Reflectance variation in boreal landscape during the snow melting period using airborne imaging spectroscopy

Anna Maaria Kirsikka Heinilä, Miia Salminen, Sari Metsämäki, Petri Kauko Emil Pellikka, Sampsa Koponen, Jouni Pulliainen

Research output: Contribution to journalArticleScientificpeer-review

Abstract

We aim a better understanding of the effect of spring-time snow melt on the remotely sensed scene reflectance by using an extensive amount of optical spectral data obtained from an airborne hyperspectral campaign in Northern Finland. We investigate the behaviour of thin snow reflectance for different land cover types, such as open areas, boreal forests and treeless fells. Our results not only confirm the generally known fact that the reflectance of a melting thin snow layer is considerably lower than that of a thick snow layer, but we also present analyses of the reflectance variation over different land covers and in boreal forests as a function of canopy coverage. According to common knowledge, the highly variating reflectance spectra of partially transparent, most likely also contaminated thin snow pack weakens the performance of snow detection algorithms, in particular in the mapping of Fractional Snow Cover (FSC) during the end of the melting period. The obtained results directly support further development of the SCAmod algorithm for FSC retrieval, and can be likewise applied to develop other algorithms for optical satellite data (e.g. spectral unmixing methods), and to perform accuracy assessments for snow detection algorithms.

A useful part of this work is the investigation of the competence of Normalized Difference Snow Index (NDSI) in snow detection in late spring, since it is widely used in snow mapping. We conclude, based on the spectral data analysis, that the NDSI-based snow mapping is more accurate in open areas than in forests. However, at the very end of the snow melting period the behavior of the NDSI becomes more unstable and unpredictable in non-forests with shallow snow, increasing the inaccuracy also in non-forested areas. For instance in peatbogs covered by melting snow layer (snow depth <30 cm) the mean NDSI-0.6 was observed, having coefficient of variation as high as 70%, whereas for deeper snow packs the mean NDSI shows positive values.

Original languageEnglish
JournalInternational Journal of Applied Earth Observation and Geoinformation
Volume76
Pages (from-to)66-76
Number of pages11
ISSN1569-8432
DOIs
Publication statusPublished - Apr 2019
MoE publication typeA1 Journal article-refereed

Fields of Science

  • 1171 Geosciences
  • 114 Physical sciences
  • Reflectance
  • AISA
  • Spectroscopy
  • Scene reflectance
  • Snow melt
  • NDSI
  • NDVI
  • Boreal forest
  • Land cover classification
  • Fell
  • Snow mapping
  • FSC
  • SCE
  • MODIS
  • GRAIN-SIZE
  • FOREST CANOPY
  • COVERED AREA
  • SPECTRAL ALBEDO
  • SPECTROMETER
  • VEGETATION
  • ACCURACY
  • PRODUCTS
  • FRACTION

Cite this

@article{bdb64f54d3074b488e4a08f096fdc3cb,
title = "Reflectance variation in boreal landscape during the snow melting period using airborne imaging spectroscopy",
abstract = "We aim a better understanding of the effect of spring-time snow melt on the remotely sensed scene reflectance by using an extensive amount of optical spectral data obtained from an airborne hyperspectral campaign in Northern Finland. We investigate the behaviour of thin snow reflectance for different land cover types, such as open areas, boreal forests and treeless fells. Our results not only confirm the generally known fact that the reflectance of a melting thin snow layer is considerably lower than that of a thick snow layer, but we also present analyses of the reflectance variation over different land covers and in boreal forests as a function of canopy coverage. According to common knowledge, the highly variating reflectance spectra of partially transparent, most likely also contaminated thin snow pack weakens the performance of snow detection algorithms, in particular in the mapping of Fractional Snow Cover (FSC) during the end of the melting period. The obtained results directly support further development of the SCAmod algorithm for FSC retrieval, and can be likewise applied to develop other algorithms for optical satellite data (e.g. spectral unmixing methods), and to perform accuracy assessments for snow detection algorithms.A useful part of this work is the investigation of the competence of Normalized Difference Snow Index (NDSI) in snow detection in late spring, since it is widely used in snow mapping. We conclude, based on the spectral data analysis, that the NDSI-based snow mapping is more accurate in open areas than in forests. However, at the very end of the snow melting period the behavior of the NDSI becomes more unstable and unpredictable in non-forests with shallow snow, increasing the inaccuracy also in non-forested areas. For instance in peatbogs covered by melting snow layer (snow depth <30 cm) the mean NDSI-0.6 was observed, having coefficient of variation as high as 70{\%}, whereas for deeper snow packs the mean NDSI shows positive values.",
keywords = "1171 Geosciences, 114 Physical sciences, Reflectance, AISA, Spectroscopy, Scene reflectance, Snow melt, NDSI, NDVI, Boreal forest, Land cover classification, Fell, Snow mapping, FSC, SCE, MODIS, GRAIN-SIZE, FOREST CANOPY, COVERED AREA, SPECTRAL ALBEDO, SPECTROMETER, VEGETATION, ACCURACY, PRODUCTS, FRACTION",
author = "Heinil{\"a}, {Anna Maaria Kirsikka} and Miia Salminen and Sari Mets{\"a}m{\"a}ki and Pellikka, {Petri Kauko Emil} and Sampsa Koponen and Jouni Pulliainen",
year = "2019",
month = "4",
doi = "10.1016/j.jag.2018.10.017",
language = "English",
volume = "76",
pages = "66--76",
journal = "International Journal of Applied Earth Observation and Geoinformation",
issn = "1569-8432",
publisher = "Elsevier Scientific Publ. Co",

}

Reflectance variation in boreal landscape during the snow melting period using airborne imaging spectroscopy. / Heinilä, Anna Maaria Kirsikka; Salminen, Miia; Metsämäki, Sari; Pellikka, Petri Kauko Emil; Koponen, Sampsa; Pulliainen, Jouni.

In: International Journal of Applied Earth Observation and Geoinformation, Vol. 76, 04.2019, p. 66-76.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - Reflectance variation in boreal landscape during the snow melting period using airborne imaging spectroscopy

AU - Heinilä, Anna Maaria Kirsikka

AU - Salminen, Miia

AU - Metsämäki, Sari

AU - Pellikka, Petri Kauko Emil

AU - Koponen, Sampsa

AU - Pulliainen, Jouni

PY - 2019/4

Y1 - 2019/4

N2 - We aim a better understanding of the effect of spring-time snow melt on the remotely sensed scene reflectance by using an extensive amount of optical spectral data obtained from an airborne hyperspectral campaign in Northern Finland. We investigate the behaviour of thin snow reflectance for different land cover types, such as open areas, boreal forests and treeless fells. Our results not only confirm the generally known fact that the reflectance of a melting thin snow layer is considerably lower than that of a thick snow layer, but we also present analyses of the reflectance variation over different land covers and in boreal forests as a function of canopy coverage. According to common knowledge, the highly variating reflectance spectra of partially transparent, most likely also contaminated thin snow pack weakens the performance of snow detection algorithms, in particular in the mapping of Fractional Snow Cover (FSC) during the end of the melting period. The obtained results directly support further development of the SCAmod algorithm for FSC retrieval, and can be likewise applied to develop other algorithms for optical satellite data (e.g. spectral unmixing methods), and to perform accuracy assessments for snow detection algorithms.A useful part of this work is the investigation of the competence of Normalized Difference Snow Index (NDSI) in snow detection in late spring, since it is widely used in snow mapping. We conclude, based on the spectral data analysis, that the NDSI-based snow mapping is more accurate in open areas than in forests. However, at the very end of the snow melting period the behavior of the NDSI becomes more unstable and unpredictable in non-forests with shallow snow, increasing the inaccuracy also in non-forested areas. For instance in peatbogs covered by melting snow layer (snow depth <30 cm) the mean NDSI-0.6 was observed, having coefficient of variation as high as 70%, whereas for deeper snow packs the mean NDSI shows positive values.

AB - We aim a better understanding of the effect of spring-time snow melt on the remotely sensed scene reflectance by using an extensive amount of optical spectral data obtained from an airborne hyperspectral campaign in Northern Finland. We investigate the behaviour of thin snow reflectance for different land cover types, such as open areas, boreal forests and treeless fells. Our results not only confirm the generally known fact that the reflectance of a melting thin snow layer is considerably lower than that of a thick snow layer, but we also present analyses of the reflectance variation over different land covers and in boreal forests as a function of canopy coverage. According to common knowledge, the highly variating reflectance spectra of partially transparent, most likely also contaminated thin snow pack weakens the performance of snow detection algorithms, in particular in the mapping of Fractional Snow Cover (FSC) during the end of the melting period. The obtained results directly support further development of the SCAmod algorithm for FSC retrieval, and can be likewise applied to develop other algorithms for optical satellite data (e.g. spectral unmixing methods), and to perform accuracy assessments for snow detection algorithms.A useful part of this work is the investigation of the competence of Normalized Difference Snow Index (NDSI) in snow detection in late spring, since it is widely used in snow mapping. We conclude, based on the spectral data analysis, that the NDSI-based snow mapping is more accurate in open areas than in forests. However, at the very end of the snow melting period the behavior of the NDSI becomes more unstable and unpredictable in non-forests with shallow snow, increasing the inaccuracy also in non-forested areas. For instance in peatbogs covered by melting snow layer (snow depth <30 cm) the mean NDSI-0.6 was observed, having coefficient of variation as high as 70%, whereas for deeper snow packs the mean NDSI shows positive values.

KW - 1171 Geosciences

KW - 114 Physical sciences

KW - Reflectance

KW - AISA

KW - Spectroscopy

KW - Scene reflectance

KW - Snow melt

KW - NDSI

KW - NDVI

KW - Boreal forest

KW - Land cover classification

KW - Fell

KW - Snow mapping

KW - FSC

KW - SCE

KW - MODIS

KW - GRAIN-SIZE

KW - FOREST CANOPY

KW - COVERED AREA

KW - SPECTRAL ALBEDO

KW - SPECTROMETER

KW - VEGETATION

KW - ACCURACY

KW - PRODUCTS

KW - FRACTION

U2 - 10.1016/j.jag.2018.10.017

DO - 10.1016/j.jag.2018.10.017

M3 - Article

VL - 76

SP - 66

EP - 76

JO - International Journal of Applied Earth Observation and Geoinformation

JF - International Journal of Applied Earth Observation and Geoinformation

SN - 1569-8432

ER -