A physically based vegetation index for improved monitoring of plant phenology
Jin, Hongxiao LU and Eklundh, Lars LU
(2014)
In Remote Sensing of Environment
152.
p.512-525
- Abstract
- Using a spectral vegetation index (VI) is an efficient approach for monitoring plant phenology from remotely-sensed data. However, the quantitative biophysical meaning of most VIs is still unclear, and, particularly at high northern latitudes characterized by low green biomass renewal rate and snow-affected VI signals, it is difficult to use them for tracking seasonal vegetation growth and retrieving phenology. In this study we propose a physically-based new vegetation index for characterizing terrestrial vegetation canopy green leaf area dynamics: the plant phenology index (PPI). PPI is derived from the solution to a radiative transfer equation, is computed from red and near-infrared (NIR) reflectance, and has a nearly linear relationship... (More)
- Using a spectral vegetation index (VI) is an efficient approach for monitoring plant phenology from remotely-sensed data. However, the quantitative biophysical meaning of most VIs is still unclear, and, particularly at high northern latitudes characterized by low green biomass renewal rate and snow-affected VI signals, it is difficult to use them for tracking seasonal vegetation growth and retrieving phenology. In this study we propose a physically-based new vegetation index for characterizing terrestrial vegetation canopy green leaf area dynamics: the plant phenology index (PPI). PPI is derived from the solution to a radiative transfer equation, is computed from red and near-infrared (NIR) reflectance, and has a nearly linear relationship with canopy green leaf area index (LAI), enabling it to depict canopy foliage density well. This capability is verified with stacked-leaf measurements, canopy reflectance model simulations, and field LAI measurements from international sites. Snow influence on PPI is shown by modeling and satellite observations to be less severe than on the Normalized Difference Vegetation Index (NDVI) or the Enhanced Vegetation Index (EVI), while soil brightness variations in general have moderate influence on PPI. Comparison of satellite-derived PPI to ground observations of plant phenology and gross primary productivity (GPP) shows strong similarity of temporal patterns over several Nordic boreal forest sites. The proposed PPI can thus serve as an efficient tool for estimating plant canopy growth, and will enable improved vegetation monitoring, particularly of evergreen needle-leaf forest phenology at high northern latitudes. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4610812
- author
- Jin, Hongxiao
LU
and Eklundh, Lars
LU
- organization
- publishing date
- 2014
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Plant phenology index (PPI) Normalized Difference Vegetation Index (NDVI) Enhanced vegetation index (EVI) Leaf area index (LAI) Snow influence High northern latitude Vegetation dynamics
- in
- Remote Sensing of Environment
- volume
- 152
- pages
- 512 - 525
- publisher
- Elsevier
- external identifiers
-
- wos:000343392200038
- scopus:84905496892
- ISSN
- 0034-4257
- DOI
- 10.1016/j.rse.2014.07.010
- language
- English
- LU publication?
- yes
- id
- c2ae96f7-1a16-433c-bff5-1023267f7d25 (old id 4610812)
- date added to LUP
- 2016-04-01 10:45:29
- date last changed
- 2022-04-28 01:06:38
@article{c2ae96f7-1a16-433c-bff5-1023267f7d25,
abstract = {{Using a spectral vegetation index (VI) is an efficient approach for monitoring plant phenology from remotely-sensed data. However, the quantitative biophysical meaning of most VIs is still unclear, and, particularly at high northern latitudes characterized by low green biomass renewal rate and snow-affected VI signals, it is difficult to use them for tracking seasonal vegetation growth and retrieving phenology. In this study we propose a physically-based new vegetation index for characterizing terrestrial vegetation canopy green leaf area dynamics: the plant phenology index (PPI). PPI is derived from the solution to a radiative transfer equation, is computed from red and near-infrared (NIR) reflectance, and has a nearly linear relationship with canopy green leaf area index (LAI), enabling it to depict canopy foliage density well. This capability is verified with stacked-leaf measurements, canopy reflectance model simulations, and field LAI measurements from international sites. Snow influence on PPI is shown by modeling and satellite observations to be less severe than on the Normalized Difference Vegetation Index (NDVI) or the Enhanced Vegetation Index (EVI), while soil brightness variations in general have moderate influence on PPI. Comparison of satellite-derived PPI to ground observations of plant phenology and gross primary productivity (GPP) shows strong similarity of temporal patterns over several Nordic boreal forest sites. The proposed PPI can thus serve as an efficient tool for estimating plant canopy growth, and will enable improved vegetation monitoring, particularly of evergreen needle-leaf forest phenology at high northern latitudes.}},
author = {{Jin, Hongxiao and Eklundh, Lars}},
issn = {{0034-4257}},
keywords = {{Plant phenology index (PPI) Normalized Difference Vegetation Index (NDVI) Enhanced vegetation index (EVI) Leaf area index (LAI) Snow influence High northern latitude Vegetation dynamics}},
language = {{eng}},
pages = {{512--525}},
publisher = {{Elsevier}},
series = {{Remote Sensing of Environment}},
title = {{A physically based vegetation index for improved monitoring of plant phenology}},
url = {{http://dx.doi.org/10.1016/j.rse.2014.07.010}},
doi = {{10.1016/j.rse.2014.07.010}},
volume = {{152}},
year = {{2014}},
}