A key part of FAO's mandate, defined through its Strategic Objectives (SO), is to contribute to a more productive and sustainable agriculture. Monitoring of agriculture systems is an important part of this task. The good news is that recent advances and trends in geospatial technologies are making it easier and more cost-effective to monitor food and agriculture resources in a timely manner. This is particularly useful in isolated or conflict-hit areas, where collecting ground data is not always possible.

FAO is taking advantage of these advances, working to develop more-efficient and more-accurate methods of using remote sensing information for crop acreage, yield estimation and crop forecasting. The goal is to set-up efficient national monitoring systems that will allow policymakers and others to take timely decisions that protect people and their livelihoods.

Remote sensing can significantly contribute to the assessment and monitoring of land resources over large areas. Analyses of high-resolution, freely available optical and radar imagery time series (also called hyper-temporal analysis) – from PROBA-V, Landsat and Sentinel satellites for instance – are used to capture the variability of seasonal events (e.g. post-flooding rice production), the phenology of natural and managed vegetation, and distinct land features (e.g. crop masks and crop types). Combined spectral indexes such as the normalized difference vegetation index (NDVI) are useful for providing an understanding of the dynamics of vegetation.

These techniques, when coupled with statistically sound sampling schemes, allow optimization of in-situ data collection. Stratification of area of interest and areal frame design, with multiple stages associated with statistical estimators, allow fast and cost-effective assessment and monitoring of accurate agricultural information over time.