Disentangling remotely-sensed plant phenology and snow seasonality at northern Europe using MODIS and the plant phenology index
Jin, Hongxiao LU ; Jönsson, Anna Maria LU ; Bolmgren, Kjell ; Langvall, Ola and Eklundh, Lars LU
(2017)
In Remote Sensing of Environment
198.
p.203-212
- Abstract
- Land surface phenology is frequently derived from remotely sensed data. However, over regions with seasonal snow cover, remotely-sensed land surface phenology may be dominated by snow seasonality, rather than showing true plant phenology. Overlooking snow influences may lead to inaccurate plant phenology estimation, and consequently to misinterpretation of climate-vegetation interactions. To address the problem we apply the recently developed plant phenology index (PPI) to Moderate Resolution Imaging Spectroradiometer (MODIS) data for estimating plant phenology metrics over northern Europe. We compare PPI-derived start and end of the growing season with ground observations by professionals (6 sites) and nonprofessional citizens (378... (More)
- Land surface phenology is frequently derived from remotely sensed data. However, over regions with seasonal snow cover, remotely-sensed land surface phenology may be dominated by snow seasonality, rather than showing true plant phenology. Overlooking snow influences may lead to inaccurate plant phenology estimation, and consequently to misinterpretation of climate-vegetation interactions. To address the problem we apply the recently developed plant phenology index (PPI) to Moderate Resolution Imaging Spectroradiometer (MODIS) data for estimating plant phenology metrics over northern Europe. We compare PPI-derived start and end of the growing season with ground observations by professionals (6 sites) and nonprofessional citizens (378 sites), with phenology metrics derived from gross primary productivity (GPP, 18 sites), and with data on the timing of snow cover. These data are also compared with land surface phenology metrics derived from the normalized difference vegetation index (NDVI) using the same MODIS data. We find that the PPI-retrieved plant phenology agrees with ground observations and GPP-derived phenology, and that the NDVI-derived phenology to a large extent agrees with the end-of-snowmelt for the start-of-season and the start-of-snowing for the end-of-season. PPI is thereby useful for more accurate estimation of plant phenology from remotely sensed data over northern Europe and other regions with seasonal snow cover. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/6926e42a-e75b-4f59-9178-6f12f2370090
- author
- Jin, Hongxiao
LU
; Jönsson, Anna Maria
LU
; Bolmgren, Kjell
; Langvall, Ola
and Eklundh, Lars
LU
- organization
- publishing date
- 2017-06-14
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- MODIS; NDVI; Plant phenology index (PPI); Land surface phenology; Snow seasonality
- in
- Remote Sensing of Environment
- volume
- 198
- pages
- 203 - 212
- publisher
- Elsevier
- external identifiers
-
- scopus:85020732637
- wos:000406818500017
- ISSN
- 0034-4257
- DOI
- 10.1016/j.rse.2017.06.015
- language
- English
- LU publication?
- yes
- id
- 6926e42a-e75b-4f59-9178-6f12f2370090
- date added to LUP
- 2017-07-03 19:54:38
- date last changed
- 2022-04-01 17:59:07
@article{6926e42a-e75b-4f59-9178-6f12f2370090,
abstract = {{Land surface phenology is frequently derived from remotely sensed data. However, over regions with seasonal snow cover, remotely-sensed land surface phenology may be dominated by snow seasonality, rather than showing true plant phenology. Overlooking snow influences may lead to inaccurate plant phenology estimation, and consequently to misinterpretation of climate-vegetation interactions. To address the problem we apply the recently developed plant phenology index (PPI) to Moderate Resolution Imaging Spectroradiometer (MODIS) data for estimating plant phenology metrics over northern Europe. We compare PPI-derived start and end of the growing season with ground observations by professionals (6 sites) and nonprofessional citizens (378 sites), with phenology metrics derived from gross primary productivity (GPP, 18 sites), and with data on the timing of snow cover. These data are also compared with land surface phenology metrics derived from the normalized difference vegetation index (NDVI) using the same MODIS data. We find that the PPI-retrieved plant phenology agrees with ground observations and GPP-derived phenology, and that the NDVI-derived phenology to a large extent agrees with the end-of-snowmelt for the start-of-season and the start-of-snowing for the end-of-season. PPI is thereby useful for more accurate estimation of plant phenology from remotely sensed data over northern Europe and other regions with seasonal snow cover.}},
author = {{Jin, Hongxiao and Jönsson, Anna Maria and Bolmgren, Kjell and Langvall, Ola and Eklundh, Lars}},
issn = {{0034-4257}},
keywords = {{MODIS; NDVI; Plant phenology index (PPI); Land surface phenology; Snow seasonality}},
language = {{eng}},
month = {{06}},
pages = {{203--212}},
publisher = {{Elsevier}},
series = {{Remote Sensing of Environment}},
title = {{Disentangling remotely-sensed plant phenology and snow seasonality at northern Europe using MODIS and the plant phenology index}},
url = {{http://dx.doi.org/10.1016/j.rse.2017.06.015}},
doi = {{10.1016/j.rse.2017.06.015}},
volume = {{198}},
year = {{2017}},
}