Food production is under threat from a combination of urban development, shrinking space for arable land, groundwater depletion and other challenges, and climate change is exacerbating these pressures. Accurate monitoring of agricultural productivity is essential for both global food security and the livelihoods of low-income rural regions, but current monitoring methods aren’t meeting this challenge.
This UK-China research collaboration has pioneered a new approach which has improved accuracy of crop monitoring by ten percent and produced crop yield estimates over large areas at an unprecedented ten metre resolution – compared to the previous one kilometre resolution estimates. The result is likely to be the most accurate portrait created to date of changing agricultural production in the North China Plain.
Previously researchers used either field surveys and mathematical models or satellite imagery to monitor crops, but both methods have their limitations. The team combined these previously incompatible data sets using new data assimilation techniques to give significantly improved estimates of agricultural productivity. The project is among the first to make use of data from the new Sentinel and Chinese GF satellites and has fed directly into agricultural production planning in China, providing more accurate analytics of crop development and responses to different stresses so that more suitable management practices can be deployed.
Besides providing better predictions of crop yield and crop growth, the team is training academics to use the software developed during the project, and the state-of-the-art techniques are already being applied to other countries including Ghana, Argentina and the UK.
"This project will lead to critical advances in quantitative remote sensing and data assimilation technology, enabling high resolution, high accuracy crop yield predictions that will benefit people both in and outside of China."
Professor Shunlin Liang, University of Maryland
Regional crop monitoring and assessment with quantitative remote sensing and data assimilation
Project leads: Professor Philip Lewis, University College London, UK and Professor Zhongxin Chen, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences
Delivery partners: Science and Technology Facilities Council, part of UK Research and Innovation and the National Natural Science Foundation of China