Package: rsat
Type: Package
Title: Tools for Downloading, Customizing, and Processing Time Series of Satellite Images from Landsat, MODIS, and Sentinel
Version: 0.1.14
Author: U Pérez - Goya [aut, cre] <unai.perez@unavarra.es>,
M Montesino - SanMartin [aut] <manuel.montesino@unavarra.es>,
A F Militino [aut] <militino@unavarra.es>,
M D Ugarte [aut] <lola@unavarra.es>
Maintainer: U Perez - Goya <unai.perez@unavarra.es>
Description: Downloading, customizing, and processing time series of satellite images for a region of interest. 'rsat' functions allow a unified access to multispectral images from Landsat, MODIS and Sentinel repositories. 'rsat' also offers capabilities for customizing satellite images, such as tile mosaicking, image cropping and new variables computation. Finally, 'rsat' covers the processing, including cloud masking, compositing and gap-filling/smoothing time series of images (Militino et al., 2018 <doi:10.3390/rs10030398> and Militino et al., 2019 <doi:10.1109/TGRS.2019.2904193>).
Depends: R (>= 3.5.0), raster, sf, stars
Imports: XML, curl, httr, leafem, leaflet, rjson, rvest, tmap, xml2, zip, methods, sp, Rdpack, fields, calendR
RdMacros: Rdpack
Suggests:
rgdal,
knitr,
rmarkdown,
covr,
testthat
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Collate:
'add2rtoi.R'
'api.R'
'extent_crs.R'
'records.R'
'rtoi.R'
'cloud_mask.R'
'connections.R'
'data.R'
'derive.R'
'download.R'
'list_data.R'
'mosaic.R'
'mosaic_fun_SYN.R'
'mosaic_fun_ls.R'
'mosaic_fun_mod.R'
'mosaic_fun_sen2.R'
'mosaic_generic.R'
'package_tools.R'
'plot.R'
'preview.R'
'search_mod.R'
'search_ls.R'
'search_sen.R'
'search.R'
'smoothing_images.R'
'variables.R'
VignetteBuilder: knitr
The most similar package could be MODIStsp. But the package only contemplates the use of Modis satellite images, while 'rsat' is focuses on the standardization and homogenization of data between different satellite programs. 'rsat' supports Modis, Landsat and Sentinel data, handling multi platform data in a database and optimizing its processing.
Confirm each of the following by checking the box.
Date accepted: 2021-09-30
Due date for @khondula: 2021-05-25Submitting Author: Unai Pérez-Goya (@unai-perez)
Other Authors: Manuel Montesino-SanMartin (@mmontesinosanmartin), Ana F Militino (@militino), María Dolores Ugarte (@lolaugartemartinez)
Repository: https://github.com/spatialstatisticsupna/rsat
Version submitted: 0.1.14
Editor: @jhollist
Reviewers: @khondula, @mhweber
Due date for @mhweber: 2021-06-23
Archive: TBD
Version accepted: TBD
Scope
Please indicate which category or categories from our package fit policies this package falls under: (Please check an appropriate box below. If you are unsure, we suggest you make a pre-submission inquiry.):
Explain how and why the package falls under these categories (briefly, 1-2 sentences):
The package is focuses on searching, downloading, and preprocessing imagery data from Landsat, Modis, and Sentinel. It also include procedures for deriving variables and cloud filling.
Who is the target audience and what are scientific applications of this package?
Anyone interested in Remote Sensing or researchers looking for satellite imagery data.
Are there other R packages that accomplish the same thing? If so, how does yours differ or meet our criteria for best-in-category?
Nothing that is functionally similar. Many source-specific packages exist, but none that aggregate across sources.
The most similar package could be MODIStsp. But the package only contemplates the use of Modis satellite images, while 'rsat' is focuses on the standardization and homogenization of data between different satellite programs. 'rsat' supports Modis, Landsat and Sentinel data, handling multi platform data in a database and optimizing its processing.
(If applicable) Does your package comply with our guidance around Ethics, Data Privacy and Human Subjects Research?
If you made a pre-submission enquiry, please paste the link to the corresponding issue, forum post, or other discussion, or @tag the editor you contacted.
Technical checks
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This package:
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