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Continental-scale land surface phenology from harmonized Landsat 8 and Sentinel-2 imagery

Bolton, Douglas K. ; Gray, Josh M. ; Melaas, Eli K. ; Moon, Minkyu ; Eklundh, Lars LU orcid and Friedl, Mark A. (2020) In Remote Sensing of Environment 240.
Abstract

Dense time series of Landsat 8 and Sentinel-2 imagery are creating exciting new opportunities to monitor, map, and characterize temporal dynamics in land surface properties with unprecedented spatial detail and quality. By combining imagery from the Landsat 8 Operational Land Imager and the MultiSpectral Instrument on-board Sentinel-2A and -2B, the remote sensing community now has access to moderate (10–30 m) spatial resolution imagery with repeat periods of ~3 days in the mid-latitudes. At the same time, the large combined data volume from Landsat 8 and Sentinel-2 introduce substantial new challenges for users. Land surface phenology (LSP) algorithms, which estimate the timing of phenophase transitions and quantify the nature and... (More)

Dense time series of Landsat 8 and Sentinel-2 imagery are creating exciting new opportunities to monitor, map, and characterize temporal dynamics in land surface properties with unprecedented spatial detail and quality. By combining imagery from the Landsat 8 Operational Land Imager and the MultiSpectral Instrument on-board Sentinel-2A and -2B, the remote sensing community now has access to moderate (10–30 m) spatial resolution imagery with repeat periods of ~3 days in the mid-latitudes. At the same time, the large combined data volume from Landsat 8 and Sentinel-2 introduce substantial new challenges for users. Land surface phenology (LSP) algorithms, which estimate the timing of phenophase transitions and quantify the nature and magnitude of seasonality in remotely sensed land surface conditions, provide an intuitive way to reduce data volumes and redundancy, while also furnishing data sets that are useful for a wide range of applications including monitoring ecosystem response to climate variability and extreme events, ecosystem modelling, crop-type discrimination, and land cover, land use, and land cover change mapping, among others. To support the need for operational LSP data sets, here we describe a continental-scale land surface phenology algorithm and data product based on harmonized Landsat 8 and Sentinel-2 (HLS) imagery. The algorithm creates high quality times series of vegetation indices from HLS imagery, which are then used to estimate the timing of vegetation phenophase transitions at 30 m spatial resolution. We present results from assessment efforts evaluating LSP retrievals, and provide examples illustrating the character and quality of information related to land cover and terrestrial ecosystem properties provided by the continental LSP dataset that we have developed. The algorithm is highly successful in ecosystems with strong seasonal variation in leaf area (e.g., deciduous forests). Conversely, results in evergreen systems are less interpretable and conclusive.

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author
; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Harmonized Landsat Sentinel, Image time series, Land surface phenology, Multi-sensor, Vegetation index
in
Remote Sensing of Environment
volume
240
article number
111685
publisher
Elsevier
external identifiers
  • scopus:85079009179
ISSN
0034-4257
DOI
10.1016/j.rse.2020.111685
language
English
LU publication?
yes
id
a32620c8-0170-44d2-8928-b74a44ec70ce
date added to LUP
2020-02-14 09:47:43
date last changed
2022-04-18 20:36:49
@article{a32620c8-0170-44d2-8928-b74a44ec70ce,
  abstract     = {{<p>Dense time series of Landsat 8 and Sentinel-2 imagery are creating exciting new opportunities to monitor, map, and characterize temporal dynamics in land surface properties with unprecedented spatial detail and quality. By combining imagery from the Landsat 8 Operational Land Imager and the MultiSpectral Instrument on-board Sentinel-2A and -2B, the remote sensing community now has access to moderate (10–30 m) spatial resolution imagery with repeat periods of ~3 days in the mid-latitudes. At the same time, the large combined data volume from Landsat 8 and Sentinel-2 introduce substantial new challenges for users. Land surface phenology (LSP) algorithms, which estimate the timing of phenophase transitions and quantify the nature and magnitude of seasonality in remotely sensed land surface conditions, provide an intuitive way to reduce data volumes and redundancy, while also furnishing data sets that are useful for a wide range of applications including monitoring ecosystem response to climate variability and extreme events, ecosystem modelling, crop-type discrimination, and land cover, land use, and land cover change mapping, among others. To support the need for operational LSP data sets, here we describe a continental-scale land surface phenology algorithm and data product based on harmonized Landsat 8 and Sentinel-2 (HLS) imagery. The algorithm creates high quality times series of vegetation indices from HLS imagery, which are then used to estimate the timing of vegetation phenophase transitions at 30 m spatial resolution. We present results from assessment efforts evaluating LSP retrievals, and provide examples illustrating the character and quality of information related to land cover and terrestrial ecosystem properties provided by the continental LSP dataset that we have developed. The algorithm is highly successful in ecosystems with strong seasonal variation in leaf area (e.g., deciduous forests). Conversely, results in evergreen systems are less interpretable and conclusive.</p>}},
  author       = {{Bolton, Douglas K. and Gray, Josh M. and Melaas, Eli K. and Moon, Minkyu and Eklundh, Lars and Friedl, Mark A.}},
  issn         = {{0034-4257}},
  keywords     = {{Harmonized Landsat Sentinel; Image time series; Land surface phenology; Multi-sensor; Vegetation index}},
  language     = {{eng}},
  publisher    = {{Elsevier}},
  series       = {{Remote Sensing of Environment}},
  title        = {{Continental-scale land surface phenology from harmonized Landsat 8 and Sentinel-2 imagery}},
  url          = {{http://dx.doi.org/10.1016/j.rse.2020.111685}},
  doi          = {{10.1016/j.rse.2020.111685}},
  volume       = {{240}},
  year         = {{2020}},
}