E. Helmer, R. Nemani, L. Oreopoulos, J. Schott, P. S. Thenkabail, E. F. Vermote, J. Vogelmann, M. A. Wulder, and R. Wynne. 2008. Free Access to Landsat Imagery Science 320 (5879): 1011-1011 [10.1126/science.320.5879.1011a]
Kotchenova, S., E. F. Vermote, R. C. Levy, and A. I. Lyapustin. 2008. Radiative transfer codes for atmospheric correction and aerosol retrieval: intercomparison study Applied Optics 47 (13): 2215 [10.1364/AO.47.002215]
Levy, R. C., R. C. Levy, S. Mattoo, E. F. Vermote, and Y. Kaufman. 2007. Second-generation operational algorithm: Retrieval of aerosol properties over land from inversion of Moderate Resolution Imaging Spectroradiometer spectral reflectance J Geophys Res 112 (D13211): 21 pp [10.1029/2006JD007815]
Sinyuk, A., O. Dubovik, B. Holben, et al. T. Eck, F. Breon, J. Martonchik, R. Kahn, D. Diner, E. Vermote, Y. Kaurman, J. Roger, T. Lapyonok, and I. Slutsker. 2007. Simultaneous retrieval of aerosol and surface properties from a combination of AERONET and satellite data Remote Sensing of Environment 107 (1-2): 90-108 [10.1016/j.rse.2006.07.022]
Myneni, R., W. Yang, R. Nemani, et al. A. Huete, R. Dickinson, Y. Knyazikhin, K. Didan, R. Fu, R. I. Juárez, S. Saatchi, H. Hashimoto, K. Ichii, N. Shabanov, B. Tan, P. Ratana, J. Privette, J. Morisette, E. F. Vermote, D. P. Roy, R. E. Wolfe, M. Friedl, S. Running, P. Votava, N. El-Saleous, S. Devadiga, Y. Su, and V. V. Salomonson. 2007. Large seasonal swings in leaf area of Amazon rainforests PNAS 104 (12): 4820-4823 [10.1073/pnas.0611338104]
Vermote, E., J. Roger, A. Sinyuk, N. Saleous, and O. Dubovik. 2007. Fusion of MODIS-MISR aerosol inversion for estimation of aerosol absorption Remote Sens Environ 107 81-89
Masek, J. G., E. F. Vermote, N. Z. Saleous, et al. R. E. Wolfe, F. G. Hall, K. F. Huemmrich, F. Gao, J. L. Kutler, and T. Lim. 2006. A Landsat Surface Reflectance Dataset for North America, 1990–2000 IEEE Geosci. Remote Sensing Lett. 3 (1): 68-72 [10.1109/LGRS.2005.857030]
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Gatebe, C. K., M. D. King, S. Platnick, et al. G. T. Arnold, E. F. Vermote, and B. Schmid. 2003. Airborne spectral measurements of surface–atmosphere anisotropy for several surfaces and ecosystems over southern Africa J. Geophys. Res. 108 (D13): 8489 [10.1029/2002JD002397]
Justice, C. O., J. R. Townshend, E. F. Vermote, et al. E. J. Masuoka, R. E. Wolfe, N. Saleous, D. P. Roy, and J. T. Morisette. 2002. An overview of MODIS Land data processing and product status Remote Sensing of Environment 83 (1-2): 3-15 [10.1016/S0034-4257(02)00084-6]
Wolfe, R. E., D. P. Roy, and E. F. Vermote. 1998. MODIS land data storage, gridding, and compositing methodology: Level 2 grid IEEE Transactions on Geoscience and Remote Sensing 36 (4): 1324-1338 [10.1109/36.701082]
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