Peer-Reviewed Journal Details
Mandatory Fields
Green S.;Cawkwell F.;Dwyer E.
Remote Sensing Applications: Society And Environment
A time-domain NDVI anomaly service for intensively managed grassland agriculture
Scopus: 1 ()
Optional Fields
Grassland management MODIS NDVI anomaly Seasonal progression
© 2018 Seasonal progress anomaly mapping uses satellite data to identify the current stage of growth of vegetation compared to the normal stage of growth at that time and place. Usually applied to crops, the potential for such a service in intensive grasslands is developed here. The research identifies the need for monitoring progress of grass growth in spring for effective herd and paddock management in the context of increasing seasonal weather variability. Using 12 Years of NASA MODIS satellite data and 12 years of ground climate station data in Ireland, NDVI was modeled against time as a proxy for grass growth. This model is the reference for estimating current seasonal progress of grass growth against a ten year average. The model is developed to estimate Seasonal Progress Anomalies in the Time domain (SPAT), giving a result in terms of days behind and days ahead of the norm instead of percentage difference which is the current reporting method for this type of service. As a comparison SPAT estimates for 2012 and 2013 are compared to ground based estimates of seasonal progress anomaly from 30 climate stations and show a correlation coefficient of 0.897 and RMSE of 15days. SPAT maps for these two seasons for the whole of Ireland are generated every 16 days with a spatial resolution of 250 m and two are given as examples. The method can successfully map current grass growth trends compared to the average and present this information to the farmer in simple everyday language. The usefulness of a producing a service based on this approach to aid farmersí planning in the context of increasing weather volatility is discussed.
Grant Details