Optical remote sensing requirements for operational crop monitoring and yield forecasting in Europe

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]  G. Genovese,et al.  Methodology of the MARS crop yield forecasting system. Vol. 2 agrometeorological data collection, processing and analysis , 2004 .

[2]  J. Wolf,et al.  WOFOST: a simulation model of crop production. , 1989 .

[3]  Pierre Defourny,et al.  A conceptual framework to define the spatial resolution requirements for agricultural monitoring using remote sensing , 2010 .

[4]  Michael E. Schaepman,et al.  A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling , 2007, Int. J. Appl. Earth Obs. Geoinformation.

[5]  Josef Aschbacher,et al.  The European Earth monitoring (GMES) programme: Status and perspectives , 2012 .

[6]  O. Hagolle,et al.  LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION: Part 1: Principles of the algorithm , 2007 .

[7]  C. Donlon,et al.  The Global Monitoring for Environment and Security (GMES) Sentinel-3 mission , 2012 .

[8]  P. Eilers,et al.  Evaluating the effectiveness of smoothing algorithms in the absence of ground reference measurements , 2011 .

[9]  F. Baret,et al.  Quantifying spatial heterogeneity at the landscape scale using variogram models , 2006 .

[10]  Michele Meroni,et al.  Evaluation of Agreement Between Space Remote Sensing SPOT-VEGETATION fAPAR Time Series , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[11]  F. Baret,et al.  Remotely sensed green area index for winter wheat crop monitoring:10-Year assessment at regional scale over a fragmented landscape , 2012 .

[12]  Christelle Vancutsem,et al.  A decision support tool for the optimization of compositing parameters , 2009 .

[13]  C. Tucker,et al.  Satellite remote sensing of primary production , 1986 .

[14]  Andrew K. Skidmore,et al.  Calibration of solar radiation models for Europe using Meteosat Second Generation and weather station data , 2013 .

[15]  Gregory Duveiller,et al.  Estimating regional winter wheat yield with WOFOST through the assimilation of green area index retrieved from MODIS observations , 2012 .

[16]  B. Holben.  Characteristics of maximum-value composite images from temporal AVHRR data , 1986 .

[17]  J. Hill,et al.  Use of coupled canopy structure dynamic and radiative transfer models to estimate biophysical canopy characteristics , 2005 .

[18]  F. Hall,et al.  Global Crop Forecasting , 1980, Science.

[19]  Herman Eerens,et al.  Evaluation of MSG-derived global radiation estimates for application in a regional crop model , 2012 .

[20]  S. Running,et al.  Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data , 2002 .