Nataliia Kussul, Andrii Shelestov, Sergii Skakun, Oleksii Kravchenko

Space Resarch Institute NASU-NSAU, Ukraine

Forecasting winter wheat Forecasting winter wheat yield in Ukraine using 3 yield in Ukraine using 3 different approachesdifferent approaches

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ContentContent

• Description of methods– NDVI-based– Meteorological data based– CGMS

• Comparison of results

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NDVI-based empirical modelNDVI-based empirical model

• NDVI-based regression models for forecasting winter wheat yields were built for each oblast

dYі = Yі - Tі = f(NDVIі) = b0 + b1*NDVIі

Min = 0.019 t/ha per yearMax = 0.197 t/ha per year

i

ii OPn

RMSE 21

Criteria

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RMSE

dYn i

iRel. eff. =

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Winter wheat yield forecastingWinter wheat yield forecasting

• Cross-validation– leave-one-out cross-validation (LOOCV)– using a single observation from the original sample

as the testing data, and the remaining observations as the training data

• Criteria– RMSE on testing data

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Zone Av. Eff.Plane Polissya 1.182Forest-Steppe 1.576Steppe 1.883

Oblast DOY RMSE EffVolyn Oblast 193 3.284 1.015

Zhytomyr Oblast 97 2.898 1.598Zakarpattia Oblast 97 4.777 0.967

Ivano-Frankivsk Oblast 193 2.771 1.22Lviv Oblast 113 2.486 0.993

Rivne Oblast 97 3.411 1.214Chernihiv Oblast 97 3.366 1.267Vinnytsia Oblast 97 5.405 1.114

Kiev Oblast 97 4.083 1.616Poltava Oblast 129 4.286 2.09Sumy Oblast 145 3.758 1.766

Ternopil Oblast 97 3.914 1.214Kharkiv Oblast 129 3.846 2.443

Khmelnytskyi Oblast 49 3.868 1.421Cherkasy Oblast 129 6.473 1.35Chernihiv Oblast 97 3.366 1.267

Dnipropetrovsk Oblast 145 5.302 2.048Donetsk Oblast 129 4.41 1.871

Zaporizhia Oblast 129 3.797 1.947Kirovohrad Oblast 129 4.506 2.324Luhansk Oblast 129 4.189 1.829Mykolaiv Oblast 129 4.086 2.116Odessa Oblast 129 5.321 1.589Kherson Oblast 129 3.927 1.796

Crimea 129 1.809 1.424

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Meteorological modelMeteorological model

• A non-linear model for winter wheat yield forecasting that incorporates climatic parameters was built for the Steppe agro-climatic zone.

• To model the relationship between crop productivity (in particular winter wheat) and main climatic parameters– Maximum temperature– Minimum temperature– Average temperature– Precipitation– Soil moisture

• 0-20 cm depth• Available for months: Sept, Oct, Apr, May, June

• Methodology– Correlation analysis– Linear multivariate regression– Non-linear multivariate regression

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Non-linear effects

Corr coef april - 0.75

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Gaussian processes regressionGaussian processes regression

Oblast Eff.Dnipropetrovsk Oblast 2.999

Donetsk Oblast 2.322Zaporizhia Oblast 1.800Kirovohrad Oblast 2.469Luhansk Oblast 1.845Mykolaiv Oblast 1.855Odessa Oblast 2.511Kherson Oblast 2.759

Crimea 3.592

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CGMSCGMS

• Results of Crop Growth Monitoring System (CGMS) adopted for Ukraine– The use of meteorological data from 180 local

weather stations at a daily time step for the last 13 years (from 1998 to 2011)

– The new soil map of Ukraine at the 1:2,500,000 scale

– The new agrometeorological data (crop data) were collected and ingested into the CGMS system

• Yield forecasting

TSbTbbTY 210ˆ

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Comparison the results of NDVI-based Comparison the results of NDVI-based regression model with CGMS regression model with CGMS

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1.50 2.00 2.50 3.00 3.50 4.00 4.50

Observed winter wheat yield, t/ha

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ed w

inte

r w

heat

yie

ld f

or 2

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t/h

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NDVI

CGMS (20 May)

CGMS (20 June)

Meteo

Prediction for 2010, models are trained for 2000-2009

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-0.5 0 0.5 1 1.50

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Error, t/ha

CGMS june

CGMS mayNDVI

Meteo

Comparison the results of NDVI-based Comparison the results of NDVI-based regression model with CGMS regression model with CGMS

Prediction for 2010, models are trained for 2000-2009: error histogram

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Comparison of modelsComparison of models

• RMSE for predicting yield for 2010, models are trained for 2000-2009– NDVI: 0.79 t/ha

• For steppe zone: 0.61 t/ha • Error can be reduced ~1.3 times when NDVI

averaged by winter wheat mask– CGMS-May: 0.37 t/ha

• For steppe zone: 0.24 t/ha– CGMS-June: 0.30 t/ha

• For steppe zone: 0.19 t/ha– Meteo: 0.86 t/ha

• Problem of over-fitting• For steppe zone: 0.26 t/ha

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NDVI averaged by maskNDVI averaged by mask

• Masks need to be estimated for each year

• For steppe zone:– NDVI: 0.61 t/ha – NDVI-mask: 0.46 t/ha– CGMS-May: 0.24 t/ha– CGMS-June: 0.19 t/ha

Kirovohradska obl.

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Geoportal: crop mapsGeoportal: crop maps

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Thank you!