Food Security Challenges
BETTER will investigate the utilization of large EO and non-EO biophysical and socio-economic heterogeneous datasets to support and optimise the different operational needs in the management of a humanitarian crisis. The requirements could focus on the development of higher-level products based on those datasets to provide reliable early warning information and support the decision-making process during operational activities. The datasets are related mostly to crop monitoring and meteorology, can be derived from the Copernicus Land, Atmosphere and Climate Change Services. Such possibilities will be explored in the framework of the project.
Main topics in these data challenge will be related to:
The United Nations World Food Programme (WFP) expects to improve its preparedness in order to better address food security issues in humanitarian crises through the development of BETTER Pipelines using Sentinels data, Copernicus Land, Atmosphere and Climate Change Services together with in-house datasets.
Food Security Data Challenges and Results
Usage of Sentinel-1 complex and backscatter data for detection and quantification of food security-related (natural) hazards and land cover/ land-use change.
WFP programmes include interventions that aim to enhance the resilience of communities and the increase in community assets. These may include things like the building of dams, restoration of irrigation capacity, afforestation, etc.
WFP runs a system for Seasonal Monitoring and Early Warning activities (Seasonal Monitor) to cover its areas of interest. It is desirable to evaluate the value-added of so far not utilised parameters as for example LST and biophysical and to simplify the access to these datasets.
Land Surface Temperature (LST) remains an under-utilized variable in the field of Seasonal Monitoring and Natural Hazards Early Warning. Using this information can help to ensure Food Security.
Information on snow cover extent is a key component of seasonal monitoring and food security early warning in WFP operations for Central Asia. Certain parameters can indicate future developments and thereby help to ensure the food supply in the appropriate region.
This challenge aims to overcome a significant problem in the usage of high-resolution optical data. A smoothing filter developed by WFP produces a noise and gap-free product, with the advantage that the output can be produced at any arbitrary time interval.
Hot-spot crop monitoring using high-resolution Sentinel and Landsat data
As a result of addressing the identified BETTER Food Security challenges, the UN’s World Food Programme was able to enhance its Climate and Earth Observation analytical procedures. These include the monitoring of natural hazards and mapping of land cover changes with humanitarian implications (e.g. cropland abandonment due to conflict)
The pipeline developed for the relevant challenge “Land cover changes and inter-annual vegetation performance” has been made accessible via a geo-browser’s graphical user interface https://ellip.terradue.com/geobrowser, enabling WFP analysts to easily acquire analysis-ready data. The data is being used to monitor end of season crop performance in selected locations and to provide first demonstrative pilots of satellite-based crop type mapping to the Ministry of Agriculture of Mozambique. Extension workers have been capturing geo-referenced ground information during the current rainfall season using smartphones. The pipeline covers nine districts in four provinces of Mozambique and has been particularly useful for the WFP Country Office to improve its capacity to assess drought impacts while being integrated into a program to develop the technical capacity of extension workers and central office technicians of the Ministry of Agriculture. Demonstration crop type mapping pilots will be carried out to analyse the feasibility to scale such an approach to province-sized areas.
The images above show a Landsat-8 false-colour-composite (left) and a Sentinel-1 coherence composite (right) with samples of captured ground data overlaid. The data consists of geo-referenced field perimeters with associated agronomic information – crop type, farming system, stage of development, crop performance. As field data builds up during the season, crop types can be mapped and performance on a pre-crop basis analysed.