Mosaicking Copernicus Sentinel-1 Data at Global Scale

Syrris, Vasileios; Corbane, Christina; Pesaresi, Martino; Soille, Pierre

doi:10.1109/tbdata.2018.2846265

Cited by 11 publications

(8 citation statements)

References 28 publications

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“…Hence, it is difficult to mosaic Sentinel‐1 images acquired over different orbits because overlapping areas are viewed with very different incident angles (Syrris et al. ), considerably impacting the magnitude of the backscattered signal as mentioned above (Frison and Mougin ; Inoue et al. ).…”

Section: Discussionmentioning

confidence: 99%

Combining optical and radar satellite image time series to map natural vegetation: savannas as an example

Lopes

Frison

Durant

et al. 2020

Remote Sens Ecol Conserv

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Up-to-date land cover maps are important for biodiversity monitoring as they are central to habitat and ecosystem distribution assessments. Satellite remote sensing is a key technology for generating these maps. Until recently, land cover mapping has been limited to static approaches, which have primarily led to the production of either global maps at coarse spatial resolutions or geographically restricted maps at high spatial resolutions. The recent availability of optical (Sentinel-2) and radar (Sentinel-1) satellite image time series (SITS) which provide access to high spatial and very high temporal resolutions, is a game changer, offering opportunities to map land cover using both temporal and spatial information. These data moreover open interesting perspectives for land cover mapping based on data combination approach. However, the usefulness of combining dense time series (more than 30 images per year) and data combination approaches to map natural vegetation has so far not been assessed. To address this gap, this contribution tests the idea that the combined consideration of optical and radar data combination and time series analyses can significantly improve natural vegetation mapping in the Pendjari National Park, a Sahelian savanna protected area in Benin. Results highlight that the combination of Sentinel-1 and Sentinel-2 SITS performs as well as Sentinel-2 SITS alone in terms of classification accuracy. Land cover maps are however qualitatively better when considering the data combination approach. Our results also clearly show that the use of dense/hypertemporal optical time series significantly improves classification outcomes compared to using multitemporal only a few images per year) or monotemporal data. Altogether, this work thus demonstrates the ability of dense SITS to improve discrimination of natural vegetation types using information on their phenology, leading to more detailed and more reliable maps for environmental management.

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Section: Discussionmentioning

confidence: 99%

Combining optical and radar satellite image time series to map natural vegetation: savannas as an example

Lopes

Frison

Durant

et al. 2020

Remote Sens Ecol Conserv

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“…This method can be used to produce wide-area backscatter composites for a variety of applications that maintain validity also in mountainous regions. Unlike "one-off" SAR mosaicking approaches [26]- [30], LRW combines all available data into a local backscatter estimate based on a weighting appropriate for the local terrain distortions applicable within each contributing image product. This opens the door to seamless composites using data from multiple imaging modes and satellites.…”

Section: Backscatter Composites Through Lrwmentioning

confidence: 99%

Wide-Area Analysis-Ready Radar Backscatter Composites

Small

Rohner

Miranda

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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The benefits of composite products are well known to users of data from optical sensors: cloud-cleared composite reflectance or index products are commonly used as an analysis-ready data (ARD) layer. No analogous composite products are currently in widespread use that is based on spaceborne radar satellite backscatter signals. Here, we present a methodology to produce wide-area ARD composite backscatter images. They build on the existing heritage of geometrically and radiometrically terrain corrected level 1 products. By combining backscatter measurements of a single region seen from multiple satellite tracks (incl. ascending and descending), they are able to provide wide-area coverage with low latency. The analysis-ready composite backscatter maps provide flattened backscatter estimates that are geometrically and radiometrically corrected for slope effects. A mask layer annotating the local quality of the composite resolution is introduced. Multiple tracks are combined by weighting each observation by its local resolution, generating seamless wide-area backscatter maps suitable for applications ranging from wet snow monitoring to land cover classification or short-term change detection.

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“…Sin embargo, estos métodos se han aplicado generalmente solo a cantidades pequeñas de escenas, en el marco de estudios de factibilidad. Las pruebas con mosaicos grandes, de resolución alta y sin nubes, para fusionar cientos de imágenes, aún son raras (Bielski et al, 2007;Syrris et al, 2018).…”

Section: Generación Automatizada Del Proto-mosaicounclassified

Un mapa de México en 100,000 millones de pixeles – generación de un mosaico nacional con imágenes RapidEye

Hruby¹,

Merino

Ressl

2020

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El presente artículo resume los pasos seguidos para la generación del primer mosaico nacional sin nubes y de alta resolución (5m). Se presentan tanto las dificultades de la producción de mosaicos en términos generales, como la metodología que hemos seguido para finalizar este proyecto con éxito. Los principales pasos son la preselección de datos de un acervo total de 50,000 imágenes RapidEye, la optimización de histogramas y de divisiones entre imágenes adyacentes y la fusión de 4,500 escenas para generar un mapa satelital del país entero. Discutimos también el pos-procesamiento para eliminar nubes y desaturar particularmente las regiones desérticas, lo cual constituye una diferencia respecto de propuestas alternativas. Adicionalmente, incluimos detalles acerca de la disponibilidad de los datos. Finalmente, cerramos el texto con posibles aplicaciones de una imagen satelital de México de aproximadamente 100,000 millones de pixeles.

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Mosaicking Copernicus Sentinel-1 Data at Global Scale

Cited by 11 publications

References 28 publications

Combining optical and radar satellite image time series to map natural vegetation: savannas as an example

Combining optical and radar satellite image time series to map natural vegetation: savannas as an example

Wide-Area Analysis-Ready Radar Backscatter Composites

Un mapa de México en 100,000 millones de pixeles – generación de un mosaico nacional con imágenes RapidEye

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