Seasonality extraction by function fitting to time-series of satellite sensor data

By Per Jönsson, Lars Eklundh. IEEE Transactions on Geoscience and Remote Sensing, 2002

1,283 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/0f796ea78b1b08f8c99caf3d66c9ba5891e2a543

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Citations in the corpus

Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas (2016, methodology): “Concerning the phenological parameters, Jönsson & Eklundh (2002, 2004„) developed the TIMESAT software, which extracts nine phenological parameters from NDVI profile, such as the beginning, the peak, or the length of the season.”
Correcting MODIS 16-day composite NDVI time-series with actual acquisition dates (2014, methodology): “This information was used in TIMESAT [Jönsson and Eklundh, 2002, 2003, 2004] to weigh NDVI values according to their quality: the lower the weight, the less the NDVI value affects the estimation of the function fitting parameters since a greater uncertainty is assigned to it [Jönsson and Eklundh,…”
Estimating Daily Land Surface Temperatures in Mountainous Environments by Reconstructed MODIS LST Data (2010, methodology): “The Temporal Fourier Analysis technique was later expanded to MODIS LST 8-day composites by Scharlemann [13], while Jönsson and Eklundh [18] applied non-linear least squares fits of asymmetric Gaussian model functions to AVHRR time-series.”
Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs (2013, methodology): “• methods estimating the timing of single phenological events [106–108]; • methods modeling the entire time series using a mathematical function [109,110].”
TIMESAT - a program for analyzing time-series of satellite sensor data (2004, methodology): “harmonic and asymmetric Gaussian model functions (Jönsson and Eklundh, 2002).”
Noise reduction of NDVI time series: An empirical comparison of selected techniques (2009, background): “…to a broad variety of strategies designed to reduce the impacts of these issues (e.g., Beck et al., 2006, 2007; Bradley et al., 2007; Holben, 1986; Jönsson & Eklundh, 2002, 2004; Ma & Veroustraete, 2006; Moody & Johnson, 2001; Roerink et al., 2000; Sellers et al., 1994; Swets et al., 1999), but…”
A comparison of methods for smoothing and gap filling time series of remote sensing observations - application to MODIS LAI products (2012, methodology): “SoS and EoS were defined similarly to Jönsson and Eklundh (2002) as the timing when LAI reaches 20 % of the whole LAI amplitude before (SoS) or after (EoS) the timing of maximum LAI (MoS).”
An evaluation of time-series smoothing algorithms for land-cover classifications using MODIS-NDVI multi-temporal data (2016, methodology): “The first three algorithms have been widely used for remote sensing time-series data analysis (Jönsson & Eklundh, 2002, 2004).”
High-Throughput Estimation of Crop Traits: A Review of Ground and Aerial Phenotyping Platforms (2020, methodology): “Optical satellites use the relative reflectance at certain spectral wavelengths to estimate crop phenotyping traits based on VIs, machine learning methods, weather data, environmental factors, and crop varieties [79], [193], [236], [289].”
Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology (2012, background): “The asymmetric Gaussian (AG) function (Jönsson & Eklundh, 2002) can be used as a basis function, and written as g t; a1; a2 ::::a5ð Þ ¼ exp − t−a1 a2 a3 ; t > a1 ¼ exp − a1−t a4 a5 ; t b a1 ð9Þ where, g(t ;a1,a2....a5) is the Gaussian-type fit function, a1 is related to position (in time) of the…”