G2 Crop CIS meeting Ispra, May 14 – 15, 2012 Presented by: Institute of Geodesy and Cartography

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

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

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.

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

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.

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

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

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

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

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

Chosen regions For each european NUTS region WA - wheat area harvested (from Eurostat, mean value of 11 considered years) TA - total arable land area (from arable land mask) 12 Ispra, May 14 – 15, 2012

Chosen regions DK Atlantic Central ES Mediterranean North DE Continental North DEE Continental North ES Mediterranean North mean lowest Ispra, May 14 – 15, 2012

Prediction errors 14 Ispra, May , 2012

Prediction errors 15 Ispra, May , 2012

Year 2009 yield prognosis 16 Ispra, May , 2012

Year 2009 yield prognosis 17 Ispra, May , 2012

Year 2009 yield prognosis 18 Ispra, May , 2012

Year 2009 yield prognosis 19 Ispra, May , 2012

Year 2009 yield prognosis 20 Ispra, May , 2012

Year 2009 prediction errors 21 Ispra, May , 2012

Year 2009 prediction errors 22 Ispra, May , 2012

Year 2009 prediction errors 23 Ispra, May , 2012

Year 2009 regression coefficients 24 Ispra, May , 2012

Year 2009 regression coefficients 25 Ispra, May , 2012

Year 2009 regression coefficients 26 Ispra, May , 2012

Models for aggregated data 27 Ispra, May , 2012 A strategy to increase the number of observations by grouping the NUTS As the number of years of yield data is small, the possibility of building PLS Regression models for aggregated data was investigated. Levels of NUTS-2 regions aggregation considered: o agro-climatic zone, o country, o country / agro-climatic zone, o NUTS-1 / agro-climatic zone.

Models for aggregated data 28 Ispra, May , 2012 For each NUTS-2 region, yield data was standardized. yield standardized = (yield – mean) / standard deviation Standardized yield values and values of vegetation indices from all NUTS-2 regions constituting one aggregated region were used to build PLS regression model for aggregated region.

Models for aggregated data 29 Ispra, May , 2012 Cross-validation The predictive ability of the model for aggregated region was assessed with cross-validation. For each year of the data: The PLS regression model was built on the basis of data that did not contain data for year considered (the standardization procedure for each NUTS-2 region was repeated). For each NUTS-2 region constituting the aggregated region, the prediction of standardized yield for year considered was performed and the destandardized yield value was calculated. This predicted yield value was compared with observed yield. Cross-validation MAPE, MPE, Nash-Sutcliffe coefficient were calculated.

Models for aggregated data 30 Ispra, May , 2012 Nash–Sutcliffe model efficiency coefficient

Models for aggregated data 31 Ispra, May , 2012 Nash–Sutcliffe efficiencies can range from −∞ to 1. An efficiency of 1 (E = 1) corresponds to a perfect match of modeled discharge to the observed data. An efficiency of 0 (E = 0) indicates that the model predictions are as accurate as the mean of the observed data, whereas an efficiency less than zero (E < 0) occurs when the observed mean is a better predictor than the model or, in other words, when the residual variance (described by the numerator in the expression above), is larger than the data variance (described by the denominator). Essentially, the closer the model efficiency is to 1, the more accurate the model is. NSC = 1 - a perfect match of modeled to the observed data. NSC = 0 - the model predictions are as accurate as the mean of the observed data NSC < 0 - the observed mean is a better predictor than the model The closer the model efficiency is to 1, the more accurate the model is.

Aggregation for agro-climatic zones 32 Ispra, May , 2012 Nash–Sutcliffe efficiencies can range from −∞ to 1. An efficiency of 1 (E = 1) corresponds to a perfect match of modeled discharge to the observed data. An efficiency of 0 (E = 0) indicates that the model predictions are as accurate as the mean of the observed data, whereas an efficiency less than zero (E < 0) occurs when the observed mean is a better predictor than the model or, in other words, when the residual variance (described by the numerator in the expression above), is larger than the data variance (described by the denominator). Essentially, the closer the model efficiency is to 1, the more accurate the model is. Number of regions NDVI_MARSfAPAR_MARSNDVI_BioParfAPAR_BioPar MPEMAPENSCMPEMAPENSCMPEMAPENSCMPEMAPENSC Alpine Atlantic Central Atlantic North Atlantic South Boreal Continental North Continental South Mediterranean North Mediterranean South

Country / agro-climatic zone 33 Ispra, May , 2012 Nash–Sutcliffe efficiencies can range from −∞ to 1. An efficiency of 1 (E = 1) corresponds to a perfect match of modeled discharge to the observed data. An efficiency of 0 (E = 0) indicates that the model predictions are as accurate as the mean of the observed data, whereas an efficiency less than zero (E < 0) occurs when the observed mean is a better predictor than the model or, in other words, when the residual variance (described by the numerator in the expression above), is larger than the data variance (described by the denominator). Essentially, the closer the model efficiency is to 1, the more accurate the model is. CountryAgro-Climatic zone Number of NUTS regions NSC MARSBioPar VCIFCIVCIFCI Austria AT_Alpine AT_Continental North BelgiumBE_Atlantic Central Germany DE_Atlantic Central DE_Continental North DenmarkDK_Atlantic Central Spain ES_Atlantic South ES_Mediterranean North ES_Mediterranean South FinlandFI_Boreal France FR_Atlantic Central FR_Atlantic South FR_Mediterranean North Hungary HU_Continental North HU_Continental South IrelandIE_Atlantic North Italy IT_Alpine IT_Mediterranean North IT_Mediterranean South LithuaniaLT_Continental North NederlandsNL_Atlantic Central PolandPL_Continental North PortugalPT_Atlantic South RomaniaRO_Continental South Sweden SE_Atlantic Central SE_Boreal SlovakiaSK_Continental North Great Britain UK_Atlantic Central UK_Atlantic North

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