Deriving plant phenology from remote sensing

Roerink, G.J.; Danes, M.H.G.I.; Prieto, Oscar Gomez; Wit, Allard de; Vliet, A.J.H. van

doi:10.1109/multi-temp.2011.6005098

Cited by 3 publications

(5 citation statements)

References 8 publications

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“…Different crops have unique phenological characteristics, which can be detected from NDVI time-series. The obtained Fourier sequence a j and b j of harmonic analysis can be used to compute the phenological features, revealing the crop growth implicit in the NDVI profile [47]. To be specific, harmonic analysis of NDVI time-series is valuable in describing changes of the start-of-season, end-of-season, duration, and fluctuation of the crop phenology [48].…”

Section: Feature Extraction and Feature Set Constructionmentioning

confidence: 99%

Large-Scale Crop Mapping From Multisource Remote Sensing Images in Google Earth Engine

Liu

Zhai

Shen

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Large-scale crop mapping is vitally important to agriculrural monitoring and management. However, traditional methods cannot well meet the needs of large-scale applications. Therefore, this study proposed a method for large-scale crop mapping based on multisource remote sensing images. To be specific, 1) harmonic analysis was conducted on normalized difference vegetation index time-series derived from moderate resolution imaging spectroradiometer images and synthetic aperture radar backscattering coefficient time-series derived from Sentinel-1 data, respectively, extracting harmonic-derived phenological features and harmonic-derived backscattering features, and then combined with spectral features from Landsat-8 and Sentinel-2 images to construct the final multisource feature set for crop classification; 2) it employed prior constraints of crop dominance and cropland distribution to reduce misclassifications in large scale crop mapping; and 3) the whole process was conducted on the Google Earth Engine online platform, which can reduce the computational burdens caused by the spatiotemporal data. In the experimental study, we evaluated three crops, including wheat, rapeseed, and corn in Qinhai in 2018, based on the classification and regression tree classifier. The results show that the Jeffries-Matusita distances between crop samples are close to 2, and the overall accuracy is 84.25%. Furthermore, this study found that the distribution of the crops in Qinghai is associated with climate, topography, and cultivation habits.

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Section: Feature Extraction and Feature Set Constructionmentioning

confidence: 99%

Large-Scale Crop Mapping From Multisource Remote Sensing Images in Google Earth Engine

Liu

Zhai

Shen

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Some further approaches are: best index slope extraction (BISE) (Viovy et al, 1992), median filters (Vandijk et al, 1987), splines and weighted least-squares (White et al, 2005), discrete Fourier transformation (DFT) (Jakubauskas et al, 2001;Geerken et al, 2005), locally adjusted cubic-splines (Chen et al, 2006), and the double logistic function (Zhang et al, 2004). More recently, Roerink et al, (2011) used HANTS (Harmonic Analysis of NDVI Time Series) to process and analyse time-series satellite sensor data. The HANTS algorithm is based on the least-squares curve fitting of cosine-functions (Atkinson et al, 2012).…”

Section: Mtci Datamentioning

confidence: 99%

“…Over the last three decades development of new satellite sensors and availability of these data at a high temporal frequency provided the opportunity to estimate vegetation phenological variables at regional to global scale (Lloyd, 1990;Reed et al, 1994;Fisher and Mustard, 2007;Roerink et al, 2011;Jeganathan et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…Phenological variables derived from temporal profiles of satellite-derived vegetation indices can be used to characterize the stages of vegetation development during the growing season (Olsson et al, 2005;Heumann et al, 2007;Seaquist et al, 2009;Reed et al, 2009;Beurs de and Henebry, 2010 ;Roerink et al, 2011). Thus, they can be related to biological definitions of plant phenology, for example, the flowering phenophase related to pollen release.…”

Section: Introductionmentioning

confidence: 99%

“…This suggests that measurements of the flowering phenophase of grass and birch from remote sensing could be used to map local pollen release nationwide (Karlsen et al, 2009). example, the start of season and end of season (Lloyd, 1990;Reed et al, 1994;Fisher and Mustard, 2007;Dash et al, 2010;Roerink et al, 2011), but to a lesser extent for the flowering phenophases which for some species are during or before budburst (e.g. for birch) and for others are at a different growth stage (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Mapping the birch and grass pollen seasons in the UK using satellite sensor time-series

Khwarahm

Dash

Skjøth

et al. 2017

Science of The Total Environment

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Grass and birch pollen are two major causes of seasonal allergic rhinitis (hay fever) in the UK and parts of Europe affecting around 15-20% of the population. Current prediction of these allergens in the UK is based on (i) measurements of pollen concentrations at a limited number of monitoring stations across the country and (ii) general information about the phenological status of the vegetation. Thus, the current prediction methodology provides information at a coarse spatial resolution only. Most station-based approaches take into account only local observations of flowering, while only a small number of approaches take into account remote observations of land surface phenology. The systematic gathering of detailed information about vegetation status nationwide would therefore be of great potential utility. In particular, there exists an opportunity to use remote sensing to estimate phenological variables that are related to the flowering phenophase and, thus, pollen release. In turn, these estimates can be used to predict pollen release at a fine spatial resolution. In this study, time-series of MERIS Terrestrial Chlorophyll Index (MTCI) data were used to predict two key phenological variables: the start of season and peak of season. A technique was then developed to estimate the flowering phenophase of birch and grass from the MTCI time-series. For birch, the timing of flowering was defined as the time after the start of the growing season when the MTCI value reached 25% of the maximum. Similarly, for grass this was defined as the time when the MTCI value reached 75% of the maximum. The predicted pollen release dates were validated with data from nine pollen monitoring stations in the UK. For both birch and grass, we obtained large positive correlations between the MTCI-derived start of pollen season and the start of the pollen season defined using station data, with a slightly larger correlation observed for birch than for grass. The technique was applied to produce detailed maps for the flowering of birch and grass across the UK for each of the years from 2003 to 2010. The results demonstrate that the remote sensing-based maps of onset flowering of birch and grass for the UK together with the pollen forecast from the Meteorology Office and National Pollen and Aerobiology Research Unit (NPARU) can potentially provide more accurate information to pollen allergy sufferers in the UK.

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Analysis of the MODIS-Based Vegetation Phenology Using the HANTS Algorithm

Choi

Jung²

2014

Journal of the Korean Association of Geographic Information Stu

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Vegetation phenology is the most important indicator of ecosystem response to climate change. Therefore it is necessary to continuously monitor forest phenology. This paper analyzes the phenological characteristics of forests in South Korea using the MODIS vegetation index with error from clouds or other sources removed using the HANTS algorithm. After using the HANTS algorithm to reduce the noise of the satellite-based vegetation index data, we were able to confirm that phenological

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Deriving plant phenology from remote sensing

Cited by 3 publications

References 8 publications

Large-Scale Crop Mapping From Multisource Remote Sensing Images in Google Earth Engine

Large-Scale Crop Mapping From Multisource Remote Sensing Images in Google Earth Engine

Mapping the birch and grass pollen seasons in the UK using satellite sensor time-series

Analysis of the MODIS-Based Vegetation Phenology Using the HANTS Algorithm

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