Modelling Impacts of Climate Variability on Agricultural Crop Yields Using Remote Sensing Derived Information

Dear Colleagues,

Feeding an increasing human population (expected to reach about 9 billion in 2050) and combating hunger and poverty worldwide remains a tremendous challenge under a more variable and changing climate. A more comprehensive and reliable assessment of environmental-related vulnerabilities and risks affecting global crop production is therefore of paramount importance.

Remote sensing can provide spatially explicit and unbiased information across different spatial and temporal scales. When integrated with process-based and statistical models, such remote sensing data can help to explore how managed agroecosystems respond to a changing climate and can greatly improve the agricultural industry’s preparedness and productivity. Indeed, utilising such improved modelling systems can substantially facilitate longer-term climate change adaptation through incrementally shifting farm and agribusiness management practices according to the seasonal and longer-term crop yield forecasts.

This Special Issue invites high-quality and innovative scientific papers describing cutting-edge research on the application of remote sensing derived information from any platform (satellite, aircraft, UAVs/drones) to the study of agricultural climate risk-related issues. Potential topics include but are not limited to the following:

• Innovative crop yield forecasting systems using RS-derived information;
• Deep learning methodologies using Earth Observational data for crop yield forecasting;
• Use of Earth Observational data to understand the impact of climate variability and change on crop growth and yield;
• Innovative methodologies of Earth Observational data integration for tackling agricultural climate risks.

Dr. Louis Kouadio
Dr. Nathaniel K. Newlands
Guest Editors

Manuscript Submission Information

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