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.

scholarly journals Replacing Missing Values in the Standard MISR Radiometric Camera-by-Camera Cloud Mask (RCCM) Data Product

2019 ◽  
Author(s):  
Michel M. Verstraete ◽  
Linda A. Hunt ◽  
Hugo De Lemos ◽  
Larry Di Girolamo
Keyword(s):  
Data Center ◽  
Missing Values ◽  
Near Infrared ◽  
Observation Data ◽  
Science Data ◽  
Data Product ◽  
Spectral Bands ◽  
Data Products ◽  
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 missions of MISR are to document the state and properties of the atmosphere, and in particular the clouds and aerosols it contains, as well as the planetary surface, on the basis of 36 data channels gathered by each of 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 Langley, VA, USA and is openly accessible (Diner et al., 1999 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 most missing values by estimates and (2) to briefly describe the software to process MISR RCCM data products, which is openly available to the community from the GitHub web site (https://github.com/mmverstraete or https://doi.org/10.5281/zenodo.3240018). Limited amounts of updated MISR RCCM data products are also archived in South Africa and can be made available upon request.

scholarly journals Improving the usability of the Multi-angle Imaging SpectroRadiometer (MISR) L1B2 Georectified Radiance Product (2000–present) in land surface applications

2020 ◽  
Vol 12 (2) ◽  
pp. 1321-1346
Author(s):  
Michel M. Verstraete ◽  
Linda A. Hunt ◽  
Veljko M. Jovanovic
Keyword(s):  
Missing Data ◽  
Land Surface ◽  
Data Repository ◽  
Local Mode ◽  
Science Data ◽  
Data Services ◽  
Spectral Bands ◽  
Digital Elevation ◽  
Virtual Machine Technology ◽  
Elevation Model

Abstract. The Multi-angle Imaging SpectroRadiometer (MISR) instrument on NASA's Terra platform has been acquiring global measurements of the spectrodirectional reflectance of the Earth since 24 February 2000 and is still operational as of this writing. The primary radiometric data product generated by this instrument is known as the Level 1B2 (L1B2) Georectified Radiance Product (GRP): it contains the 36 radiometric measurements acquired by the instrument's nine cameras, each observing the planet in four spectral bands. The product version described here is projected on a digital elevation model and is available from the NASA Langley Atmospheric Science Data Center (ASDC; http://doi.org/10.5067/Terra/MISR/MI1B2T_L1.003; Jovanovic et al., 1999). The MISR instrument is highly reliable. Nevertheless, its onboard computer occasionally becomes overwhelmed by the number of raw observations coming from the cameras' focal planes, especially when switching into or out of Local Mode acquisitions that are often requested in conjunction with field campaigns. Whenever this occurs, one or more lines of data are dropped while the computer resets and readies itself for accepting new data. Although this type of data loss is minuscule compared to the total number of measurements acquired and is marginal for atmospheric studies dealing with large areas and long periods of time, this outcome can be crippling for land surface studies that focus on the detailed analysis of particular scenes at specific times. This paper describes the problem, reports on the prevalence of missing data, proposes a practical solution to optimally estimate the values of the missing data and provides evidence of the performance of the algorithm through specific examples in southern Africa. The software to process MISR L1B2 GRP data products as described here is openly available to the community from the GitHub website (https://github.com/mmverstraete or https://doi.org/10.5281/zenodo.3519988). 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.2020.012) includes five items: (A1) a compressed archive (L1B2_Out.zip) containing all intermediary, final and ancillary outputs created while generating the figures of this paper; (A2) a user manual (L1B2_Out.pdf) describing how to install, uncompress and explore those files; (A3) an additional compressed archive (L1B2_Suppl.zip) containing a similar set of results, only for eight other sites, spanning a much wider range of geographical, climatic and ecological conditions; (A4) a companion user manual (L1B2_Suppl.pdf) describing how to install, uncompress and explore those additional files; and (A5) a separate input MISR data archive (L1B2_input_68050.zip) for Path 168, Orbit 68050. This latter archive is usable with the second set (B; Verstraete and Vogt, 2020; https://doi.org/10.5880/fidgeo.2020.011), which includes (B1) a stand-alone, self-contained, executable version of the L1B2 correction codes (L1B2_Soft_Win.zip) that uses the IDL Virtual Machine technology and does not require a paid IDL license as well as (B2) a user manual (L1B2_Soft_Win.pdf) that explains how to install, uncompress and use this software.

scholarly journals Sea Ice Albedo from MISR and MODIS: Production, Validation, and Trend Analysis

2018 ◽  
Vol 11 (1) ◽  
pp. 9 ◽  
Author(s):  
Said Kharbouche ◽  
Jan-Peter Muller
Keyword(s):  
Sea Ice ◽  
Near Infrared ◽  
Time Window ◽  
Solar Spectrum ◽  
High Accuracy ◽  
Dynamic Surface ◽  
Spectral Bands ◽  
Langley Research Center ◽  
Terra Satellite ◽  
Cloud Mask

The Multi-angle Imaging SpectroRadiometer (MISR) sensor onboard the Terra satellite provides high accuracy albedo products. MISR deploys nine cameras each at different view angles, which allow a near-simultaneous angular sampling of the surface anisotropy. This is particularly important to measure the near-instantaneous albedo of dynamic surface features such as clouds or sea ice. However, MISR’s cloud mask over snow or sea ice is not yet sufficiently robust because MISR’s spectral bands are only located in the visible and the near infrared. To overcome this obstacle, we performed data fusion using a specially processed MISR sea ice albedo product (that was generated at Langley Research Center using Rayleigh correction) combining this with a cloud mask of a sea ice mask product, MOD29, which is derived from the MODerate Resolution Imaging Spectroradiometer (MODIS), which is also, like MISR, onboard the Terra satellite. The accuracy of the MOD29 cloud mask has been assessed as >90% due to the fact that MODIS has a much larger number of spectral bands and covers a much wider range of the solar spectrum. Four daily sea ice products have been created, each with a different averaging time window (24 h, 7 days, 15 days, 31 days). For each time window, the number of samples, mean and standard deviation of MISR cloud-free sea ice albedo is calculated. These products are publicly available on a predefined polar stereographic grid at three spatial resolutions (1 km, 5 km, 25 km). The time span of the generated sea ice albedo covers the months between March and September of each year from 2000 to 2016 inclusive. In addition to data production, an evaluation of the accuracy of sea ice albedo was performed through a comparison with a dataset generated from a tower based albedometer from NOAA/ESRL/GMD/GRAD. This comparison confirms the high accuracy and stability of MISR’s sea ice albedo since its launch in February 2000. We also performed an evaluation of the day-of-year trend of sea ice albedo between 2000 and 2016, which confirm the reduction of sea ice shortwave albedo with an order of 0.4–1%, depending on the day of year and the length of observed time window.

Ensuring Scientific Reproducibility within the Earth Observation Community: Standardized Algorithm Documentation for Improved Scientific Data Understanding

2020 ◽  
Author(s):  
Aaron Kaulfus ◽  
Kaylin Bugbee ◽  
Alyssa Harris ◽  
Rahul Ramachandran ◽  
Sean Harkins ◽  
...  
Keyword(s):  
Earth Science ◽  
Scientific Data ◽  
Earth Observation ◽  
Data Reuse ◽  
Observation Data ◽  
Science Data ◽  
Dynamic Documents ◽  
Common Information ◽  
Earth Observation Data ◽  
Earth Science Data

<p>Algorithm Theoretical Basis Documents (ATBDs) accompany Earth observation data generated from algorithms. ATBDs describe the physical theory, mathematical procedures and assumptions made for the algorithms that convert radiances received by remote sensing instruments into geophysical quantities. While ATBDs are critical to scientific reproducibility and data reuse, there have been technical, social and informational issues surrounding the creation and maintenance of these key documents. A standard ATBD structure has been lacking, resulting in inconsistent documents of varying levels of detail. Due to the lack of a minimum set of requirements, there has been very little formal guidance on the ATBD publication process.  Additionally, ATBDs have typically been provided as static documents that are not machine readable, making search and discovery of the documents and the content within the documents difficult for users. To address the challenges surrounding ATBDs, NASA has prototyped the Algorithm Publication Tool (APT), a centralized cloud-based publication tool that standardizes the ATBD content model and streamlines the ATBD authoring process. This presentation will describe our approach in developing a common information model for ATBDs and our efforts to provide ATBDs as dynamic documents that are available for both human and machine utilization. We will also include our vision for APT within the broader NASA Earth science data system and how this tool may assist in standardizes and easing the ATBD creation and maintenance process.</p>

Retrieval of Earth observation data in visible and near-infrared bands

2001 ◽  
Author(s):  
Tasuku Tanaka ◽  
Haruhisa Shimoda ◽  
Yoshihide Katoh ◽  
Hiroshi Murakami ◽  
Osamu Tanaka ◽  
...  
Keyword(s):  
Near Infrared ◽  
Earth Observation ◽  
Observation Data ◽  
Earth Observation Data

scholarly journals Pull vs. Push: How OmniEarth Delivers Better Earth Observation Information to Subscribers

2015 ◽  
Vol XL-7/W3 ◽  
pp. 1083-1087
Author(s):  
C. Fish ◽  
S. Slagowski ◽  
L. Dyrud ◽  
J. Fentzke ◽  
B. Hargis ◽  
...  
Keyword(s):  
Low Cost ◽  
Contextual Information ◽  
Earth Observation ◽  
Observation Data ◽  
Science Data ◽  
Bottom Line ◽  
Commercial Use ◽  
Mobile Interfaces ◽  
Observation Systems ◽  
Earth Observation Data

Until very recently, the commercialization of Earth observation systems has largely occurred in two ways: either through the detuning of government satellites or the repurposing of NASA (or other science) data for commercial use. However, the convergence of cloud computing and low-cost satellites is enabling Earth observation companies to tailor observation data to specific markets. Now, underserved constituencies, such as agriculture and energy, can tap into Earth observation data that is provided at a cadence, resolution and cost that can have a real impact to their bottom line. To connect with these markets, OmniEarth fuses data from a variety of sources, synthesizes it into useful and valuable business information, and delivers it to customers via web or mobile interfaces. The “secret sauce” is no longer about having the highest resolution imagery, but rather it is about using that imagery – in conjunction with a number of other sources – to solve complex problems that require timely and contextual information about our dynamic and changing planet. OmniEarth improves subscribers’ ability to visualize the world around them by enhancing their ability to see, analyze, and react to change in real time through a solutions-as-a-service platform.

scholarly journals Long-Term Data Preservation Data Lifecycle, Standardisation Process, Implementation and Lessons Learned

2020 ◽  
Vol 15 (1) ◽  
pp. 10
Author(s):  
Mirko Albani ◽  
Iolanda Maggio ◽  
CEOS Data Stewardship Interest Group
Keyword(s):  
Earth Science ◽  
Data Curation ◽  
Observation Data ◽  
Science Data ◽  
Data Preservation ◽  
Data Stewardship ◽  
Data Lifecycle ◽  
Earth Observation Data ◽  
Earth Science Data

Science and Earth Observation data represent today a unique and valuable asset for humankind that should be preserved without time constraints and kept accessible and exploitable by current and future generations. In Earth Science, knowledge of the past and tracking of the evolution are at the basis of our capability to effectively respond to the global changes that are putting increasing pressure on the environment, and on human society. This can only be achieved if long time series of data are properly preserved and made accessible to support international initiatives. Within ESA Member States and beyond, Earth Science data holders are increasingly coordinating data preservation efforts to ensure that the valuable data are safeguarded against loss and kept accessible and useable for current and future generations. This task becomes increasingly challenging in view of the existing 40 years’ worth of Earth Science data stored in archives around the world and the massive increase of data volumes expected over the next years from e.g., the European Copernicus Sentinel missions. Long Term Data Preservation (LTDP) aims at maintaining information discoverable and accessible in an independent and understandable way, with supporting information, which helps ensuring authenticity, over the long term. A focal aspect of LTDP is data Curation. Data Curation refers to the management of data throughout its life cycle. Data Curation activities enable data discovery and retrieval, maintain its quality, add value, and allow data re-use over time. It includes all the processes that involve data management, such as pre-ingest initiatives, ingest functions, archival storage and preservation, dissemination, and provision of access for a designated community. The paper presents specific aspects, of importance during the entire Earth observation data lifecycle, with respect to evolving data volumes and application scenarios. These particular issues are introduced in the section on 'Big Data' and LTDP. The Data Stewardship Reference lifecycle section describes how the data stewardship activities can be efficiently organised, while the following section addresses the overall preservation workflow and shows the technical steps to be taken during Data Curation. Earth Science Data Curation and preservation should be addressed during all mission stages - from the initial mission planning, throughout the entire mission lifetime, and during the post- mission phase. The Data Stewardship Reference Lifecycle gives a high-level overview of the steps useful for implementing Curation and preservation rules on mission data sets from initial conceptualisation or receipt through the iterative Curation cycle.

Our goal is to establish the earth observation data in the business world Unser Ziel ist es, die Erdbeobachtungsdaten in der Geschäftswelt zu etablieren

GIS Business ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 12-14
Author(s):  
Eicher, A
Keyword(s):  
Earth Observation ◽  
Observation Data ◽  
Business World ◽  
The Earth ◽  
Earth Observation Data

Our goal is to establish the earth observation data in the business world Unser Ziel ist es, die Erdbeobachtungsdaten in der Geschäftswelt zu etablieren

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