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submission: rsat #437

@unai-perez

Description

@unai-perez

Date accepted: 2021-09-30
Submitting 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 @khondula: 2021-05-25

Due date for @mhweber: 2021-06-23
Archive: TBD
Version accepted: TBD


  • Paste the full DESCRIPTION file inside a code block below:
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

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.):

    • data retrieval
    • data extraction
    • data munging
    • data deposition
    • workflow automation
    • version control
    • citation management and bibliometrics
    • scientific software wrappers
    • field and lab reproducibility tools
    • database software bindings
    • geospatial data
    • text analysis
  • 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.

Technical checks

Confirm each of the following by checking the box.

This package:

Publication options

  • Do you intend for this package to go on CRAN?

  • Do you intend for this package to go on Bioconductor?

  • Do you wish to submit an Applications Article about your package to Methods in Ecology and Evolution? If so:

MEE Options
  • The package is novel and will be of interest to the broad readership of the journal.
  • The manuscript describing the package is no longer than 3000 words.
  • You intend to archive the code for the package in a long-term repository which meets the requirements of the journal (see MEE's Policy on Publishing Code)
  • (Scope: Do consider MEE's Aims and Scope for your manuscript. We make no guarantee that your manuscript will be within MEE scope.)
  • (Although not required, we strongly recommend having a full manuscript prepared when you submit here.)
  • (Please do not submit your package separately to Methods in Ecology and Evolution)

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