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

DSpace Repository

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

Details

Files for download
Icon
Overview of item record
Publication Article, peer reviewed scientific
Title A Method for Robust Estimation of Vegetation Seasonality from Landsat and Sentinel-2 Time Series Data
Author Jönsson, Per ; Cai, Zhanzhang ; Melaas, Eli ; Friedl, Mark A. ; Eklundh, Lars
Date 2018
English 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.
DOI https://doi.org/10.3390/rs10040635 (link to publisher's fulltext.)
Link https://www.mdpi.com/2072-4292/10/4/635 .Icon
Publisher MDPI
Host/Issue Remote Sensing;4
Volume 10
ISSN 2072-4292
Language eng (iso)
Subject time series
vegetation index
Landsat
Sentinel-2
separable least squares
seasonality
shape prior
robust statistics
data quality
gap filling
Technology
Research Subject Categories::TECHNOLOGY
Handle http://hdl.handle.net/2043/26700 Permalink to this page
Facebook

This item appears in the following Collection(s)

Details

Search


Browse

My Account

Statistics