G2 Crop CIS meeting Ispra, May 14 – 15, 2012

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

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 2012-01-25 ISPRA

Objective 10400 - 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. 2012-01-25 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 24107 EN; DOI 10.2791/33218; EC 2009. 2012-01-25 ISPRA

Another grouping of regions mean ordinal number of the decade in which the annual maximum of NDVI occurred 2012-01-25 ISPRA

Statistical model Partial Least Squares Regression(PLSR) - to choose a few components being linear combinations of explanatory variablesXand to perform linear regression of response variableY on these variables instead of performing regression with use of all X-variables Y - response variable (yield value); Xn - 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); cNn - function f – coefficients generated by the PLS regression algorithm. 2012-01-25 ISPRA

Statistical model 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 2012-01-25 ISPRA

Model evaluation One-leave-out cross-validation: - for each year of data the PLS regression model was built with this yearexcluded - the yield prediction for excluded year was performed - predicted and actual yield values were compared 2012-01-25 ISPRA

Model evaluation One-leave-out cross-validation: Performances were evaluated in terms of cross-validation mean errors: Mean Percentage Error (MPE) Mean Absolute Percentage Error (MAPE) Root Mean Square Error (RMSE) Yield_obsi– actual yield in year i, Yield_predi –yield prediction made for year i, N – number of observations (years) taken into account 2012-01-25 ISPRA

Results - cross validation Agro-climatic zones M A R S B i o P a r 2012-01-25 ISPRA

Results - cross validation maxNDVI M A R S B i o P a r 2012-01-25 ISPRA

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) Ispra, May 14 – 15, 2012

Chosen regions DK0 153.02 Atlantic Central ES41 133.69 Mediterranean North DE2 128.95 Continental North DEE87.51 Continental North ES24 87.31 Mediterranean North mean29.69 lowest0.09 Ispra, May 14 – 15, 2012

Prediction errors Ispra, May 14 - 15, 2012

Year 2009 yield prognosis Ispra, May 14 - 15, 2012

Year 2009 prediction errors Ispra, May 14 - 15, 2012

Year 2009 regression coefficients Ispra, May 14 - 15, 2012

Models for aggregated data • 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: • agro-climatic zone, • country, • country / agro-climatic zone, • NUTS-1 / agro-climatic zone. Ispra, May 14 - 15, 2012

Models for aggregated data 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. Ispra, May 14 - 15, 2012

Models for aggregated data 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. Ispra, May 14 - 15, 2012

Models for aggregated data Nash–Sutcliffe model efficiency coefficient Ispra, May 14 - 15, 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. Models for aggregated data 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. Ispra, May 14 - 15, 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. Aggregation for agro-climatic zones Ispra, May 14 - 15, 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. Country / agro-climatic zone Ispra, May 14 - 15, 2012

THANK YOU VERY MUCH

G2 Crop CIS meeting Ispra, May 14 – 15, 2012