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The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) was launched to the International Space Station (ISS) on June 29, 2018, and currently provides the highest spatial resolution thermal infrared (TIR) data (38 m x 69 m) available from space. In this study, we validated the ECOSTRESS level-2 Land Surface Temperature (LST) and emissivity product at fourteen global sites to Stage-1 status. Two primary methods are recommended for the validation of LST data: Temperature-based (T-based) and Radiance-based (R-based) methods. The T-based method requires calibrated measurements of the ground leaving radiance concurrent with the satellite overpass. In contrast, the R-based method uses a radiative closure simulation with external atmospheric profiles and an a priori knowledge of surface emissivity. Using these standard methods, we validated 1139 ECOSTRESS clear-sky observations between August 1, 2018, and March 31, 2020. For LST, the results show good agreement with ground-based measurements with an average root mean square error (RMSE) of 1.07 K, mean absolute error (MAE) of 0.40 K, and r²>0.988 at all sites. However, a cold bias of ~0.75 K was identified for temperatures below 295 K linked to calibration issues that will be addressed in future reprocessing of the data. Retrieved emissivity comparisons with laboratory spectra had an RMSE of 0.023 (2.3%) for all bands on average. With the decommissioning of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) on Terra in 2023, the multispectral and high-spatial-resolution characteristics of ECOSTRESS data serve as a pathfinder to the National Aeronautics and Space Administration's (NASA) Surface Biology and Geology (SBG) designated observable with an expected launch in 2026.
Glynn C. Hulley; Frank M. Gottsche; Gerardo Rivera; Simon J. Hook; Robert J. Freepartner; Maria Anna Martin; Kerry Cawse-Nicholson; William R. Johnson. Validation and Quality Assessment of the ECOSTRESS Level-2 Land Surface Temperature and Emissivity Product. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -23.
AMA StyleGlynn C. Hulley, Frank M. Gottsche, Gerardo Rivera, Simon J. Hook, Robert J. Freepartner, Maria Anna Martin, Kerry Cawse-Nicholson, William R. Johnson. Validation and Quality Assessment of the ECOSTRESS Level-2 Land Surface Temperature and Emissivity Product. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-23.
Chicago/Turabian StyleGlynn C. Hulley; Frank M. Gottsche; Gerardo Rivera; Simon J. Hook; Robert J. Freepartner; Maria Anna Martin; Kerry Cawse-Nicholson; William R. Johnson. 2021. "Validation and Quality Assessment of the ECOSTRESS Level-2 Land Surface Temperature and Emissivity Product." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-23.
Surface-atmosphere fluxes and their drivers vary across space and time. A growing area of interest is in downscaling, localizing, and/or resolving sub-grid scale energy, water, and carbon fluxes and drivers. Existing downscaling methods require inputs of land surface properties at relatively high spatial (e.g., sub-kilometer) and temporal (e.g., hourly) resolutions, but many observed land surface drivers are not available at these resolutions. We evaluate an approach to overcome this challenge for land surface temperature (LST), a World Meteorological Organization Essential Climate Variable and a key driver for surface heat fluxes. The Chequamegon Heterogenous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors (CHEESEHEAD19) field experiment provided a scalable testbed. We downscaled LST from satellites (GOES-16 and ECOSTRESS) with further refinement using airborne hyperspectral imagery. Temporally and spatially downscaled LST compared well to observations from a network of 20 micrometeorological towers and airborne in addition to Landsat-based LST retrieval and drone-based LST observed at one tower site. The downscaled 50-meter hourly LST showed good relationships with tower (r2=0.79, precision=3.5 K) and airborne (r2=0.75, precision=2.4 K) observations over space and time, with precision lower over wetlands and lakes, and some improvement for capturing spatio-temporal variation compared to geostationary satellite. Further downscaling to 10 m using hyperspectral imagery resolved hotspots and cool spots on the landscape detected in drone LST, with significant improvement in precision by 1.3 K. These results demonstrate a simple pathway for multi-sensor retrieval of high space and time resolution LST.
Ankur Rashmikant DesaiiD; Anam Munir Khan; Ting Zheng; Sreenath Paleri; Brian J. ButterworthiD; Temple R. LeeiD; Joshua B FisheriD; Glynn Hulley; Tania KleynhansiD; Aaron Gerace; Philip A Townsend; Paul Christopher Stoy; Stefan MetzgeriD. Multi-sensor approach for high space and time resolution land surface temperature. 2021, 1 .
AMA StyleAnkur Rashmikant DesaiiD, Anam Munir Khan, Ting Zheng, Sreenath Paleri, Brian J. ButterworthiD, Temple R. LeeiD, Joshua B FisheriD, Glynn Hulley, Tania KleynhansiD, Aaron Gerace, Philip A Townsend, Paul Christopher Stoy, Stefan MetzgeriD. Multi-sensor approach for high space and time resolution land surface temperature. . 2021; ():1.
Chicago/Turabian StyleAnkur Rashmikant DesaiiD; Anam Munir Khan; Ting Zheng; Sreenath Paleri; Brian J. ButterworthiD; Temple R. LeeiD; Joshua B FisheriD; Glynn Hulley; Tania KleynhansiD; Aaron Gerace; Philip A Townsend; Paul Christopher Stoy; Stefan MetzgeriD. 2021. "Multi-sensor approach for high space and time resolution land surface temperature." , no. : 1.
The Combined ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) MODIS (Moderate Resolution Imaging Spectroradiometer) Emissivity over Land (CAMEL) Version 2 (V002) has been available since March 2019 from the NASA LP DAAC (Land Processes Distributed Active Archive Center) and provides global, monthly infrared land surface emissivity and uncertainty at 0.05 degrees (~5 km) resolution. A climatology of the CAMEL V002 product is now available at the same spatial, temporal, and spectral resolution, covering the CAMEL record from 2000 to 2016. Characterization of the climatology over case sites and IGBP (International Geosphere-Biosphere Programme) land cover categories shows the climatology is a stable representation of the monthly CAMEL emissivity. Time series of the monthly CAMEL V002 product show realistic seasonal changes but also reveal subtle artifacts known to be from calibration and processing errors in the MODIS MxD11 emissivity. The use of the CAMEL V002 climatology mitigates many of these time dependent errors by providing an emissivity estimate which represents the complete 16-year record. The CAMEL V002 climatology’s integration into RTTOV (Radiative Transfer for TOVS) v12 is demonstrated through the simulation of IASI (Infrared Atmospheric Sounding Interferometer) radiances. Improved stability in CAMEL Version 3 is expected in the future with the incorporation of the new MxD21 and VIIRS VNP21 emissivity products in MODIS Collection 6.1.
Michelle Loveless; E. Borbas; Robert Knuteson; Kerry Cawse-Nicholson; Glynn Hulley; Simon Hook. Climatology of the Combined ASTER MODIS Emissivity over Land (CAMEL) Version 2. Remote Sensing 2020, 13, 111 .
AMA StyleMichelle Loveless, E. Borbas, Robert Knuteson, Kerry Cawse-Nicholson, Glynn Hulley, Simon Hook. Climatology of the Combined ASTER MODIS Emissivity over Land (CAMEL) Version 2. Remote Sensing. 2020; 13 (1):111.
Chicago/Turabian StyleMichelle Loveless; E. Borbas; Robert Knuteson; Kerry Cawse-Nicholson; Glynn Hulley; Simon Hook. 2020. "Climatology of the Combined ASTER MODIS Emissivity over Land (CAMEL) Version 2." Remote Sensing 13, no. 1: 111.
It is important to understand the distribution of irrigated and non-irrigated vegetation in rapidly expanding urban areas that are experiencing climate-induced changes in water availability, such as Los Angeles, California. Mapping irrigated vegetation in Los Angeles is necessary for developing sustainable water use practices and accurately accounting for the megacity’s carbon exchange and water balance changes. However, pre-existing maps of irrigated vegetation are largely limited to agricultural regions and are too coarse to resolve heterogeneous urban landscapes. Previous research suggests that irrigation has a strong cooling effect on vegetation, especially in semi-arid environments. The July 2018 launch of the ECOsystem Spaceborne Thermal Radiometer on Space Station (ECOSTRESS) offers an opportunity to test this hypothesis using retrieved land surface temperature (LST) data in complex, heterogeneous urban/non-urban environments. In this study, we leverage Landsat 8 optical imagery and 30 m sharpened afternoon summertime ECOSTRESS LST, then apply very high-resolution (0.6–10 m) vegetation fraction weighting to produce a map of irrigated and non-irrigated vegetation in Los Angeles. This classification was compared to other classifications using different combinations of sensors in order to offer a preliminary accuracy and uncertainty assessment. This approach verifies that ECOSTRESS LST data provides an accurate map (98.2% accuracy) of irrigated urban vegetation in southern California that has the potential to reduce uncertainties in regional carbon and hydrological cycle models.
Red Coleman; Natasha Stavros; Glynn Hulley; Nicholas Parazoo. Comparison of Thermal Infrared-Derived Maps of Irrigated and Non-Irrigated Vegetation in Urban and Non-Urban Areas of Southern California. Remote Sensing 2020, 12, 4102 .
AMA StyleRed Coleman, Natasha Stavros, Glynn Hulley, Nicholas Parazoo. Comparison of Thermal Infrared-Derived Maps of Irrigated and Non-Irrigated Vegetation in Urban and Non-Urban Areas of Southern California. Remote Sensing. 2020; 12 (24):4102.
Chicago/Turabian StyleRed Coleman; Natasha Stavros; Glynn Hulley; Nicholas Parazoo. 2020. "Comparison of Thermal Infrared-Derived Maps of Irrigated and Non-Irrigated Vegetation in Urban and Non-Urban Areas of Southern California." Remote Sensing 12, no. 24: 4102.
Research on heatwaves has gained significant impetus over the past decade due to a warming planet and rapid 21st century urbanization. This study examines driving factors influencing heatwave trends and interannual variability across Southern California (SoCal) from 1950–2020. Inland urban areas of Los Angeles county are the most susceptible to heatwaves with strong increasing trends in frequency, duration, and intensity that are closely tied to nighttime warming. Coastal and rural areas are less impacted but show a significant increase in heatwave frequency over the past two decades. Heatwave nighttime temperatures combined with high humidity have been increasing at a rapid rate of ~1°C/decade since the 1980's—elevating heat‐stress and mortality risk to vulnerable urban communities. The increased nighttime humidity is associated with an anomalous moisture source off the coast of Baja California that has intensified over the past decade and is linked to ocean warming trends and changes in the California current system. Heatwaves are starting earlier and ending later in the year for urban regions. This augments public health risks and sets the stage for more intense fall wildfires by enhancing the drying of fuels. Droughts and heatwaves are strongly linked, particularly in inland urban and rural areas that have a high statistical probability of heatwaves increasing in frequency (42%), duration (26%), and daily mean temperature (2.2%) during severe drought conditions. Better understanding of heatwave climate drivers and underlying physical processes could help with prediction skill, in addition to providing effective data‐driven recommendations for mitigation efforts in SoCal's vulnerable urban regions.
Glynn C. Hulley; Benedicte Dousset; Brian H. Kahn. Rising Trends in Heatwave Metrics Across Southern California. Earth's Future 2020, 8, 1 .
AMA StyleGlynn C. Hulley, Benedicte Dousset, Brian H. Kahn. Rising Trends in Heatwave Metrics Across Southern California. Earth's Future. 2020; 8 (7):1.
Chicago/Turabian StyleGlynn C. Hulley; Benedicte Dousset; Brian H. Kahn. 2020. "Rising Trends in Heatwave Metrics Across Southern California." Earth's Future 8, no. 7: 1.
The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) was launched to the International Space Station on 29 June 2018 by the National Aeronautics and Space Administration (NASA). The primary science focus of ECOSTRESS is centered on evapotranspiration (ET), which is produced as Level‐3 (L3) latent heat flux (LE) data products. These data are generated from the Level‐2 land surface temperature and emissivity product (L2_LSTE), in conjunction with ancillary surface and atmospheric data. Here, we provide the first validation (Stage 1, preliminary) of the global ECOSTRESS clear‐sky ET product (L3_ET_PT‐JPL, Version 6.0) against LE measurements at 82 eddy covariance sites around the world. Overall, the ECOSTRESS ET product performs well against the site measurements (clear‐sky instantaneous/time of overpass: r2 = 0.88; overall bias = 8%; normalized root‐mean‐square error, RMSE = 6%). ET uncertainty was generally consistent across climate zones, biome types, and times of day (ECOSTRESS samples the diurnal cycle), though temperate sites are overrepresented. The 70‐m‐high spatial resolution of ECOSTRESS improved correlations by 85%, and RMSE by 62%, relative to 1‐km pixels. This paper serves as a reference for the ECOSTRESS L3 ET accuracy and Stage 1 validation status for subsequent science that follows using these data.
Joshua B. Fisher; Brian Lee; Adam J. Purdy; Gregory H. Halverson; Matthew B. Dohlen; Kerry Cawse‐Nicholson; Audrey Wang; Ray G. Anderson; Bruno Aragon; M. Altaf Arain; Dennis D. Baldocchi; John M. Baker; Hélène Barral; Carl J. Bernacchi; Christian Bernhofer; Sébastien C. Biraud; Gil Bohrer; Nathaniel Brunsell; Bernard Cappelaere; Saulo Castro‐Contreras; Junghwa Chun; Bryan J. Conrad; Edoardo Cremonese; Jérôme Demarty; Ankur R. Desai; Anne De Ligne; Lenka Foltýnová; Michael L. Goulden; Timothy J. Griffis; Thomas Grünwald; Mark S. Johnson; Minseok Kang; Dave Kelbe; Natalia Kowalska; Jong‐Hwan Lim; Ibrahim Maïnassara; Matthew McCabe; Justine E.C. Missik; Binayak P. Mohanty; Caitlin E. Moore; Laura Morillas; Ross Morrison; J. William Munger; Gabriela Posse; Andrew D. Richardson; Eric S. Russell; Youngryel Ryu; Arturo Sanchez‐Azofeifa; Marius Schmidt; Efrat Schwartz; Iain Sharp; Ladislav Šigut; Yao Tang; Glynn Hulley; Martha Anderson; Christopher Hain; Andrew French; Eric Wood; Simon Hook. ECOSTRESS: NASA's Next Generation Mission to Measure Evapotranspiration From the International Space Station. Water Resources Research 2020, 56, 1 .
AMA StyleJoshua B. Fisher, Brian Lee, Adam J. Purdy, Gregory H. Halverson, Matthew B. Dohlen, Kerry Cawse‐Nicholson, Audrey Wang, Ray G. Anderson, Bruno Aragon, M. Altaf Arain, Dennis D. Baldocchi, John M. Baker, Hélène Barral, Carl J. Bernacchi, Christian Bernhofer, Sébastien C. Biraud, Gil Bohrer, Nathaniel Brunsell, Bernard Cappelaere, Saulo Castro‐Contreras, Junghwa Chun, Bryan J. Conrad, Edoardo Cremonese, Jérôme Demarty, Ankur R. Desai, Anne De Ligne, Lenka Foltýnová, Michael L. Goulden, Timothy J. Griffis, Thomas Grünwald, Mark S. Johnson, Minseok Kang, Dave Kelbe, Natalia Kowalska, Jong‐Hwan Lim, Ibrahim Maïnassara, Matthew McCabe, Justine E.C. Missik, Binayak P. Mohanty, Caitlin E. Moore, Laura Morillas, Ross Morrison, J. William Munger, Gabriela Posse, Andrew D. Richardson, Eric S. Russell, Youngryel Ryu, Arturo Sanchez‐Azofeifa, Marius Schmidt, Efrat Schwartz, Iain Sharp, Ladislav Šigut, Yao Tang, Glynn Hulley, Martha Anderson, Christopher Hain, Andrew French, Eric Wood, Simon Hook. ECOSTRESS: NASA's Next Generation Mission to Measure Evapotranspiration From the International Space Station. Water Resources Research. 2020; 56 (4):1.
Chicago/Turabian StyleJoshua B. Fisher; Brian Lee; Adam J. Purdy; Gregory H. Halverson; Matthew B. Dohlen; Kerry Cawse‐Nicholson; Audrey Wang; Ray G. Anderson; Bruno Aragon; M. Altaf Arain; Dennis D. Baldocchi; John M. Baker; Hélène Barral; Carl J. Bernacchi; Christian Bernhofer; Sébastien C. Biraud; Gil Bohrer; Nathaniel Brunsell; Bernard Cappelaere; Saulo Castro‐Contreras; Junghwa Chun; Bryan J. Conrad; Edoardo Cremonese; Jérôme Demarty; Ankur R. Desai; Anne De Ligne; Lenka Foltýnová; Michael L. Goulden; Timothy J. Griffis; Thomas Grünwald; Mark S. Johnson; Minseok Kang; Dave Kelbe; Natalia Kowalska; Jong‐Hwan Lim; Ibrahim Maïnassara; Matthew McCabe; Justine E.C. Missik; Binayak P. Mohanty; Caitlin E. Moore; Laura Morillas; Ross Morrison; J. William Munger; Gabriela Posse; Andrew D. Richardson; Eric S. Russell; Youngryel Ryu; Arturo Sanchez‐Azofeifa; Marius Schmidt; Efrat Schwartz; Iain Sharp; Ladislav Šigut; Yao Tang; Glynn Hulley; Martha Anderson; Christopher Hain; Andrew French; Eric Wood; Simon Hook. 2020. "ECOSTRESS: NASA's Next Generation Mission to Measure Evapotranspiration From the International Space Station." Water Resources Research 56, no. 4: 1.
The NASA ESA Temperature Sensing Experiment (NET-Sense) is a NASA and ESA funded campaign in support of the Copernicus Land Surface Temperature Monitoring (LSTM) satellite mission.
The LSTM mission would carry a calibrated, high spatial-temporal resolution thermal infrared imager whose data would be used to provide the land-surface temperature information required for such applications as evapotranspiration estimation at the European field-scale. The LSTM mission responds to priority requirements of the agricultural user community for improving sustainable agricultural productivity in a world of increasing water scarcity and variability.
As part of the effort to LSTM mission development effort, the first non-US flights of NASA JPL’s state-of-the-art Hyperspectral Thermal Emission Spectrometer (HyTES) were conducted on a UK research aircraft in both the UK and Italy in June and July 2019. HyTES is an airborne thermal hyperspectral imager providing extremely high quality and radiometrically precise infrared radiances within 256 spectral channels across the spectral range 7.5 to 12 µm, with the primary aim to map LST and surface spectral emissivity. Flights in Italy were accompanied by the HyPLANT and TASI instruments, operated by FZ-Juelich, Germany installed aboard a second aircraft from CzechGlobe (CZ).
We provide an overview of the NET-Sense campaign, example results from HyTES and comparisons to in situ LST and surface spectral emissivity data collected co-incident with the aircraft overflights using tower-mounted radiometers and portable FTIR spectrometers adapted for the purpose. We explain the integration of NET-Sense into the broader science strategy for the LSTM mission, and highlight planned activities for the coming years, including NET-Sense 2020 European campaign plans.
Martin Wooster; James Johnson; Tom Dowling; Mark De Jong; Mark Grosvenor; Mary Langsdale; Simon Hook; Bjorn Eng; William Johnson; Gerardo Rivera; Glynn Hulley; Dirk Schüttemeyer; Benjamin Koetz. Airborne mapping and in situ validation of European land surface temperature using the NASA-JPL HyTES sensor. Results from the 2019 European NET-Sense Campaign in support of the Copernicus High Priority Candidate satellite mission development. 2020, 1 .
AMA StyleMartin Wooster, James Johnson, Tom Dowling, Mark De Jong, Mark Grosvenor, Mary Langsdale, Simon Hook, Bjorn Eng, William Johnson, Gerardo Rivera, Glynn Hulley, Dirk Schüttemeyer, Benjamin Koetz. Airborne mapping and in situ validation of European land surface temperature using the NASA-JPL HyTES sensor. Results from the 2019 European NET-Sense Campaign in support of the Copernicus High Priority Candidate satellite mission development. . 2020; ():1.
Chicago/Turabian StyleMartin Wooster; James Johnson; Tom Dowling; Mark De Jong; Mark Grosvenor; Mary Langsdale; Simon Hook; Bjorn Eng; William Johnson; Gerardo Rivera; Glynn Hulley; Dirk Schüttemeyer; Benjamin Koetz. 2020. "Airborne mapping and in situ validation of European land surface temperature using the NASA-JPL HyTES sensor. Results from the 2019 European NET-Sense Campaign in support of the Copernicus High Priority Candidate satellite mission development." , no. : 1.
Validation of emissivity (ε) retrievals from spaceborne thermal infrared (TIR) sensors typically requires spatial extrapolations over several orders of magnitude for a comparison between centimeter-scale laboratory ε measurements and the common decameter and lower resolution of spaceborne TIR data. In the case of NASA’s Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) temperature and ε separation algorithm (TES), this extrapolation becomes especially challenging because TES was originally designed for the geologic surface of Earth, which is typically heterogeneous even at centimeter and decameter scales. Here, we used the airborne TIR hyperspectral Mako sensor with its 2.2 m/pixel resolution, to bridge this scaling issue and robustly link between ASTER TES 90 m/pixel emissivity retrievals and laboratory ε measurements from the Algodones dune field in southern California, USA. The experimental setup included: (i) Laboratory XRD, grain size, and TIR spectral measurements; (ii) radiosonde launches at the time of the two Mako overpasses for atmospheric corrections; (iii) ground-based thermal measurements for calibration, and (iv) analyses of ASTER day and night ε retrievals from 21 different acquisitions. We show that while cavity radiation leads to a 2% to 4% decrease in the effective emissivity contrast of fully resolved scene elements (e.g., slipface slopes and interdune flats), spectral variability of the site when imaged at 90 m/pixel is below 1%, because at this scale the dune field becomes an effectively homogeneous mixture of the different dune elements. We also found that adsorption of atmospheric moisture to grain surfaces during the predawn hours increased the effective ε of the dune surface by up to 0.04. The accuracy of ASTER’s daytime emissivity retrievals using each of the three available atmospheric correction protocols was better than 0.01 and within the target performance of ASTER’s standard emissivity product. Nighttime emissivity retrievals had lower precision (
Amit Mushkin; Alan R. Gillespie; Elsa A. Abbott; Jigjidsurengiin Batbaatar; Glynn Hulley; Howard Tan; David M. Tratt; Kerry N. Buckland. Validation of ASTER Emissivity Retrieval Using the Mako Airborne TIR Imaging Spectrometer at the Algodones Dune Field in Southern California, USA. Remote Sensing 2020, 12, 815 .
AMA StyleAmit Mushkin, Alan R. Gillespie, Elsa A. Abbott, Jigjidsurengiin Batbaatar, Glynn Hulley, Howard Tan, David M. Tratt, Kerry N. Buckland. Validation of ASTER Emissivity Retrieval Using the Mako Airborne TIR Imaging Spectrometer at the Algodones Dune Field in Southern California, USA. Remote Sensing. 2020; 12 (5):815.
Chicago/Turabian StyleAmit Mushkin; Alan R. Gillespie; Elsa A. Abbott; Jigjidsurengiin Batbaatar; Glynn Hulley; Howard Tan; David M. Tratt; Kerry N. Buckland. 2020. "Validation of ASTER Emissivity Retrieval Using the Mako Airborne TIR Imaging Spectrometer at the Algodones Dune Field in Southern California, USA." Remote Sensing 12, no. 5: 815.
Rapid 21st century urbanization combined with anthropogenic climate warming are significantly increasing heat-related health threats in cities worldwide. In Los Angeles (LA), increasing trends in extreme heat are expected to intensify and exacerbate the urban heat island effect, leading to greater health risks for vulnerable populations. Partnerships between city policymakers and scientists are becoming more important as the need to provide data-driven recommendations for sustainability and mitigation efforts becomes critical. Here we present a model to produce heat vulnerability index (HVI) maps driven by surface temperature data from National Aeronautics and Space Administration’s (NASA) new Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) thermal infrared sensor. ECOSTRESS was launched in June 2018 with the capability to image fine-scale urban temperatures at a 70 m resolution throughout different times of the day and night. The HVI model further includes information on socio-demographic data, green vegetation abundance, and historical heatwave temperatures from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the Aqua spacecraft since 2002. During a period of high heat in July 2018, we identified the five most vulnerable communities at a sub-city block scale in the LA region. The persistence of high HVI throughout the day and night in these areas indicates a clear and urgent need for implementing cooling technologies and green infrastructure to curb future warming.
Glynn Hulley; Sarah Shivers; Erin Wetherley; Robert Cudd. New ECOSTRESS and MODIS Land Surface Temperature Data Reveal Fine-Scale Heat Vulnerability in Cities: A Case Study for Los Angeles County, California. Remote Sensing 2019, 11, 2136 .
AMA StyleGlynn Hulley, Sarah Shivers, Erin Wetherley, Robert Cudd. New ECOSTRESS and MODIS Land Surface Temperature Data Reveal Fine-Scale Heat Vulnerability in Cities: A Case Study for Los Angeles County, California. Remote Sensing. 2019; 11 (18):2136.
Chicago/Turabian StyleGlynn Hulley; Sarah Shivers; Erin Wetherley; Robert Cudd. 2019. "New ECOSTRESS and MODIS Land Surface Temperature Data Reveal Fine-Scale Heat Vulnerability in Cities: A Case Study for Los Angeles County, California." Remote Sensing 11, no. 18: 2136.
Le Kuai; Olga V. Kalashnikova; Francesca M. Hopkins; Glynn C. Hulley; Huikyo Lee; Michael J. Garay; Riley M. Duren; John R. Worden; Simon J. Hook. Quantification of Ammonia Emissions With High Spatial Resolution Thermal Infrared Observations From the Hyperspectral Thermal Emission Spectrometer (HyTES) Airborne Instrument. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2019, 12, 4798 -4812.
AMA StyleLe Kuai, Olga V. Kalashnikova, Francesca M. Hopkins, Glynn C. Hulley, Huikyo Lee, Michael J. Garay, Riley M. Duren, John R. Worden, Simon J. Hook. Quantification of Ammonia Emissions With High Spatial Resolution Thermal Infrared Observations From the Hyperspectral Thermal Emission Spectrometer (HyTES) Airborne Instrument. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2019; 12 (12):4798-4812.
Chicago/Turabian StyleLe Kuai; Olga V. Kalashnikova; Francesca M. Hopkins; Glynn C. Hulley; Huikyo Lee; Michael J. Garay; Riley M. Duren; John R. Worden; Simon J. Hook. 2019. "Quantification of Ammonia Emissions With High Spatial Resolution Thermal Infrared Observations From the Hyperspectral Thermal Emission Spectrometer (HyTES) Airborne Instrument." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12, no. 12: 4798-4812.
Our objective is to develop a framework for deriving long term, consistent Land Surface Temperatures (LSTs) from Geostationary (GEO) satellites that is able to account for satellite sensor updates. Specifically, we use the Radiative Transfer for TOVS (RTTOV) model driven with Modern-Era Retrospective Analysis for Research and Applications (MERRA-2) information and Combined ASTER and MODIS Emissivity over Land (CAMEL) products. We discuss the results from our comparison of the Geostationary Operational Environmental Satellite East (GOES-E) with the MODIS Land Surface Temperature and Emissivity (MOD11) products, as well as several independent sources of ground observations, for daytime and nighttime independently. Based on a six-year record at instantaneous time scale (2004–2009), most LST estimates are within one std from the mean observed value and the bias is under 1% of the mean. It was also shown that at several ground sites, the diurnal cycle of LST, as averaged over six years, is consistent with a similar record generated from satellite observations. Since the evaluation of the GOES-E LST estimates occurred at every hour, day and night, the data are well suited to address outstanding issues related to the temporal variability of LST, specifically, the diurnal cycle and the amplitude of the diurnal cycle, which are not well represented in LST retrievals form Low Earth Orbit (LEO) satellites.
Rachel T. Pinker; Yingtao Ma; Wen Chen; Glynn Hulley; Eva Borbas; Tanvir Islam; Chris Hain; Kerry Cawse-Nicholson; Simon Hook; Jeff Basara. Towards a Unified and Coherent Land Surface Temperature Earth System Data Record from Geostationary Satellites. Remote Sensing 2019, 11, 1399 .
AMA StyleRachel T. Pinker, Yingtao Ma, Wen Chen, Glynn Hulley, Eva Borbas, Tanvir Islam, Chris Hain, Kerry Cawse-Nicholson, Simon Hook, Jeff Basara. Towards a Unified and Coherent Land Surface Temperature Earth System Data Record from Geostationary Satellites. Remote Sensing. 2019; 11 (12):1399.
Chicago/Turabian StyleRachel T. Pinker; Yingtao Ma; Wen Chen; Glynn Hulley; Eva Borbas; Tanvir Islam; Chris Hain; Kerry Cawse-Nicholson; Simon Hook; Jeff Basara. 2019. "Towards a Unified and Coherent Land Surface Temperature Earth System Data Record from Geostationary Satellites." Remote Sensing 11, no. 12: 1399.
Fire is a widespread Earth system process with important carbon and climate feedbacks. Multispectral remote sensing has enabled mapping of global spatiotemporal patterns of fire and fire effects, which has significantly improved our understanding of interactions between ecosystems, climate, humans and fire. With several upcoming spaceborne hyperspectral missions like the Environmental Mapping And Analysis Program (EnMAP), the Hyperspectral Infrared Imager (HyspIRI) and the Precursore Iperspettrale Della Missione Applicativa (PRISMA), we provide a review of the state-of-the-art and perspectives of hyperspectral remote sensing of fire. Hyperspectral remote sensing leverages information in many (often more than 100) narrow (smaller than 20 nm) spectrally contiguous bands, in contrast to multispectral remote sensing of few (up to 15) non-contiguous wider (greater than 20 nm) bands. To date, hyperspectral fire applications have primarily used airborne data in the visible to short-wave infrared region (VSWIR, 0.4 to 2.5 μm). This has resulted in detailed and accurate discrimination and quantification of fuel types and condition, fire temperatures and emissions, fire severity and vegetation recovery. Many of these applications use processing techniques that take advantage of the high spectral resolution and dimensionality such as advanced spectral mixture analysis. So far, hyperspectral VSWIR fire applications are based on a limited number of airborne acquisitions, yet techniques will approach maturity for larger scale application when spaceborne imagery becomes available. Recent innovations in airborne hyperspectral thermal (8 to 12 μm) remote sensing show potential to improve retrievals of temperature and emissions from active fires, yet these applications need more investigation over more fires to verify consistency over space and time, and overcome sensor saturation issues. Furthermore, hyperspectral information and structural data from, for example, light detection and ranging (LiDAR) sensors are highly complementary. Their combined use has demonstrated advantages for fuel mapping, yet its potential for post-fire severity and combustion retrievals remains largely unexplored.
Sander Veraverbeke; Philip Dennison; Ioannis Gitas; Glynn Hulley; Olga Kalashnikova; Thomas Katagis; Le Kuai; Ran Meng; Dar Roberts; Natasha Stavros. Hyperspectral remote sensing of fire: State-of-the-art and future perspectives. Remote Sensing of Environment 2018, 216, 105 -121.
AMA StyleSander Veraverbeke, Philip Dennison, Ioannis Gitas, Glynn Hulley, Olga Kalashnikova, Thomas Katagis, Le Kuai, Ran Meng, Dar Roberts, Natasha Stavros. Hyperspectral remote sensing of fire: State-of-the-art and future perspectives. Remote Sensing of Environment. 2018; 216 ():105-121.
Chicago/Turabian StyleSander Veraverbeke; Philip Dennison; Ioannis Gitas; Glynn Hulley; Olga Kalashnikova; Thomas Katagis; Le Kuai; Ran Meng; Dar Roberts; Natasha Stavros. 2018. "Hyperspectral remote sensing of fire: State-of-the-art and future perspectives." Remote Sensing of Environment 216, no. : 105-121.
Infrared surface emissivity is needed for the calculation of net longwave radiation, a critical parameter in weather and climate models and Earth’s radiation budget. Due to a prior lack of spatially and temporally variant global broadband emissivity (BBE) measurements of the surface, it is common practice in land surface and climate models to set BBE to a single constant over the globe. This can lead to systematic biases in the estimated net and longwave radiation for any particular location and time of year. Under the National Aeronautics and Space Administration’s (NASA) Making Earth System Data Records for Use in Research Environments (MEaSUREs) project, a new global, high spectral resolution land surface emissivity dataset has recently been made available at monthly at 0.05 degree resolution since 2000. Called the Combined ASTER MODIS Emissivity over Land (CAMEL), this dataset is created by the merging of the MODIS baseline-fit emissivity database developed at the University of Wisconsin-Madison and the ASTER Global Emissivity Dataset (GED) produced at the Jet Propulsion Laboratory. CAMEL has 13 hinge points between 3.6–14.3 µm which are expanded to cover 417 infrared spectral channels within the same wavelength region using a principal component regression approach. This work presents the method for calculating BBE using the new CAMEL dataset. BBE is computed via numerical integration over the CAMEL High Spectral Resolution product for two different wavelength ranges—3.6–14.3 µm which takes advantage of the full, available CAMEL spectra and 8.0–13.5 µm which has been determined to be an optimal range for computing the most representative all wavelength, longwave net radiation. CAMEL BBE uncertainty estimates are computed, and comparisons are made to BBE computed from lab validation data for selected case sites. Variations of BBE over time and land cover classification schemes are investigated and converted into flux to demonstrate the equivalent error in longwave radiation which would be made by the use of a single, constant BBE value. Misrepresentations in BBE by 0.05 at 310 K corresponds to potential errors in longwave radiation of over 25 W/m2.
Michelle Feltz; Eva Borbas; Robert Knuteson; Glynn Hulley; Simon Hook. The Combined ASTER and MODIS Emissivity over Land (CAMEL) Global Broadband Infrared Emissivity Product. Remote Sensing 2018, 10, 1027 .
AMA StyleMichelle Feltz, Eva Borbas, Robert Knuteson, Glynn Hulley, Simon Hook. The Combined ASTER and MODIS Emissivity over Land (CAMEL) Global Broadband Infrared Emissivity Product. Remote Sensing. 2018; 10 (7):1027.
Chicago/Turabian StyleMichelle Feltz; Eva Borbas; Robert Knuteson; Glynn Hulley; Simon Hook. 2018. "The Combined ASTER and MODIS Emissivity over Land (CAMEL) Global Broadband Infrared Emissivity Product." Remote Sensing 10, no. 7: 1027.
Thermal sensors onboard Landsat satellites have been underutilized due to the lack of consistent and accurate methodologies for retrieving the land surface temperature (LST) at global scales over all land cover types. We present an operational algorithm for generating Landsat LST consistently for all sensors that will be implemented by the United States Geological Survey/The National Aeronautics and Space Administration and made available at the Land Processes Distributed Active Archive Center. The LST algorithm involves three steps. The observed thermal radiance is atmospherically corrected using a radiative transfer model and reanalysis data. The Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Emissivity Data Set version 3 is spectrally adjusted and then modified to account for vegetation phenology and snow cover using Landsat visible-shortwave infrared data. The LST is retrieved by inverting the atmospherically and emissivity corrected Landsat radiances with a lookup-table approach. Landsat-derived emissivities were validated at two pseudoinvariant sand dune sites within an average absolute error of 0.54% when compared with laboratory measurements. The Landsat LST retrievals were validated with in situ observations from four surface radiation budget network (SURFRAD) sites, and two inland water bodies (Salton Sea and Lake Tahoe) in the USA. The LST retrievals for Landsat 5 and 7 had a mean bias (root mean square error) of 0.7 K (2.2 K) and 0.9 K (2.3 K) for the SURFRAD sites, and -0.3 K (0.6 K) and 0.4 K (0.7 K) for the inland water bodies, respectively. The operational algorithm will provide a consistent LST record from four decades of historical Landsat thermal data enabling the long-term monitoring of temperature and trends, land cover and land use changes, and improved utilization in models.
Nabin K. Malakar; Glynn C. Hulley; Simon J. Hook; Kelly Laraby; Monica Cook; John R. Schott. An Operational Land Surface Temperature Product for Landsat Thermal Data: Methodology and Validation. IEEE Transactions on Geoscience and Remote Sensing 2018, 56, 5717 -5735.
AMA StyleNabin K. Malakar, Glynn C. Hulley, Simon J. Hook, Kelly Laraby, Monica Cook, John R. Schott. An Operational Land Surface Temperature Product for Landsat Thermal Data: Methodology and Validation. IEEE Transactions on Geoscience and Remote Sensing. 2018; 56 (10):5717-5735.
Chicago/Turabian StyleNabin K. Malakar; Glynn C. Hulley; Simon J. Hook; Kelly Laraby; Monica Cook; John R. Schott. 2018. "An Operational Land Surface Temperature Product for Landsat Thermal Data: Methodology and Validation." IEEE Transactions on Geoscience and Remote Sensing 56, no. 10: 5717-5735.
Under the National Aeronautics and Space Administration’s (NASA) Making Earth System Data Records for Use in Research Environments (MEaSUREs) Land Surface Temperature and Emissivity project, a new global land surface emissivity dataset has been produced by the University of Wisconsin–Madison Space Science and Engineering Center and NASA’s Jet Propulsion Laboratory (JPL). This new dataset termed the Combined ASTER MODIS Emissivity over Land (CAMEL), is created by the merging of the UW–Madison MODIS baseline-fit emissivity dataset (UWIREMIS) and JPL’s ASTER Global Emissivity Dataset v4 (GEDv4). CAMEL consists of a monthly, 0.05° resolution emissivity for 13 hinge points within the 3.6–14.3 µm region and is extended to 417 infrared spectral channels using a principal component regression approach. An uncertainty product is provided for the 13 hinge point emissivities by combining temporal, spatial, and algorithm variability as part of a total uncertainty estimate. Part 1 of this paper series describes the methodology for creating the CAMEL emissivity product and the corresponding high spectral resolution algorithm. This paper, Part 2 of the series, details the methodology of the CAMEL uncertainty calculation and provides an assessment of the CAMEL emissivity product through comparisons with (1) ground site lab measurements; (2) a long-term Infrared Atmospheric Sounding Interferometer (IASI) emissivity dataset derived from 8 years of data; and (3) forward-modeled IASI brightness temperatures using the Radiative Transfer for TOVS (RTTOV) radiative transfer model. Global monthly results are shown for different seasons and International Geosphere-Biosphere Programme land classifications, and case study examples are shown for locations with different land surface types.
Michelle Feltz; Eva Borbas; Robert Knuteson; Glynn Hulley; Simon Hook. The Combined ASTER MODIS Emissivity over Land (CAMEL) Part 2: Uncertainty and Validation. Remote Sensing 2018, 10, 664 .
AMA StyleMichelle Feltz, Eva Borbas, Robert Knuteson, Glynn Hulley, Simon Hook. The Combined ASTER MODIS Emissivity over Land (CAMEL) Part 2: Uncertainty and Validation. Remote Sensing. 2018; 10 (5):664.
Chicago/Turabian StyleMichelle Feltz; Eva Borbas; Robert Knuteson; Glynn Hulley; Simon Hook. 2018. "The Combined ASTER MODIS Emissivity over Land (CAMEL) Part 2: Uncertainty and Validation." Remote Sensing 10, no. 5: 664.
As part of a National Aeronautics and Space Administration (NASA) MEaSUREs (Making Earth System Data Records for Use in Research Environments) Land Surface Temperature and Emissivity project, the Space Science and Engineering Center (UW-Madison) and the NASA Jet Propulsion Laboratory (JPL) developed a global monthly mean emissivity Earth System Data Record (ESDR). This new Combined ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) and MODIS (Moderate Resolution Imaging Spectroradiometer) Emissivity over Land (CAMEL) ESDR was produced by merging two current state-of-the-art emissivity datasets: the UW-Madison MODIS Infrared emissivity dataset (UW BF) and the JPL ASTER Global Emissivity Dataset Version 4 (GEDv4). The dataset includes monthly global records of emissivity and related uncertainties at 13 hinge points between 3.6–14.3 µm, as well as principal component analysis (PCA) coefficients at 5-km resolution for the years 2000 through 2016. A high spectral resolution (HSR) algorithm is provided for HSR applications. This paper describes the 13 hinge-points combination methodology and the high spectral resolutions algorithm, as well as reports the current status of the dataset.
E. Eva Borbas; Glynn Hulley; Michelle Feltz; Robert Knuteson; Simon Hook. The Combined ASTER MODIS Emissivity over Land (CAMEL) Part 1: Methodology and High Spectral Resolution Application. Remote Sensing 2018, 10, 643 .
AMA StyleE. Eva Borbas, Glynn Hulley, Michelle Feltz, Robert Knuteson, Simon Hook. The Combined ASTER MODIS Emissivity over Land (CAMEL) Part 1: Methodology and High Spectral Resolution Application. Remote Sensing. 2018; 10 (4):643.
Chicago/Turabian StyleE. Eva Borbas; Glynn Hulley; Michelle Feltz; Robert Knuteson; Simon Hook. 2018. "The Combined ASTER MODIS Emissivity over Land (CAMEL) Part 1: Methodology and High Spectral Resolution Application." Remote Sensing 10, no. 4: 643.
Land surface temperature and emissivity (LST&E) determine the total amount of upward long-wave infrared radiation emitted from the Earth's surface, making them key variables in a wide range of studies, including climate variability, land cover/use change, and the energy balance between the land and the atmosphere. LST&E products are currently produced on a routine basis using data from the MODIS instruments on the NASA EOS platforms and by the VIIRS instrument on the Suomi-NPP platform that serves as a bridge between NASA EOS and the next-generation JPSS platforms. Two new NASA LST&E products for MODIS (MxD21) and VIIRS (VNP21) will be produced during 2017 using a new approach that addresses discrepancies in accuracy and consistency between the current suite of MODIS and VIIRS LST split-window-based products. The new approach uses a temperature emissivity separation (TES) algorithm, originally developed for the ASTER instrument, to physically retrieve both LST and spectral emissivity consistently for both sensors with high accuracy and well-defined uncertainties. This study demonstrates continuity between the new MYD21 and VNP21 LST products at the <;±0.5 K level, with differences that are invariant to environmental conditions and land cover type. Furthermore, MYD21 and VNP21 retrieved emissivities matched closely in magnitude and temporal variation to within 1%-2% over two land validation sites consisting of quartz sands and grassland. Continuity between the new suite of MODIS and VIIRS LST&E products will ensure a consistent and well-characterized long-term LST&E data record for better monitoring and understanding trends in Earth system behavior.
Glynn C. Hulley; Nabin K. Malakar; Tanvir Islam; Robert J. Freepartner. NASA's MODIS and VIIRS Land Surface Temperature and Emissivity Products: A Long-Term and Consistent Earth System Data Record. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2017, 11, 522 -535.
AMA StyleGlynn C. Hulley, Nabin K. Malakar, Tanvir Islam, Robert J. Freepartner. NASA's MODIS and VIIRS Land Surface Temperature and Emissivity Products: A Long-Term and Consistent Earth System Data Record. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2017; 11 (2):522-535.
Chicago/Turabian StyleGlynn C. Hulley; Nabin K. Malakar; Tanvir Islam; Robert J. Freepartner. 2017. "NASA's MODIS and VIIRS Land Surface Temperature and Emissivity Products: A Long-Term and Consistent Earth System Data Record." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11, no. 2: 522-535.
Land surface temperature (LST) is a key climate variable for studying the energy and water balance of the earth surface and monitoring the effects of climate change. This paper presents a physics-based temperature emissivity separation (TES) algorithm for the simultaneous retrieval of LST and emissivity (LST&E) from the thermal infrared bands of the Suomi National Polar-Orbiting Partnership's Visible Infrared Imaging Radiometer Suite (VIIRS) payload. The new VIIRS LST&E product (VNP21) was developed to provide continuity with the Moderate-Resolution Imaging Spectroradiometer (MODIS) equivalent LST&E product (MxD21) product, which is available in Collection 6, and to address inconsistencies between the current MODIS and VIIRS split-window LST products. The TES algorithm uses full radiative transfer simulations to isolate the surface emitted radiance, and an emissivity calibration curve based on the variability in the surface radiance data to dynamically retrieve both LST and spectral emissivity. Furthermore, an improved water vapor scaling model was implemented to improve the accuracy and stability of the atmospheric correction for conditions with high atmospheric water vapor content. An independent assessment of the VIIRS LST retrievals was performed against in situ LST measurements over two dedicated validation sites at Lake Tahoe and Salton Sea in the Southwestern USA, while the VIIRS emissivity retrievals were evaluated with the latest ASTER Global Emissivity Dataset Version 3 (GEDv3). The bias and root-mean-square error (RMSE) in retrieved VIIRS LST were 0.50 and 1.40 K, respectively for the two sites combined, while mean emissivity differences between VIIRS and ASTER GEDv3 were 0.2%, 0.1%, and 0.3% for bands M14 ($8.55~\mu \text{m}$ ), M15 ($10.76~\mu \text{m}$ ), and M16 ($12.01~\mu \text{m}$ ), respectively, with an RMSE of 1%. We further demonstrate close agreement between the MODIS and VIIRS TES algorithm LST products to within ~0.3 K difference, as opposed to the current MODIS and VIIRS split window products, which had an average difference of 3 K.
Tanvir Islam; Glynn C. Hulley; Nabin K. Malakar; Robert G. Radocinski; Pierre C. Guillevic; Simon J. Hook. A Physics-Based Algorithm for the Simultaneous Retrieval of Land Surface Temperature and Emissivity From VIIRS Thermal Infrared Data. IEEE Transactions on Geoscience and Remote Sensing 2016, 55, 563 -576.
AMA StyleTanvir Islam, Glynn C. Hulley, Nabin K. Malakar, Robert G. Radocinski, Pierre C. Guillevic, Simon J. Hook. A Physics-Based Algorithm for the Simultaneous Retrieval of Land Surface Temperature and Emissivity From VIIRS Thermal Infrared Data. IEEE Transactions on Geoscience and Remote Sensing. 2016; 55 (1):563-576.
Chicago/Turabian StyleTanvir Islam; Glynn C. Hulley; Nabin K. Malakar; Robert G. Radocinski; Pierre C. Guillevic; Simon J. Hook. 2016. "A Physics-Based Algorithm for the Simultaneous Retrieval of Land Surface Temperature and Emissivity From VIIRS Thermal Infrared Data." IEEE Transactions on Geoscience and Remote Sensing 55, no. 1: 563-576.
We present an improved water vapor scaling (WVS) model for atmospherically correcting MODIS thermal infrared (TIR) bands in the temperature emissivity separation (TES) algorithm. TES is used to retrieve the land surface temperature and emissivity (LST&E) from MODIS TIR bands 29, 31, and 32. The WVS model improves the accuracy of the atmospheric correction parameters in TES on a band-by-band and pixel-by-pixel basis. We used global atmospheric radiosondes profiles to generate view angle and day–night-dependent WVS coefficients that are valid for all MODIS scan angles up to 65°. We demonstrate the effects of applying the improved WVS model on the retrieval accuracy of MODIS-TES (MODTES) LST&E using a case study for a granule over the southwest USA during very warm and moist monsoonal atmospheric conditions. Furthermore, a comprehensive validation of the MODTES LST&E retrieval was performed over two sites at the quartz-rich Algodones Dunes in California and a grassland site in Texas, USA using three full years of MODIS Aqua data. Results from the case study showed that absolute errors in the emissivity retrieval for the three MODIS TIR bands were reduced on average from 1.4% to 0.4% when applying the WVS method. A Radiance-based method was used to validate the MODTES LST retrievals for and the results showed that application of the WVS method with the MODTES algorithm led to significant reduction in both bias and root mean square error (RMSE) of the LST retrievals at both sites. When the WVS model was applied, LST RMSE's were reduced on average from 1.3 K to 1.0 K at the Algodones Dunes site, and from 1.2 K to 0.7 K at the Texas Grassland site. This study demonstrated that the WVS atmospheric correction model is critical for retrieving MODTES LST with < 1 K accuracy and emissivity with < 1% consistently for a wide range of challenging atmospheric conditions and land surface types.
Nabin K. Malakar; Glynn C. Hulley. A water vapor scaling model for improved land surface temperature and emissivity separation of MODIS thermal infrared data. Remote Sensing of Environment 2016, 182, 252 -264.
AMA StyleNabin K. Malakar, Glynn C. Hulley. A water vapor scaling model for improved land surface temperature and emissivity separation of MODIS thermal infrared data. Remote Sensing of Environment. 2016; 182 ():252-264.
Chicago/Turabian StyleNabin K. Malakar; Glynn C. Hulley. 2016. "A water vapor scaling model for improved land surface temperature and emissivity separation of MODIS thermal infrared data." Remote Sensing of Environment 182, no. : 252-264.
We introduce a retrieval algorithm to estimate lower tropospheric methane (CH4) concentrations from the surface to 1 km with uncertainty estimates using Hyperspectral Thermal Emission Spectrometer (HyTES) airborne radiance measurements. After resampling, retrievals have a spatial resolution of 6 × 6 m2. The total error from a single retrieval is approximately 20 %, with the uncertainties determined primarily by noise and spectral interferences from air temperature, surface emissivity, and atmospheric water vapor. We demonstrate retrievals for a HyTES flight line over storage tanks near Kern River Oil Field (KROF), Kern County, California, and find an extended plume structure in the set of observations with elevated methane concentrations (3.0 ± 0.6 to 6.0 ± 1.2 ppm), well above mean concentrations (1.8 ± 0.4 ppm) observed for this scene. With typically a 20 % estimated uncertainty, plume enhancements with more than 1 ppm are distinguishable from the background values with its uncertainty. HyTES retrievals are consistent with simultaneous airborne and ground-based in situ CH4 mole fraction measurements within the reported accuracy of approximately 0.2 ppm (or ∼ 8 %), due to retrieval interferences related to air temperature, emissivity, and H2O.
Le Kuai; John R. Worden; King-Fai Li; Glynn C. Hulley; Francesca M. Hopkins; Charles E. Miller; Simon J. Hook; Riley M. Duren; Andrew D. Aubrey. Characterization of anthropogenic methane plumes with the Hyperspectral Thermal Emission Spectrometer (HyTES): a retrieval method and error analysis. Atmospheric Measurement Techniques 2016, 9, 3165 -3173.
AMA StyleLe Kuai, John R. Worden, King-Fai Li, Glynn C. Hulley, Francesca M. Hopkins, Charles E. Miller, Simon J. Hook, Riley M. Duren, Andrew D. Aubrey. Characterization of anthropogenic methane plumes with the Hyperspectral Thermal Emission Spectrometer (HyTES): a retrieval method and error analysis. Atmospheric Measurement Techniques. 2016; 9 (7):3165-3173.
Chicago/Turabian StyleLe Kuai; John R. Worden; King-Fai Li; Glynn C. Hulley; Francesca M. Hopkins; Charles E. Miller; Simon J. Hook; Riley M. Duren; Andrew D. Aubrey. 2016. "Characterization of anthropogenic methane plumes with the Hyperspectral Thermal Emission Spectrometer (HyTES): a retrieval method and error analysis." Atmospheric Measurement Techniques 9, no. 7: 3165-3173.