qc_sentinel2

<table class="table">
<tr><td>SCL Value</td><td>Description</td><td>Quality</td><td>weight</td></tr>
<tr><td>1</td><td>Saturated or defective</td><td>Bad</td><td>\(w_{min}\)</td></tr>
<tr><td>2</td><td>Dark Area Pixels</td><td>Bad</td><td>\(w_{min}\)</td></tr>
<tr><td>3</td><td>Cloud Shadows</td><td>Bad</td><td>\(w_{min}\)</td></tr>
<tr><td>4</td><td>Vegetation</td><td>Good</td><td>\(w_{max}\)</td></tr>
<tr><td>5</td><td>Bare Soils</td><td>Good</td><td>\(w_{max}\)</td></tr>
<tr><td>6</td><td>Water</td><td>Good</td><td>\(w_{max}\)</td></tr>
<tr><td>7</td><td>Clouds Low Probability / Unclassified</td><td>Good</td><td>\(w_{max}\)</td></tr>
<tr><td>8</td><td>Clouds Medium Probability</td><td>Marginal</td><td>\(w_{mid}\)</td></tr>
<tr><td>9</td><td>Clouds High Probability</td><td>Bad</td><td>\(w_{mid}\)</td></tr>
<tr><td>10</td><td>Cirrus</td><td>Good</td><td>\(w_{mid}\)</td></tr>
<tr><td>11</td><td>Snow / Ice</td><td>Bad</td><td>\(w_{mid}\)</td></tr>
</table>

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

qc_sentinel2 function

<dl><dt>SCL</dt>
<dd>quality control variable for sentinel2</dd>
<dt>wmin</dt>
<dd>Double, minimum weigth (i.e. weight of snow, ice and cloud).</dd>
<dt>wmid</dt>
<dd>Dougle, middle weight, i.e. marginal</dd>
<dt>wmax</dt>
<dd>Double, maximum weight, i.e. good</dd></dl>

Arguments

<table class='table'>
<tr><td>SCL Value</td><td>Description</td><td>Quality</td><td>weight</td></tr>
<tr><td>1</td><td>Saturated or defective</td><td>Bad</td><td>\(w_{min}\)</td></tr>
<tr><td>2</td><td>Dark Area Pixels</td><td>Bad</td><td>\(w_{min}\)</td></tr>
<tr><td>3</td><td>Cloud Shadows</td><td>Bad</td><td>\(w_{min}\)</td></tr>
<tr><td>4</td><td>Vegetation</td><td>Good</td><td>\(w_{max}\)</td></tr>
<tr><td>5</td><td>Bare Soils</td><td>Good</td><td>\(w_{max}\)</td></tr>
<tr><td>6</td><td>Water</td><td>Good</td><td>\(w_{max}\)</td></tr>
<tr><td>7</td><td>Clouds Low Probability / Unclassified</td><td>Good</td><td>\(w_{max}\)</td></tr>
<tr><td>8</td><td>Clouds Medium Probability</td><td>Marginal</td><td>\(w_{mid}\)</td></tr>
<tr><td>9</td><td>Clouds High Probability</td><td>Bad</td><td>\(w_{mid}\)</td></tr>
<tr><td>10</td><td>Cirrus</td><td>Good</td><td>\(w_{mid}\)</td></tr>
<tr><td>11</td><td>Snow / Ice</td><td>Bad</td><td>\(w_{mid}\)</td></tr>
</table>

Initial weights for sentinel2 according to SCL band — qc_sentinel2

<dl>

<dt>SCL</dt>
<dd>quality control variable for sentinel2</dd>


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


<dt>wmid</dt>
<dd>Dougle, middle weight, i.e. marginal</dd>


<dt>wmax</dt>
<dd>Double, maximum weight, i.e. good</dd>

</dl>

qc_sentinel2: Initial weights for sentinel2 according to SCL band

Description

Usage

Arguments

References

Examples

SCL Value	Description	Quality	weight
1	Saturated or defective	Bad	\(w_{min}\)
2	Dark Area Pixels	Bad	\(w_{min}\)
3	Cloud Shadows	Bad	\(w_{min}\)
4	Vegetation	Good	\(w_{max}\)
5	Bare Soils	Good	\(w_{max}\)
6	Water	Good	\(w_{max}\)
7	Clouds Low Probability / Unclassified	Good	\(w_{max}\)
8	Clouds Medium Probability	Marginal	\(w_{mid}\)
9	Clouds High Probability	Bad	\(w_{mid}\)
10	Cirrus	Good	\(w_{mid}\)
11	Snow / Ice	Bad	\(w_{mid}\)