## 4 Conclusions  
An automated satellite image processing workflow for smallholder agriculture based on free software has been developed and is presented here. The key characteristics are the automated capability, the delivery of near-real-time application-ready products, the free and open-source nature of the algorithmic approach, and the development tailored to smallholder agriculture. This workflow can be set up on a Linux machine and requires no cost for direct or indirect software licensing. The individual steps have been presented, and the adjustments made to accommodate smallholder-specific analysis have been discussed. It supports VHSR satellite images and is currently being tested to see if it can accommodate the use of Sentinel-2 images. Calibrated data and efficient and timely processing of the vault of satellite images that are gradually becoming available is important for shifting from a data-centric to a product-centric solution based on remotely sensed images. This work has demonstrated how an automated workflow, once set up, can deliver, in near-real-time, information to end-users and decision makers, who need no remote sensing knowledge to apprehend the results. However, despite the wealth of information produced, the value of the product can be appreciated only if the information aids a decision resulting in higher profitability [4].  
In order to adequately monitor the temporal evolution of smallholder farms with remote sensing, a high spatial and temporal resolution dataset is needed. This requires new advancement in satellite remote sensing; an attempt to integrate free data satisfying the requirements for smallholder agriculture (e.g., Sentinel-2, Venus and SPOT-5) can enrich the available information vault. An investigation of the usability of Unmanned Aerial Vehicles (UAV), a development signaling a new era in remote sensing regarding spatial accuracy, temporal frequency, and radiometric quality, and similar automated workflows encompassing virtual constellations could be a sound basis, not only for delivering near-real-time information, but also for delivering results for further scientific analysis.  
The Faculty of Geo-Information Science and Earth Observation (ITC) team aims to publish the presented workflow as an open-source, global public good, together with its partner crop spectro-temporal signature library (CSSL). We invite research and development teams with an interest in the image-based monitoring of smallholder farming to collaborate with us and to make use of this workflow for their work. Teams that have an interest in such collaboration should write to [PERSON] at [EMAIL_ADDRESS].  
This research was supported by the Bill and Melinda Gates Foundation, the STARS Grant Agreement, under number 1094229-2014. We wish to express our gratitude to the STARS partners for their useful feedback during the algorithmic development. Our thanks also go to our colleagues from ITC, who contributed ideas and workarounds, especially [PERSON]. Our appreciation goes to the open-source community of R, who offered valuable advice when needed.  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] conceived the workflow and designed the experiments. [PERSON], [PERSON], [PERSON] and [PERSON] implemented most of the code, [PERSON] realized the tree detection and image co-registration, [PERSON] delivered the atmospheric correction code and [PERSON] integrated it in the workflow. [PERSON] wrote the draft of the manuscript and [PERSON], [PERSON], [PERSON], [PERSON]-[PERSON], [PERSON] and [PERSON] reviewed and expanded it.  
The authors declare no conflicts of interest. The funding sponsors had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.