Climate change, increased drought occurrence, and limited water resources put immense pressure on the sustainability of agricultural systems in an increasingly over-populated world. Improved management of available resources are critically needed to avoid impending risks of over exploitation. At the same time, optimization of water and nutrient use efficiencies is fundamental for improving crop yields and the long-term sustainability of agriculture. Traditional and emerging remote sensing capabilities combined with developments in machine learning present unrealized opportunities to inform upon agricultural systems at relevant spatiotemporal scales and address impending food security concerns.
The focus of this article collection is broadly to explore the utility of remote sensing to improve food security in a changing climate. We are particularly interested in submissions with a high resolution focus both spatially (field scale or smaller) and temporally in order to facilitate timely in-field monitoring and modeling of surface characteristics. Studies that demonstrate the utility of remote sensing for directing sustainable management practices for increasing agricultural efficiency, optimize productivity, and enhance profitability, are highly encouraged. We also welcome contributions that attempt to tackle integrated field level to continental scale crop monitoring and prediction using multiple sensors and advanced modeling. We will be accepting contributions related to one or more of the following overall topics in the context of agricultural monitoring and management:
- Use of high resolution remote sensing to: monitor, model, and optimize water and nutrient use efficiencies; diagnose within-field variations in surface characteristics and vegetation function on a routine basis; optimize crop production via spatially explicit management practices
- Evaluation and integration of proximal sensors for spatial irrigation and fertility management
- Synergistic cross-platform (e.g., geostationary, polar-orbiting, unmanned aerial vehicles) approaches to enhance monitoring capacity, spatiotemporal resolution, and retrieval robustness from field to continental scales
- Integration of remote sensing, process modeling, and machine learning to advance agricultural monitoring and management
- Monitoring and management of climate induced impacts on crop functioning and yield
- Integration of short-term climate projections or weather forecasting for improved crop yield monitoring and prediction
- Integrated cloud-based agricultural monitoring systems to deliver informed decision support, early warning and directed location-specific management in near real-time.
This article collection welcomes diverse article types, including Original Research, Reviews, and Perspective Papers. Upon consultation with the Editors, we may also include Hypothesis & Theory papers, Technology Reports, Mini Reviews, Code, Data Report, General Commentaries, and other article types.
Climate change, increased drought occurrence, and limited water resources put immense pressure on the sustainability of agricultural systems in an increasingly over-populated world. Improved management of available resources are critically needed to avoid impending risks of over exploitation. At the same time, optimization of water and nutrient use efficiencies is fundamental for improving crop yields and the long-term sustainability of agriculture. Traditional and emerging remote sensing capabilities combined with developments in machine learning present unrealized opportunities to inform upon agricultural systems at relevant spatiotemporal scales and address impending food security concerns.
The focus of this article collection is broadly to explore the utility of remote sensing to improve food security in a changing climate. We are particularly interested in submissions with a high resolution focus both spatially (field scale or smaller) and temporally in order to facilitate timely in-field monitoring and modeling of surface characteristics. Studies that demonstrate the utility of remote sensing for directing sustainable management practices for increasing agricultural efficiency, optimize productivity, and enhance profitability, are highly encouraged. We also welcome contributions that attempt to tackle integrated field level to continental scale crop monitoring and prediction using multiple sensors and advanced modeling. We will be accepting contributions related to one or more of the following overall topics in the context of agricultural monitoring and management:
- Use of high resolution remote sensing to: monitor, model, and optimize water and nutrient use efficiencies; diagnose within-field variations in surface characteristics and vegetation function on a routine basis; optimize crop production via spatially explicit management practices
- Evaluation and integration of proximal sensors for spatial irrigation and fertility management
- Synergistic cross-platform (e.g., geostationary, polar-orbiting, unmanned aerial vehicles) approaches to enhance monitoring capacity, spatiotemporal resolution, and retrieval robustness from field to continental scales
- Integration of remote sensing, process modeling, and machine learning to advance agricultural monitoring and management
- Monitoring and management of climate induced impacts on crop functioning and yield
- Integration of short-term climate projections or weather forecasting for improved crop yield monitoring and prediction
- Integrated cloud-based agricultural monitoring systems to deliver informed decision support, early warning and directed location-specific management in near real-time.
This article collection welcomes diverse article types, including Original Research, Reviews, and Perspective Papers. Upon consultation with the Editors, we may also include Hypothesis & Theory papers, Technology Reports, Mini Reviews, Code, Data Report, General Commentaries, and other article types.