Winter Wheat Yield Forecasting: a Comparative Analysis of Results of Regression and Biophysical Models

Kogan, Felix; Kussul, Nataliya N.; Adamenko, T.; Skakun, Sergii; Kravchenko, A. N.; Krivobok, A.; Shelestov, Andrii; Kolotii, Andrii; Kussul, Olga; Lavrenyuk, Alla N.

doi:10.1615/jautomatinfscien.v45.i6.70

Cited by 41 publications

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“…Many studies have shown that forecasting models based on remote sensing data can give similar or better performance comparing to the more sophisticated crop growth models (Gallego et al, 2012, Kogan et al, 2013a, 2013bKowalik et al, 2014). Usually, remote sensing derived indicators are connected to crop yield using empirical regression-based models.…”

Section: Introductionmentioning

confidence: 99%

“…In our previous study, we have estimated efficiency of using predictors of different nature (vegetation indices, biophysical parameters, and a crop growth model adopted for the territory of Ukraine) at oblast level (Kogan et al, 2013a(Kogan et al, , 2013bKussul et al, 2013;Kussul et al, 2014). No previous studies assessed efficiency of satellite-derived indicators at multiple scales.…”

Section: Introductionmentioning

confidence: 99%

“…Since reliable statistical data are available for Ukraine only since 2000, we use a single factor regression model for crop yield estimation. Our previous study have demonstrated the effect of overfitting when more complex models are used (Kogan et al, 2013b). The main goal of the paper is to determine the best predictors for regression models at different scales among satellite and biophysical model (WOFOST) input parameters.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine

Kolotii

Kussul

Shelestov

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Winter wheat crop yield forecasting at national, regional and local scales is an extremely important task. This paper aims at assessing the efficiency (in terms of prediction error minimization) of satellite and biophysical model based predictors assimilation into winter wheat crop yield forecasting models at different scales (region, county and field) for one of the regions in central part of Ukraine. Vegetation index NDVI, as well as different biophysical parameters (LAI and fAPAR) derived from satellite data and WOFOST crop growth model are considered as predictors of winter wheat crop yield forecasting model. Due to very short time series of reliable statistics (since 2000) we consider single factor linear regression. It is shown that biophysical parameters (fAPAR and LAI) are more preferable to be used as predictors in crop yield forecasting regression models at each scale. Correspondent models possess much better statistical properties and are more reliable than NDVI based model. The most accurate result in current study has been obtained for LAI values derived from SPOT-VGT (at 1 km resolution) on county level. At field level, a regression model based on satellite derived LAI significantly outperforms the one based on LAI simulated with WOFOST.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine

Kolotii

Kussul

Shelestov

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Since last decade, Ukraine experienced six droughts (2003, 2007, 2008, 2009, 2010, and 2012) that covered between 40% and 60% of the country and up to 80% of the major grain crop area (Kogan, Adamenko, & Guo 2013;Kogan & Guo 2014). Therefore, drought risk mapping and quantification in Ukraine became a key element in providing support to policy-makers in food security (Kogan et al 2013a(Kogan et al , 2013bShelestov et al 2013). Drought hazard mapping is done for the whole territory of Ukraine to map drought return periods.…”

Section: Study Area and Materials Descriptionmentioning

confidence: 99%

“…Potential crop yield can be estimated using biophysical models of crop growth or satellite-derived indices (Salazar et al 2007;Kogan et al 2013aKogan et al , 2013b, while global and regional crop land maps or crop specific maps can be derived from satellite imagery (Pittman et al 2010;Gallego et al 2012;Gallego et al 2014). S. Skakun et al…”

Section: Loss Function Estimationmentioning

confidence: 99%

The use of satellite data for agriculture drought risk quantification in Ukraine

Skakun

Kussul

Shelestov

et al. 2015

Geomatics, Natural Hazards and Risk

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This paper focuses on drought risk assessment using satellite data. Methods of the extreme value theory (EVT) are applied for a time series of vegetation health index (VHI) derived from the National Oceanic and Atmospheric Administration satellites in order to provide drought hazard mapping. A Poisson-GP (generalized Pareto) model is applied for modelling VHI extreme values. The model allows estimation and mapping of return periods of different categories of droughts. An approach to economical risk assessment due to droughts is presented that relies on the following components: damage function assessment, crop yield assessment, and crop area estimation. The advantage of the proposed approach is that it allows quantification of drought hazard through the drought return period, damages, and ultimately drought risk using satellite data. The derived drought hazard map is integrated with high-resolution crop map to provide final estimates of risk. The proposed approach is implemented for quantitative assessment of drought risk for the Kyiv region in Ukraine. The derived map shows that risk is distributed non-uniformly over the region, thus allowing identification of areas with higher risk. Such a map would be of great benefit to both local authorities to take directed actions to reduce the risk and insurance companies operating in agriculture sector.

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Crop Growth Modeling and Yield Forecasting

Pan

Chen

2021

Springer Remote Sensing/Photogrammetry

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Winter Wheat Yield Forecasting: a Comparative Analysis of Results of Regression and Biophysical Models

Cited by 41 publications

References 0 publications

Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine

Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine

The use of satellite data for agriculture drought risk quantification in Ukraine

Crop Growth Modeling and Yield Forecasting

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