## 1 Introduction
The system emphasizes the analysis of heterogeneous and  
Figure 1: Example of smallholder fields in Barisal, Bangladesh, as depicted by different remote sensors: (**a**) Unmanned Aerial Vehicle (UAV) (Tetracam five-band mini-MCA (multi-camera array) false-colour composite), (**b**) WorldView-2/3 multispectral false-colour composite, (**c**) WorldView-2/3 panchromatic, and (**d**) RapidEye false-colour composite. The overlay parallelograms indicate nine adjacent farm fields. The apparent misalignment between the satellite images at (**b–d**) and the polygons are a result of the relatively low horizontal positional accuracy with which the satellite images were delivered. multi-sensor images on a global scale. A system developed by [14] requires little human interaction to derive vegetative phenological metrics from traffic webcams. Another work, presented in [15], focuses on training and validating satellite data; it, however, requires the user to select reference data to establish an operational system for crop type maps. Last but not least, [PERSON] et al. [16] built a modular system accessed through Python to conduct Geographical Object-Based Image Analysis (GEOBIA) as an open-source package with functionality similar to existing GEOBIA packages. Other remote sensing domains have also encompassed efforts for automation; for instance, [PERSON] et al. [17] developed a semi-automated and open-source processing chain for urban object classification. Next to specialized automated workflows, more general-purpose platforms that offer access and processing capabilities have been released lately. Google Earth Engine is a platform that offers parallel computing, allowing a tremendous speed-up of processing workflows [18]. The DigitalGlobe (DG) company (now part of MDA Corporation) has the GBDX platform for computing with their images, which allows the user to acquire post-processed results instead of purchasing the data, a fact which addresses the concern of cost-effectiveness in obtaining derived image products [19]. ImageQuerying is yet another system for automatic, near-real-time image information extraction from Big Earth data [20]. This system helps the user, through an easy-to-use graphical user interface, to perform semantic content-based image retrieval and to build decision rules with spatial and temporal operators. More generic cloud computing platforms are also available such as the Amazon EC2 [21] and the Microsoft Azure [22]. Despite the fact that many processing systems are available, as presented above, many of them will either not be able to ingest Very High Spatial Resolution (VHSR) data, not be based on free and open-source software, or not meet the requirements of agriculture remote sensing. The Spurring a Transformation for Agriculture through Remote Sensing (STARS) project aimed to investigate the potential of remote sensing in assisting smallholder farming in sub-Saharan Africa and Southeast Asia [23]. To this end, we developed an automated processing chain that ingests VHSR satellite images and derives spectral and textural information for each smallholder field known to the system. This paper aims to showcase the established workflow and demonstrate its potential. We discuss algorithmic development and present examples of products derived by concentrating on three functions of the workflow, namely to:  
1. Produce higher processing level products, which can support various scientific purposes. The surface reflectance products aim specifically at vegetation studies. 2. Prepare the data for object-specific statistical information extraction to derive on-demand results and feed a spectral library. 3. Showcase the feasibility of an automated workflow, as a case study of monitoring smallholder agriculture farms from space.