Improved characterization of forest transmissivity within the L-MEB model using multisensor SAR data

Jaakko Seppänen, Oleg Antropov, Thomas Jagdhuber, Martti Hallikainen, Janne Heiskanen, Jaan Praks

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)

Abstract

This letter proposes a novel way to assimilate synthetic aperture radar (SAR) data to L-band Microwave Emission of the Biosphere (L-MEB) model to enhance model performance over forested areas. L- and C-band satellite SAR data are used in order to characterize the forest transmissivity within the emission model, instead of the optical satellite imagery-based leaf area index (LAI) parameter. Examination of several combinations of satellite SAR data as a substitute for LAI within the L-MEB model showed that when ALOS PALSAR (L-band) and multitemporal composite Sentinel-1 (C-band) data are applied, an improved agreement was achieved between the measured and simulated brightness temperatures (TBs) over forests. The root mean squared difference between modeled and measured TBs was reduced from 6.1 to 4.7 K with single PALSAR scene-based transmissivity correction and down to 4.1 K with multitemporal Sentinel-1 composite-based transmissivity correction. Suitability of single Sentinel-1 scenes varied based on seasonal and weather conditions. Overall, this indicates the potential of an SAR-based estimation of forest volume transmissivity and opens a possible way of fruitful active-passive microwave satellite data integration.
Original languageEnglish
Article number7968354
Pages (from-to)1408-1412
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Volume14
Issue number8
DOIs
Publication statusPublished - 1 Aug 2017
MoE publication typeA1 Journal article-refereed

Fingerprint

transmissivity
Synthetic aperture radar
biosphere
synthetic aperture radar
Microwaves
PALSAR
Satellites
leaf area index
ALOS
Satellite imagery
Data integration
Composite materials
brightness temperature
satellite imagery
satellite data
Luminance
microwave
Temperature

Keywords

  • Forestry
  • Microwave radiometry
  • Remote sensing
  • Soil moisture
  • Synthetic aperture radar (SAR)

Cite this

Seppänen, Jaakko ; Antropov, Oleg ; Jagdhuber, Thomas ; Hallikainen, Martti ; Heiskanen, Janne ; Praks, Jaan. / Improved characterization of forest transmissivity within the L-MEB model using multisensor SAR data. In: IEEE Geoscience and Remote Sensing Letters. 2017 ; Vol. 14, No. 8. pp. 1408-1412.
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abstract = "This letter proposes a novel way to assimilate synthetic aperture radar (SAR) data to L-band Microwave Emission of the Biosphere (L-MEB) model to enhance model performance over forested areas. L- and C-band satellite SAR data are used in order to characterize the forest transmissivity within the emission model, instead of the optical satellite imagery-based leaf area index (LAI) parameter. Examination of several combinations of satellite SAR data as a substitute for LAI within the L-MEB model showed that when ALOS PALSAR (L-band) and multitemporal composite Sentinel-1 (C-band) data are applied, an improved agreement was achieved between the measured and simulated brightness temperatures (TBs) over forests. The root mean squared difference between modeled and measured TBs was reduced from 6.1 to 4.7 K with single PALSAR scene-based transmissivity correction and down to 4.1 K with multitemporal Sentinel-1 composite-based transmissivity correction. Suitability of single Sentinel-1 scenes varied based on seasonal and weather conditions. Overall, this indicates the potential of an SAR-based estimation of forest volume transmissivity and opens a possible way of fruitful active-passive microwave satellite data integration.",
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Improved characterization of forest transmissivity within the L-MEB model using multisensor SAR data. / Seppänen, Jaakko; Antropov, Oleg; Jagdhuber, Thomas; Hallikainen, Martti; Heiskanen, Janne; Praks, Jaan.

In: IEEE Geoscience and Remote Sensing Letters, Vol. 14, No. 8, 7968354, 01.08.2017, p. 1408-1412.

Research output: Contribution to journalArticleScientificpeer-review

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