Noise reduction of NDVI time series: An empirical comparison of selected techniques

By Jennifer N. Hird, Gregory J. McDermid. 2009

571 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/424c3193b6358154409f2bad2b3999588905e370

How researchers see this paper

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

In Ecological Modeling

1. Cheryl Stephen Jeganathan
2. J. Dash
3. Clement Atzberger
4. Ronald E. McRoberts
5. Peter M. Atkinson

In Ecology

1. Frédéric Baret
2. Aleixandre Verger
3. Marie Weiss
4. Cheryl Stephen Jeganathan
5. B. Wilson
6. J. Dash
7. James B. Campbell
8. Philippe Neveux

In Global and Planetary Change

1. Cheryl Stephen Jeganathan
2. B. Wilson
3. J. Dash
4. James B. Campbell
5. Philippe Neveux
6. Clement Atzberger
7. John S. Iiames
8. Ronald E. McRoberts

Citations in the corpus

• A comparison of methods for smoothing and gap filling time series of remote sensing observations - application to MODIS LAI products (2012, methodology): “More recently, (Hird and McDermid, 2009) reviewed the abundant literature on 20 time series processing in remote sensing, mostly focusing on NDVI (Normalized Difference Vegetation Index) (Rouse et al.”
• An evaluation of time-series smoothing algorithms for land-cover classifications using MODIS-NDVI multi-temporal data (2016, methodology): “Although a variety of algorithms have been examined and implemented for smoothing time-series data, comparisons of the relative effectiveness of each algorithm is difficult due to lack of in-situ reference data (Hird & McDermid, 2009) and standard statistical measures (Atzberger & Eilers, 2011a).”
• Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology (2012, methodology): “Several studies have reported the utility of the Asymmetric Gaussian and Double Logistic models over other fitting techniques and filters (Beck et al., 2006; Hird & McDermid, 2009; Jönsson & Eklundh, 2002, 2004).”
• Harmonic regression of Landsat time series for modeling attributes from national forest inventory data (2018, background): “Hird and McDermid (2009) conducted a thorough comparison of several of these techniques for reducing noise in NVDI time series of MODIS imagery and concluded that the double logistic and asymmetric Gaussian approaches were generally superior to the others in the study.”
• Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs (2013, background): “, from MODIS) still contain substantial amounts of noise [139] (Figure 11).”