There is a large number of data archives and web services offering free access to multispectral satellite imagery. Images from multiple sources are increasingly combined to improve the spatio-temporal coverage of measurements while achieving more accurate results. Archives and web services differ in their protocols, formats, and data standards, which are barriers to combine datasets. Here, we present RGISTools, an R package to create time-series of multispectral satellite images from multiple platforms in a harmonized and standardized way. We first provide an overview of the package functionalities, namely downloading, customizing, and processing multispectral satellite imagery for a region and time period of interest as well as a recent statistical method for gap-filling and smoothing series of images, called interpolation of the mean anomalies. We further show the capabilities of the package through a case study that combines Landsat-8 and Sentinel-2 satellite optical imagery to estimate the level of a water reservoir in Northern Spain. We expect RGISTools to foster research on data fusion and spatio-temporal modelling using satellite images from multiple programs.

The aim of this study was to compare the available tools in R for downloading and processing Moderate Resolution Imaging Spectroradiometer (MODIS) data, specifically the Enhanced Vegetation Index (EVI) product. The R tools evaluated were the MODIS package, RGISTools, MODISTools, R Google Earth Engine (RGEE) package, MODIStsp, and the Application for Extracting and Exploring Analysis Ready Samples (AppEEARS) application. Each tool was used to download the same product (EVI) corresponding to the same day (3 December 2015), and downloaded data were used to analyze the urban growth of Tarija (Bolivia) as an interesting application. The following features were analyzed: download time and memory used during the download, additional post-processing time, local memory occupied on the computer, and downloaded file formats. Results showed that the most efficient R tools were those that work directly in the “cloud” or use text queries (RGEE and AppEEARS, respectively) and provide, as a final product, a cropped.tif image according to the area of interest.