CEOS Analysis Ready Data For Land – An Overview on the Current and Future Work

Siqueira, Andreia; Tadono, Takeo; Rosenqvist, Åke; Lacey, Jennifer; Lewis, Adam; Thankappan, Medhavy; Szantoi, Zoltan; Goryl, Philippe; Labahn, Steven; Ross, Jonathon; Hosford, Steven; Mecklenburg, Susanne

doi:10.1109/igarss.2019.8899846

Cited by 16 publications

(7 citation statements)

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“…The global Landsat archive holds more than 6 M unique scenes that provide more than 40 years of data, and higher-level science products such as surface reflectance are available through on-demand interfaces ( Wulder et al, 2019 ). In recognition of the value of Analysis Ready Data (ARD), which is gridded to a standard tile size, atmospherically corrected and complemented with masks for clouds, cloud shadows, snow/ice, etc., ARD is now produced routinely for the US land area and global ARD production is being planned ( Siqueira et al, 2019 ).…”

Section: The Current State Of Eo Data Repositories Platforms and Resmentioning

confidence: 99%

“…This is because many products will require data sourced from different providers and, without standards, users will be forced to work with a cumbersome number of independently developed systems. Fortunately, the largest satellite data providers are already moving in this direction ( Siqueira et al, 2019 ). For example, NASA now provides a Harmonized Landsat and Sentinel-2 (HLS) surface reflectance dataset for North America as well as some globally distributed areas at 30 m, while ESA is working on a pre-operational demonstration service, where they harmonize and fuse Sentinel-2 (S2) and Landsat 8 data at a common 10 m resolution.…”

Section: The Current State Of Eo Data Repositories Platforms and Resmentioning

confidence: 99%

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Addressing the need for improved land cover map products for policy support

Szantoi

Geller

Tsendbazar

et al. 2020

Environmental Science & Policy

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Highlights Despite the widespread importance of land cover products, current production approaches leave many end users unsatisfied. Frequent, global coverage of satellite imagery is now available, enabling new approaches for land cover product generation. New approaches and land cover products can better serve end user needs. A dynamic, automated land cover mapping system is proposed, with challenges outlined and their solutions proposed. Realization of the concept will require additional government support.

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Section: The Current State Of Eo Data Repositories Platforms and Resmentioning

confidence: 99%

Section: The Current State Of Eo Data Repositories Platforms and Resmentioning

confidence: 99%

Addressing the need for improved land cover map products for policy support

Szantoi

Geller

Tsendbazar

et al. 2020

Environmental Science & Policy

Self Cite

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“…Further, there can be a significant mismatch between the Ground Sample Distances (GSDs) of agency craft (10 −> 300 m), typical small-sat and airborne (<5 m), or UAV (<<1 m) platforms, calling into question the comparison approach especially for small targets and heterogeneous areas of interest. Data interoperability and harmonization from multiple instruments require, therefore, both thorough characterization of the individual sensors and suitable methods of intercalibration or calibration transfer [24][25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

The Ground to Space CALibration Experiment (G-SCALE): Simultaneous Validation of UAV, Airborne, and Satellite Imagers for Earth Observation Using Specular Targets

Russell¹,

Soffer

Ientilucci

et al. 2023

Remote Sensing

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The objective of the Ground to Space CALibration Experiment (G-SCALE) is to demonstrate the use of convex mirrors as a radiometric and spatial calibration and validation technology for Earth Observation assets, operating at multiple altitudes and spatial scales. Specifically, point sources with NIST-traceable absolute radiance signal are evaluated for simultaneous vicarious calibration of multi- and hyperspectral sensors in the VNIR/SWIR range, aboard Unmanned Aerial Vehicles (UAVs), manned aircraft, and satellite platforms. We introduce the experimental process, field site, instrumentation, and preliminary results of the G-SCALE, providing context for forthcoming papers that will detail the results of intercomparison between sensor technologies and remote sensing applications utilizing the mirror-based calibration approach, which is scalable across a wide range of pixel sizes with appropriate facilities. The experiment was carried out at the Rochester Institute of Technology’s Tait Preserve in Penfield, NY, USA on 23 July 2021. The G-SCALE represents a unique, international collaboration between commercial, academic, and government entities for the purpose of evaluating a novel method to improve vicarious calibration and validation for Earth Observation.

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“…The Committee on Earth Observations Satellites (CEOS) definition of ARD stands for "satellite data processed to a minimum set of requirements and organized into a form that allows immediate analysis with a minimum of additional user effort and interoperability both through time and with other data sets" (Siqueira et al, 2019). ARD are procedurally generated data that ensures consistency and interoperability from data acquisition to a level required by users, which can be TOA (Topof-atmosphere) reflectance, surface reflectance (SR) or other standardized data (Giuliani et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Impact of Lasrc and Sen2cor Atmospheric Correction Algorithms on Landsat-8/Oli and Sentinel-2/Msi Data Over Aeronet Stations in Brazilian Territory

Marujo

Fronza

Soares

et al. 2021

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Accurate and consistent Surface Reflectance estimation from optical remote sensor observations is directly dependant on the used atmospheric correction processor and the differences caused by it may have implications on further processes, e.g. classification. Brazil is a continental scale country with different biomes. Recently, new initiatives, as the Brazil Data Cube Project, are emerging and using free and open data policy data, more specifically medium spatial resolution sensor images, to create image data cubes and classify the Brazilian territory crops. For this reason, the purpose of this study is to verify, on Landsat-8 and Sentinel-2 images for the Brazilian territory, the suitability of the atmospheric correction processors maintained by their image providers, LaSRC from USGS and Sen2cor from ESA, respectively. To achieve this, we tested the surface reflectance products from Landsat-8 processed through LaSRC and Sentinel-2 processed through LaSRC and Sen2cor comparing to a reference dataset computed by ARCSI and AERONET. The obtained results point that Landsat-8/OLI images atmospherically corrected using the LaSRC corrector are consistent to the surface reflectance reference and other atmospheric correction processors studies, while for Sentinel-2/MSI images, Sen2cor performed best. Although corrections over Sentinel-2/MSI data weren’t as consistent as in Landsat-8/OLI corrections, in comparison to the surface reflectance references, most of the spectral bands achieved acceptable APU results.

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CEOS Analysis Ready Data For Land – An Overview on the Current and Future Work

Cited by 16 publications

References 0 publications

Addressing the need for improved land cover map products for policy support

Addressing the need for improved land cover map products for policy support

The Ground to Space CALibration Experiment (G-SCALE): Simultaneous Validation of UAV, Airborne, and Satellite Imagers for Earth Observation Using Specular Targets

Evaluating the Impact of Lasrc and Sen2cor Atmospheric Correction Algorithms on Landsat-8/Oli and Sentinel-2/Msi Data Over Aeronet Stations in Brazilian Territory

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