STAIR: A generic and fully-automated method to fuse multiple sources of optical satellite data to generate a high-resolution, daily and cloud-/gap-free surface reflectance product

Yunan Luo, Kaiyu Guan, Jian Peng

Research output: Contribution to journalArticle

Abstract

Surface reflectance data with high resolutions in both space and time have been desired and demanded by scientific research and societal applications. Standard satellite missions could not provide such data at both high resolutions. Fusion approaches that leverage the complementary strengths in various satellite sources (e.g. MODIS/VIIRS/GOES-R's sub-daily revisiting frequency and Landsat/Sentinel-2's high spatial resolution) provide a viable means to simultaneously achieve both high resolutions in the fusion data. In this paper, we presented a novel, generic and fully-automated method, STAIR, for fusing multi-spectral satellite data to generate a high-frequency, high-resolution and cloud-/gap-free data. Building on the time series of multiple sources of satellite data, STAIR first imputes the missing-value pixels (due to cloud cover or sensor mechanical issues) in satellite images using an adaptive-average correction process, which takes into account different land covers and neighborhood information of miss-value pixels through an automatic segmentation. To fuse satellite images, it employs a local interpolation model to capture the most informative spatial information provided by the high spatial resolution data (e.g., Landsat) and then performs an adjustment step to incorporate the temporal patterns provided by the high-frequency data (e.g., MODIS). The resulting fused products contain daily, high spatial resolution and cloud-/gap-free fused images. We tested our algorithm to fuse surface reflectance data of MODIS and Landsat in Champaign County at Illinois and generated daily time series for all the growing seasons (Apr 1 to Nov 1) from 2000 to 2015 at 30 m resolution. Extensive experiments demonstrated that STAIR not only captures correct texture patterns but also predicts accurate reflectance values in the generated images, with a significant performance improvement over the classic STARFM algorithm. This method is computationally efficient and ready to be scaled up to continental scales. It is also sufficiently generic to easily include various optical satellite data for fusion. We envision this novel algorithm can provide effective means to leverage historical optical satellite data to build long-term daily, 30 m surface reflectance record (e.g. from 2000 to present) at continental scales for various applications, as well as produce operational near-realtime daily and high-resolution data for future earth observation applications.

Original languageEnglish (US)
Pages (from-to)87-99
Number of pages13
JournalRemote Sensing of Environment
Volume214
DOIs
StatePublished - Sep 1 2018

Fingerprint

surface reflectance
Electric fuses
reflectance
remote sensing
satellite data
moderate resolution imaging spectroradiometer
Landsat
Satellites
time series analysis
MODIS
spatial resolution
methodology
land cover
space and time
sensors (equipment)
Time series
pixel
Pixels
texture
growing season

Keywords

  • Fusion
  • Gap filling
  • Landsat
  • MODIS

ASJC Scopus subject areas

  • Soil Science
  • Geology
  • Computers in Earth Sciences

Cite this

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title = "STAIR: A generic and fully-automated method to fuse multiple sources of optical satellite data to generate a high-resolution, daily and cloud-/gap-free surface reflectance product",
abstract = "Surface reflectance data with high resolutions in both space and time have been desired and demanded by scientific research and societal applications. Standard satellite missions could not provide such data at both high resolutions. Fusion approaches that leverage the complementary strengths in various satellite sources (e.g. MODIS/VIIRS/GOES-R's sub-daily revisiting frequency and Landsat/Sentinel-2's high spatial resolution) provide a viable means to simultaneously achieve both high resolutions in the fusion data. In this paper, we presented a novel, generic and fully-automated method, STAIR, for fusing multi-spectral satellite data to generate a high-frequency, high-resolution and cloud-/gap-free data. Building on the time series of multiple sources of satellite data, STAIR first imputes the missing-value pixels (due to cloud cover or sensor mechanical issues) in satellite images using an adaptive-average correction process, which takes into account different land covers and neighborhood information of miss-value pixels through an automatic segmentation. To fuse satellite images, it employs a local interpolation model to capture the most informative spatial information provided by the high spatial resolution data (e.g., Landsat) and then performs an adjustment step to incorporate the temporal patterns provided by the high-frequency data (e.g., MODIS). The resulting fused products contain daily, high spatial resolution and cloud-/gap-free fused images. We tested our algorithm to fuse surface reflectance data of MODIS and Landsat in Champaign County at Illinois and generated daily time series for all the growing seasons (Apr 1 to Nov 1) from 2000 to 2015 at 30 m resolution. Extensive experiments demonstrated that STAIR not only captures correct texture patterns but also predicts accurate reflectance values in the generated images, with a significant performance improvement over the classic STARFM algorithm. This method is computationally efficient and ready to be scaled up to continental scales. It is also sufficiently generic to easily include various optical satellite data for fusion. We envision this novel algorithm can provide effective means to leverage historical optical satellite data to build long-term daily, 30 m surface reflectance record (e.g. from 2000 to present) at continental scales for various applications, as well as produce operational near-realtime daily and high-resolution data for future earth observation applications.",
keywords = "Fusion, Gap filling, Landsat, MODIS",
author = "Yunan Luo and Kaiyu Guan and Jian Peng",
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T2 - A generic and fully-automated method to fuse multiple sources of optical satellite data to generate a high-resolution, daily and cloud-/gap-free surface reflectance product

AU - Luo, Yunan

AU - Guan, Kaiyu

AU - Peng, Jian

PY - 2018/9/1

Y1 - 2018/9/1

N2 - Surface reflectance data with high resolutions in both space and time have been desired and demanded by scientific research and societal applications. Standard satellite missions could not provide such data at both high resolutions. Fusion approaches that leverage the complementary strengths in various satellite sources (e.g. MODIS/VIIRS/GOES-R's sub-daily revisiting frequency and Landsat/Sentinel-2's high spatial resolution) provide a viable means to simultaneously achieve both high resolutions in the fusion data. In this paper, we presented a novel, generic and fully-automated method, STAIR, for fusing multi-spectral satellite data to generate a high-frequency, high-resolution and cloud-/gap-free data. Building on the time series of multiple sources of satellite data, STAIR first imputes the missing-value pixels (due to cloud cover or sensor mechanical issues) in satellite images using an adaptive-average correction process, which takes into account different land covers and neighborhood information of miss-value pixels through an automatic segmentation. To fuse satellite images, it employs a local interpolation model to capture the most informative spatial information provided by the high spatial resolution data (e.g., Landsat) and then performs an adjustment step to incorporate the temporal patterns provided by the high-frequency data (e.g., MODIS). The resulting fused products contain daily, high spatial resolution and cloud-/gap-free fused images. We tested our algorithm to fuse surface reflectance data of MODIS and Landsat in Champaign County at Illinois and generated daily time series for all the growing seasons (Apr 1 to Nov 1) from 2000 to 2015 at 30 m resolution. Extensive experiments demonstrated that STAIR not only captures correct texture patterns but also predicts accurate reflectance values in the generated images, with a significant performance improvement over the classic STARFM algorithm. This method is computationally efficient and ready to be scaled up to continental scales. It is also sufficiently generic to easily include various optical satellite data for fusion. We envision this novel algorithm can provide effective means to leverage historical optical satellite data to build long-term daily, 30 m surface reflectance record (e.g. from 2000 to present) at continental scales for various applications, as well as produce operational near-realtime daily and high-resolution data for future earth observation applications.

AB - Surface reflectance data with high resolutions in both space and time have been desired and demanded by scientific research and societal applications. Standard satellite missions could not provide such data at both high resolutions. Fusion approaches that leverage the complementary strengths in various satellite sources (e.g. MODIS/VIIRS/GOES-R's sub-daily revisiting frequency and Landsat/Sentinel-2's high spatial resolution) provide a viable means to simultaneously achieve both high resolutions in the fusion data. In this paper, we presented a novel, generic and fully-automated method, STAIR, for fusing multi-spectral satellite data to generate a high-frequency, high-resolution and cloud-/gap-free data. Building on the time series of multiple sources of satellite data, STAIR first imputes the missing-value pixels (due to cloud cover or sensor mechanical issues) in satellite images using an adaptive-average correction process, which takes into account different land covers and neighborhood information of miss-value pixels through an automatic segmentation. To fuse satellite images, it employs a local interpolation model to capture the most informative spatial information provided by the high spatial resolution data (e.g., Landsat) and then performs an adjustment step to incorporate the temporal patterns provided by the high-frequency data (e.g., MODIS). The resulting fused products contain daily, high spatial resolution and cloud-/gap-free fused images. We tested our algorithm to fuse surface reflectance data of MODIS and Landsat in Champaign County at Illinois and generated daily time series for all the growing seasons (Apr 1 to Nov 1) from 2000 to 2015 at 30 m resolution. Extensive experiments demonstrated that STAIR not only captures correct texture patterns but also predicts accurate reflectance values in the generated images, with a significant performance improvement over the classic STARFM algorithm. This method is computationally efficient and ready to be scaled up to continental scales. It is also sufficiently generic to easily include various optical satellite data for fusion. We envision this novel algorithm can provide effective means to leverage historical optical satellite data to build long-term daily, 30 m surface reflectance record (e.g. from 2000 to present) at continental scales for various applications, as well as produce operational near-realtime daily and high-resolution data for future earth observation applications.

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