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:
https://lup.lub.lu.se/record/25a1c0a9-a5ca-4acb-a298-e45d8bb4b78e
- author
- Jönsson, Per
; Cai, Zhanzhang
LU
; Melaas, Eli
; Friedl, Mark
and Eklundh, Lars
LU
- organization
- publishing date
- 2018-04-19
- 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
- article number
- 635
- pages
- 13 pages
- publisher
- MDPI AG
- external identifiers
-
- scopus:85045994794
- ISSN
- 2072-4292
- DOI
- 10.3390/rs10040635
- project
- TIMESAT - software to analyze time-series of satellite sensor data
- 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
- 2022-04-25 06:48:09
@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.}},
author = {{Jönsson, Per and Cai, Zhanzhang and Melaas, Eli and Friedl, Mark and Eklundh, Lars}},
issn = {{2072-4292}},
keywords = {{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}},
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}},
doi = {{10.3390/rs10040635}},
volume = {{10}},
year = {{2018}},
}