index.qmd

## Greetings {-}

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Welcome to the age of big Earth observation data! With free access to massive data sets, we need new methods to measure change on our planet. This book will help you to use state-of-the-art tools to work with image time series. Combined with Earth observation data cubes, time series are a powerful tool for monitoring change, providing insights and information that single snapshots cannot achieve.  Time series analysis are a new and exciting paradigm. This book offers a comprehensive appraisal of this emerging discipline.
:::

## What is this book about? {-}

This book introduces `sits`, an open-source **R** package for big Earth observation data analysis using satellite image time series. Users build regular data cubes from cloud services such as Amazon Web Services, Microsoft Planetary Computer, Copernicus Data Space Ecosystem, NASA Harmonized Landsat-Sentinel, Brazil Data Cube, Swiss Data Cube, Digital Earth Australia, and Digital Earth Africa. The `sits` API includes training sample quality measures, machine learning and deep learning classification algorithms, and Bayesian post-processing methods for smoothing and uncertainty assessment. To evaluate results, `sits` supports best-practice accuracy assessments. The authors also provide a **Python** API that interfaces with the **R** API, and thus allows Python users to directly run `sits` and convert its data structures to Python `data.frames` and `xarrays`.

## Target audience{-}

The `sits` package is designed for remote sensing experts in the Earth Sciences field who want to use advanced data analysis techniques with only basic programming knowledge. The package provides a clear and direct set of functions that are easy to learn and master. 

## About the authors{-}

- Gilberto Camara is a Senior Research Fellow at Brazil's National Institute for Space Reseach (INPE). 
- Rolf Simoes is a Research Engineer at Open Geo Hub (Netherlands). 
- Felipe Souza and Pedro Brito are PhD students at INPE. 
- Felipe Carlos is a Software Engineer at the Group on Earth Observations (GEO). 
- Pedro Andrade and Karine Ferreira are Senior Researchers at INPE. 
- Lorena Santos is a Researcher at CTrees.org. 
- Alexandre Assunção is a software consultant. 
- Charlotte Pelletier is an Associate Professor at Université Bretagne-Sud in France. 


## How much R knowledge is required? {.unnumbered}

To quickly master what is needed to run sits in R, please read Parts 1 and 2 of Garrett Grolemund’s book, [Hands-On Programming with R](https://rstudio-education.github.io/hopr/). Although not needed to run `sits`, your **R** skills will benefit from the book by Hadley Wickham and Gareth Grolemund, [R for Data Science (2nd edition)](https://r4ds.hadley.nz/). Important concepts of spatial analysis are presented by Edzer Pebesma and Roger Bivand in their book [Spatial Data Science](https://r-spatial.org/book/).

## How does one run SITS in Python? {.unnumbered}

From version 1.5.3 onwards, users can run `sits` in Python. Follow the instructions in the “Setup” chapter on how to set your Python environment to interface with R. Then follow the book examples provided for using `sits` in Python. The book provides code in both R and Python. Therefore, after correctly setting up their working environment, Python experts can run sits functions in their favorite tools, such as Jupyter Notebooks.

## Software version described in this book {.unnumbered}

The version of the `sits` package described in this book is 1.5.4.

## API documentation 

The `sits` API documentation is available at [this website]( https://e-sensing.github.io/sits/). 

## Main reference for `sits` {.unnumbered}

If you use `sits` in your work, please cite the following paper:

Rolf Simoes, Gilberto Camara, Gilberto Queiroz, Felipe Souza, Pedro R. Andrade, Lorena Santos, Alexandre Carvalho, and Karine Ferreira. [Satellite Image Time Series Analysis for Big Earth Observation Data](%5Bhttps://doi.org/10.3390/rs13132428). Remote Sensing, 13, p. 2428, 2021.

## Intellectual property rights {.unnumbered}

This book is licensed as [Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/) by Creative Commons. The `sits` package is licensed under the GNU General Public License, version 3.0.