Presentation on theme: "Status Report CROP CIS Geoland2 Project Review Ispra, 25 th of January 2012 Institute of Geodesy and Cartography."— Presentation transcript:
Status Report CROP CIS Geoland2 Project Review Ispra, 25 th of January 2012 Institute of Geodesy and Cartography
ISPRA2012-01-25 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
ISPRA2012-01-25 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. Objective
ISPRA2012-01-25 NDVI and FAPAR images from - MARS OP - BioPar databases resolution 1km 2 10-day periods 1998 – 2011 unsmoothed Satellite indices
ISPRA2012-01-25 [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
ISPRA2012-01-25 Arable fraction image - from JRC
ISPRA 2012-01-25 - 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
ISPRA2012-01-25 Missing yield data for all years: 76 NUTS2 regions Wheat yield data
ISPRA2012-01-25 Missing yield data for more than two years: 92 NUTS2 regions Wheat yield data
ISPRA2012-01-25 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
ISPRA2012-01-25 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
ISPRA2012-01-25 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
ISPRA2012-01-25 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.
ISPRA2012-01-25 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
ISPRA2012-01-25 Another grouping of regions mean ordinal number of the decade in which the annual maximum of NDVI occurred
ISPRA2012-01-25 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.
ISPRA2012-01-25 Growing seasons Number of the decade Number of regions January February March April May June July August September October November December 113 128 138 148 1516 37 1719 1811 1915 205
ISPRA2012-01-25 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.
ISPRA2012-01-25 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
ISPRA2012-01-25 Statistical model Partial Least Squares Regression Partial Least Squares Regression (PLSR) http://www.youtube.com/watch?v=AxmqUKYeD-U&feature=related PLS PACKAGE the PLS PACKAGE R R software environment
ISPRA2012-01-25 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
ISPRA2012-01-25 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
ISPRA2012-01-25 Agro-climatic zone Mean yield (dt/ha) Number of regions RMSE (dt/ha)MPE (%)MAPE (%) BioParMARS Null model BioParMARS Null model BioParMARS Null model Alpine52.454.485.625.23-0.71-0.83-0.867.209.368.04 Atlantic Central75.1487.597.496.25-2.05-2.46-0.768.508.206.88 Atlantic North86.636.826.625.18-0.20-0.43-0.336.81 5.26 Atlantic South45.676.986.954.68-4.00-4.73-2.1714.9814.7711.88 Boreal36.845.825.217.65-3.04-3.03-3.0413.8312.3612.55 Continental North44.3304.214.365.72-1.51-1.68-1.898.258.3911.40 Continental South35.295.685.616.81-2.54-2.97-3.6313.5213.6916.16 Mediterranean North38.6185.225.705.08-1.85-1.55-2.8712.0612.7313.14 Mediterranean South22.063.953.915.05-4.61-4.24-6.7316.6815.9023.34 Cross-validation prediction errors Agro-climatic zones Small differences in errors (MPE, MAPE) of yield prognosis for both MARS and BioPar databases
ISPRA2012-01-25 Results - cross validation for Agroc-limatic zones B i o P a r M A R S
ISPRA2012-01-25 Cross-validation prediction errors Agro-climatic zones Mean errors for indices Index RMSE (dt/ha)MPE (%)MAPE (%) BioParMARSBioParMARSBioParMARS NDVI6.096.15-2.16-2.3510.299.57 Fapar6.066.00-2.15-2.2210.159.56 VCI5.866.04-2.02-2.4110.039.47 FCI5.825.94-2.03-2.249.879.44
ISPRA2012-01-25 Results - cross validation Agro-climatic zones B i o P a r M A R S
ISPRA2012-01-25 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 1117.733.483.534.61-4.07-5.36-6.9619.0918.2723.41 1228.584.564.659.71-3.86-3.76-5.2915.1014.7419.81 1334.984.564.997.96-2.62-2.35-2.6111.1111.4712.32 1446.887.697.2010.39-3.79-4.31-2.3413.9512.7912.56 1559.4166.636.537.51-1.98-3.02-2.4210.159.9712.28 1657.7375.525.505.56-1.78-1.98-1.338.408.209.22 1758.0194.584.865.05-1.06-1.26-220.127.116.118.52 1868.318.104.22.168-0.33-0.57-0.836.316.377.51 1964.5156.466.676.15-1.30-1.25-1.168.288.808.04 2044.056.125.745.82-2.11-2.59-2.4912.7111.4112.25
ISPRA2012-01-25 Results - cross validation maxNDVI decades B i o P a r M A R S
ISPRA2012-01-25 Cross-validation prediction errors maxNDVI decades Mean errors for indices Index RMSE (dt/ha)MPE (%)MAPE (%) BioParMARSBioParMARSBioParMARS NDVI5.655.66-1.94-2.129.7310.31 Fapar5.565.66-1.92-2.139.4410.08 VCI5.585.61-1.84-2.179.6410.10 FCI5.545.59-1.85-2.129.479.92
ISPRA2012-01-25 Results - cross validation maxNDVI B i o P a r M A R S
ISPRA2012-01-25 Cross-validation prediction errors - annual MPEs Index19992000200120022003200420052006200720082009 NDVI-1.44-2.87-7.02-1.73-10.496.472.280.51-11.243.90-2.10 fAPAR-2.07-3.46-7.45-2.54-10.096.262.780.49-9.264.04-0.46 VCI-1.69-2.42-6.37-1.58-11.656.042.210.10-10.923.67-1.61 FCI-2.32-3.26-7.06-2.74-10.936.192.910.48-9.044.14-0.21 Average-1.88-3.00-6.97-2.15-10.796.242.540.39-10.123.94-1.10 Index19992000200120022003200420052006200720082009 NDVI2.07-0.91-5.210.90-10.706.000.71-0.69-14.372.22-2.47 fAPAR1.29-1.62-6.150.43-9.566.351.05-0.48-13.532.17-2.07 VCI2.510.31-4.081.41-11.335.570.07-1.50-13.371.97-2.73 FCI1.550.09-5.190.79-10.165.900.48-1.30-12.671.76-2.46 Average1.86-0.53-5.160.88-10.445.960.58-0.99-13.482.03-2.43 MARS BioPar The largest errors: 2003 (drought in Europe) and 2007
ISPRA2012-01-25 Cross-validation prediction errors - annual MPEs MARS BioPar Agroclim zone19992000200120022003200420052006200720082009 Number of regions Alpine-6.29-0.324.081.69-11.035.114.00-1.24-1.680.85-4.795 Atlantic Central1.940.02-6.22-0.10-8.042.83-0.09-3.04-19.564.336.6448 Atlantic North3.438.400.12-9.39-4.909.67-2.425.55-7.250.27-11.903 Atlantic South2.360.47-27.8817.69-12.775.25-9.934.19-15.713.22-30.287 Boreal-19.039.32-4.19-2.296.15-3.43-3.23-1.033.12-12.31-7.554 Continental North-5.90-14.85-3.39-3.84-4.329.896.60-3.68-5.727.37-0.7030 Continental South-9.63-3.393.20-7.58-26.7815.3213.3511.95-21.390.72-8.439 Mediterranean North1.392.92-15.01-3.69-19.917.282.954.384.572.30-4.3018 Mediterranean South-5.87-3.25-12.23-19.87-25.455.312.3813.542.4116.206.026 Average-4.18-0.08-6.84-3.04-11.896.361.513.40-6.802.55-6.14 Agroclim zone19992000200120022003200420052006200720082009 Number of regions Alpine-5.510.992.711.20-7.164.54-0.48-1.32-0.331.66-4.795 Atlantic Central3.330.54-4.051.97-7.253.36-1.04-2.98-20.372.356.0648 Atlantic North0.358.186.15-7.56-6.2012.06-5.024.55-7.782.03-13.403 Atlantic South4.12-0.24-26.3616.54-13.594.42-8.686.37-14.394.28-23.907 Boreal-21.2712.19-8.263.845.474.88-2.32-3.715.52-20.42-10.164 Continental North0.57-10.770.021.35-5.779.214.44-5.94-12.014.13-1.9330 Continental South-0.783.997.27-2.83-26.7916.6711.505.47-32.252.85-13.039 Mediterranean North6.335.61-14.20-1.11-17.953.80-0.711.780.060.57-7.3318 Mediterranean South2.884.56-16.86-14.87-23.961.48-3.6211.55-5.9313.61-3.206 Average-1.112.78-5.95-0.16-11.476.71-0.661.75-9.721.23-7.96
ISPRA2012-01-25 Cross-validation prediction errors - annual MAPEs MARS BioPar Agroclim zone19992000200120022003200420052006200720082009 Number of regions Alpine9.4912.468.853.8811.577.919.4611.8211.449.267.485 Atlantic Central4.474.0811.565.7810.427.694.146.0120.396.627.6648 Atlantic North7.628.405.329.394.909.675.905.557.251.1611.903 Atlantic South4.875.2727.8817.6913.4611.7116.166.7425.4018.5330.287 Boreal21.7112.4510.885.897.5712.169.3815.095.4921.8514.794 Continental North7.4015.456.636.7511.139.907.638.076.908.143.9830 Continental South9.708.246.6911.2233.4915.3213.3513.4321.397.6910.029 Mediterranean North6.699.2719.157.6420.038.9413.079.8220.9411.7115.2318 Mediterranean South13.4220.9312.9520.5125.6211.0715.5413.5410.1416.206.026 Average9.4810.7312.219.8615.3510.4910.5110.0114.3711.2411.93 Agroclim zone19992000200120022003200420052006200720082009 Number of regions Alpine11.668.356.334.967.686.766.688.985.974.907.865 Atlantic Central5.735.2410.756.7410.778.124.495.1420.885.578.0348 Atlantic North22.214.171.124.566.3812.066.714.557.782.0313.403 Atlantic South6.735.0126.3616.5414.2414.9316.627.7825.3418.1123.907 Boreal23.5216.749.429.065.8112.585.8515.235.6127.7221.454 Continental North6.6613.425.296.2011.209.326.748.2012.076.165.2630 Continental South5.9311.748.818.2827.9716.6711.507.5332.255.0113.039 Mediterranean North9.3310.1317.176.5818.239.6412.0310.0817.1112.9812.3518 Mediterranean South14.8020.6817.1520.6123.9611.3619.7412.2712.3213.613.206 Average9.7211.0511.949.6114.0311.2710.048.8615.4810.6812.05
ISPRA2012-01-25 Cross validation annual prediction errors B i o P a r M A R S
ISPRA2012-01-25 2009 forecast B i o P a r M A R S Differences between prediction errors and errors of Null Model
ISPRA2012-01-25 2009 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.
ISPRA2012-01-25 2009 forecast – MARS data Percentage of regions with forecast better than Null Model NDVI prognosis decade AustriaBelgiumDenmarkFinlandGermanyHungaryIreland The Nederlands PolandPortugalRomaniaSlovakiaSpainSweden 12088100334229067100506750 75 22088100334214067100503350 75 320881006733430671005033503875 42088100 3314067100500 3875 52088100674229056100500 6375 62088100674229056100500 6375 7063100674229056100500 6375 8063100674229056100500 6375 940630674214056100500 6375 102063067421406710000506375 1120750674229506710000256375 1220880100504306710000256375 mean187867 41264621003811465675 fAPAR prognosis decade AustriaBelgiumDenmarkFinlandGermanyHungaryIreland The Nederlands PolandPortugalRomaniaSlovakiaSpainSweden 12567033079566700 50 75 22570002586446700 503875 350670050862250100033503875 450590332582225010000503875 525670 258222501000050 75 6256703325821167100500 75 725670050821167100500 75 850700025791167100 0506375 925520025791167100 0756375 1025560025791167100500756375 11255603325791167100500756375 12255903375791167100500 75 mean316301931812063833814565175 Number of regions 5813127291623484
ISPRA2012-01-25 2009 forecast – BioPar data Percentage of regions with forecast better than Null Model NDVI prognosis decade AustriaBelgiumDenmarkFinlandGermanyHungaryIreland The Nederlands PolandPortugalRomaniaSlovakiaSpainSweden 120100 05829067100503350 240100 336757056100503350 360100067582905694503350 46088033422905694100050 25 5407510033422905694500 6207510033502906794500 6350 7407510033501406794500 6350 820751003350140679400506350 920751003358140569400506325 1020751003358140789400506325 11207510033581450789400506325 12207510033581450789400506325 mean32828333542486595338505740 fAPAR prognosis decade AustriaBelgiumDenmarkFinlandGermanyHungaryIreland The Nederlands PolandPortugalRomaniaSlovakiaSpainSweden 120100 05843044100506750 240100 33674305610003350 360100 67 290561000050 25 460880335829056100500 25 54088100335029067100 050 64075100335014078100500 6350 7407510067501407810000506350 8407510033501408910000506350 920631003350140671000050630 1020751003350140671000050630 112075100335014067100500 630 122088100 5014067100500 630 mean358392425423066100298505729 Number of regions 5813127291623484
ISPRA2012-01-25 In 2009 forecast – percentage of regions with lower error (MAPE) than error (MAPE) of Null Model
ISPRA2012-01-25 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.
ISPRA2012-01-25 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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