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Status Report CROP CIS Geoland2 Project Review Ispra, 25 th of January 2012 Institute of Geodesy and Cartography

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ISPRA Utility assessment of BioPAR products for wheat yield forecasting in Europe. Crop yield estimation. Detailed description of methods and comparison of results on MARSOP and BioPar data

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ISPRA Utility Assessment – IGiK contribution The objective of the work is to test the performance of MARS and BioPar indicators for yield forecast on an European window. The purpose is to show and assess their practical use in crop monitoring/yield forecasting. The work is aimed at comparing the differences in yield estimation accuracy, based on the two data sets. Objective

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ISPRA NDVI and FAPAR images from - MARS OP - BioPar databases resolution 1km 2 10-day periods 1998 – 2011 unsmoothed Satellite indices

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ISPRA [VALUE] max - the highest (of all years) index value for a given pixel in a given decade [VALUE] min - the lowest (of all years) index value for a given pixel in a given decade Satellite indices

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ISPRA Arable fraction image - from JRC

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ISPRA arable land fraction > 50 % - clumps, which are contiguous groups of pixels in one thematic class (region) > 10 pixels - number of arable pixels in one region (thematic class) > 100 geometric correction to NDVI images Arable land mask – created in IGIK

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ISPRA Indices profiles

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ISPRA Indices profiles

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ISPRA Eurostat, Regional Agriculture Statistics Database NUTS0 109 NUTS1 299 NUTS2 Wheat yield data

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ISPRA Missing yield data for all years: 76 NUTS2 regions Wheat yield data

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ISPRA Missing yield data for more than two years: 92 NUTS2 regions Wheat yield data

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ISPRA Adding NUTS1 regions for DE, DK and UK. Number of added NUTS1 polygons: 25 Adding the last 3 years (2008; 2009; 2010) of yield data for Spanish regions from Spanish National Statistical Office Wheat yield data

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ISPRA NUTS 2 regions FR81, FR82 and RO21, RO22, RO31,RO31,RO41, PT18 excluded due to erroneous yield data (one order of magnitude less than other) Wheat yield data

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ISPRA Wheat yield data These NUTS 2 regions which have less than 100 pixels representing arable land were excluded. Number of excluded polygons: 17 AT13 FI20 ITC3FI13 AT32 FR83 AT33 NL21 BE21NL22 BE34NL31 DECPT15 PT17 UKI UKL

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ISPRA European agro-climatic zones Iglesias, A., Garrote, L., Quiroga, S., Moneo, M.: Impacts of climate change in agriculture in Europe. PESETA-Agriculture study. EUR EN; DOI /33218; EC 2009.

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ISPRA Analized regions

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ISPRA Agro-climatic Zone Number of regions January February March April May June July August September October November December Alpine5 Atlantic Central48 Atlantic North3 Atlantic South7 Boreal4 Continental North30 Continental South9 Mediterranean North18 Mediterranean South6 Growing seasons

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ISPRA Another grouping of regions mean ordinal number of the decade in which the annual maximum of NDVI occurred

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ISPRA Another grouping of regions The starts and the ends of the growing seasons: in each zone, the season starts two decades before the lowest - occurred in this zone - ordinal number of the decade with annual maximum NDVI; in each zone the season ends two decades after the highest - occurred in this zone - ordinal number of the decade with annual maximum NDVI.

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ISPRA Growing seasons Number of the decade Number of regions January February March April May June July August September October November December

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ISPRA Statistical model Partial Least Squares Regression Partial Least Squares Regression (PLSR) - to choose a few components being linear combinations of explanatory variables X and to perform linear regression of response variable Y on these variables instead of performing regression with use of all X-variables Y - response variable (yield value); X n - explanatory variables (values of vegetation indices); n - sequential number of ten-day period taken into account; d_beg, d_end – number of ten-day period corresponding to the beginning and the end of growing season, respectively (different for different agro-climatic zones); c Nn - function f – coefficients generated by the PLS regression algorithm.

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ISPRA Statistical model Partial Least Squares Regression Partial Least Squares Regression (PLSR) - generalization of multiple regression - many (correlated) predictor variables - few observations - to derive orthogonal components using the cross-covariance matrix between the response variable and the explanatory variables - dimension reduction technique similar to Principal Component Regression (PCR) PCR - the coefficients reflect the covariance structure between the predictor variables X PLSR – the coefficients reflect the covariance structure between the predictor X and response Y variables

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ISPRA Statistical model Partial Least Squares Regression Partial Least Squares Regression (PLSR) PLS PACKAGE the PLS PACKAGE R R software environment

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ISPRA Model evaluation One-leave-out One-leave-out cross-validation: - for each year of data the PLS regression model was built with this year excluded - the yield prediction for excluded year was performed - predicted and actual yield values were compared

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ISPRA Model evaluation One-leave-out One-leave-out cross-validation: Performances were evaluated in terms of cross-validation mean errors: MPE Mean Percentage Error (MPE) MAPE Mean Absolute Percentage Error (MAPE) RMSE Root Mean Square Error (RMSE) Yield_obs i – actual yield in year i, Yield_pred i –yield prediction made for year i, N – number of observations (years) taken into account

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ISPRA Agro-climatic zone Mean yield (dt/ha) Number of regions RMSE (dt/ha)MPE (%)MAPE (%) BioParMARS Null model BioParMARS Null model BioParMARS Null model Alpine Atlantic Central Atlantic North Atlantic South Boreal Continental North Continental South Mediterranean North Mediterranean South Cross-validation prediction errors Agro-climatic zones Small differences in errors (MPE, MAPE) of yield prognosis for both MARS and BioPar databases

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ISPRA Results - cross validation for Agroc-limatic zones B i o P a r M A R S

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ISPRA Cross-validation prediction errors Agro-climatic zones Mean errors for indices Index RMSE (dt/ha)MPE (%)MAPE (%) BioParMARSBioParMARSBioParMARS NDVI Fapar VCI FCI

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ISPRA Results - cross validation Agro-climatic zones B i o P a r M A R S

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ISPRA Cross-validation prediction errors maxNDVI decades NDVImax decade Mean yield (dt/ha) Number of regions RMSE (dt/ha)MPE (%)MAPE (%) BioParMARS Null model BioParMARS Null model BioParMARS Null model

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ISPRA Results - cross validation maxNDVI decades B i o P a r M A R S

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ISPRA Cross-validation prediction errors maxNDVI decades Mean errors for indices Index RMSE (dt/ha)MPE (%)MAPE (%) BioParMARSBioParMARSBioParMARS NDVI Fapar VCI FCI

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ISPRA Results - cross validation maxNDVI B i o P a r M A R S

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ISPRA Cross-validation prediction errors - annual MPEs Index NDVI fAPAR VCI FCI Average Index NDVI fAPAR VCI FCI Average MARS BioPar The largest errors: 2003 (drought in Europe) and 2007

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ISPRA Cross-validation prediction errors - annual MPEs MARS BioPar Agroclim zone Number of regions Alpine Atlantic Central Atlantic North Atlantic South Boreal Continental North Continental South Mediterranean North Mediterranean South Average Agroclim zone Number of regions Alpine Atlantic Central Atlantic North Atlantic South Boreal Continental North Continental South Mediterranean North Mediterranean South Average

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ISPRA Cross-validation prediction errors - annual MAPEs MARS BioPar Index NDVI fAPAR VCI FCI Average Index NDVI fAPAR VCI FCI Average

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ISPRA Cross-validation prediction errors - annual MAPEs MARS BioPar Agroclim zone Number of regions Alpine Atlantic Central Atlantic North Atlantic South Boreal Continental North Continental South Mediterranean North Mediterranean South Average Agroclim zone Number of regions Alpine Atlantic Central Atlantic North Atlantic South Boreal Continental North Continental South Mediterranean North Mediterranean South Average

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ISPRA Cross validation annual prediction errors B i o P a r M A R S

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ISPRA forecast B i o P a r M A R S Differences between prediction errors and errors of Null Model

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ISPRA forecast Differences between prediction errors and errors of Null Model L - number of 10-day periods within growing season; Yield_obs – actual yield in year 2009; Yield_pred n – yield prediction made with knowledge of decadal indices from d_beg to n.

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ISPRA forecast – MARS data Percentage of regions with forecast better than Null Model NDVI prognosis decade AustriaBelgiumDenmarkFinlandGermanyHungaryIreland The Nederlands PolandPortugalRomaniaSlovakiaSpainSweden mean fAPAR prognosis decade AustriaBelgiumDenmarkFinlandGermanyHungaryIreland The Nederlands PolandPortugalRomaniaSlovakiaSpainSweden mean Number of regions

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ISPRA forecast – BioPar data Percentage of regions with forecast better than Null Model NDVI prognosis decade AustriaBelgiumDenmarkFinlandGermanyHungaryIreland The Nederlands PolandPortugalRomaniaSlovakiaSpainSweden mean fAPAR prognosis decade AustriaBelgiumDenmarkFinlandGermanyHungaryIreland The Nederlands PolandPortugalRomaniaSlovakiaSpainSweden mean Number of regions

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ISPRA In 2009 forecast – percentage of regions with lower error (MAPE) than error (MAPE) of Null Model

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ISPRA Conclusions The investigations did not reveal the substantial differences between MARS and BioPar databases, although the results from comparison are very close, and the differences are minimal in favour of BioPar dataset. Observing the spatial distribution of the prediction errors, it can be noticed that the largest errors occurred in the countries in the periphery of Europe, while in the central, geographically close countries, the performance of the model is better for both datasets. For two methods of regions grouping the better results were obtained for division of regions into zones according to maxNDVI decades (more than half of zones with better performance than for Null model) than for classical division into Agro-climatic zones. Again, the results are similar for both databases. In the Annual predictions the averages of MPEs and MAPEs are lower for BioPar data.

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ISPRA Conclusions In the yield forecast for the year 2009 the spatial stratification of the results can be observed. The best results were obtained in northern part of Central Europe (Poland, North-eastern Germany, Denmark) and in the large regions of Spain. The worst results were obtained for the countries of the northern part of Europe and located in the periphery of the continent (Sweden, Ireland, Portugal) and in southern part of Central Europe (southern Germany, Romania, Hungary). The overall performance of the statistical model for both databases is not good enough. It can be justified by too short time series of data (11 years) and the large gaps in the yield data. Gathering more data over the years and complementing yield data for European NUTS regions are expected to improve the performance of the statistical model. The investigations of the methods of regions grouping (affecting the period of conducting the forecast) different from the classical one (agro-climatic zones) should also be done. The effort should be done to get the yield statistic data for 2010 to do the yield prognosis for another year than 2009

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