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Optical remote sensing requirements for operational crop monitoring and yield forecasting in Europe
- In Proceedings of Sentinel-3 OLCI/SLSTR and MERIS/(A) ATSR Workshop, ESA SP-711
, 2012
"... The European Commission requires in-season crop yield forecasts at a European level as part of the decision making process on market intervention and for policy support. For the past twenty years, the Monitoring Agricultural Resources (MARS) Unit of the European Commission Joint Research Centre (JRC ..."
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The European Commission requires in-season crop yield forecasts at a European level as part of the decision making process on market intervention and for policy support. For the past twenty years, the Monitoring Agricultural Resources (MARS) Unit of the European Commission Joint Research Centre (JRC) has operationally produced such forecasts using the MARS Crop Yield Forecasting System (MCYFS), a modelling infrastructure driven by agro-meteorological data and assisted by remotely sensed observations. The potential of quantitative assessments of crop canopy status by remote sensing is currently underexploited in MCYFS because the available data do not satisfy the requirements for crop specific monitoring and yield forecasting. After presenting the current MCYFS, this paper discusses these ideal data requirements with the objective to see how the forthcoming Sentinel3-OLCI data could satisfy them. 1.
From Anopheles to Spatial Surveillance: A Roadmap Through a Multidisciplinary Challenge
, 2013
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www.mdpi.com/journal/ijgi/ Article Investigating Within-Field Variability of Rice from High Resolution Satellite Imagery in Qixing Farm County,
"... Abstract: Rice is a primary staple food for the world population and there is a strong need to map its cultivation area and monitor its crop status on regional scales. This study was conducted in the Qixing Farm County of the Sanjiang Plain, Northeast China. First, the rice cultivation areas were id ..."
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Abstract: Rice is a primary staple food for the world population and there is a strong need to map its cultivation area and monitor its crop status on regional scales. This study was conducted in the Qixing Farm County of the Sanjiang Plain, Northeast China. First, the rice cultivation areas were identified by integrating the remote sensing (RS) classification maps from three dates and the Geographic Information System (GIS) data obtained from a local agency. Specifically, three FORMOSAT-2 (FS-2) images captured during the growing season in 2009 and a GIS topographic map were combined using a knowledge-based classification method. A highly accurate classification map (overall accuracy = 91.6%) was generated based on this Multi-Data-Approach (MDA). Secondly, measured agronomic variables that include biomass, leaf area index (LAI), plant nitrogen (N) concentration and plant N uptake were correlated with the date-specific FS-2 image spectra using stepwise multiple linear regression models. The best model validation results with a relative error
Article A Framework for Defining Spatially Explicit Earth Observation Requirements for a Global Agricultural Monitoring Initiative (GEOGLAM)
"... www.mdpi.com/journal/remotesensing ..."
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Article Assessing the Performance of MODIS NDVI and EVI for Seasonal Crop Yield Forecasting at the Ecodistrict Scale
, 2014
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