18 results on '"Benjamin Marchant"'
Search Results
2. The NASA MODIS-VIIRS Continuity Cloud Optical Properties Products
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Steven Platnick, Kerry Meyer, Galina Wind, Robert E. Holz, Nandana Amarasinghe, Paul A. Hubanks, Benjamin Marchant, Steven Dutcher, and Paolo Veglio
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satellite remote sensing ,satellite climate data records ,clouds ,Science - Abstract
The NASA Aqua MODIS and Suomi National Polar-Orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) climate data record continuity cloud properties products (CLDPROP) were publicly released in April 2019 with an update later that year (Version 1.1). These cloud products, having heritage with the NASA Moderate-resolution Imaging Spectroradiometer (MODIS) MOD06 cloud optical properties product and the NOAA GOES-R Algorithm Working Group (AWG) Cloud Height Algorithm (ACHA), represent an effort to bridge the multispectral imager records of NASA’s Earth Observing System (EOS) and NOAA’s current generation of operational weather satellites to achieve a continuous, multi-decadal climate data record for clouds that can extend well into the 2030s. CLDPROP offers a “continuity of approach,” applying common algorithms and ancillary datasets to both MODIS and VIIRS, including utilizing only a subset of spectral channels available on both sensors to help mitigate instrument differences. The initial release of the CLDPROP_MODIS and CLDPROP_VIIRS data records spans the SNPP observational record (2012-present). Here, we present an overview of the algorithms and an evaluation of the intersensor continuity of the core CLDPROP_MODIS and CLDPROP_VIIRS cloud optical property datasets, i.e., cloud thermodynamic phase, optical thickness, effective particle size, and derived water path. The evaluation includes analyses of pixel-level MODIS/VIIRS co-locations as well as spatial and temporal aggregated statistics, with a focus on identifying and understanding the root causes of individual dataset discontinuities. The results of this evaluation will inform future updates to the CLDPROP products and help scientific users determine the appropriate use of the product datasets for their specific needs.
- Published
- 2020
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3. Reply to ‘Pseudoreplication and greenhouse-gas emissions from rivers'
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Sophie A. Comer-Warner, Paul Romeijn, Daren C. Gooddy, Sami Ullah, Nicholas Kettridge, Benjamin Marchant, David M. Hannah, and Stefan Krause
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Science - Published
- 2019
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4. Addendum: Thermal sensitivity of CO2 and CH4 emissions varies with streambed sediment properties
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Sophie A. Comer-Warner, Paul Romeijn, Daren C. Gooddy, Sami Ullah, Nicholas Kettridge, Benjamin Marchant, David M. Hannah, and Stefan Krause
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Science - Published
- 2019
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5. Characterization of a Highly Biodiverse Floodplain Meadow Using Hyperspectral Remote Sensing within a Plant Functional Trait Framework
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Suvarna Punalekar, Anne Verhoef, Irina V. Tatarenko, Christiaan van der Tol, David M. J. Macdonald, Benjamin Marchant, France Gerard, Kevin White, and David Gowing
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MG4 community ,field spectroscopy ,optical functional types ,radiative transfer model ,biophysical parameters ,sedge and rush ,Science - Abstract
We assessed the potential for using optical functional types as effective markers to monitor changes in vegetation in floodplain meadows associated with changes in their local environment. Floodplain meadows are challenging ecosystems for monitoring and conservation because of their highly biodiverse nature. Our aim was to understand and explain spectral differences among key members of floodplain meadows and also characterize differences with respect to functional traits. The study was conducted on a typical floodplain meadow in UK (MG4-type, mesotrophic grassland type 4, according to British National Vegetation Classification). We compared two approaches to characterize floodplain communities using field spectroscopy. The first approach was sub-community based, in which we collected spectral signatures for species groupings indicating two distinct eco-hydrological conditions (dry and wet soil indicator species). The other approach was “species-specific”, in which we focused on the spectral reflectance of three key species found on the meadow. One herb species is a typical member of the MG4 floodplain meadow community, while the other two species, sedge and rush, represent wetland vegetation. We also monitored vegetation biophysical and functional properties as well as soil nutrients and ground water levels. We found that the vegetation classes representing meadow sub-communities could not be spectrally distinguished from each other, whereas the individual herb species was found to have a distinctly different spectral signature from the sedge and rush species. The spectral differences between these three species could be explained by their observed differences in plant biophysical parameters, as corroborated through radiative transfer model simulations. These parameters, such as leaf area index, leaf dry matter content, leaf water content, and specific leaf area, along with other functional parameters, such as maximum carboxylation capacity and leaf nitrogen content, also helped explain the species’ differences in functional dynamics. Groundwater level and soil nitrogen availability, which are important factors governing plant nutrient status, were also found to be significantly different for the herb/wetland species’ locations. The study concludes that spectrally distinguishable species, typical for a highly biodiverse site such as a floodplain meadow, could potentially be used as target species to monitor vegetation dynamics under changing environmental conditions.
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- 2016
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6. The CHIMAERA system for retrievals of cloud top, optical and microphysical properties from imaging sensors.
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Galina Wind, Steven Platnick, Kerry Meyer, G. Thomas Arnold, Nandana Amarasinghe, Benjamin Marchant, and Chenxi Wang
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- 2020
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7. The CHIMAERA System for Retrievals of Cloud Top, Optical and Microphysical Properties from Imaging Sensors
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Galina (Gala) Wind, Steven Platnick, Kerry Meyer, Tom Arnold, Nandana Amarasinghe, Benjamin Marchant, and Chenxi Wang
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Computer Systems ,Earth Resources And Remote Sensing - Abstract
Continuity and consistency of geophysical retrieval products obtained from different Earth-observing spaceborne or airborne atmospheric multispectral imagers can be challenging due to inherent differences in the instruments and/or the use of different retrieval algorithms. The Cross-platform HIgh resolution Multi-instrument AtmosphEric Retrieval Algorithms (CHIMAERA) system addresses the latter aspect of the inter-sensor continuity problem for cloud property retrievals by removing retrieval methodology and implementation as a source of inconsistency when applied to instruments that share common measurement capabilities. Transferring an existing retrieval algorithm to a new sensor oftentimes is a nontrivial task, as it is common for an algorithm code to be tightly coupled to the sensor for which it was developed. By creating a clear division between the science algorithm and the instrument I/O codes, CHIMAERA allows easy migration of science algorithms to different sensors. CHIMAERA is built from C and FORTRAN source code, and can operate in a variety of environments ranging from a personal laptop to a high-performance computing environment for near real-time satellite data production. It is highly adaptable, low-maintenance and allows for easy expansion such that adding new instruments into the system requires only instrument-specific I/O and provision of any external lookup tables specific to the instrument's spectral characteristics. CHIMAERA currently supports 14 spaceborne and airborne atmospheric imagers from a single code base, and has been in use since 2007. In this paper we describe the engineering aspects of CHIMAERA and briefly discuss a few examples from its many current applications.
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- 2021
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8. Evaluation of the MODIS Collection 6 Multilayer Cloud Detection Algorithm Through Comparisons with Cloudsat Cloud Profiling Radar and CALIPSO CALIOP Products
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Benjamin Marchant, Steven Platnick, Kerry Meyer, and Galina Wind
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Earth Resources And Remote Sensing - Abstract
Since multilayer cloud scenes are common in the atmosphere and can be an important source of uncertainty in passive satellite sensor cloud retrievals, the MODIS MOD06 and MYD06 standard cloud optical property products include a multilayer cloud detection algorithm to assist with data quality assessment. This paper presents an evaluation of the Aqua MODIS MYD06 Collection 6 multilayer cloud detection algorithm through comparisons with active Cloud Profiling Radar (CPR) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) products that have the ability to provide cloud vertical distributions and directly classify multilayer cloud scenes and layer properties. To compare active sensor products with an imager such as MODIS, it is first necessary to define multilayer clouds in the context of their radiative impact on cloud retrievals. Three main parameters have thus been considered in this evaluation: (1) the maximum separation distance between two cloud layers, (2) the thermodynamic phase of those layers and (3) the upper-layer cloud optical thickness. The impact of including the Pavolonis–Heidinger multilayer cloud detection algorithm, introduced in Collection 6, to assist with multilayer cloud detection has also been assessed. For the year 2008, the MYD06 C6 multilayer cloud detection algorithm identifies roughly 20% of all cloudy pixels as multilayer (decreasing to about 13% if the Pavolonis–Heidinger algorithm output is not used). Evaluation against the merged CPR and CALIOP2B-CLDCLASS-lidar product shows that the MODIS multilayer detection results are quite sensitive to how multilayer clouds are defined in the radar and lidar product and that the algorithm performs better when the optical thickness of the upper cloud layer is greater than about 1.2 with a minimum layer separation distance of 1 km. Finally, we find that filtering the MYD06 cloud optical properties retrievals using the multilayer cloud flag improves aggregated statistics, particularly for ice cloud effective radius.
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- 2020
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9. The MODIS Cloud Optical and Microphysical Products: Collection 6 Updates and Examples From Terra and Aqua.
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Steven Platnick, Kerry Meyer, Michael D. King, Galina Wind, Nandana Amarasinghe, Benjamin Marchant, G. Thomas Arnold, Zhibo Zhang, Paul A. Hubanks, Robert E. Holz, Ping Yang 0007, William L. Ridgway, and Jérôme C. Riedi
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- 2017
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10. Corrigendum to 'The CHIMAERA system for retrievals of cloud top, optical and microphysical properties from imaging sensors' [Comput. Geosci. 134 (2019) 104345].
- Author
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Galina Wind, Steven Platnick, Kerry Meyer, G. Thomas Arnold, Nandana Amarasinghe, Benjamin Marchant, and Chenxi Wang
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- 2021
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11. Driver Drowsiness Estimation Based on Factorized Bilinear Feature Fusion and a Long-Short-Term Recurrent Convolution-al Network
- Author
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Steven Platnick, Kerry Meyer, Galina Wind, Robert E. Holz, Nandana Amarasinghe, Paul A. Hubanks, Benjamin Marchant, Steven Dutcher, and Paolo Veglio
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satellite remote sensing ,Science ,clouds ,satellite climate data records - Abstract
The NASA Aqua MODIS and Suomi National Polar-Orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) climate data record continuity cloud properties products (CLDPROP) were publicly released in April 2019 with an update later that year (Version 1.1). These cloud products, having heritage with the NASA Moderate-resolution Imaging Spectroradiometer (MODIS) MOD06 cloud optical properties product and the NOAA GOES-R Algorithm Working Group (AWG) Cloud Height Algorithm (ACHA), represent an effort to bridge the multispectral imager records of NASA’s Earth Observing System (EOS) and NOAA’s current generation of operational weather satellites to achieve a continuous, multi-decadal climate data record for clouds that can extend well into the 2030s. CLDPROP offers a “continuity of approach,” applying common algorithms and ancillary datasets to both MODIS and VIIRS, including utilizing only a subset of spectral channels available on both sensors to help mitigate instrument differences. The initial release of the CLDPROP_MODIS and CLDPROP_VIIRS data records spans the SNPP observational record (2012-present). Here, we present an overview of the algorithms and an evaluation of the intersensor continuity of the core CLDPROP_MODIS and CLDPROP_VIIRS cloud optical property datasets, i.e., cloud thermodynamic phase, optical thickness, effective particle size, and derived water path. The evaluation includes analyses of pixel-level MODIS/VIIRS co-locations as well as spatial and temporal aggregated statistics, with a focus on identifying and understanding the root causes of individual dataset discontinuities. The results of this evaluation will inform future updates to the CLDPROP products and help scientific users determine the appropriate use of the product datasets for their specific needs.
- Published
- 2021
12. Anonymous Referee #4 (Answers)
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Benjamin Marchant
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- 2020
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13. Anonymous Referee #2 (Answers)
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Benjamin Marchant
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- 2020
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14. Anonymous Referee #3 (Answers)
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Benjamin Marchant
- Published
- 2020
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15. MODIS Collection 6 shortwave-derived cloud phase classification algorithm and comparisons with CALIOP
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Benjamin Marchant, Jerome Riedi, Steven Platnick, Kerry Meyer, G. Thomas Arnold, Laboratoire d’Optique Atmosphérique - UMR 8518 (LOA), and Institut national des sciences de l'Univers (INSU - CNRS)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)
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Atmospheric Science ,010504 meteorology & atmospheric sciences ,Meteorology ,0211 other engineering and technologies ,Cloud computing ,02 engineering and technology ,01 natural sciences ,Article ,Physics::Geophysics ,Sea ice ,lcsh:TA170-171 ,Physics::Atmospheric and Oceanic Physics ,Astrophysics::Galaxy Astrophysics ,021101 geological & geomatics engineering ,0105 earth and related environmental sciences ,Remote sensing ,geography ,geography.geographical_feature_category ,lcsh:TA715-787 ,business.industry ,Cloud top ,lcsh:Earthwork. Foundations ,Cloud fraction ,Cloud physics ,Snow ,lcsh:Environmental engineering ,[SDU]Sciences of the Universe [physics] ,Environmental science ,Moderate-resolution imaging spectroradiometer ,Astrophysics::Earth and Planetary Astrophysics ,business ,Shortwave ,Algorithm - Abstract
Cloud thermodynamic phase (ice, liquid, undetermined) classification is an important first step for cloud retrievals from passive sensors such as MODIS (Moderate-Resolution Imaging Spectroradiometer). Because ice and liquid phase clouds have very different scattering and absorbing properties, an incorrect cloud phase decision can lead to substantial errors in the cloud optical and microphysical property products such as cloud optical thickness or effective particle radius. Furthermore, it is well established that ice and liquid clouds have different impacts on the Earth's energy budget and hydrological cycle, thus accurately monitoring the spatial and temporal distribution of these clouds is of continued importance. For MODIS Collection 6 (C6), the shortwave-derived cloud thermodynamic phase algorithm used by the optical and microphysical property retrievals has been completely rewritten to improve the phase discrimination skill for a variety of cloudy scenes (e.g., thin/thick clouds, over ocean/land/desert/snow/ice surface, etc). To evaluate the performance of the C6 cloud phase algorithm, extensive granule-level and global comparisons have been conducted against the heritage C5 algorithm and CALIOP. A wholesale improvement is seen for C6 compared to C5.
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- 2016
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16. The economics of precision agriculture
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Thomas F. A. Bishop, Brett Whelan, Benjamin Marchant, and Margaret Oliver
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Economics ,Precision agriculture ,Agricultural economics - Published
- 2013
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17. Spatial distribution of lindane concentration in topsoil across France
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Dominique Arrouays, Estelle Villanneau, Enrique Barriuso, Giovanni Caria, Claudy Jolivet, Olivier Briand, Nicolas Saby, Benjamin Marchant, Antonio Bispo, Thomas G. Orton, InfoSol (InfoSol), Institut National de la Recherche Agronomique (INRA), Unité de recherche Science du Sol (USS), Laboratoire d'Analyses des Sols (LAS), Environnement et Grandes Cultures (EGC), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Département de Recherche des Déchets et des Sols, Agence de l'Environnement et de la Maitrise de l'Energie, Laboratoire de Sécurité des Aliments, Agence nationale de sécurité sanitaire de l'alimentation, de l'environnement et du travail (ANSES), Unité INFOSOL, and AgroParisTech-Institut National de la Recherche Agronomique (INRA)
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model-based geostatistics ,[SDV.SA]Life Sciences [q-bio]/Agricultural sciences ,Insecticides ,Environmental Engineering ,010504 meteorology & atmospheric sciences ,Soil test ,Climate ,Geostatistics ,persistent organic pollutants ,010501 environmental sciences ,Spatial distribution ,01 natural sciences ,covariate selection ,chemistry.chemical_compound ,organochlorine pesticides ,Agricultural land ,Limit of Detection ,seasonal trends ,Environmental Chemistry ,Soil Pollutants ,geostatistics ,uncertainty ,Waste Management and Disposal ,0105 earth and related environmental sciences ,2. Zero hunger ,Hydrology ,Topsoil ,Land use ,area ,15. Life on land ,Hydrogen-Ion Concentration ,Pollution ,6. Clean water ,hexachlorocyclohexane isomers ,chemistry ,13. Climate action ,Soil water ,Environmental science ,censored data ,maximum-likelihood ,France ,agricultural soils ,Lindane ,quantification limit ,china ,gamma-hch ,Hexachlorocyclohexane ,Environmental Monitoring - Abstract
Sci Total Environ 098OZ Times Cited:0 Cited References Count:68; Lindane [gamma-hexachlorocyclohexane (gamma-HCH)] is an organochlorine pesticide with toxic effects on humans. It is bioaccumulative and can remain in soils for long periods, and although its use for crop spraying was banned in France in 1998, it is possible that residues from before this time remain in the soil. The RMQS soil monitoring network consists of soil samples from 2200 sites on a 16 km regular grid across France, collected between 2002 and 2009. We use 726 measurements of the Lindane concentration in these samples to (i) investigate the main explanatory factors for its spatial distribution across France, and (ii) map this distribution. Geostatistics provides an appropriate framework to analyze our spatial dataset, though two issues regarding the data are worth special consideration: first, the harmonization of two subsets of the data (which were analyzed using different measurement processes), and second, the large proportion of data from one of these subsets that fell below a limit of quantification. We deal with these issues using recent methodological developments in geostatistics. Results demonstrate the importance of land use and rainfall for explaining part of the variability of Lindane across France: land use due to the past direct input of Lindane on cropland and its subsequent persistence in the soil, and rainfall due to the re-deposition of volatilized Lindane. Maps show the concentrations to be generally largest in the north and northwest of France, areas of more intensive agricultural land. We also compare levels to some contamination thresholds taken from the literature, and present maps showing the probability of Lindane concentrations exceeding these thresholds across France. These maps could be used as guidelines for deciding which areas require further sampling before some possible remediation strategy could be applied. (C) 2012 Elsevier B.V. All rights reserved.
- Published
- 2013
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18. Sensitivity of passive measurements in VIS, SWIR, and TIR to cirrus microphysical vertical profile: application to cloud remote sensing from MODIS
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Gérard Brogniez, Jerome Riedi, Philippe Dubuisson, Laurent C.-Labonnote, and Benjamin Marchant
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Wavelength ,Spectroradiometer ,Meteorology ,Ice crystals ,Brightness temperature ,Near-infrared spectroscopy ,Radiative transfer ,Environmental science ,Cirrus ,Astrophysics::Galaxy Astrophysics ,Physics::Atmospheric and Oceanic Physics ,Atmospheric optics ,Remote sensing - Abstract
For inferring cirrus optical and microphysical properties from satellite imagery, a common assumption is that the radiative properties of a cirrus cloud may be represented by those associated with a specific ice crystal habit, a single particle size distribution and Ice Water Content (IWC). Various algorithms have been developed to retrieve cirrus optical and microphysical properties in the past 20 years. They can be categorized into the techniques based on either thermal infrared or solar reflection measurements. However, in-situ measurements have shown that shapes, sizes and IWC of ice crystals may vary substantially with height within the clouds. Given the different sensitivity of thermal infrared and solar wavelength to cloud microphysics, it is unlikely that a single cloud layer with homogeneous cloud properties can be used to reproduce both type of measurements. Thus, it is necessary to assess the effect of vertical inhomogeneity within cirrus on the radiative transfer calculations and on the retrieval techniques. The purpose of this study is to investigate a microphysical cirrus model composed of different layers in terms of ice crystal habit, size and IWC. The vertical structure will be given by simple analytic formula derived from various prescribed physical constraints. The primary goal of this study is to determine a simple cloud model that can be used to retrieve consistent information from both solar and thermal measurements. For this purpose, we examine the sensitivity of cirrus reflectances and brightness temperature to its vertical description for a suite of MODIS (MODerate-resolution Imaging Spectroradiometer) bands spanning visible, near infrared and thermal infrared wavelengths. Results of this study are presented and potential application to remote sensing of cirrus clouds with MODIS are discussed.
- Published
- 2007
- Full Text
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