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4 results for “PROBA-V”
PROBA-V Global Dataset 5 km - BHR
<p>In the framework of the Spot/PROBA-V Surface Aerosol Retrieval at MEP (<a href="mailto:SPAR@MEP">SPAR@MEP</a>) ESA project, the CISAR algorithm, originally developed by Rayference for the joint retrieval of surface reflectance, aerosol and cloud single scattering properties, has been applied to PROBA-V observation globally during 2019 at 5km resolution. CISAR retrieves simultaneously the surface reflectance (represented by the RPV model) and the aerosol optical depth (AOD) in all PROBA-V bands plus the AOD at 500nm, with their corresponding pixel-level uncertainty. The retrieval uncertainty results from the propagation of all input, prior and inversion uncertainty through the inversion process. The processing has been performed in the Mission Exploitation Platform (MEP), developed by VITO. This Global Dataset includes the surface reflectance products. Specification on the filename convention, format and content of the products can be found on the Product Specification Document (PSD).</p> <p>The products related to the <a href="https://zenodo.org/record/7462676">aerosol retrieval</a> products.</p>
PROBA-V Global Dataset 5 km - AOT/COT
<p>In the framework of the Spot/PROBA-V Surface Aerosol Retrieval at MEP (<a href="mailto:SPAR@MEP">SPAR@MEP</a>) ESA project, the CISAR algorithm, originally developed by Rayference for the joint retrieval of surface reflectance, aerosol and cloud single scattering properties, has been applied to PROBA-V observation globally during 2019 at 5km resolution. CISAR retrieves simultaneously the surface reflectance (represented by the RPV model) and the aerosol optical depth (AOD) in all PROBA-V bands plus the AOD at 500nm, with their corresponding pixel-level uncertainty. The retrieval uncertainty results from the propagation of all input, prior and inversion uncertainty through the inversion process. The processing has been performed in the Mission Exploitation Platform (MEP), developed by VITO. This Global Dataset includes products for the aerosol retrieval. Specification on the filename convention, format and content of the products can be found on the Product Specification Document (PSD).</p> <p>The products related to the <a href="https://zenodo.org/record/7457917">surface reflectance</a> products.</p>
PROBA-V Land Use @100m
<p>The PROBA-V Land Use @100m map for 2015-2020 covers the pan-tropical zone (23°S to 23°N) and consists of six annual maps, each with four bands. Bands 1 (B1) and 2 (B2) represent the land use at the beginning and end of each year, classified into four categories: "oil palm plantation" (value 1), "other perennial plantation" (value 2), "tropical forest" (value 3), and "other land use" (value 0). For full years (2016-2019), change detection runs from January 1 to December 16, with B1 and B2 corresponding to land use just before January 1 (as the first change is detected on this date) and after December 16, respectively. For the first year (2015) the period runs from May 1 to December 16, and for the last year (2020) from January 1 to April 30. Band 3 (B3) assigns a biweekly cutting date and band 4 (B4) assigns a biweekly planting date (value 1-24). </p>
PROBA-V Super-Resolution dataset
<p>The PROBA-V Super-Resolution dataset is the official dataset of <strong>ESA's Kelvins</strong> <strong>competition for "PROBA-V Super Resolution"</strong>. It contains satellite data from 74 hand-selected regions around the globe at different points in time. The data is composed of radiometrically and geometrically corrected Top-Of-Atmosphere (TOA) reflectances for the RED and NIR spectral bands at <strong>300m</strong> and <strong>100m</strong> resolution in Plate Carrée projection. The <strong>300m</strong> resolution data is delivered as <strong>128x128</strong> grey-scale pixel images, the <strong>100m</strong> resolution data as <strong>384x384</strong> grey-scale pixel images. Additionally, a quality map is provided for each pixel, indicating whether the pixels are concealed (i.e. by clouads, ice, water, missing information, etc.).</p> <p>The goal of the challenge can be described as <strong>Multi-Image Super-resolution</strong>: Construct a single high-resolution image out of a series of more frequent low resolution images.</p> <p>Detailed information about the related competition can be found at <a href="https://kelvins.esa.int/proba-v-super-resolution">https://kelvins.esa.int/proba-v-super-resolution</a>.</p> <p>A publication about the generation of this dataset exists as well: </p> <ul> <li><strong>Märtens M., Izzo D., Krzic A. and Cox D.</strong> "Super-resolution of PROBA-V images using convolutional neural networks." Astrodynamics 3.4 (2019): 387-402. (<a href="https://arxiv.org/pdf/1907.01821.pdf">arxiv version</a>)</li> </ul>
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