## 1 Introduction
Earth Observation has fundamentally contributed to large-scale agricultural information systems in high-income countries for some time, especially for tasks in crop scouting for diagnosing soil fertility, the occurrence of insects, diseases, and weed and water problems; monitoring vegetation growth; and estimating potential yield, e.g., [7]. While crop scouting is time- and labor-intensive and thus expensive, Earth Observation presents a viable alternative, even though few studies have reported economic benefit estimates and evidence of on-farm profitability remains fuzzy [8]. The agricultural information landscape in low-income countries is contrastingly different. For one, few farmers have access to important farm inputs, whether products or management recommendations. Consequently, crop production often just covers the basic nutritional needs of the family. Secondly, mixed-cropping and inter-cropping are common practices in smallholder fields, and these practices increase land use intensity. This sketches a landscape in which the established management techniques and data sources of large-scale farming systems are essentially inapplicable. Low-income farming is dominated by small farms that are family-run. Farm size in these areas decreased in the period from 1960 to 2000 [9], and quite possibly the same happened to the average farm field size. This calls for the use of fine spatial resolution images to accrue a sufficient number of pixels per field (Figure 1). While, on the one hand, this will eliminate pixels covering multiple fields and reduce errors in field delineation, increased ground sampling frequency is also known to lead to an increased within-class variability in crop classification. Therefore, spatial resolution requirements differ considerably over different landscapes, even in the framework of a given application [10]. As a consequence, determining the optimal ground sampling distance for an application is a task of identifying the coarsest acceptable pixel size, given the specific landscape. To monitor crop development throughout the season, a time series of image acquisitions is required. Single satellite platforms cannot trivially satisfy such needs, especially where cloud presence is a limiting factor. For instance, [11] describes the observation requirements for a global agricultural monitoring activity and claims that no current satellite platform with fine-to-moderate spatial resolution can accommodate it. However, a synergistic use of data from multiple agencies and missions may meet the requirements; this leads to the idea of a virtual satellite constellation, which forces significant development in image analytics, especially in the harmonized use of Earth Observation information and the on-time availability of ready-to-use products. The two requirements of high spatial and high temporal resolution lead to secondary dependencies for our specific application; first, images in a time series must be georegistered with very high accuracy, and, secondly, surface reflectance products acquired at different times and possile, possibly by different platforms, must be carefully spectrally calibrated. A number of automated image processing methods have been developed in the past. For instance, STORM [12] is a successful example initially developed for 6.5 m RapidEye processing, though challenges surfaced when the workflow was used to tackle 2 m WorldView-2 (WV-2) images. These challenges were encountered especially during geometric correction and were attributed to the finer spatial resolution. While considered successful, the workflow depended on proprietary software (i.e., IDL and ATCOR), the licenses of which are not always available in image processing laboratories, especially when used in low-income countries, where smallholder farming prevails. A similar automatic processing and analysis system for multi-sensor satellite data, named GeoMultiSens, is currently under development [13].