## 1 Introduction  
The world's population reached 7.3 billion people in mid-2015 [1] and is expected to increase to 9.6 billion by 2050 [2]. Consequently, global demand for food by that year is predicted to double at least that of 2005, not only because of population growth, but also because of a shift to nutrient-richer diets in especially developing nations [3]. The latter scenario calls for technical solutions that help to improve crop yield, provide accurate information to aid in-field management decisions, increase the efficiency of applications of farm inputs, and boost profit margins in the agricultural sector [4]. In some high-income countries, the technology to aid this decision-making may have been established; however, in low-income countries, which are the most populous parts of our planet and which depend strongly on agriculture, information on crops and farm management practices is still primitive. Remote sensing has been recognized as an important source of information over the last decades in a wide spectrum of Earth Observation applications. The requirements for agriculture-oriented remote sensing systems have long been outlined [5] as the frequency of coverage, high spatial resolution (5 m to 20 m), timeliness, and integration in models. Of these, timeliness is considered the number one requirement as meeting it allows addressing identified problems in real time. Over the past decade, these requirements have been increasingly met by a fleet of reconnaissance satellites with advanced capabilities that allow cost-effective agricultural applications [6]. Moreover, various space agencies and satellite product providers have adopted a free and unrestricted data access policy; for instance, the European Space Agency (ESA) for the Copernicus programme (including the Sentinel-1 and Sentinel-2 satellites) exercises a free, full, and open data policy. Since 2008, the U.S. Geological Survey (USGS) has been providing open access to over five million Landsat images from 1972 onwards. The Japan Aerospace Exploration Agency (JAXA) and the National Aeronautics and Space Administration (NASA) recently made their 30 m Digital Elevation Models (DEM) freely available. The VITO Vision website offers access to PROBA-V and SPOT-Vegetation, and the National Oceanic and Atmospheric Administration (NOAA) is committed to full and open data access. Several partnerships between governments and institutes such as the Brazilian National Institute for Space Research (INPE) Image Catalog and the University Navstar Consortium (UNAVCO) have created catalogues with available free images, and international bodies have been dedicated to the exchange and open-access of ocean- and climate-related data such as the International bodies Global Earth Observation System of Systems (GEOSS), the Committee on Earth Observing Satellites (CEOS), and the World Data System (WDS) of the International Council of Science (ICSU). This policy shift, along with simultaneous developments in image processing software, has fostered easier access to satellite data and lower costs of image deployment. Despite the fact that the image-processing steps are well studied, few of them are fully automated. This can be attributed to their complexity and the required know-how from the user. As we are entering the big data era, the need to establish operational image workflows that produce actionable information in a trusted, robust, and stand-alone fashion is arising. One application in which Earth Observation can be an important information source is smallholder farming. The fine spatial resolution required to sense smallholder farm fields, the radiometric resolution to discriminate between plant types in this heterogeneous environment, and the temporal resolution required to monitor events and developments (farm practices and crop growth, for instance) during the crop season has become available lately from satellite sensors.