modisfast

modisfast: An R package for fast and efficient access to MODIS, VIIRS and GPM Earth Observation data - Published in JOSS (2024)

https://github.com/ptaconet/modisfast

Keywords

Scientific Fields

Repository

Fast and efficient access to MODIS, VIIRS and GPM Earth Observation data with R

README.Rmd

---
output: github_document  
always_allow_html: yes
---


```{r include=FALSE}
library(magrittr)
library(modisfast)
library(dplyr)
library(kableExtra)
library(knitr)

knitr::opts_chunk$set(
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```
# modisfast 



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[![DOI](https://joss.theoj.org/papers/10.21105/joss.07343/status.svg)](https://doi.org/10.21105/joss.07343)
![CRAN_downloads](https://cranlogs.r-pkg.org/badges/last-month/modisfast)





## Table of contents


  • Overview

  • Installation

  • Get started

  • Data collections available

  • Manual testing of the functionality

  • Foundational framework

  • Comparison with similar R packages

  • Citation

  • Future developments

  • Contributing

  • Acknowledgments



## News

2025-07-17 : `modisfast` v1.0.2 available on CRAN ! See [here](https://github.com/ptaconet/modisfast/blob/master/NEWS.md) for fixes. 

## Overview

**`modisfast`** is an R package designed for **easy** and **fast** downloads of [**MODIS**](https://www.earthdata.nasa.gov/data/instruments/modis) Land products, [**VIIRS**](https://www.earthdata.nasa.gov/data/instruments/viirs) Land products, and [**GPM**](https://gpm.nasa.gov/data) (Global Precipitation Measurement Mission) Earth Observation data.

`modisfast` uses the abilities offered by the [OPeNDAP](https://www.opendap.org/about/) framework (*Open-source Project for a Network Data Access Protocol*) to download a subset of Earth Observation data cube, along spatial, temporal or any other data dimension (depth, ...). This way, it reduces downloading time and disk usage to their minimum : no more 1° x 1° MODIS tiles with 10 bands when your region of interest is only 30 km x 30 km wide and you need 2 bands ! Moreover, `modisfast` enables parallel downloads of data.

This package is hence particularly suited for retrieving MODIS or VIIRS data **over long time series** and **over areas**, rather than short time series and points.

Importantly, the robust, sustainable, and cost-free [foundational framework](#foundational-framework) of `modisfast`, both for the data provider (NASA) and the software (R, OPeNDAP, the `tidyverse` and `GDAL` suite of packages and software), guarantees the long-term reliability and open-source nature of the package.

By enabling to download subsets of data cubes, `modisfast` facilites the access to Earth science data for R users in places where internet connection is slow or expensive and promotes digital sobriety for our research work. 

## Installation

You can install the released version of `modisfast` from [CRAN](https://CRAN.R-project.org) with :

``` r
install.packages("modisfast")
```

or the development version (to get a bug fix or to use a feature from the development version) with : 

``` r
if(!require(devtools)){install.packages("devtools")}
devtools::install_github("ptaconet/modisfast")
```

## Get Started 

Accessing and opening MODIS data with `modisfast` is a simple 3-steps workflow. This example shows how to download and import a one-year-long monthly time series of MODIS Normalized Difference Vegetation Index (NDVI) at 1 km spatial resolution over the whole country of Madagascar.

**1/ First, define the variables of interest (ROI, time frame, collection, and bands) :**

```{r , eval = F, message=F}
# Load the packages
library(modisfast)
library(sf)
library(terra)

# ROI and time range of interest
roi <- st_as_sf(data.frame(id = "madagascar", geom = "POLYGON((41.95 -11.37,51.26 -11.37,51.26 -26.17,41.95 -26.17,41.95 -11.37))"), wkt = "geom", crs = 4326) # a ROI of interest, format sf polygon
time_range <- as.Date(c("2023-01-01", "2023-12-31")) # a time range of interest

# MODIS collections and variables (bands) of interest
collection <- "MOD13A3.061" # run mf_list_collections() for an exhaustive list of collections available
variables <- c("_1_km_monthly_NDVI") # run mf_list_variables("MOD13A3.061") for an exhaustive list of variables available for the collection "MOD13A3.061"
```

**2/ Then, get the URL of the data and download them :**

```{r , eval = F, message=F}
## Login to Earthdata servers with your EOSDIS credentials.
# To create an account (free) go to : https://urs.earthdata.nasa.gov/.
log <- mf_login(credentials = c("username", "password")) # set your own EOSDIS username and password

## Get the URLs of the data
urls <- mf_get_url(
  collection = collection,
  variables = variables,
  roi = roi,
  time_range = time_range
)

## Download the data. By default the data is downloaded in a temporary directory, but you can specify a folder
res_dl <- mf_download_data(urls, parallel = TRUE)
```

**3/ And finally, import the data in R as a `terra::SpatRaster` object using the function `mf_import_data()`** ( :warning: see [here](https://ptaconet.github.io/modisfast/articles/get_started.html#warning-import) why you should use this function, instead of the original `terra::rast()`, in the context of `modisfast`) :

```{r , eval = F, message=F}
r <- mf_import_data(
  path = dirname(res_dl$destfile[1]),
  collection = collection,
  proj_epsg = 4326
)

terra::plot(r, col = rev(terrain.colors(20)))
```

![Time series of monthly 1-km MODIS NDVI over Madagascar for the year 2023, retrieved with `modisfast`](.Rplot_readme.png)

\
et voilà ! 

Want more examples ? `modisfast` provides three long-form documentations and examples to learn more about the package :

- a ["Get started" article](https://ptaconet.github.io/modisfast/articles/get_started.html) describing the core features of the package;
- a ["Get data on several regions or periods of interest simultaneously" article](https://ptaconet.github.io/modisfast/articles/modisfast2.html) detailing advanced functionalities of `modisfast` (for multi-time frame or multi-regions data access);
- a ["Full use case" article](https://ptaconet.github.io/modisfast/articles/use_case.html) showcasing an example of use of the package in a scientific context (here: landscape epidemiology).






## Collections available in `modisfast`

Currently `modisfast` supports download of `r nrow(mf_list_collections())` data collections, extracted from the following meta-collections : 

* [MODIS land products](https://www.earthdata.nasa.gov/data/instruments/modis) made available by the [NASA / USGS LP DAAC](https://www.earthdata.nasa.gov/centers/lp-daac) ( :arrow_right: [source OPeNDAP server](https://opendap.cr.usgs.gov/opendap/hyrax/)) ;
* [VIIRS land products](https://www.earthdata.nasa.gov/data/instruments/viirs) made available by the [NASA / USGS LP DAAC](https://www.earthdata.nasa.gov/centers/lp-daac) ( :arrow_right: [source OPeNDAP server](https://opendap.cr.usgs.gov/opendap/hyrax/))
* [Global Precipitation Measurement](https://gpm.nasa.gov/missions/GPM) (GPM) made available by the [NASA / JAXA GES DISC](https://disc.gsfc.nasa.gov/) ( :arrow_right: [source OPeNDAP server](https://gpm1.gesdisc.eosdis.nasa.gov/opendap/hyrax/GPM_L3/)).

Details of each product available for download are provided in the tables below or through the function `mf_list_collections()`.


```{r, eval = TRUE, echo=F,message=F}

fun_display_coll <- function(filts){
  
  cols <- mf_list_collections() %>%
  dplyr::filter(nature %in% filts) %>%
  dplyr::mutate(collection = cell_spec(collection, "html", link = doi)) %>%
  dplyr::mutate(spatial_resolution_m = paste0(spatial_resolution_m, " m")) %>%
  dplyr::mutate(temporal_resolution = paste0(temporal_resolution," ",temporal_resolution_unit)) %>%
  dplyr::mutate(temporal_resolution = ifelse(temporal_resolution=="1 day", "Daily", temporal_resolution)) %>%
  dplyr::mutate(start_date = paste0(start_date," to present")) %>%
  dplyr::select(collection, source, nature, long_name, spatial_resolution_m, temporal_resolution, start_date) %>%
  dplyr::arrange(nature, source, spatial_resolution_m, rev(temporal_resolution)) %>% 
  dplyr::rename(Collection = collection, Type = nature, Name = long_name, Source = source, 'Spatial resolution' = spatial_resolution_m, 'Temporal resolution' = temporal_resolution, 'Temporal extent' = start_date) %>%
  kable("html", escape = FALSE) %>%
  kable_styling(bootstrap_options = c("hover", "condensed"))
  
  return(cols)
  
}

cols_natures <- sort(unique(mf_list_collections()$nature))

```


```{r, eval = TRUE, echo=F, message=F, results='asis'}
for(i in seq_along(cols_natures)){
  if(i==1){
     cat('
',cols_natures[i],' data collections (click to expand)
')
  } else {
      cat('
',cols_natures[i],' data collections
')
  }
  cat(knitr::asis_output(fun_display_coll(cols_natures[i])))
  cat('
')
}
```


## Manual testing of the functionality

Since most `modisfast` functions depend on EarthData credentials, automated tests are disabled. However, after installation, users can manually test the package's functionality by running these lines of code :

```{r, eval = F, echo=T,message=F}
# replace "username" and "password" with your own EOSDIS (Earthdata) credentials 
earthdata_un <- "username" 
earthdata_pw <- "password"

devtools::test("modisfast")
```

## Foundational framework  {#foundational-framework}

Technically, `modisfast` is a programmatic interface (R wrapper) to several NASA [OPeNDAP](https://www.opendap.org/) servers. OPeNDAP is the acronym for *Open-source Project for a Network Data Access Protocol* and designates both the software, the access protocol, and the corporation that develops them. The OPeNDAP is designed to simplify access to structured and high-volume data, such as satellite products, over the Web. It is a collaborative effort involving multiple institutions and companies, with open-source code, free software, and adherence to the [Open Geospatial Consortium](https://www.ogc.org/) (OGC) standards. It is widely used by NASA, which partly finances it. 

A key feature of OPeNDAP is its capability to apply filters at the data download process, ensuring that only the necessary data is retrieved. These filters, specified within a URL, can be spatial, temporal, or dimensional. Although powerful, OPeNDAP URLs are not trivial to build. `modisfast` facilitates this process by constructing the URL based on the spatial, temporal, and dimensional filters provided by the user in the function `mf_get_url()`.

Let's take an example to understand.

The following URL :arrow_down:

https://opendap.cr.usgs.gov/opendap/hyrax/MOD11A2.061/h17v08.ncml.nc4?MODIS_Grid_8Day_1km_LST_eos_cf_projection,LST_Day_1km[775:793][55:140][512:560],LST_Night_1km[775:793][55:140][512:560],QC_Day[775:793][55:140][512:560],QC_Night[775:793][55:140][512:560],time[775:793],YDim[55:140],XDim[512:560]

is a link to download the following subset of MOD11A2.061 data in netCDF :

- bands LST_Day_1km, LST_Night_1km, QC_Day, QC_Night ;
- each available date between the 2017-01-01 and the 2017-06-01 ; 
- within the following bounding box (lon/lat): -5.41 8.84, -5.82 9.54. 

The indices within the `[]` refer to values encoding for the spatial and temporal filters.

These OPeNDAP URLs are not trivial to build. `modisfast` converts the spatial, temporal and dimensional filters (R objects) provided by the user through the function `mf_get_url()` into the appropriate OPeNDAP URL(s). Subsequently, the function `mf_download_data()` allows for downloading the data using the [`httr`](https://cran.r-project.org/package=httr) and `parallel` packages. 

## Comparison with similar R packages

There are other R packages available for accessing MODIS data. Below is a comparison of modisfast with other packages available for downloading chunks of MODIS or VIIRS data :

|    Package              | Data  |  Available on CRAN | Utilizes open standards for data access protocols | Spatial subsetting* | Dimensional subsetting*   | Maximum area size allowed for download  | Speed** | 
| :---------------------: | :------------------------------------------: | :-------------: |  :-------------: | :-------------: | :-------------: | :-------------------: | :-------------------: |
| [`modisfast`](https://github.com/ptaconet/modisfast)      | MODIS, VIIRS, GPM |  :white_check_mark: |  :white_check_mark:  |   :white_check_mark:  |   :white_check_mark:  | unlimited | :white_check_mark:
| [`appeears`](https://github.com/bluegreen-labs/appeears)  |  MODIS, VIIRS, and many others  |  :white_check_mark: |   :white_check_mark:   |   :white_check_mark: |  :white_check_mark: | unlimited | variable
| [`MODISTools`](https://github.com/bluegreen-labs/MODISTools/)  |  MODIS, VIIRS  |  :white_check_mark: |   :x:   |   :white_check_mark: |   :white_check_mark:   | 200 km x 200 km | :white_check_mark: 
| [`rgee`](https://github.com/r-spatial/rgee)  |  MODIS, VIIRS, GPM, and many others  |  :white_check_mark: |   :x:   |   :white_check_mark: |  :white_check_mark: | unlimited | not tested
| [`MODIStsp`](https://github.com/ropensci/MODIStsp)   |  MODIS |  :x: |     |  :x: |   :white_check_mark:   | unlimited | NA
| [`MODIS`](https://github.com/fdetsch/MODIS)       | MODIS  |  :x: |  :x:  |  :x:  |   :x:   | NA | NA

\* at the downloading phase

\** Take a look at the article ["Comparison of performance with other similar R packages"](https://ptaconet.github.io/modisfast/articles/perf_comp.html) to get an overview of how `modisfast` compares to these packages in terms of data access time.

## Citation

This package is licensed under a [GNU General Public License v3.0 or later](https://www.gnu.org/licenses/gpl-3.0-standalone.html) license. 

We thank in advance people that use `modisfast` for citing it in their work / publication(s). For this, please use the following citation : 

> Taconet et al., (2024). modisfast: An R package for fast and efficient access to MODIS, VIIRS and GPM Earth Observation data. Journal of Open Source Software, 9(103), 7343, https://doi.org/10.21105/joss.07343

## Future developments

Future developments of the package may include access to additional data collections from other OPeNDAP servers, and support for a variety of data formats as they become available from data providers through their OPeNDAP servers. Furthermore, the creation of an RShiny application on top of the package is being considered, as a means of further simplifying data access for users with limited coding skills.

## Contributing

All types of contributions are encouraged and valued. For more information, check out our [Contributor Guidelines](https://github.com/ptaconet/modisfast/blob/master/CONTRIBUTING.md).

Please note that the `modisfast` project is released with a [Contributor Code of Conduct](https://contributor-covenant.org/version/2/1/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.

## Acknowledgments

We thank NASA and its partners for making all their Earth science data freely available, and implementing open data access protocols such as OPeNDAP. `modisfast` heavily builds on top of the OPeNDAP, so we thank the non-profit [OPeNDAP, Inc.](https://www.opendap.org/about/) for developing the eponym tool in an open and collaborative way.

We also thank the contributors that have tested the package, reviewed the documentation and brought valuable feedbacks to improve the package : [Florian de Boissieu](https://github.com/floriandeboissieu), Julien Taconet.

This work has been developed over the course of several research projects (REACT 1, REACT 2, ANORHYTHM and DIV-YOO) funded by Expertise France, the French National Research Agency (ANR), and the French National Research Institute for Sustainable Development (IRD).

Owner

JOSS Publication

modisfast: An R package for fast and efficient access to MODIS, VIIRS and GPM Earth Observation data

Published

November 13, 2024

DOI

10.21105/joss.07343

Volume 9, Issue 103, Page 7343

Authors

Paul Taconet
MIVEGEC, Université de Montpellier, CNRS, IRD, Montpellier, France

Nicolas Moiroux
MIVEGEC, Université de Montpellier, CNRS, IRD, Montpellier, France

Editor

Michael Mahoney

Tags

MODIS VIIRS GPM Earth observation Datacubes Remote sensing OPeNDAP

View PDF Review Thread Software Archive

Citation (CITATION.cff)

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authors:
- family-names: Taconet
  given-names: Paul
  orcid: "https://orcid.org/0000-0001-7429-7204"
- family-names: Moiroux
  given-names: Nicolas
  orcid: "https://orcid.org/0000-0001-6755-6167"
contact:
- family-names: Taconet
  given-names: Paul
  orcid: "https://orcid.org/0000-0001-7429-7204"
doi: 10.5281/zenodo.14100599
message: If you use this software, please cite our article in the
  Journal of Open Source Software.
preferred-citation:
  authors:
  - family-names: Taconet
    given-names: Paul
    orcid: "https://orcid.org/0000-0001-7429-7204"
  - family-names: Moiroux
    given-names: Nicolas
    orcid: "https://orcid.org/0000-0001-6755-6167"
  date-published: 2024-11-13
  doi: 10.21105/joss.07343
  issn: 2475-9066
  issue: 103
  journal: Journal of Open Source Software
  publisher:
    name: Open Journals
  start: 7343
  title: "modisfast: An R package for fast and efficient access to
    MODIS, VIIRS and GPM Earth Observation data"
  type: article
  url: "https://joss.theoj.org/papers/10.21105/joss.07343"
  volume: 9
title: "modisfast: An R package for fast and efficient access to MODIS,
  VIIRS and GPM Earth Observation data"

CodeMeta (codemeta.json)

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  "dateCreated": "2020-01-01",
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  "downloadUrl": "https://github.com/ptaconet/modisfast/archive/refs/heads/master.zip",
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