The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Hongxiao Jin

Hongxiao Jin

Researcher

Hongxiao Jin

Disentangling remotely-sensed plant phenology and snow seasonality at northern Europe using MODIS and the plant phenology index

Author

  • Hongxiao Jin
  • Anna Maria Jönsson
  • Kjell Bolmgren
  • Ola Langvall
  • Lars Eklundh

Summary, in English

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.

Department/s

  • Dept of Physical Geography and Ecosystem Science
  • MERGE: ModElling the Regional and Global Earth system
  • BECC: Biodiversity and Ecosystem services in a Changing Climate

Publishing year

2017-06-14

Language

English

Pages

203-212

Publication/Series

Remote Sensing of Environment

Volume

198

Document type

Journal article

Publisher

Elsevier

Topic

  • Physical Geography
  • Botany

Keywords

  • MODIS; NDVI; Plant phenology index (PPI); Land surface phenology; Snow seasonality

Status

Published

ISBN/ISSN/Other

  • ISSN: 0034-4257