Provides a set of temporal metrics derived from satellite image time series.
The basics metrics are composed of basic statistics, histogram-based statistics, and methods
based on time series analysis.
The polar metrics, an approach proposed by Korting et al. (2013)

R package for extracting metrics from time series of satellite images
The goal of the sitsfeats package is to extract temporal metrics from satellite images time series. The metrics implemented in this version are:
The implementations of this package are based on the stmetrics library in Python.
You can install the development version of sitsfeats from github with:
library(devtools)
devtools::install_github("oldlipe/sitsfeats")
# loading sitsfeats and tibble packages
library(sitsfeats)
library(tibble)
# loading example time series
data("timeseries")
polar_metrics <- tibble::tibble(area_q1 = sitsfeats::area_q1(timeseries),
area_q2 = sitsfeats::area_q2(timeseries),
area_q3 = sitsfeats::area_q3(timeseries),
area_q4 = sitsfeats::area_q3(timeseries))
polar_metrics
#> # A tibble: 5 x 4
#> area_q1[,1] area_q2[,1] area_q3[,1] area_q4[,1]
#> <dbl> <dbl> <dbl> <dbl>
#> 1 29443841. 14207184. 30180681. 30180681.
#> 2 28600327. 11275172. 32912031. 32912031.
#> 3 39626598. 26313817. 39566583. 39566583.
#> 4 37874557. 16023772. 34952318. 34952318.
#> 5 23932473. 15016832. 27336259. 27336259.
# get one instance from timeseries
one_instance <- timeseries[1,]
# plot first time series example
sitsfeats::polar_plot(one_instance)
Feel free to create issues if you have any questions.