The application of time-series MODIS NDVI profiles for the acquisition of crop information across Afghanistan

Abstract

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.

Characterizing and Mitigating Sensor Generated Spatial Correlations in Airborne Hyperspectral Imaging Data

“…Furthermore, in [37], the performances of onboard lossless compression of hyperspectral raw data are analyzed considering the blurring effects. In the literature, many studies acknowledge the potential for error due to sensor induced spatial blurring effects (e.g., [38][39][40][41][42]) but do not characterize the implications.…”

AirSurf-Lettuce: an aerial image analysis platform for ultra-scale field phenotyping and precision agriculture using computer vision and deep learning

“…The flying route and the imaging protocol were designed to facilitate cross-site crop layout assessment, yield prediction (based on vegetation indices), and disease monitoring (Fig. 1A), which has been described previously 35 . In this study, we used a series of collected ultra-large NDVI images (1.5-2GB per image) at 3cm ground sample distance (GSD) spatial resolution, collecting iceberg lettuce signals between H1 and H2 stages (i.e.…”

“…The NDVI imaging sensor used is an industrial standard camera described previously 35 . The aerial imaging was carried out by a ‘Sky Arrow’ light aircraft, the lightest weight class (Very Light Aircraft, VLA) of any commercial aircraft that is allowed for commercial work.…”

Assessing changes in the above ground biomass and carbon stocks of Lidder valley, Kashmir Himalaya, India

“…NDVI has been used at regional levels by many workers for assessing above-ground biomass (Lu et al 2012;Ahmad 2013;Simms et al 2014;Zhu & Liu 2015). During the present study, a linear regression model was applied to develop AGB estimation from NDVI for 2005 and 2013 separately.…”