## 4 Space-Based Crop Mapping
### Our Approach  
As mentioned above, we focused on outlining homogeneous areas rather than directly classifying crop types. As expected, single-date spectral responses are very similar to each other, so multitemporal trends had to be exploited. In this context, MODIS data was the first choice, supported by two very important strongpoints it possesses in this context:  
* open availability;
* high temporal frequency (roughly every second day in the equatorial band, daily elsewhere). Given the scarce spectral separability of the species to be mapped, temporal frequency becomes of paramount importance here as discrimination will have to rely upon phenological cycles as reflected in temporal trends of vegetation indexes. Terra and Aqua were launched in December 1999 and May 2002, respectively, as the first items in a series of multi-instrument spacecraft forming NASA's Earth Observing System (EOS). The latter includes a science component and a data information system (EOSDIS) supporting global observations of both land and water and distributing data through its Distributed Active Archives [22]. Copernicus Sentinel-2 data, distributed through the Copernicus Open Access Hub [23], was included where deemed necessary to improve the situation in the case of poor separability of crop areas. Sentinel-2 data was used only as an integrative source because, with respect to MODIS, it has:  
* lower temporal frequency; 5-days revisit time may appear very short, but in areas where cloud coverage is frequent such as the predominantly tropical Caribbean areas, the daily revisit time of Terra/Aqua can be crucial in preventing data gaps;
* higher spatial resolution, while unnecessary to the foreseen application, results in gigantic files to be stored and processed. This is only worthwhile when the additional data makes a difference in terms of separability of relevant land cover classes. As a consequence, we selected MODIS as the main data source for our work, and Sentinel-2 as an integrative source where necessary; Table 1 summarizes the Earth Observation data types used in this work and their main features involved in our choice. Multitemporal MODIS data was thus searched on the \"Level-1 and Atmosphere Archive and Distribution System\" (LAADS) repository, maintained by the \"Distributed Active Archive Center\" (DAAC) of the \"National Aeronautics and Space Administration\" (NASA). Images that turned out to be too cloudy on solid land were discarded, while valid Level-1 images were retained and organized by country covered. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Spaceborne EO System** & **Ground Resolution (m)** & **Swath Width (km)** & **Revisit Time (days)** & **Usage in Project** \\ \hline MODIS & 250–1000 & 2330 & 6 & baseline \\ Sentinel-2 & 10–60 & 290 & 2–3 & occasional \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Spaceborne Earth Observation (EO) data types used in this work, and their respective roles. The geographic scope of the contracted study, highlighted in blue in Figure 2, was ample both in latitude and longitude. Thus, one of the first issues was to ensure geographical consistency of the multitemporal datasets and seamless merging of adjacent tiles on land; the ocean surface is not relevant in a vegetation-related study. This entailed welding together several tiles. An automated procedure based on SIFT [24] involving identification and matching [25] of feature points [26] was developed [27] considering the size and characteristics of MODIS images, with special attention to geometric consistence; uniformity of control points was not a priority, considering that in many cases most of the imaged area was ocean surface. Such methodology was then applied to the downloaded, dense time series. Next, cloudy areas were removed using an algorithm specifically developed for large-scale multispectral datasets [28; 29].