Led by Marshall Burke and David Lobell, researchers at the Center on Food Security and the Environment are exploring new analytical techniques to harness data sets with the potential to solve challenges of food security. Voluminous data, including those obtained from satellite sensors, mobile phone carriers, or social media companies, provide opportunities to assess issues such as farm productivity, food prices, nutritional status, household assets, rural infrastructure, fishing patterns, human conflict, and social unrest. FSE is focused on the long-term opportunities in understanding and improving both food security and environmental quality through big data analysis.
Developing and testing remote-sensing based approaches to gather aid-relevant data on household assets, focusing on Sub-Saharan Africa.
Using high-resolution satellite imagery with household survey data to develop algorithms and measure crop yields to improve productivity and eventually reduce hunger.
Using data and spatial analysis to atribute local-level factors to health and child mortality.
Using satellite data to identify the causes of and potential solutions for yield gaps in India's Wheat Belt
Environmental Research Letters, September 2017
Mapping Smallholder Yield Heterogeneity at Multiple Scales in Eastern Africa
Remote Sensing, September 2017
The shared and unique values of optical, fluorescence, thermal and microwave satellite data for estimating large-scale crop yields
Remote Sensing of Environment, August 2017
Towards fine resolution global maps of crop yields: Testing multiple methods and satellites in three countries
Remote Sensing of Environment, May 2017
Satellite-based assessment of yield variation and its determinants in smallholder African systems
Proceedings of the National Academy of Sciences, February 2017
Using remotely sensed temperature to estimate climate response functions
Environmental Research Letters, January 2017
Sources of variation in under-5 mortality across sub-Saharan Africa: a spatial analysis
Lancet Global Health, October 2016
Combining satellite imagery and machine learning to predict poverty
Science, August 2016
Improving the monitoring of crop productivity using spaceborne solar-induced fluorescence
Global Change Biology, November 2015