scholarly journals Multispectral Thermal Imager science, data product and ground data processing overview

2002 ◽  
Author(s):  
J.J. Szymanski ◽  
W. Atkins ◽  
L. Balick ◽  
C.C. Borel ◽  
W.B. Clodius ◽  
...  
Keyword(s):  
Data Processing ◽  
Thermal Imager ◽  
Science Data ◽  
Data Product

scholarly journals Replacing missing values in the standard Multi-angle Imaging SpectroRadiometer (MISR) radiometric camera-by-camera cloud mask (RCCM) data product

2020 ◽  
Vol 12 (1) ◽  
pp. 611-628 ◽  
Author(s):  
Michel M. Verstraete ◽  
Linda A. Hunt ◽  
Hugo De Lemos ◽  
Larry Di Girolamo
Keyword(s):  
Missing Values ◽  
Near Infrared ◽  
Data Repository ◽  
Observation Data ◽  
Science Data ◽  
Data Services ◽  
Data Product ◽  
Spectral Bands ◽  
Earth Observation Data ◽  
Cloud Mask

Abstract. The Multi-angle Imaging SpectroRadiometer (MISR) is one of the five instruments hosted on board the NASA Terra platform, launched on 18 December 1999. This instrument has been operational since 24 February 2000 and is still acquiring Earth observation data as of this writing. The primary mission of the MISR is to document the state and properties of the atmosphere, in particular the clouds and aerosols it contains, as well as the planetary surface, on the basis of 36 data channels collectively gathered by its nine cameras (pointing in different directions along the orbital track) in four spectral bands (blue, green, red and near-infrared). The radiometric camera-by-camera cloud mask (RCCM) is derived from the calibrated measurements at the nominal top of the atmosphere and is provided separately for each of the nine cameras. This RCCM data product is permanently archived at the NASA Atmospheric Science Data Center (ASDC) in Hampton, VA, USA, and is openly accessible (Diner et al., 1999b, and https://doi.org/10.5067/Terra/MISR/MIRCCM_L2.004). For various technical reasons described in this paper, this RCCM product exhibits missing data, even though an estimate of the clear or cloudy status of the environment at each individual observed location can be deduced from the available measurements. The aims of this paper are (1) to describe how to replace over 99 % of the missing values by estimates and (2) to briefly describe the software to replace missing RCCM values, which is openly available to the community from the GitHub website, https://github.com/mmverstraete/MISR\\ RCCM/ (last access: 12 March 2020), or https://doi.org/10.5281/ZENODO.3240017 (Verstraete, 2019e). Two additional sets of resources are also made available on the research data repository of GFZ Data Services in conjunction with this paper. The first set (A; Verstraete et al., 2020; https://doi.org/10.5880/fidgeo.2020.004) includes three items: (A1) a compressed archive, RCCM_Out.zip, containing all intermediary, final and ancillary outputs created while generating the figures of this paper; (A2) a user manual, RCCM_Out.pdf, describing how to install, uncompress and explore those files; and (A3) a separate input MISR data archive, RCCM_input_68050.zip, for Path 168, Orbit 68050. This latter archive is usable with (B), the second set (Verstraete and Vogt, 2020; https://doi.org/10.5880/fidgeo.2020.008), which includes (B1), a stand-alone, self-contained, executable version of the RCCM correction codes, RCCM_Soft_Win.zip, using the IDL Virtual Machine technology that does not require a paid IDL license, as well as (B2), a user manual, RCCM_Soft_Win.pdf, to explain how to install, uncompress and use this software.

Ziggy, a Portable, Scalable Infrastructure for Science Data Processing Pipelines

2021 ◽  
Author(s):  
Peter Tenenbaum ◽  
Bill Wohler ◽  
Jon Jenkins ◽  
Yohei Shinozuka ◽  
Jennifer Dungan ◽  
...  
Keyword(s):  
Data Processing ◽  
Science Data

Onboard science data processing for NGST: algorithms and architectures

2000 ◽  
Author(s):  
Luca Scandelli ◽  
Arnaldo Bonati ◽  
Immacolata Bruno ◽  
Patricia A. Caraveo ◽  
Koos W. Cornelisse ◽  
...  
Keyword(s):  
Data Processing ◽  
Science Data

scholarly journals Analytical guidelines to increase the value of citizen science data: using eBird data to estimate species occurrence

2019 ◽  
Author(s):  
A Johnston ◽  
WM Hochachka ◽  
ME Strimas-Mackey ◽  
V Ruiz Gutierrez ◽  
OJ Robinson ◽  
...  
Keyword(s):  
Data Processing ◽  
Citizen Science ◽  
Model Performance ◽  
Species Distributions ◽  
Ecological Knowledge ◽  
Sample Sizes ◽  
Science Data ◽  
Science Projects ◽  
Wide Range ◽  
The Impact

AbstractCitizen science data are valuable for addressing a wide range of ecological research questions, and there has been a rapid increase in the scope and volume of data available. However, data from large-scale citizen science projects typically present a number of challenges that can inhibit robust ecological inferences. These challenges include: species bias, spatial bias, and variation in effort.To demonstrate addressing key challenges in analysing citizen science data, we use the example of estimating species distributions with data from eBird, a large semi-structured citizen science project. We estimate two widely applied metrics of species distributions: encounter rate and occupancy probability. For each metric, we assess the impact of data processing steps that either degrade or refine the data used in the analyses. We also test whether differences in model performance are maintained at different sample sizes.Model performance improved when data processing and analytical methods addressed the challenges arising from citizen science data. The largest gains in model performance were achieved with: 1) the use of complete checklists (where observers report all the species they detect and identify); and 2) the use of covariates describing variation in effort and detectability for each checklist. Occupancy models were more robust to a lack of complete checklists and effort variables. Improvements in model performance with data refinement were more evident with larger sample sizes.Here, we describe processes to refine semi-structured citizen science data to estimate species distributions. We demonstrate the value of complete checklists, which can inform the design and adaptation of citizen science projects. We also demonstrate the value of information on effort. The methods we have outlined are also likely to improve other forms of inference, and will enable researchers to conduct robust analyses and harness the vast ecological knowledge that exists within citizen science data.

scholarly journals James Webb Space Telescope: L2 communications for science data processing

2008 ◽  
Author(s):  
Alan Johns ◽  
Bonita Seaton ◽  
Jonathan Gal-Edd ◽  
Ronald Jones ◽  
Curtis Fatig ◽  
...  
Keyword(s):  
Data Processing ◽  
Science Data ◽  
Space Telescope ◽  
James Webb Space Telescope
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