How can remote sensing be used to strengthen food security in low- and middle-income countries? 

How can remote sensing be used to strengthen food security in low- and middle-income countries? 

 By Daniel Lapidus, Senior Economic and Policy Analyst, RTI International


Agricultural decisionmakers in developing countries need better information to make policy and investment decisions that could have major implications for their country’s food security. For instance, which crops are growing where? What yields can be expected? What are average plot sizes? Unfortunately, this data can be extremely expensive to collect and existing data is often outdated, error prone, or without the right metadata to make sense of it.


Advancements in remote sensing technologies—including drones and high-resolution satellite imagery—and the advent of improved data processing and machine learning all hold promise to revolutionize the cost, resolution, and accuracy of collecting agricultural information. However, these technologies can also be difficult to apply and sustain.  RTI’s internally funded Grand Challenge project in Rwanda is applying recent advances in remote sensing to determine how insights and data from these technologies can be harnessed to promote food security in areas of need.


We recently explored this important topic with several remote sensing technical experts and international development practitioners. Here are some key takeaways:


Tailor the technology to the decision maker and purpose. How one uses remote sensing data is very dependent on who is making decisions. Many tools are geared toward farmers or agribusinesses to inform individual decisions. Precision agriculture or site-specific management should not be confused with the kinds of data and information that would be required by regional- or national-level decisionmakers. Different remote sensing products should be tailored to different end users, including government, farmers, cooperatives, and financial institutions.


A helpful construct for considering the usefulness and applicability of the information is assessing whether the needs of the end-user are tactical, operational, and/or strategic. Highly detailed and timely information might be more valuable to farmers to make immediate, tactical decisions. For those that need to make more operational decisions (for example, where to prioritize resources), such as wholesalers, cooperatives, or extension agencies, the data can be more zoomed out. More forward-thinking and strategic decision makers would benefit from data and information that is highly accurate but might not need the same resolution or time sensitivity.


Be aware of the tradeoffs between information quality and cost. The better the accuracy, timeliness, and accessibility of the data, the more valuable it is for decision making—and the more costly it is to produce. And with technologies rapidly advancing, it’s becoming less costly to get better and better data. It is a dynamic situation, and we need to make sure that decision support tools are fit-for-purpose but also stay relevant in a changing environment.


Data privacy and control are lagging the technology. With high resolution data becoming more widely available so quickly, regulations have not caught up. Remote sensing data has become like the wild west. How can we ensure that the privacy of data is protected and that it is in hands of the right people, especially when there is an increasing call for more open and freely available data? These are questions that have not been resolved but need to be addressed if we want open and reliable data to become more available and actionable to address food security.


While important questions remain, leveraging advancements in remote sensing technology to make smarter agricultural decisions is emerging as a potent approach for better understanding—and strengthening—food security in some of the world’s most vulnerable countries.