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

Senty, Paul ; Guzinski, Radoslaw ; Grogan, Kenneth ; Buitenwerf, Robert ; Ardö, Jonas LU orcid ; Eklundh, Lars LU orcid ; Koukos, Alkiviadis ; Tagesson, Torbern LU and Munk, Michael (2024) In Remote Sensing 16(11).
Abstract

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... (More)

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.

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author
; ; ; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
data fusion, interpolation, phenology, rangelands, Sentinel-2, Sentinel-3, spatiotemporal fusion, STARFM, time series
in
Remote Sensing
volume
16
issue
11
article number
1833
publisher
MDPI AG
external identifiers
  • scopus:85196215140
ISSN
2072-4292
DOI
10.3390/rs16111833
language
English
LU publication?
yes
id
7f81f0da-49f3-42df-898d-02a7f25fadc0
date added to LUP
2024-08-19 14:22:11
date last changed
2024-08-19 14:23:35
@article{7f81f0da-49f3-42df-898d-02a7f25fadc0,
  abstract     = {{<p>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.</p>}},
  author       = {{Senty, Paul and Guzinski, Radoslaw and Grogan, Kenneth and Buitenwerf, Robert and Ardö, Jonas and Eklundh, Lars and Koukos, Alkiviadis and Tagesson, Torbern and Munk, Michael}},
  issn         = {{2072-4292}},
  keywords     = {{data fusion; interpolation; phenology; rangelands; Sentinel-2; Sentinel-3; spatiotemporal fusion; STARFM; time series}},
  language     = {{eng}},
  number       = {{11}},
  publisher    = {{MDPI AG}},
  series       = {{Remote Sensing}},
  title        = {{Fast Fusion of Sentinel-2 and Sentinel-3 Time Series over Rangelands}},
  url          = {{http://dx.doi.org/10.3390/rs16111833}},
  doi          = {{10.3390/rs16111833}},
  volume       = {{16}},
  year         = {{2024}},
}