Modeling MODIS LAI time series using three statistical methods

By B. Jiang, Shunlin Liang, Jindi Wang, Zhiqiang Xiao. 2010

115 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering, Plant Science

View Paper: https://www.semanticscholar.org/paper/ae9143d9bcd470a75c8e0eb60315554ab0e24b28

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecology

1. Aleixandre Verger
2. Frédéric Baret
3. Marie Weiss
4. Philippe Neveux
5. Sivasathivel Kandasamy

In Environmental Engineering

1. Frédéric Baret
2. Philippe Neveux
3. Sivasathivel Kandasamy
4. Aleixandre Verger
5. Marie Weiss

In Plant Science

1. Frédéric Baret
2. Marie Weiss
3. Aleixandre Verger

Citations in the corpus

• Spatiotemporal social media analytics for abnormal event detection and examination using seasonal-trend decomposition (2012, methodology): “There are many alternative statistical models for this task, such as DHR (Dynamic Harmonics Regression) [38] and SARIMA (Seasonal AutoRegressive Intergrated Moving Average) [3].”
• A comparison of methods for smoothing and gap filling time series of remote sensing observations - application to MODIS LAI products (2012, methodology): “…on the improvement of the MODIS products using specific mathematical filters which use either temporal or spatial techniques to get temporally smoothed and spatially continuous products (Gao et al., 2008; Borak and Jasinski, 2009; Jiang et al., 2010; Verger et al., 2011; Yuan et al., 2011).”