Crop identification using harmonic analysis of time-series AVHRR NDVI data

By M. Jakubauskas, D. Legates, J. Kastens. 2002

550 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Atmospheric Science, Ecology

View Paper: https://www.semanticscholar.org/paper/7fb2fe487764412ef43f1e3793cc0efae4f47d68

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In Analytical Chemistry

1. Clement Atzberger
2. Anatoly A. Gitelson
3. Hugh Turral
4. D. Molden
5. Francis Canisius

In Atmospheric Science

1. Clement Atzberger
2. Peter M. Atkinson
3. Hugh Turral
4. D. Molden
5. Francis Canisius

In Ecology

1. Frédéric Baret
2. Toshihiro Sakamoto
3. Clement Atzberger
4. Aleixandre Verger
5. Timothy J. Arkebauer
6. Marie Weiss
7. Cheryl Stephen Jeganathan
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Citations in the corpus

• A Two-Step Filtering approach for detecting maize and soybean phenology with time-series MODIS data (2010, background): “…observations of the land surface which have been widely used for monitoring seasonal vegetation growth across large geographic areas (Azzali & Menenti, 2000; de Beurs & Henebry, 2004; Jakubauskas et al., 2001, 2002; Kang et al., 2003; Reed et al., 1994; Reed et al., 1996; Xiao et al., 2002).”
• Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs (2013, background): “• principle component analysis [113]; • Fourier analysis [114,99]; • harmonic analysis [115]; • wavelet decomposition [12,116]; • and curve fitting [98,105,117].”
• Stabilizing high‐order, non‐classical harmonic analysis of NDVI data for average annual models by damping model roughness (2007, background): “…the 4th annual harmonic may be significant for discriminating between certain crop types, such as corn, alfalfa, pasture, native prairie grasslands and winter wheat, leaving some question as to how high the order should be for truncating a harmonic series (see also Jakubauskas et al. 2002a; 2002b).”
• Temporal segmentation of MODIS time series for improving crop classification in Central Asian irrigation systems (2011, methodology): “( 2002 ) successfully used 1 km National Oceanic and Atmospheric Administration (NOAA)-Advanced Very High Resolution Radiometer (AVHRR) time series to identify wheat, milo, corn and alfalfa in the Great Plains.”
• Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology (2012, methodology): “…data are; median smoothing (Reed et al., 1994), curve fitting (Bradley et al., 2007), moving average (White et al., 2009), Fourier decomposition (Jakubauskas et al., 2001; Moody & Johnson, 2001; Roerink et al., 2000; Verhoef et al., 1996; Wagenseil & Samimi, 2006), logistic function (Beck et…”
• A comparison of methods for smoothing and gap filling time series of remote sensing observations - application to MODIS LAI products (2012, background): “…climate change (Pettorelli et al., 2005; Kobayashi et al., 2007), global carbon fluxes (Wylie et al., 2007; Schubert et al., 2010) land cover (Jakubauskas et al., 2002; Boles et al., 2004; Thenkabail et al., 2005; Heiskanen and Kivinen, 2008) and land-cover change (Hansen et al., 2002, 2008;…”
• Fourier analysis of historical NOAA time series data to estimate bimodal agriculture (2007, methodology): “At finer scales, it reveals anthropogenic effects on the ecosystem such as rainfed and irrigated agriculture (Jakubauskas et al. 2002, Moody and Johnson 2001).”