smooth_wHANTS

The merits of 'TIMESAT' and 'phenopix' are adopted. Besides, a simple and
growing season dividing method and a practical snow elimination method
based on Whittaker were proposed. 7 curve fitting methods and 4 phenology
extraction methods were provided. Parameters boundary are considered for
every curve fitting methods according to their ecological meaning.
And 'optimx' is used to select best optimization method for different
curve fitting methods.
Reference:
Kong, D., (2020). R package: A state-of-the-art Vegetation Phenology extraction
package, phenofit version 0.3.1, <doi:10.5281/zenodo.5150204>;
Kong, D., Zhang, Y., Wang, D., Chen, J., & Gu, X. (2020). Photoperiod Explains
the Asynchronization Between Vegetation Carbon Phenology and Vegetation Greenness
Phenology. Journal of Geophysical Research: Biogeosciences, 125(8), e2020JG005636.
<doi:10.1029/2020JG005636>;
Kong, D., Zhang, Y., Gu, X., & Wang, D. (2019). A robust method for reconstructing
global MODIS EVI time series on the Google Earth Engine.
ISPRS Journal of Photogrammetry and Remote Sensing, 155, 13–24;
Zhang, Q., Kong, D., Shi, P., Singh, V.P., Sun, P., 2018. Vegetation phenology
on the Qinghai-Tibetan Plateau and its response to climate change (1982–2013).
Agric. For. Meteorol. 248, 408–417. <doi:10.1016/j.agrformet.2017.10.026>.

Dongdong Kong

phenofit

Extract Remote Sensing Vegetation Phenology

Mingzhong Xiao

Yongqiang Zhang

Xihui Gu

Jianjian Cui

smooth_wHANTS function

<dl><dt>y</dt>
<dd>Numeric vector, vegetation index time-series</dd>
<dt>t</dt>
<dd>Numeric vector, <code>Date</code> variable</dd>
<dt>w</dt>
<dd>(optional) Numeric vector, weights of <code>y</code>. If not specified,
weights of all <code>NA</code> values will be <code>wmin</code>, the others will be 1.0.</dd>
<dt>nf</dt>
<dd>number of frequencies to be considered above the zero frequency</dd>
<dt>ylu</dt>
<dd><code>[low, high]</code> of time-series y (curve fitting values are constrained
in the range of <code>ylu</code>.</dd>
<dt>periodlen</dt>
<dd>length of the base period, measured in virtual samples
(days, dekads, months, etc.). nptperyear in timesat.</dd>
<dt>nptperyear</dt>
<dd>Integer, number of images per year.</dd>
<dt>wFUN</dt>
<dd>weights updating function, can be one of 'wTSM', 'wChen' and
'wBisquare'.</dd>
<dt>iters</dt>
<dd>How many times curve fitting is implemented.</dd>
<dt>wmin</dt>
<dd>Double, minimum weigth (i.e. weight of snow, ice and cloud).</dd>
<dt>...</dt>
<dd>Additional parameters are passed to <code>wFUN</code>.</dd></dl>

Arguments

Wout Verhoef, NLR, Remote Sensing Dept. June 1998
Mohammad Abouali (2011), Converted to MATLAB
Dongdong Kong (2018), introduced to R and modified into weighted model.

Author

Weighted HANTS SMOOTH — smooth_wHANTS

<dl>

<dt>y</dt>
<dd>Numeric vector, vegetation index time-series</dd>


<dt>t</dt>
<dd>Numeric vector, <code>Date</code> variable</dd>


<dt>w</dt>
<dd>(optional) Numeric vector, weights of <code>y</code>. If not specified,
weights of all <code>NA</code> values will be <code>wmin</code>, the others will be 1.0.</dd>


<dt>nf</dt>
<dd>number of frequencies to be considered above the zero frequency</dd>


<dt>ylu</dt>
<dd><code>[low, high]</code> of time-series y (curve fitting values are constrained
in the range of <code>ylu</code>.</dd>


<dt>periodlen</dt>
<dd>length of the base period, measured in virtual samples
(days, dekads, months, etc.). nptperyear in timesat.</dd>


<dt>nptperyear</dt>
<dd>Integer, number of images per year.</dd>


<dt>wFUN</dt>
<dd>weights updating function, can be one of 'wTSM', 'wChen' and
'wBisquare'.</dd>


<dt>iters</dt>
<dd>How many times curve fitting is implemented.</dd>


<dt>wmin</dt>
<dd>Double, minimum weigth (i.e. weight of snow, ice and cloud).</dd>


<dt>...</dt>
<dd>Additional parameters are passed to <code>wFUN</code>.</dd>

</dl>

smooth_wHANTS: Weighted HANTS SMOOTH

Description

Usage

Value

Arguments

Author

Examples