We investigated and developed a prototype crop information system integrating 250 m MODIS Normalised Difference Vegetation Index (NDVI) data with other available remotely sensed imagery, field data and knowledge as part of a wider project monitoring opium and cereal crops. NDVI profiles exhibited large geographical variations in timing, height, shape and number of peaks with characteristics determined by underlying crop mixes, growth cycles and agricultural practices. MODIS pixels were typically bigger than the field sizes but profiles were indicators of crop phenology as the growth stages of the main first-cycle crops (opium poppy and cereals) were in phase. Profiles were used to investigate crop rotations, areas of newly exploited agriculture, localised variation in land management and environmental factors such as water availability and disease. Near-real time tracking of the current year's profile provided forecasts of crop growth stages, early warning of drought and mapping of affected areas. Derived data-products and bulletins provided timely crop information to the UK Government and other international stakeholders to assist the development of counter-narcotic policy, plan activity and measure progress. Results show the potential for transferring these techniques to other agricultural systems.

An integrated application of remote-sensing technology was devised and applied in Afghanistan during 2003-2009 providing critical information on cereal and poppy cultivation and poppy eradication. The results influenced UK and international policy and counter-narcotics actions in Afghanistan.

Yearly estimates of illicit opium production are key metrics for assessing the effectiveness of counter narcotics policy in Afghanistan. Poor security often prevents access to sample locations and puts pressure on field surveyors, resulting in biased sampling and errors in data recording. Supportive methods using aerial digital photography for improving yield estimates were investigated in the UK in 2004, 2005 and 2010. There were good empirical relationships between NDVI and poppy yield indicators (mature capsule volume and dry capsule yield) for individual fields. The results suggested a good generalised relationship across all sampled fields and years (R 2 >0.70) during the 3-4 week period including poppy flowering. Regression estimates using this relationship with the imagery counteracted bias in the sample estimate of yield, reduced sample error and enabled the production of detailed maps showing the poppy yield distribution. The application of this approach using VHR satellite imagery was investigated in the context of the annual opium survey in Afghanistan. Initial results indicated the potential for bias correction of yield estimates using a smaller and targeted collection of ground observations as an alternative to random sampling.