| Title: | 'Copernicus' HR-VPP Vegetation Time-Series Processing |
| Version: | 2.0.0 |
| Description: | Provides tools for acquiring, extracting, quality-assessing, and analysing vegetation time series derived from 'Copernicus' High Resolution Vegetation Phenology and Productivity (HR-VPP) products. Supports Seasonal Trajectories (ST) and Vegetation Phenology and Productivity (VPP) products through native R access to 'WEkEO', geospatial processing, product-quality assessment, temporal completeness analysis, missingness modelling, and integrated graphical workflows. Selected functions from 'TSGenerator' 1.x are retained to support migration and legacy workflows. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.3 |
| URL: | https://github.com/avalero92/TSGenerator |
| BugReports: | https://github.com/avalero92/TSGenerator/issues |
| Imports: | hdar, jsonlite, stringr, utils, ggplot2, mgcv, scales, shiny, shinydashboard, shinyjs, shinycssloaders, DT, readr, terra |
| Suggests: | imputeTS, knitr, rmarkdown, testthat (≥ 3.0.0) |
| VignetteBuilder: | knitr |
| Config/testthat/edition: | 3 |
| NeedsCompilation: | no |
| Packaged: | 2026-10-10 17:34:03 UTC; runner |
| Author: | Alexey Valero-Jorge
 [aut, cre],
M. Auxiliadora Casterad Seral
[aut],
Jose-Tomas Alcala Nalvaiz
[aut] |
| Maintainer: | Alexey Valero-Jorge <avalero@cita-aragon.es> |
| Repository: | CRAN |
| Date/Publication: | 2026-10-21 14:50:19 UTC |
TSGenerator: Copernicus HR-VPP vegetation time-series processing
Description
TSGenerator provides a native-R workflow for acquiring, extracting, quality-assessing, and analysing vegetation time series derived from Copernicus High Resolution Vegetation Phenology and Productivity (HR-VPP) products, including Seasonal Trajectories (ST) and Vegetation Phenology and Productivity (VPP) products.
Details
The stable 2.0 API covers WEkEO acquisition, terra-based geospatial
extraction, product-quality assessment, temporal completeness diagnostics,
optional time-series imputation, missingness modelling, visualization, and
integrated graphical workflows. Selected TSGenerator 1.x entry points are
retained as deprecated compatibility wrappers or migration guards.
Main workflow
Use check_wekeo() and hda_client() to validate WEkEO access;
download_st() or download_vpp() for acquisition; extract_ts() or
extract_vpp() for polygon-level geospatial extraction; quality_info(),
classify_quality(), summarize_quality(), and mask_quality() for product
quality; temporal_plan(), summarize_missingness(), and
assess_ts_quality() for temporal diagnostics; impute_ts() for optional
gap filling; and model_missingness() with plot_missingness() for
missingness analysis. The integrated graphical workflow is launched with
runTSapp().
Legacy wrapper for VPP download
Description
Deprecated TSGenerator 1.x compatibility wrapper. It delegates to download_vpp() using the native R/hdar backend and no longer requires Python.
Usage
Download.HRVPP(
user = NULL, password = NULL, dataset_id = .VPP_DATASET_ID,
productType = "SOSD", productGroupId = "s1", tileId = NULL,
start, end, bbox = NULL, download_path = NULL
)
Arguments
user, password |
Optional legacy WEkEO credentials. Current workflows should use the native authentication configuration. |
dataset_id |
Legacy WEkEO dataset identifier. |
productType |
VPP product type. |
productGroupId |
VPP season/product group identifier. |
tileId |
Optional MGRS tile identifier. |
start, end |
Start and end dates of the search interval. |
bbox |
Optional bounding box. |
download_path |
Explicit destination required for downloads; no default directory is created. |
Value
A tsg_vpp_download object.
Legacy wrapper for Seasonal Trajectories download
Description
Deprecated TSGenerator 1.x compatibility wrapper. It delegates to download_st() using the native R/hdar backend and no longer requires Python.
Usage
Download.STPPI(
user = NULL, password = NULL, dataset_id = .ST_DATASET_ID,
productType = "PPI", platformSerialIdentifier = "S2A, S2B",
tileId = NULL, start, end, bbox = NULL, download_path = NULL
)
Arguments
user, password |
Optional legacy WEkEO credentials. Current workflows should use the native authentication configuration. |
dataset_id |
Legacy WEkEO dataset identifier. |
productType |
Seasonal Trajectories product type. |
platformSerialIdentifier |
Sentinel-2 platform identifier(s). |
tileId |
Optional MGRS tile identifier. |
start, end |
Start and end dates of the search interval. |
bbox |
Optional bounding box. |
download_path |
Explicit destination required for downloads; no default directory is created. |
Value
A tsg_st_download object.
Discontinued legacy VI downloader
Description
The HR-VPP VI products targeted by the TSGenerator 1.x downloader are no longer disseminated. This compatibility function stops with migration guidance.
Usage
Download.VI(...)Arguments
... |
Ignored legacy arguments, retained only for compatibility. |
Value
No value; the function raises an error.
GAM.missing
Description
GAM.missing
Usage
GAM.missing(data, year_col, doy_col, missing_col)
Arguments
data |
Data frame containing the missingness time-series information. |
year_col |
Name of the column containing years. |
doy_col |
Name of the column containing day-of-year values. |
missing_col |
Name of the column containing the missingness/value indicator used by the legacy wrapper. |
Value
A list object where the prediction results and the GAM model data are stored.
Examples
# Model fitting is illustrated with synthetic observations.
example_data <- expand.grid(
Year = 2020:2021, DOY = seq(10, 350, by = 10), replicate = 1:4
)
example_data$NDVI_median <- 0.5
example_data$NDVI_median[
(seq_len(nrow(example_data)) %% 7L) < 2L
] <- NA_real_
result <- suppressWarnings(GAM.missing(
example_data, year_col = "Year", doy_col = "DOY",
missing_col = "NDVI_median"
))
GAM.plot
Description
GAM.plot
Usage
GAM.plot(
data,
doy_col = "DOY",
year_col = "Year",
proporcion_col = "Proporcion_Missing",
prediccion_col = "Predicted",
sos = NULL,
eos = NULL,
max_doy = NULL
)
Arguments
data |
A DataFrame that contains the data to be plotted. |
doy_col |
Name of the column representing the Julian Day (DOY). |
year_col |
Name of the column representing the Year. |
proporcion_col |
Name of the column representing the Proportion of Missing Data. |
prediccion_col |
Name of the column representing the Prediction. |
sos |
Optional value for the SOS line. |
eos |
Optional value for the EOS line. |
max_doy |
Optional value for the MAX line. |
Value
Object of type ggplot that shows the results of the prediction obtained with the GAM.missing function.
Examples
proportions <- data.frame(
Year = rep(2020L, 5), DOY = c(20, 50, 80, 110, 140),
Proporcion_Missing = c(0.1, 0.2, 0.3, 0.2, 0.1),
Predicted = c(0.12, 0.18, 0.25, 0.18, 0.12)
)
p <- suppressWarnings(GAM.plot(proportions))
inherits(p, "ggplot")
Legacy VI QFLAG2 masking workflow
Description
QFLAG2.Mask() targets the discontinued VI QFLAG2 product. It is retained only as a migration guard. Use quality_info(), summarize_quality(), classify_quality(), and mask_quality() for current ST/VPP quality products.
Usage
QFLAG2.Mask(...)
Arguments
... |
Legacy arguments. They are accepted only so existing calls fail with explicit migration guidance. |
Value
This function does not return a value; it stops with migration guidance.
Examples
## Not run:
QFLAG2.Mask()
## End(Not run)
TsImpute
Description
TsImpute
Usage
TsImpute(data, group_col, value_col)
Arguments
data |
data frame where VI time series containing missing data are stored. |
group_col |
column by which the grouping is to be performed (example: ID column) |
value_col |
column where the time series of the VI data is stored (example: NDVI) |
Value
A new file similar to the original data frame but with a new column where the imputed data is stored.
Examples
# Kalman imputation requires the optional imputeTS package.
if (requireNamespace("imputeTS", quietly = TRUE)) {
example_data <- data.frame(
ID = rep("parcel_1", 12),
Date = as.Date("2020-01-01") + 0:11,
NDVI = c(0.2, 0.3, NA, 0.4, 0.5, NA, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5)
)
completed <- suppressWarnings(TsImpute(
example_data, group_col = "ID", value_col = "NDVI"
))
}
Assess time-series completeness quality
Description
Classifies time-series completeness from the proportion of expected dates containing usable values. This metric is separate from Copernicus ST/VPP QFLAG product quality.
Usage
assess_ts_quality(
data, id_col = "ID", date_col = "Date", value_col = "Value",
expected_step = NULL, window_days = NULL,
thresholds = c(high = 0.90, medium = 0.75, low = 0.50),
start = NULL, end = NULL
)
Arguments
data |
Input time-series data frame. |
id_col, date_col, value_col |
Column names identifying the series, observation date, and value. |
expected_step |
Expected temporal step in days; if |
window_days |
Optional assessment-window length in days. |
thresholds |
Named completeness thresholds used to assign quality classes. |
start, end |
Optional start and end dates restricting the assessment period. |
Value
An object of class tsg_ts_quality (also a data.frame) with
one row per series and assessment window. It contains the completeness
columns documented in summarize_missingness(), a numeric window
index, and an ordered factor Quality with levels Very low,
Low, Medium, and High, based on
value_completeness. Attributes thresholds and
window_days record the assessment settings.
Check the TSGenerator WEkEO acquisition environment
Description
Performs non-destructive checks of the native R acquisition stack. Offline checks are the default.
Usage
check_wekeo(client = NULL, online = FALSE)Arguments
client |
Optional client created by |
online |
Logical; perform live WEkEO authentication and schema checks. |
Value
A data frame of diagnostic checks with class tsg_wekeo_check.
See Also
hda_client, download_st, download_vpp
Run a live WEkEO integration smoke test
Description
Performs authentication, live schema discovery, small ST/VPP searches and, optionally, download/readability checks.
Usage
check_wekeo_integration(
client = NULL,
st_tile = "32TPS",
vpp_tile = "30TXR",
st_start = "2018-03-01",
st_end = "2018-03-11",
vpp_start = "2018-01-01",
vpp_end = "2018-12-31",
vpp_product = "TPROD",
vpp_season = "s1",
download = FALSE,
output_dir = NULL,
quiet = FALSE
)
Arguments
client |
Optional client created by |
st_tile |
Sentinel-2 MGRS tile used for the ST smoke test. |
vpp_tile |
Sentinel-2 MGRS tile used for the VPP smoke test. |
st_start, st_end |
Small ST date range. |
vpp_start, vpp_end |
VPP date range. |
vpp_product |
VPP parameter used for the smoke test. |
vpp_season |
VPP season ( |
download |
Logical. Search only by default; optionally download matching files. |
output_dir |
Explicit destination required when downloading; no default directory is created. |
quiet |
Logical; suppress progress messages. |
Value
A list of class tsg_wekeo_integration.
Decode HR-VPP quality codes
Description
Decodes HR-VPP quality codes while retaining their categorical meaning.
Usage
classify_quality(values, type = c("ST", "VPP"))Arguments
values |
Numeric or integer quality codes. |
type |
Quality-flag family: |
Value
A data frame with code, class, rank, default acceptance, and definition.
See Also
quality_info, summarize_quality, mask_quality
Count missing observations (legacy name)
Description
Retired TSGenerator 1.x compatibility wrapper. Calling this function produces a deprecation warning followed by a migration error. Use summarize_missingness() instead.
Usage
count.NA(
data, sos, maxd, eos, doy_col = "DOY", year_col = "Year",
na_col = "NDVI", fid_col = "ID"
)
Arguments
data |
Input data frame. |
sos, maxd, eos |
Phenological day-of-year boundaries. |
doy_col, year_col, na_col, fid_col |
Column names for day of year, year, assessed value, and feature identifier. |
Value
No return value. This retired compatibility function signals an error with
migration guidance; use summarize_missingness() instead.
Legacy phenology-specific missing-count interface
Description
Retired TSGenerator 1.x interface. The former phenology-specific missing-count workflow is no longer executed.
Usage
count_missing(
data, sos, maxd, eos, doy_col = "DOY", year_col = "Year",
na_col = "NDVI", fid_col = "ID"
)
Arguments
data |
A data frame containing the time series. |
sos, maxd, eos |
Day-of-year thresholds. |
doy_col, year_col, na_col, fid_col |
Column names. |
Value
No return value. This retired function signals an error with migration guidance; use summarize_missingness() for missingness counts and assess_ts_quality() for completeness classes.
Download Copernicus HR-VPP Seasonal Trajectories
Description
Searches and optionally downloads HR-VPP Seasonal Trajectories through WEkEO HDA V2 using the native R hdar backend.
Usage
download_st(start, end, output_dir = NULL, product = "PPI",
tile_id = NULL, bbox = NULL, platform = "S2A, S2B", resolution = 10L,
product_version = NULL, client = NULL, download = TRUE, limit = NULL,
overwrite = FALSE, prompt = interactive(), quiet = FALSE, retries = 2L,
backoff = 2)Arguments
start, end |
Start and end dates of the WEkEO search interval. |
output_dir |
Explicit destination required when |
product |
Seasonal Trajectories product type. |
tile_id |
Optional MGRS tile identifier. |
bbox |
Optional bounding box used to spatially restrict the query. |
platform |
Sentinel-2 platform identifier(s). |
resolution |
Requested spatial resolution in metres. |
product_version |
Optional HR-VPP product version. |
client |
Optional preconfigured native HDA client. |
download |
Logical; download results when |
limit |
Optional maximum number of search results. |
overwrite |
Logical; overwrite existing files. |
prompt |
Logical; allow interactive download confirmation. |
quiet |
Logical; suppress progress messages when possible. |
retries |
Number of download retries. |
backoff |
Backoff interval used between retries. |
Value
An object of class tsg_st_download.
Download Copernicus HR-VPP VPP parameters
Description
Searches and optionally downloads HR-VPP phenology and productivity parameters through WEkEO HDA V2 using the native R hdar backend.
Usage
download_vpp(start, end, output_dir = NULL,
product = c("SOSD", "MAXD", "EOSD", "LENGTH"), season = "s1",
tile_id = NULL, bbox = NULL, platform = "S2A, S2B", product_version = NULL,
client = NULL, download = TRUE, limit = NULL, overwrite = FALSE,
prompt = interactive(), quiet = FALSE, retries = 2L, backoff = 2)Arguments
start, end |
Start and end dates of the WEkEO search interval. |
output_dir |
Explicit destination required when |
product |
VPP parameter or parameters to query. |
season |
VPP season identifier. |
tile_id |
Optional MGRS tile identifier. |
bbox |
Optional bounding box used to spatially restrict the query. |
platform |
Sentinel-2 platform identifier(s). |
product_version |
Optional HR-VPP product version. |
client |
Optional preconfigured native HDA client. |
download |
Logical; download results when |
limit |
Optional maximum number of search results. |
overwrite |
Logical; overwrite existing files. |
prompt |
Logical; allow interactive download confirmation. |
quiet |
Logical; suppress progress messages when possible. |
retries |
Number of download retries. |
backoff |
Backoff interval used between retries. |
Value
An object of class tsg_vpp_download.
extract_dates_from_tiff_files
Description
extract_dates_from_tiff_files
extract_dates_from_tiff_files
extract_dates_from_tiff_files
Usage
extract_dates_from_tiff_files(tiff_files)
extract_dates_from_tiff_files(tiff_files)
extract_dates_from_tiff_files(tiff_files)
Arguments
tiff_files |
List of files in .tif format |
Value
The names of each of the files in the formats %Y-%m-%d or YYYYYYYMMDD
The names of each of the files in the formats %Y-%m-%d or YYYYYYYMMDD
The names of each of the files in the formats %Y-%m-%d or YYYYYYYMMDD
Extract polygon-level raster time series
Description
Terra-first replacement for the duplicated TSGenerator 1.x mean and median extraction workflows.
Usage
extract_ts(x, polygons, id_col = NULL, fun = "median", dates = NULL,
transform = TRUE, scale_factor = 1, offset = 0, na.rm = TRUE,
exact = FALSE, touches = FALSE)
Arguments
x |
A terra SpatRaster, TIFF path, directory, or vector of TIFF paths. |
polygons |
An sf/sfc object or terra SpatVector containing polygons. |
id_col |
Optional unique polygon ID column. |
fun |
Summary function; mean, median, min, max, sum, or a function. |
dates |
Optional vector with one date per raster layer. |
transform |
Transform polygons to raster CRS when needed. |
scale_factor |
Numeric divisor applied after extraction. |
offset |
Numeric offset added after scaling. |
na.rm |
Remove missing raster values during summarisation. |
exact |
Use exact polygon-cell fractions when supported. |
touches |
Include cells touched by polygon boundaries. |
Value
A tidy data frame of class tsg_time_series with ID, Date, Layer and Value columns.
Extract HR-VPP phenology and productivity parameters
Description
Extracts polygon statistics from Copernicus HR-VPP VPP rasters using product-aware decoding, scaling, NoData handling and season metadata.
Usage
extract_vpp(x, polygons, id_col = NULL, product = NULL, season = NULL,
year = NULL, fun = "median", transform = TRUE, na.rm = TRUE,
exact = FALSE, touches = FALSE)Arguments
x |
VPP raster input, file paths, or directory accepted by TSGenerator. |
polygons |
Polygon |
id_col |
Optional polygon identifier column. |
product |
Optional VPP product filter. |
season |
Optional season filter. |
year |
Optional year filter. |
fun |
Polygon summary function. |
transform |
Logical; transform polygons to raster CRS when required. |
na.rm |
Logical; remove missing raster values in summaries. |
exact |
Logical passed to the extraction backend for exact polygon extraction. |
touches |
Logical controlling inclusion of touched cells. |
Value
A data frame of class tsg_vpp. Date products populate Date; numeric products populate Value.
general.Quality
Description
general.Quality
Usage
general.Quality(df, period_col, observations_col)
Arguments
df |
Data base generated from the use of the function quality.Series |
period_col |
Column where the periods are stored (every 91 days) |
observations_col |
Columna donde se almacenan el numero de observaciones por periodo y |
Value
A list of files that stores a new data frame and an interactive graphic.
Inspect inputs for the TSGenerator 2.0 geospatial engine
Description
Validates raster/vector geometry, CRS, IDs, and scaling before terra-based extraction.
Usage
geospatial_plan(
x, polygons, id_col = NULL, transform = TRUE,
scale_factor = 1, offset = 0
)
Arguments
x |
A |
polygons |
An |
id_col |
Optional unique polygon ID column. |
transform |
Logical; transform polygons to the raster CRS when required. |
scale_factor |
Numeric divisor applied after extraction. |
offset |
Numeric offset applied after scaling. |
Value
An object of class tsg_geospatial_plan; no extraction is performed.
See Also
Legacy VI cleaning workflow
Description
get.Clean.IV() belongs to the discontinued VI/QFLAG2 workflow. It is retained only as a migration guard. Use mask_quality() for current ST/VPP quality handling.
Usage
get.Clean.IV(...)
Arguments
... |
Legacy arguments. They are accepted only so existing calls fail with explicit migration guidance. |
Value
This function does not return a value; it stops with migration guidance.
Examples
## Not run:
get.Clean.IV()
## End(Not run)
Retired legacy VPP extractor
Description
Retired because one generic scale factor is not scientifically valid across VPP products. Use extract_vpp().
Usage
get.Series.VPP(pathRaster = NULL, shapefile = NULL, factorR = NULL)Arguments
pathRaster |
Legacy raster directory. |
shapefile |
Legacy polygon object. |
factorR |
Legacy scale factor. |
Value
No return value. This retired function always signals an error explaining
the migration to extract_vpp().
Legacy mean time-series extractor
Description
Deprecated compatibility wrapper around extract_ts(..., fun = "mean").
Usage
get.Series.mean(pathRaster = NULL, shapefile = NULL, factorR = NULL)Arguments
pathRaster |
Directory containing TIFF rasters. |
shapefile |
Polygon sf/sfc or terra SpatVector. |
factorR |
Legacy numeric scale divisor. |
Value
A data frame with legacy columns ID, Date, and Mean.
Legacy median time-series extractor
Description
Deprecated compatibility wrapper around extract_ts(..., fun = "median").
Usage
get.Series.median(pathRaster = NULL, shapefile = NULL, factorR = NULL)Arguments
pathRaster |
Directory containing TIFF rasters. |
shapefile |
Polygon sf/sfc or terra SpatVector. |
factorR |
Legacy numeric scale divisor. |
Value
A data frame with legacy columns ID, Date, and Mean. The historical column name is preserved.
Legacy VI/QFLAG stack workflow
Description
get.Stack() belongs to the discontinued VI/QFLAG2 workflow. It is retained only as a migration guard; current ST and QFLAG products should remain separate.
Usage
get.Stack(...)
Arguments
... |
Legacy arguments. They are accepted only so existing calls fail with explicit migration guidance. |
Value
This function does not return a value; it stops with migration guidance.
Examples
## Not run:
get.Stack()
## End(Not run)
Test native WEkEO authentication
Description
Checks that the configured WEkEO credentials can obtain an HDA authentication token.
Usage
hda_auth_check(client = NULL, ...)Arguments
client |
Optional client created by |
... |
Arguments passed to |
Value
Invisibly returns TRUE after a token is obtained.
See Also
Create a native WEkEO HDA client
Description
Creates the native R client used by TSGenerator to access WEkEO HDA. If credentials are omitted, hdar uses ‘~/.hdarc’.
Usage
hda_client(username = NULL, password = NULL, save_credentials = FALSE)Arguments
username |
Optional WEkEO username. |
password |
Optional WEkEO password. |
save_credentials |
Logical; allow hdar to save credentials in ‘~/.hdarc’. |
Value
An authenticated hdar::Client object.
See Also
hda_auth_check, check_wekeo, download_st, download_vpp
Discover WEkEO datasets
Description
Queries the WEkEO catalogue through the native HDA client.
Usage
hda_datasets(pattern = NULL, client = NULL)Arguments
pattern |
Optional text used to filter the WEkEO catalogue. |
client |
Optional authenticated HDA client. |
Value
Dataset records returned by hdar.
See Also
Download HDA search results
Description
Downloads WEkEO HDA search results with controlled retry behaviour.
Usage
hda_download(
results, output_dir, force = FALSE, retries = 2L,
backoff = 2, prompt = interactive()
)
Arguments
results |
SearchResults returned by |
output_dir |
Destination directory. |
force |
Logical; re-download existing files when supported. |
retries |
Number of retries after transient failures. |
backoff |
Initial retry delay in seconds. |
prompt |
Logical; allow hdar to request confirmation. |
Value
The value returned by the hdar download method, invisibly.
See Also
Generate an HDA V2 query template
Description
Retrieves the current queryable schema for a WEkEO dataset rather than hard-coding provider-specific query fields.
Usage
hda_query_template(dataset_id, client = NULL)Arguments
dataset_id |
WEkEO dataset identifier. |
client |
Optional authenticated HDA client. |
Value
A query template returned by hdar.
See Also
Search WEkEO using an HDA V2 query
Description
Submits an HDA V2 search with controlled retry and exponential backoff.
Usage
hda_search(query, client = NULL, limit = NULL, retries = 2L, backoff = 2)Arguments
query |
HDA V2 query, normally based on |
client |
Optional authenticated HDA client. |
limit |
Optional maximum number of results. |
retries |
Number of retries after transient failures. |
backoff |
Initial retry delay in seconds. |
Value
An hdar SearchResults object.
See Also
Impute missing values in vegetation time series
Description
Explicit, opt-in imputation with provenance and safeguards for processed Seasonal Trajectories.
Usage
impute_ts(data, id_col = "ID", date_col = "Date", value_col = "Value",
method = c("linear", "kalman"), series_type = c("user", "raw", "st"),
allow_processed = FALSE, complete_grid = FALSE, expected_step = NULL,
max_gap = NULL, allow_edge = FALSE, output_col = NULL)
Arguments
data |
Input time-series data frame. |
id_col, date_col, value_col |
Column names identifying the series, observation date, and value. |
method |
Imputation method. |
series_type |
Series provenance: user, raw, or processed ST. |
allow_processed |
Logical; explicitly permit additional imputation of processed ST series. |
complete_grid |
Logical; complete the expected temporal grid before imputation. |
expected_step |
Expected temporal step in days. |
max_gap |
Optional maximum gap eligible for imputation. |
allow_edge |
Logical; permit filling gaps at series edges where supported. |
output_col |
Optional name for the imputed output column. |
Value
A data frame of class tsg_imputed_series.
Mask raster values using HR-VPP quality flags
Description
Masks values according to explicit quality rules. Categorical QFLAG rasters are never resampled automatically.
Usage
mask_quality(
x,
quality,
type = c("ST", "VPP"),
min_quality = NULL,
keep_codes = NULL
)
Arguments
x |
Target |
quality |
Quality |
type |
Quality-flag family, either |
min_quality |
Optional minimum quality code. |
keep_codes |
Optional explicit quality codes to retain; overrides |
Value
A masked terra::SpatRaster.
See Also
quality_info, classify_quality
Model temporal missingness with a generalized additive model
Description
Aggregates missing/available counts and fits a binomial GAM. Year is treated as a factor by default.
Usage
model_missingness(
data, year_col = "Year", doy_col = "DOY", value_col = "Value",
k = NULL, year_effect = c("factor", "none"), cyclic = TRUE
)
Arguments
data |
Data frame containing repeated observations. |
year_col, doy_col, value_col |
Column names. |
k |
Optional GAM basis dimension. |
year_effect |
Factor year effect or none. |
cyclic |
Use a cyclic DOY smooth. |
Value
An object of class tsg_missingness_model.
Plot observed and modelled temporal missingness
Description
Plots observed missingness proportions and GAM predictions without printing automatically.
Usage
plot_missingness(x, sos = NULL, max_doy = NULL, eos = NULL, facet = TRUE)Arguments
x |
A tsg_missingness_model or compatible data frame. |
sos, max_doy, eos |
Optional DOY markers. |
facet |
Facet by year. |
Value
A ggplot object.
quality.Series
Description
quality.Series
Usage
quality.Series(df, id_col, fecha_col, value_col)
Arguments
df |
Data base |
id_col |
Column where the observation grid identifiers are located. |
fecha_col |
Column where the date is located (%yyyyy%mm%dd) |
value_col |
Column from which the quality of the time series will be determined (estimating the number of observations). |
Value
A list of files where a dataframe with the results of the quality assessment and an interactive graphic are stored.
HR-VPP quality-code definitions
Description
Returns the TSGenerator definitions used to interpret current HR-VPP ST-QFLAG or VPP-QFLAG values.
Usage
quality_info(type = c("ST", "VPP"))Arguments
type |
Quality-flag family: |
Value
A data frame describing the supported quality codes and classes.
See Also
classify_quality, summarize_quality, mask_quality
Legacy VI file renaming helper
Description
renames.image.IV() belongs to the discontinued VI workflow and is retained only as a migration guard.
Usage
renames.image.IV(...)
Arguments
... |
Legacy arguments. They are accepted only so existing calls fail with explicit migration guidance. |
Value
This function does not return a value; it stops with migration guidance.
Examples
## Not run:
renames.image.IV()
## End(Not run)
renames.image.VPP
Description
Rename TIF files in a directory according to the year extracted from the name.
Usage
renames.image.VPP(...)
Arguments
... |
Legacy arguments. |
Details
This function searches for TIF files in a specified directory and renames them using the year extracted from the filename. The new file name shall be in the format 'yyyy.tif'.
Value
Does not return a value, but renames the TIF files in the specified directory.
Run the TSGenerator graphical interface
Description
Launches the unified TSGenerator 2.0 Shiny application. Selected 1.x/early-2.0 acquisition mini-apps remain temporarily accessible for compatibility, but new development is concentrated in the unified interface.
Usage
runTSapp(app = "TSGenerator2")
Arguments
app |
Character string. Application to launch. The default, |
Details
The unified application is a graphical layer over the public TSGenerator 2.0 API. Scientific algorithms are not reimplemented in the Shiny server. The historical standalone GAM interface has been retired; missingness modelling remains available through model_missingness() and plot_missingness().
Value
Invisibly returns the result of shiny::runApp().
See Also
model_missingness(), plot_missingness()
Other applications: runTSapp()
Examples
## Not run:
TSGenerator::runTSapp()
TSGenerator::runTSapp("DownloadVPP")
## End(Not run)
Summarise missingness and temporal completeness
Description
Separates absent expected dates from explicit NA values and reports temporal and usable-value completeness for each series.
Usage
summarize_missingness(
data, id_col = "ID", date_col = "Date", value_col = "Value",
expected_step = NULL, start = NULL, end = NULL
)
Arguments
data |
Input time-series data frame. |
id_col, date_col, value_col |
Column names identifying the series, observation date, and value. |
expected_step |
Expected temporal step in days; if |
start, end |
Optional start and end dates restricting the summary period. |
Value
An object of class tsg_missingness (also a data.frame) with
one row per series. Columns are the identifier (named by id_col),
start, end, expected_step_days, n_expected,
n_observed_dates, n_absent_dates, n_value_na,
n_usable, date_completeness, value_completeness,
and missing_fraction. Counts distinguish absent expected dates from
observed dates with missing values. Fractions use n_expected as the
denominator and are NA when that count is zero.
Summarize HR-VPP quality flags
Description
Summarizes the distribution of HR-VPP quality flags without masking observations.
Usage
summarize_quality(x, type = c("ST", "VPP"), quality_col = "QFLAG")Arguments
x |
Quality codes, a |
type |
Quality-flag family. |
quality_col |
Column containing codes when |
Value
A data frame with counts and percentages by documented quality code.
See Also
quality_info, classify_quality, mask_quality
Inspect and validate a vegetation time series
Description
Validates IDs, dates, missing values, duplicate observations, and temporal regularity without modifying the series.
Usage
temporal_plan(
data, id_col = "ID", date_col = "Date", value_col = "Value",
expected_step = NULL, allow_duplicates = FALSE
)
Arguments
data |
A data frame containing one or more time series. |
id_col |
Series identifier column. |
date_col |
Date column. |
value_col |
Value column. |
expected_step |
Optional expected temporal step in days. |
allow_duplicates |
Logical; allow duplicate ID-date observations. |
Value
An object of class tsg_temporal_plan containing validated data and diagnostics.