Advanced

A Method for Robust Estimation of Vegetation Seasonality from Landsat and Sentinel-2 Time Series Data

Jönsson, Per; Cai, Zhanzhang LU ; Melaas, Eli; Friedl, Mark and Eklundh, Lars LU (2018) In Remote Sensing 10(4).
Abstract
Time series from Landsat and Sentinel-2 satellites have great potential for modeling vegetation seasonality. However, irregular time sampling and frequent data loss due to clouds, snow, and short growing seasons, makes this modeling a challenge. We describe a new method for modeling seasonal vegetation index dynamics from satellite time series data. The method is based on box constrained separable least squares fits to logistic model functions combined with seasonal shape priors. To enable robust estimates, we extract a base level (i.e., the minimum dormant season value) from the frequency distribution of clear-sky vegetation index values. A seasonal shape prior is computed from several years of data, and in the final fits local parameters... (More)
Time series from Landsat and Sentinel-2 satellites have great potential for modeling vegetation seasonality. However, irregular time sampling and frequent data loss due to clouds, snow, and short growing seasons, makes this modeling a challenge. We describe a new method for modeling seasonal vegetation index dynamics from satellite time series data. The method is based on box constrained separable least squares fits to logistic model functions combined with seasonal shape priors. To enable robust estimates, we extract a base level (i.e., the minimum dormant season value) from the frequency distribution of clear-sky vegetation index values. A seasonal shape prior is computed from several years of data, and in the final fits local parameters are box constrained. More specifically, if enough data values exist in a certain time period, the corresponding local parameters determining the shape of the model function over this period are relaxed and allowed to vary freely. If there are no observations in a period, the corresponding local parameters are locked to the parameters of the shape prior. The method is flexible enough to model interannual variations, yet robust enough when data are sparse. We test the method with Landsat, Sentinel-2, and MODIS data over a forested site in Sweden, demonstrating the feasibility and potential of the method for operational modeling of growing seasons. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
time series, vegetation index, Landsat, Sentinel-2, separable least squares, seasonality, shape prior, robust statistics, data quality, gap filling
in
Remote Sensing
volume
10
issue
4
pages
13 pages
publisher
MDPI AG
external identifiers
  • scopus:85045994794
ISSN
2072-4292
DOI
10.3390/rs10040635
language
English
LU publication?
yes
id
25a1c0a9-a5ca-4acb-a298-e45d8bb4b78e
alternative location
http://www.mdpi.com/2072-4292/10/4/635
date added to LUP
2018-04-19 18:56:14
date last changed
2018-06-10 05:28:23
@article{25a1c0a9-a5ca-4acb-a298-e45d8bb4b78e,
  abstract     = {Time series from Landsat and Sentinel-2 satellites have great potential for modeling vegetation seasonality. However, irregular time sampling and frequent data loss due to clouds, snow, and short growing seasons, makes this modeling a challenge. We describe a new method for modeling seasonal vegetation index dynamics from satellite time series data. The method is based on box constrained separable least squares fits to logistic model functions combined with seasonal shape priors. To enable robust estimates, we extract a base level (i.e., the minimum dormant season value) from the frequency distribution of clear-sky vegetation index values. A seasonal shape prior is computed from several years of data, and in the final fits local parameters are box constrained. More specifically, if enough data values exist in a certain time period, the corresponding local parameters determining the shape of the model function over this period are relaxed and allowed to vary freely. If there are no observations in a period, the corresponding local parameters are locked to the parameters of the shape prior. The method is flexible enough to model interannual variations, yet robust enough when data are sparse. We test the method with Landsat, Sentinel-2, and MODIS data over a forested site in Sweden, demonstrating the feasibility and potential of the method for operational modeling of growing seasons.},
  articleno    = {635},
  author       = {Jönsson, Per and Cai, Zhanzhang and Melaas, Eli and Friedl, Mark and Eklundh, Lars},
  issn         = {2072-4292},
  keyword      = {time series,vegetation index,Landsat,Sentinel-2,separable least squares,seasonality,shape prior,robust statistics,data quality,gap filling},
  language     = {eng},
  month        = {04},
  number       = {4},
  pages        = {13},
  publisher    = {MDPI AG},
  series       = {Remote Sensing},
  title        = {A Method for Robust Estimation of Vegetation Seasonality from Landsat and Sentinel-2 Time Series Data},
  url          = {http://dx.doi.org/10.3390/rs10040635},
  volume       = {10},
  year         = {2018},
}