6. Accessing and processing the data

The capacity to acquire information from remote sensing data has been improved to an unprecedented level, accumulating overwhelming amounts of information. For example, the Google Earth Engine (GEE) [105] is updated at a rate of nearly 6000 scenes per day from active missions (a typical image 10 km by 10 km requires 50–200 million bytes of memory). Such a large amount of data requires not only vast amounts of memory data but also higher-level services with high-performance computing systems [106]. Successful experiences have already been recorded [97], but the GEE is worth mentioning [105]. GGE stores a multipetabyte catalog of satellite imagery and geospatial data sets collected from different resources and provides high-performance computing systems that can be

Satellite Data and Supervised Learning to Prevent Impact of Drought on Crop… DOI: http://dx.doi.org/10.5772/intechopen.85471

accessed and controlled through an Internet-accessible application programming interface (API) and an associated web-based interactive development environment (IDE). It also possess a library with more than 800 functions, ranging from simple mathematical functions to powerful geostatistical, machine learning, and image processing operations [105].

In many situations, intense computing resources are required for image processing operations [105], but more friendly solutions can be suggested to those who do have not the necessary skills.

As mentioned before, many satellite precipitation products are freely available (Table 1). Most of them are in network Common Data Form (netCDF) format [95]. R users can access this format using the "ncdf4" [96] or "raster" [97] packages. These data were already processed and can be used to forecast and perform complementary analyses [98]. We have already mentioned the SPEI [59] or the SPI [60] packages used to generate, for example, the SPI index.

Regarding the ML methods discussed here, almost all of them are available in packages deposited at the CRAN or CRAN-like repositories, for example, "Random Forest" package [43], "rminer" [99] that implements ANN, SVR and boosting [99], etc. A full list of packages implementing ML algorithms is available at https://cran.rproject.org/web/views/MachineLearning.html.

Finally, also available at the repositories are plenty of packages that are really helpful for visualizing and interpreting the results [107, 108].
