Using spatial and aerial imagery to estimate crop surfaces in developing countries

Agricultural statistics are essential for monitoring production changes, planning government interventions and future investments, and estimating crop outputs for policymakers, researchers, and organizations. Poor agricultural data can lead to disastrous misallocations of resources and unsuccessful policies, as well as having a dire impact on populations and farmers alike.

Rwanda fields


Jacques Delincé, a veteran agricultural statistician and former head of Agrilife and MARS units at the European Commission, is currently working as a consultant for the Food and Agriculture Organization – or FAO – on the Global Strategy to improve Agriculture and Rural Statistics. The team is looking for more cost-efficient methods for agricultural statistics in developing countries and, in particular, comparing the accuracy and costs of list and area frames for farmer surveys.

Estimating crop areas by conducting farmer interviews involves collecting data through regular household questionnaires, asking farmers to estimate the superficies of planted crops for an individual field or farm. Area frame surveys, on the other hand, is a global estimate drawn from a sample collection of well-defined land units.

In Nepal and Brazil, the FAO ran ground data collection surveys. Ground surveys have many strengths, but can also be costly and strict quality control procedures are needed to ensure data integrity. And, despite rigorous statistical modeling approaches, accuracy remains an issue as cost considerations often restrict sample sizes.

This could drastically change in the next few years as Earth observation data becomes more accessible and affordable satellite imagery can be used to supplement ground based systems. And in areas where surveys are unsafe due to civil wars and violence, aerial images may be the only approach.

In the interest of saving costs while working in Rwanda, Delincé and his team opted to use ground surveys from the current year and combine them with recent remote-sensed data. If the team could obtain crop area results similar enough to the ground surveys by analyzing aerial images from the same location, then Delincé could apply the same discriminating methodology to satellite images of the entire country to get an accurate estimate of crop surface areas.

For their analysis, Global Strategy’s team started with images from Sentinel-2, Landsat-8, and Sentinel-1. Rwanda is around 25,000 km2, roughly the size of Maryland. With a country of this size, it is often more economical to use satellite imagery over drones or planes.

Drones can be a great tool to cover small, defined areas, but regulations can vary greatly by location. For example, rules such as line-of-sight, requiring the pilot to be able to see the drone at all times, or large no-fly zones around buildings, like airports or hospital with helipads, represent a serious hurdle to full area coverage.

Planes, on the other hand, efficiently cover very large surfaces, are less susceptible to cloud coverage issues often plaguing satellites, and offer significant savings over very high-resolution satellite images. But, as with drones, military or government restrictions prevent statisticians and scientists to fly over certain areas.

Accurately estimating crop areas from aerial images is never easy and is even more of a challenge in the context of African farming systems. Crop areas in Sub-Saharan Africa are often characterized by smallholder farms that produce a wide range of diverse crops, non-uniform plots in a wide range of sizes – sometimes of the order of a few meters square – and intercropping, where farmers plant different crops within the same field.

Rwanda is no exception and the preliminary results from the study were inconclusive. The spatial resolution of open data used was too coarse, at 15 and 10 m, to properly distinguish between cultures. Despite this, through Global Strategy’s research, the Rwandan governments and NGOs, both global and local, will be able to better estimate future crop allocation, develop help plans for farmers, and even model the impact of specific food subsidies on the local economy.

To improve results accuracy further and widen the applicability, the team is now looking into sub-meter satellite data. As satellite sensors continue to sharpen and Earth observation data access improves over time, programs like the FAO Global Strategy will develop even more cost-efficient methods to improve Agriculture and Rural Statistic for developing countries.


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