Icesat) Spacecraft Immediately Following Its Initial Mechanical Integration on June 18Th, 2002
Total Page:16
File Type:pdf, Size:1020Kb

Load more
Recommended publications
-
High-Temporal-Resolution Water Level and Storage Change Data Sets for Lakes on the Tibetan Plateau During 2000–2017 Using Mult
Earth Syst. Sci. Data, 11, 1603–1627, 2019 https://doi.org/10.5194/essd-11-1603-2019 © Author(s) 2019. This work is distributed under the Creative Commons Attribution 4.0 License. High-temporal-resolution water level and storage change data sets for lakes on the Tibetan Plateau during 2000–2017 using multiple altimetric missions and Landsat-derived lake shoreline positions Xingdong Li1, Di Long1, Qi Huang1, Pengfei Han1, Fanyu Zhao1, and Yoshihide Wada2 1State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, China 2International Institute for Applied Systems Analysis (IIASA), 2361 Laxenburg, Austria Correspondence: Di Long ([email protected]) Received: 21 February 2019 – Discussion started: 15 March 2019 Revised: 4 September 2019 – Accepted: 22 September 2019 – Published: 28 October 2019 Abstract. The Tibetan Plateau (TP), known as Asia’s water tower, is quite sensitive to climate change, which is reflected by changes in hydrologic state variables such as lake water storage. Given the extremely limited ground observations on the TP due to the harsh environment and complex terrain, we exploited multiple altimetric mis- sions and Landsat satellite data to create high-temporal-resolution lake water level and storage change time series at weekly to monthly timescales for 52 large lakes (50 lakes larger than 150 km2 and 2 lakes larger than 100 km2) on the TP during 2000–2017. The data sets are available online at https://doi.org/10.1594/PANGAEA.898411 (Li et al., 2019). With Landsat archives and altimetry data, we developed water levels from lake shoreline posi- tions (i.e., Landsat-derived water levels) that cover the study period and serve as an ideal reference for merging multisource lake water levels with systematic biases being removed. -
Algorithm Theoretical Basis Document (ATBD) for Land
ICE, CLOUD, and Land Elevation Satellite (ICESat-2) Algorithm Theoretical Basis Document (ATBD) for Land - Vegetation Along-track products (ATL08) Contributions by Land/VEG SDT Team Members and ICESAt-2 Project Science Office (Amy Neuenschwander, Sorin Popescu, Ross Nelson, David Harding, Katherine Pitts, John Robbins, Dylan Pederson, and Ryan Sheridan) ATBD Document prepared by Amy Neuenschwander June 2018 Content reviewed: technical approach, assumptions, scientific soundness, maturity, scientific utility of the data product 21 Contents 1 INTRODUCTION 8 1.1. Background 9 1.2 Photon Counting Lidar 11 1.3 The ICESat-2 concept 12 1.4 Height Retrieval from ATLAS 16 1.5 Accuracy Expected from ATLAS 17 1.6 Additional Potential Height Errors from ATLAS 19 1.7 Dense Canopy Cases 20 1.8 Sparse Canopy Cases 20 2. ATL08: DATA PRODUCT 21 2.1 Subgroup: Land Parameters 23 2.1.1 Georeferenced_segment_number_beg 24 2.1.2 Georeferenced_segment_number_end 25 2.1.3 Segment_terrain_height_mean 25 2.1.4 Segment_terrain_height_med 25 2.1.5 Segment_terrain_height_min 25 2.1.6 Segment_terrain_height_max 26 2.1.7 Segment_terrain_height_mode 26 2.1.8 Segment_terrain_height_skew 26 2.1.9 Segment_number_terrain_photons 26 2.1.10 Segment height_interp 27 2.1.11 Segment h_te_std 27 2.1.12 Segment_terrain_height_uncertainty 27 2.1.13 Segment_terrain_slope 27 21 2.1.14 Segment number_of_photons 27 2.1.15 Segment_terrain_height_best_fit 28 2.2 Subgroup: Vegetation Parameters 28 2.2.1 Georeferenced_segment_number_beg 31 2.2.2 Georeferenced_segment_number_end 31 2.2.3 -
GLAS HDF Standard Data Product Specification Revision - November 01, 2012
ICE, CLOUD, and Land Elevation Satellite (ICESat) Project GLAS_HDF Standard Data Product Specification Revision - November 01, 2012 SGT/Jeffrey Lee Cryospheric Sciences Laboratory Hydrospheric and Biospheric Processes NASA Goddard Space Flight Center Goddard Space Flight Center Greenbelt, Maryland National Aeronautics and Space Administration GLAS_HDF Standard Data Product Specification Revision - Table of Contents Table of Contents ............................................................................................... 1-1 List of Figures ..................................................................................................... 1-3 List of Tables ...................................................................................................... 1-3 1.0 Introduction ................................................................................................ 1-1 1.1 Identification of Document ...................................................................... 1-1 1.2 Scope ..................................................................................................... 1-1 1.3 Purpose and Objectives ......................................................................... 1-1 1.4 Acknowledgements ................................................................................ 1-1 1.5 Document Status and Schedule ............................................................. 1-2 1.6 Document Change History ..................................................................... 1-2 2.0 Related Documentation ............................................................................ -
Cloudsat-CALIPSO Launch
NATIONAL AERONAUTICS AND SPACE ADMINISTRATION CloudSat-CALIPSO Launch Press Kit April 2006 Media Contacts Erica Hupp Policy/Program (202) 358-1237 NASA Headquarters, Management [email protected] Washington Alan Buis CloudSat Mission (818) 354-0474 NASA Jet Propulsion Laboratory, [email protected] Pasadena, Calif. Emily Wilmsen Colorado Role - CloudSat (970) 491-2336 Colorado State University, [email protected] Fort Collins, Colo. Julie Simard Canada Role - CloudSat (450) 926-4370 Canadian Space Agency, [email protected] Saint-Hubert, Quebec, Canada Chris Rink CALIPSO Mission (757) 864-6786 NASA Langley Research Center, [email protected] Hampton, Va. Eliane Moreaux France Role - CALIPSO 011 33 5 61 27 33 44 Centre National d'Etudes [email protected] Spatiales, Toulouse, France George Diller Launch Operations (321) 867-2468 NASA Kennedy Space Center, [email protected] Fla. Contents General Release ......................................................................................................................... 3 Media Services Information ........................................................................................................ 5 Quick Facts ................................................................................................................................. 6 Mission Overview ....................................................................................................................... 7 CloudSat Satellite .................................................................................................................... -
An Overview of the Seawifs Project and Strategies for Producing a Climate Research Quality Global Ocean Bio-Optical Time Series
ARTICLE IN PRESS Deep-Sea Research II 51 (2004) 5–42 An overview of the SeaWiFS project andstrategies for producing a climate research quality global ocean bio-optical time series Charles R. McClain*, Gene C. Feldman, Stanford B. Hooker Code 970.2, Office for Global Carbon Studies, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA Received1 April 2003; accepted19 November 2003 Abstract The Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Project Office was formally initiated at the NASA Goddard Space Flight Center in 1990. Seven years later, the sensor was launched by Orbital Sciences Corporation under a data- buy contract to provide 5 years of science quality data for global ocean biogeochemistry research. To date, the SeaWiFS program has greatly exceeded the mission goals established over a decade ago in terms of data quality, data accessibility andusability, ocean community infrastructure development,cost efficiency, andcommunity service. The SeaWiFS Project Office andits collaborators in the scientific community have madesubstantial contributions in the areas of satellite calibration, product validation, near-real time data access, field data collection, protocol development, in situ instrumentation technology, operational data system development, and desktop level-0 to level-3 processing software. One important aspect of the SeaWiFS program is the high level of science community cooperation andparticipation. This article summarizes the key activities andapproaches the SeaWiFS Project Office pursuedto define,achieve, and maintain the mission objectives. These achievements have enabledthe user community to publish a large andgrowing volume of research such as those contributedto this special volume of Deep-Sea Research. Finally, some examples of major geophysical events (oceanic, atmospheric, andterrestrial) capturedby SeaWiFS are presentedto demonstratethe versatility of the sensor. -
Quality Control of Modis Data on Behalf of the European Space Agency A
QUALITY CONTROL OF MODIS DATA ON BEHALF OF THE EUROPEAN SPACE AGENCY A. Borg1, S. Lavender1, J. Jackson1, C. Kent1, D. Sautreau1, G. Ottavianelli2 1: ARGANS Ltd, Plymouth, United Kingdom, 2: ESA ESRIN, Frascati, Italy INTRODUCTION The European Space Agency (ESA) acquires and distributes MODIS data as a Third Party Mission (TPM). Data is acquired over Europe and used to populate the MERCI MODIS and GMES MyOcean Catalogues, available to Users in Near Real Time (NRT). The GMES MyOcean dataset also contains SeaWiFS data collected up to the mission end in December 2010. Plans to reprocess ESA archives of SeaWiFS data using the most recently available NASA code and calibration configurations (available via SeaDAS 6.2) are imminent, as well as research activities aimed to increase awareness of ESA acquired MODIS and SeaWiFS data products. ARGANS Ltd is a UK based company, Plymouth and Harwell Oxford, run by Earth Observation expert Samantha Lavender. It has close links to ESA and works collaboratively on a number of projects, such as the VEGA Space Ltd lead IDEAS SPPA service which supports the Quality Control (QC) and handling of MODIS and SeaWiFS data. IDEAS SPPA QUALITY CONTROL GMES is the European Programme for the establishment of a European capacity for Earth MODIS SPPA activities carried out at Observation. It consists of data collected from multiple ARGANS include the daily QC of products sources (earth observation satellites and in situ through download and processing using the sensors). SeaDAS processor. Assessment of products Policymakers and public authorities, the major users of is fed back to ESA and the GMES GMES, can use the information to prepare Coordinated Quality Control (CQC). -
Analyzing Performances of Different Atmospheric Correction Techniques for Landsat 8: Application for Coastal Remote Sensing
remote sensing Article Analyzing Performances of Different Atmospheric Correction Techniques for Landsat 8: Application for Coastal Remote Sensing Christopher O. Ilori 1,*, Nima Pahlevan 2,3 and Anders Knudby 4 1 Simon Fraser University, 8888 University Drive, Burnaby, BC V5A 1S6, Canada 2 NASA Goddard Space Flight Center, 8800 Greenbelt Road, Greenbelt, MD 20771, USA; [email protected] 3 Science Systems and Applications, Inc., 10210 Greenbelt Road, Suite 600 Lanham, MD 20706, USA 4 University of Ottawa, 60 University Private, Ottawa, ON K1N 6N5, Canada; [email protected] * Correspondence: [email protected]; Tel.: +1-778-929-5350 Received: 28 January 2019; Accepted: 18 February 2019; Published: 25 February 2019 Abstract: Ocean colour (OC) remote sensing is important for monitoring marine ecosystems. However, inverting the OC signal from the top-of-atmosphere (TOA) radiance measured by satellite sensors remains a challenge as the retrieval accuracy is highly dependent on the performance of the atmospheric correction as well as sensor calibration. In this study, the performances of four atmospheric correction (AC) algorithms, the Atmospheric and Radiometric Correction of Satellite Imagery (ARCSI), Atmospheric Correction for OLI ‘lite’ (ACOLITE), Landsat 8 Surface Reflectance (LSR) Climate Data Record (Landsat CDR), herein referred to as LaSRC (Landsat 8 Surface Reflectance Code), and the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) Data Analysis System (SeaDAS), implemented for Landsat 8 Operational Land Imager (OLI) data, were evaluated. The OLI-derived remote sensing reflectance (Rrs) products (also known as Level-2 products) were tested against near-simultaneous in-situ data acquired from the OC component of the Aerosol Robotic Network (AERONET-OC). -
The Future of Remote Sensing
The Future of Remote Sensing YI CHAO 1993-2011: Jet Propulsion Laboratory 2012-present: Remote Sensing Solutions, Inc. October 7, 2013 1 California Institute of Technology ------OUTLINE------ • Current state-of-the-art • Future challenges and mission concepts • Remote sensing data integrated with in situ data and assimilative/forecasting models 2 Emerging Field of Satellite Oceanography TOPEX (1992) Seasat (1978) Courtesy: D. Menemenlis Golden Era of Satellite Oceanography SeaStar TRMM NSCAT QuikSCAT TOPEX Terra Jason Seasat SeaWinds All the satellite missions that were either dedicated to or Aqua partly capable of ocean GRACE Courtesy: D. Menemenlis ICESat OSTM Aquarius remote sensing. 1st Weather (Meteorological) Satellite (1960) 5 1st Oceanographic Satellite (1978) Satellite’s view of the Gulf Stream 1770 Benjamin Franklin (postmaster) collected information about ships sailing between New England and England, discovering and mapping the Gulf Stream 6 Sea Surface Temperature as measured by thermal infrared sensor via multi-channel • Atmosphere absorbs and emits radiation (wavelength dependent, use multi- channel) • Reflection of solar radiation (avoid solar radiation band) • ~0.5oC accuracy 7 MODIS Terra & Aqua Satellites Infrared cannot penetrate cloud MetOp Satellites by EUMETSAT (VIIRS 8 on NPP) Geostationary Satellites 36,000 km 9 TRMM Microwave Imager (TMI) & AMSR-E (cloud-free, but coarse resolution Δ ~ Hλ/D ~ 25-km) 10 Microwave Radiometer on the Aquarius satellite: The first NASA satellite to measure salinity L-Band Vertical -
An Overview of Available Satellite Data and Services
Awareness Event 28 September 2018 | ADB, Manila, Philippines An overview of available satellite data and services EO4SD consortium, presented by Rolf A. de By (ITC, University of Twente) The consortium of EO4SD – Agriculture and Rural Development The Netherlands Denmark Service provision Austria Data integration Belgium Communication Capacity development Remote sensing … is the science of acquiring information about the Earth's surface without actually being in contact with it. A: Source of energy D G B: Interaction with A atmosphere C: Interaction with earth surface B D: Recording energy B E E: Transmission, F reception, processing F: Analysis C G: Application slide 3 28 September 2018 Ready for take-off • To get there, first steps are: ▪ identify observation requirements ▪ build sensor & design satellites ▪ get rocket to send each satellite into a (pre-defined) orbit slide 4 28 September 2018 Space: a busy place Infographics from https://spaceoneers.io Infographics from https://spaceoneers.io Status on 31-7-2017 Infographics from https://spaceoneers.io ~215 European 12% Copernicus programme • The world's largest single earth observation programme, directed by the European Commission in partnership with the European Space Agency (ESA). • Headed by the European Commission (EC) ▪ Acting on behalf of the European Union, setting requirements, managing the services • in partnership with the European Space Agency (ESA) ▪ Provision of 30 satellites (Sentinels) for the operational needs, space segment & ground segment. • Objectives: ▪ Global, continuous, autonomous, high quality, wide-range EO capacity. ▪ Providing accurate, timely and easily accessible information for, a.o. improving the management of the environment, understanding and mitigating the effects of climate change, and ensure civil security. -
Putting Together the Big Picture: Remote-Sensing Observations of Ocean Color James A
Putting Together the Big Picture: Remote-sensing Observations of Ocean Color James A. Yoder National Science Foundation • Arlington, Virginia USA J. Keith Moore National Center for Atmospheric Research • Boulder, Colorado USA Robert N. Swift EG&G Technical Services, Inc. • Wallops Island, Virginia USA Introduction Observations of ocean color from space have been Nevertheless, satellite and aircraft ocean-color part of the U.S. JGOFS strategy for discerning temporal measurements had an important role in JGOFS. First, and spatial variations in upper-ocean productivity on retrospective analyses of CZCS chlorophyll images the global scale since the first planning workshops for a were important for planning all of the U.S. JGOFS U.S. global ocean flux program (National Academy of process studies (Feldman et al., 1992). Second, aircraft Sciences, 1984). From the start, remote measurements of remote-sensing measurements were part of the field near-surface chlorophyll a concentrations were envis- studies in the North Atlantic, equatorial Pacific and aged as the major tool for extrapolating upper-ocean Arabian Sea. Third, SeaWiFS data collected during chemical and biological measurements in time and AESOPS were used to extrapolate in situ measurements space and linking calculations of new and primary pro- made during the field study, making possible a basin- duction with the flux of particulate material through scale calculation of primary production and particulate the water column. organic carbon (POC). Finally, data from CZCS, In 1986, researchers at the U.S. National SeaWiFS, the Ocean Color and Temperature Sensor Aeronautics and Space Administration (NASA) (OCTS), and the Polarization and Directionality of the Goddard Space Flight Center published the first Earth's Reflectances (POLDER) sensor are contributing monthly composite of images from the Nimbus-7 substantially to the analyses and modeling studies of Coastal Zone Color Scanner (CZCS), an ocean color the U.S. -
Solar Data Analysis for Seawifs Calibration
NASA Technical Memorandum 2000–206892, Volume 9 SeaWiFS Postlaunch Technical Report Series Stanford B. Hooker, Editor NASA Goddard Space Flight Center Greenbelt, Maryland Elaine R. Firestone, Senior Technical Editor SAIC General Sciences Corporation Beltsville, Maryland Volume 9, SeaWiFS Postlaunch Calibration and Validation Analyses, Part 1 Charles R. McClain NASA Goddard Space Flight Center Greenbelt, Maryland Ewa J. Ainsworth, Robert A. Barnes, Robert E. Eplee, Jr., Frederick S. Patt, and Wayne D. Robinson SAIC General Sciences Corporation Beltsville, Maryland Menghua Wang University of Maryland, Baltimore County Baltimore, Maryland Sean W. Bailey Futuretech Corporation Greenbelt, Maryland McClain, Ainsworth, Barnes, Eplee, Patt, Robinson, Wang, and Bailey Table of Contents Prologue .................................................................................................1 1. SeaWiFS Postlaunch Calibration and Validation Overview ............................................4 1.1 Introduction .........................................................................................4 1.2 Data Processing Algorithms ..........................................................................5 1.3 Sensor Calibration ...................................................................................5 1.4 Product Validation .................................................................................10 1.5 Summary ...........................................................................................11 2. Cloud-Top Radiance Analysis -
Satellite Remote Sensing: Ocean Color☆ P Jeremy Werdell and Charles R Mcclain, NASA Goddard Space Flight Center, Greenbelt, MD, United States
Satellite Remote Sensing: Ocean Color☆ P Jeremy Werdell and Charles R McClain, NASA Goddard Space Flight Center, Greenbelt, MD, United States © 2019 Elsevier Ltd. All rights reserved. Introduction 444 Ocean Color Theoretical and Observational Basis 444 Satellite Ocean Color Methodology 446 Sensor Design and Performance 447 Postlaunch Sensor Calibration Stability 449 Atmospheric Correction 449 Bio-optical Algorithms 450 Product Validation 450 Satellite Ocean Color Example Applications 451 Conclusions and Future Directions 454 References 454 Nomenclature Symbol Description (Units) À a Absorption coefficient (m 1) À1 aCDOM Absorption coefficient for colored dissolved organic matter (m ) À1 aNAP Absorption coefficient for nonalgal particles (m ) À1 aph Absorption coefficient for phytoplankton (m ) À1 aw Absorption coefficient for seawater (m ) À1 bb Backscattering coefficient (m ) À1 bb,NAP Backscattering coefficient for nonalgal particles (m ) À1 bb,ph Backscattering coefficient for phytoplankton (m ) À1 bb,w Backscattering coefficient for seawater (m ) À [Chla] Concentration of chlorophyll-a (mg m 3) À2 À1 Ed Downwelling irradiance (mWcm nm ) À2 À1 Eu Upwelling irradiance (mWcm nm ) f Factor that relates R to a and bb (unitless) À2 À1 F0 Solar irradiance (mWcm nm ) À1 Kd Diffuse attenuation coefficient of downwelling irradiance (m ) À1 KLu Diffuse attenuation coefficient of upwelling radiance (m ) À2 À1 À1 La Aerosol radiance (mWcm nm sr ) À2 À1 À1 Lf Foam (white cap) radiance (mWcm nm sr ) À2 À1 À1) Lg Sun glint radiance (mWcm nm sr À2