process_season

Integer, number of images per year.

nptperyear

Boolean. In south hemisphere, growing year is 1 July to the
following year 31 June; In north hemisphere, growing year is 1 Jan to 31 Dec.

south

If true, it will use V-curve theory to optimize Whittaker
parameter, lambda.

.v_curve

data.frame, with the columns of <code>t</code>, <code>y</code> and <code>w</code>.

options of <code><a rd-options="=season_mov" href="/link/season_mov()?package=phenofit&version=0.3.2&to=%3Dseason_mov" data-mini-rdoc="=season_mov::season_mov()">season_mov()</a></code>

options_season

internal

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

process_season function

<dl><dt>nptperyear</dt>
<dd>Integer, number of images per year.</dd>
<dt>south</dt>
<dd>Boolean. In south hemisphere, growing year is 1 July to the
following year 31 June; In north hemisphere, growing year is 1 Jan to 31 Dec.</dd>
<dt>.v_curve</dt>
<dd>If true, it will use V-curve theory to optimize Whittaker
parameter, lambda.</dd>
<dt>...</dt>
<dd>Others will be ignored.</dd>
<dt>d</dt>
<dd>data.frame, with the columns of <code>t</code>, <code>y</code> and <code>w</code>.</dd>
<dt>options_season</dt>
<dd>options of <code>season_mov()</code></dd></dl>

Arguments

divide_seasons — process_season

<dl>

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


<dt>south</dt>
<dd>Boolean. In south hemisphere, growing year is 1 July to the
following year 31 June; In north hemisphere, growing year is 1 Jan to 31 Dec.</dd>


<dt>.v_curve</dt>
<dd>If true, it will use V-curve theory to optimize Whittaker
parameter, lambda.</dd>


<dt>...</dt>
<dd>Others will be ignored.</dd>


<dt>d</dt>
<dd>data.frame, with the columns of <code>t</code>, <code>y</code> and <code>w</code>.</dd>


<dt>options_season</dt>
<dd>options of <code>season_mov()</code></dd>

</dl>

process_season: divide_seasons

Description

Usage

Arguments