The along track scanning radiometer for ERS-1-scan geometry and data simulation

Prata, A. J.; Cechet, R. P.; Barton, I. J.; Llewellyn‐Jones, David

doi:10.1109/36.45741

Cited by 68 publications

(29 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…AVHRR sensors are, at the time of this work, the only polar orbiting Earth observing satellite system able to provide MIR and TIR as well as consecutive day/night data. The Along Track Scanning Radiometer (ATSR) onboard ERS1/2 satellites has spectral channels very similar to AVHRR but its swath is not wide enough to provide consecutive day and night images (Prata et al 1990). ATSR capability to observe in two directions (nadir and forward, 47°to nadir) might oVer the possibility to assess directional quantities such as the bi-directional re ectivity or, studied by François and Ottlé (1994) over sea, the directional emissivity.…”

Section: Introductionmentioning

confidence: 99%

Mapping directional emissivity at 3.7 μ m using a simple model of bi-directional reflectivity

Petitcolin¹,

Nerry²,

Stoll³

2002

International Journal of Remote Sensing

View full text Add to dashboard Cite

Algorithms for Land Surface Temperature determination from satellite data need incorporating pixel-wise emissivity values to take care of the variability of this parameter over land and achieve a high accuracy in the surface emissivity. This parameter should be determined at the pixel scale and thus directly from the sensor's data. This work addresses the issue of extracting the land emissivity from AVHRR data. The necessary emissivity-temperature decoupling is achieved thanks to a method (Becker and Li 1990) that uses a combination of day/night channels 3, 4 and 5 data. Single channel atmospheric corrections are performed using MODTRAN and atmospheric pro les from outputs of GCMs (Météo-France AVISO database). The channel 3 emissivity is extracted in two steps: rst the channel 3 bi-directional re ectivity is retrieved using the emissivity-temperature decoupling; second, emissivity is related to the re ectivity thanks to KirchoV 's relation. Accuracy assessment indicates that the expected overall error on channel 3 emissivity ranges between 3% and 6% at most (for low emissivity value). Series of day/night AVHRR images are processed, resulting in a wide range of view angles. A simple empirical model is used to t the angular variation of the bi-directional re ectivity, from which a form factor can be calculated, allowing directional emissivity to be obtained. A clear distinction is observed in the angular behaviour between bare soils and vegetation covered surfaces where backscattering appears dominant. Directional channel 3 emissivity shows a large dynamical ranges, from above 0.95 for fully vegetation covered surfaces, to below 0.6 for desert surfaces. Amplitude of emissivity angular variation is small for vegetation, whereas it may be up to 10% between nadir and 60°-view angle for desert areas.

show abstract

Section: Introductionmentioning

confidence: 99%

Mapping directional emissivity at 3.7 μ m using a simple model of bi-directional reflectivity

Petitcolin¹,

Nerry²,

Stoll³

2002

International Journal of Remote Sensing

View full text Add to dashboard Cite

show abstract

“…The along-track scanning radiometer series of sensors have provided a 512 km wide swath stereo-viewing capability since 1991 (Prata et al, 1990), and recently Fisher et al (2014) developed an automated stereo-height retrieval algorithm (M6) working with data from the advanced along-track scanning radiometer (AATSR). Unlike MINX, M6 is not able to correct for the plume displacement induced by the ambient wind.…”

Section: Stereo-imagersmentioning

confidence: 99%

A review of approaches to estimate wildfire plume injection height within large-scale atmospheric chemical transport models

et al. 2016

View full text Add to dashboard Cite

Abstract. Landscape fires produce smoke containing a very wide variety of chemical species, both gases and aerosols. For larger, more intense fires that produce the greatest amounts of emissions per unit time, the smoke tends initially to be transported vertically or semi-vertically close by the source region, driven by the intense heat and convective energy released by the burning vegetation. The column of hot smoke rapidly entrains cooler ambient air, forming a rising plume within which the fire emissions are transported. The characteristics of this plume, and in particular the height to which it rises before releasing the majority of the smoke burden into the wider atmosphere, are important in terms of how the fire emissions are ultimately transported, since for example winds at different altitudes may be quite different. This difference in atmospheric transport then may also affect the longevity, chemical conversion, and fate of the plumes chemical constituents, with for example very high plume injection heights being associated with extreme long-range atmospheric transport. Here we review how such landscape-scale fire smoke plume injection heights are represented in largerscale atmospheric transport models aiming to represent the impacts of wildfire emissions on component of the Earth system. In particular we detail (i) satellite Earth observation data sets capable of being used to remotely assess wildfire plume height distributions and (ii) the driving characteristics of the causal fires. We also discuss both the physical mechanisms and dynamics taking place in fire plumes and investigate the efficiency and limitations of currently available injection height parameterizations. Finally, we conclude by suggesting some future parameterization developments and ideas on Earth observation data selection that may be relevant to the instigation of enhanced methodologies aimed at injection height representation.

show abstract

“…The cone is tilted with part of the scan providing the forward look data and part the nadir look data. The resampling, geolocation and co-location of the two looks is achieved by the use of spherical trigonometry and an orbital model which incorporates altitude adjustments for the ERS-l platform (Prata et al 1990). In this study, only the nadir look has been used due to its finer spatial resolution than the forward look.…”

Section: Description Of the Test Sitementioning

confidence: 99%

Comparison of the detection of deforested areas using the ERS-1 ATSR and the NOAA-11 AVHRR with reference to ERS-1 SAR data: a case study in the Brazilian Amazon

Conway

Eva

D'Souza³

1996

International Journal of Remote Sensing

View full text Add to dashboard Cite

Two coarse spatial resolution classifications of nominally 1·1 km resolution of deforested areas over a test site covering part of the south-west Amazon basin are compared. One classification is derived from data acquired by NOAA's Advanced Very High Resolution Radiometer (AVHRR) and the other from the ERS-I Along Track Scanning Radiometer (ATSR-I). A forest/non-forest classification derived from finer spatial resolution ERS-l Synthetic Aperture Radar (SAR) data is used as a reference for the comparison. Specificpatterns of deforestation in the area are identified and their depiction on each of the coarse spatial resolution datasets is examined in relation to their depiction on the fine spatial resolution classification. The results show that significant differencesexist between the two coarse spatial resolution classifications and that special rigorous integration methodologies will have to be developed before the results from either source can be used interchangeably with those from the other.

show abstract

The along track scanning radiometer for ERS-1-scan geometry and data simulation

Cited by 68 publications

References 7 publications

Mapping directional emissivity at 3.7 μ m using a simple model of bi-directional reflectivity

Mapping directional emissivity at 3.7 μ m using a simple model of bi-directional reflectivity

A review of approaches to estimate wildfire plume injection height within large-scale atmospheric chemical transport models

Comparison of the detection of deforested areas using the ERS-1 ATSR and the NOAA-11 AVHRR with reference to ERS-1 SAR data: a case study in the Brazilian Amazon

Contact Info

Product

Resources

About