Webbläsaren som du använder stöds inte av denna webbplats. Alla versioner av Internet Explorer stöds inte längre, av oss eller Microsoft (läs mer här: * https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Var god och använd en modern webbläsare för att ta del av denna webbplats, som t.ex. nyaste versioner av Edge, Chrome, Firefox eller Safari osv.

Default user image.

Lars Eklundh

Professor

Default user image.

Fast Fusion of Sentinel-2 and Sentinel-3 Time Series over Rangelands

Författare

  • Paul Senty
  • Radoslaw Guzinski
  • Kenneth Grogan
  • Robert Buitenwerf
  • Jonas Ardö
  • Lars Eklundh
  • Alkiviadis Koukos
  • Torbern Tagesson
  • Michael Munk

Summary, in English

Monitoring ecosystems at regional or continental scales is paramount for biodiversity conservation, climate change mitigation, and sustainable land management. Effective monitoring requires satellite imagery with both high spatial resolution and high temporal resolution. However, there is currently no single, freely available data source that fulfills these needs. A seamless fusion of data from the Sentinel-3 and Sentinel-2 optical sensors could meet these monitoring requirements as Sentinel-2 observes at the required spatial resolution (10 m) while Sentinel-3 observes at the required temporal resolution (daily). We introduce the Efficient Fusion Algorithm across Spatio-Temporal scales (EFAST), which interpolates Sentinel-2 data into smooth time series (both spatially and temporally). This interpolation is informed by Sentinel-3’s temporal profile such that the phenological changes occurring between two Sentinel-2 acquisitions at a 10 m resolution are assumed to mirror those observed at Sentinel-3’s resolution. The EFAST consists of a weighted sum of Sentinel-2 images (weighted by a distance-to-clouds score) coupled with a phenological correction derived from Sentinel-3. We validate the capacity of our method to reconstruct the phenological profile at a 10 m resolution over one rangeland area and one irrigated cropland area. The EFAST outperforms classical interpolation techniques over both rangeland (−72% in the mean absolute error, MAE) and agricultural areas (−43% MAE); it presents a performance comparable to the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) (+5% MAE in both test areas) while being 140 times faster. The computational efficiency of our approach and its temporal smoothing enable the creation of seamless and high-resolution phenology products on a regional to continental scale.

Avdelning/ar

  • Institutionen för naturgeografi och ekosystemvetenskap
  • BECC: Biodiversity and Ecosystem services in a Changing Climate
  • LU profilområde: Naturbaserade framtidslösningar
  • MERGE: ModElling the Regional and Global Earth system

Publiceringsår

2024-06

Språk

Engelska

Publikation/Tidskrift/Serie

Remote Sensing

Volym

16

Issue

11

Dokumenttyp

Artikel i tidskrift

Förlag

MDPI AG

Ämne

  • Remote Sensing

Nyckelord

  • data fusion
  • interpolation
  • phenology
  • rangelands
  • Sentinel-2
  • Sentinel-3
  • spatiotemporal fusion
  • STARFM
  • time series

Aktiv

Published

ISBN/ISSN/Övrigt

  • ISSN: 2072-4292