Yield forecasting for wheat and corn in Hungary by satellite remote sensing

Bognár, Péter; Ferencz, Csaba; Pásztor, Szilárd; Molnár, Gábor; Timár, Gábor; Hamar, D.; Lichtenberger, János; Székely, Balázs; Steinbach, Péter; Ferencz, Orsolya E.

doi:10.1080/01431161.2010.493566

Cited by 21 publications

(22 citation statements)

References 10 publications

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“…This method was originally developed for NOAA AVHRR Greenness (GN) data (Ferencz et al 2004) -where GN is the difference of reflectance values of near-infrared and visible red channels (e.g. Jackson 1983; Hamar et al 1996) -and has been successfully applied for the 1996-2000 interval both for corn and wheat (Bognár et al 2011). In this work, we adopted a similar method for the MODIS NDVI data.…”

Section: The Robust Methodsmentioning

confidence: 99%

“…Moreover, because of the crop rotation, the classification must be made in every year. The essential point of our robust method is that there is no need for a detailed classification to identify the acreage of different crops since the general conditions of the vegetation-covered surfaces also represent the individual crop conditions in the case of the species with similar vegetation cycles (Dabrowska-Zielenska et al 2002;Ferencz et al 2004;Bognár et al 2011), and thus we use the general (non-crop specific) cropland mask described in the previous section.…”

Section: The Robust Methodsmentioning

confidence: 99%

“…These studies became even more intensive with the appearance of the Advanced Very High Resolution Radiometer (AVHRR) instrument on board the National Oceanic and Atmospheric Administration (NOAA) satellites (e.g. Hochheim and Barber 1998;Doraiswamy et al 2003;Vicente-Serrano, Cuadrat-Prats, and Romo 2006;Bognár et al 2011). The methodology of the yield estimation procedures has a wide range (Atzberger 2013).…”

Section: Introductionmentioning

confidence: 99%

“…First, we attempted to improve our estimation procedure developed earlier (Bognár et al 2011). Using the data of the period 2003-2015, we tested a number of new curve-fitting techniques and developed a forecast method which enables yield forecast to be given at different dates preceding harvest.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Yield estimation and forecasting for winter wheat in Hungary using time series of MODIS data

Bognár

Kern

Pásztor

et al. 2017

International Journal of Remote Sensing

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Wheat is one of the most important crops in Hungary, which represents approximately 20% of the entire agricultural area of the country, and about 40% of cereals. A robust yield method has been improved for estimating and forecasting wheat yield in Hungary in the period of 2003-2015 using normalized difference vegetation index (NDVI) derived from the data of the Moderate Resolution Imaging Spectroradiometer. Estimation was made at the end of June -it is generally the beginning of harvest of winter wheat in Hungary -while the forecasts were performed 1-7 weeks earlier. General yield unified robust reference index (GYURRI) vegetation index was calculated each year using different curve-fitting methods to the NDVI time series. The correlation between GYURRI and country level yield data gave correlation coefficient (r) of 0.985 for the examined 13 years in the case of estimation. Simulating a quasi-operative yield estimation process, 10 years ' (2006-2015) yield data was estimated. The differences between the estimated and actual yield data provided by the Hungarian Central Statistical Office were less than 5%, the average difference was 2.5%. In the case of forecasting, these average differences calculated approximately 2 and 4 weeks before the beginning of harvest season were 4.5% and 6.8%, respectively. We also tested the yield estimation procedure for smaller areas, for the 19 counties (Nomenclature of Territorial Units for Statistics-3 level) of Hungary. We found that, the relationship between GYURRI and the county level yield data had r of 0.894 for the years 2003-2014, and by simulating the quasi-operative forecast for 2015, the resulting 19 county average yield values differed from the actual yield as much as 8.7% in average.

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Section: The Robust Methodsmentioning

confidence: 99%

Section: The Robust Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Yield estimation and forecasting for winter wheat in Hungary using time series of MODIS data

Bognár

Kern

Pásztor

et al. 2017

International Journal of Remote Sensing

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“…Satellite spectra derived vegetation indexes have been useful in relating satellite imagery to yield in corn (Zea may. L.) (Mkhabela and Mashinini, 2005;Prasad et al, 2006;Bognár et al, 2011), winter wheat (Triticum aestivum, L.) (Salazar et al, 2007;Ren et al, 2008;Becker-Reshef et al, 2010;Bognár et al, 2011;), potato (Solanum tuberosum, L.) (Bala and Islam, 2009), barley (Hordeum vulgare, L.), canola (Brassica napus, L.), field pea (Pisum sativum, L.), spring wheat (Triticum aestivum. L.) (Mkhabela et al, 2011), and sorghum (Sorghum bicolor, L.) (Shamseddin and Adeeb, 2012).…”

Section: Comparison Of Satellite Imagery and Ground-based Active Opticalmentioning

confidence: 99%

Comparison of Satellite Imagery and Ground‐Based Active Optical Sensors as Yield Predictors in Sugar Beet, Spring Wheat, Corn, and Sunflower

Sharma

Denton

et al. 2017

Agronomy Journal

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Algorithms using active-optical (AO) sensors have been developed to direct in-season N application to crops. Many farmers in the United States have a large number of farm fi elds to manage. Farmers using AO technology must visit each fi eld and operate the sensor across the entire fi eld in order to conduct in-season N application. A fi eld might be driven over with an on-the-go N fertilizer applicator, but the application might not be required. Th e objective of this study was to determine whether satellite imagery might be used to predict yield in sugar beet, spring wheat, corn and sunfl ower similar to the yield prediction possible using AO sensors. If so, the algorithms produced could be used to select fi elds that would benefi t from in-season N application. Two N-rate studies in sugar beet, spring wheat, corn and sunfl ower, were conducted with experimental unit size of 9 by 9 m large enough to fi t a satellite pixel of 5 by 5 m size within each unit. Th e AO sensor and satellite imagery data were related to yield of sugar beet, spring wheat, corn and sunfl ower in some site-years. Th e problem is the ability to acquire the satellite imagery early enough in the season to be useful as a screening tool. Th ese results indicate that even though satellite imagery could be used as a fi eld screening tool, a better option may be to mount an AO sensor on a farm implement for an early season activity, or to explore the use of unmanned aerial vehicles (UAVs).

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Mid‐season county‐level corn yield forecast for US Corn Belt integrating satellite imagery and weather variables

et al. 2020

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Yield estimations are of great interest to support interventions from governmental policies and to increase global food security. This study presents a novel model to perform in-season corn yield predictions at the US-county level, providing robust results under different weather and yield levels. The objectives of this study were to: (i) evaluate the performance of a random forest classification to identify corn fields using Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and weather variables (temperature, precipitation, and vapor pressure deficit, VPD); (ii) evaluate the contribution of weather variables when forecasting corn yield via remote sensing data, and perform a sensitivity analysis to explore the model performance in different dates; and (iii) develop a model pipeline for performing in-season corn yield predictions at county-scale. Main outcomes from this study were: (i) high accuracy (87% on average) for corn field classification achieved in late August, (ii) corn yield forecasts with a mean absolute error (MAE) of 0.89 Mg ha −1 , (iii) weather variables (VPD and temperature) highly influenced the model performance, and (iv) model performance decreased when predictions were performed early in the season (mid-July), with MAE increasing from 0.87-1.36 Mg ha −1 when forecast timing changed from day of year 232-192. This research portrays the benefits of integrating statistical techniques and remote sensing to field survey data in order to perform more reliable in-season corn yield forecasts.

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Yield forecasting for wheat and corn in Hungary by satellite remote sensing

Cited by 21 publications

References 10 publications

Yield estimation and forecasting for winter wheat in Hungary using time series of MODIS data

Yield estimation and forecasting for winter wheat in Hungary using time series of MODIS data

Comparison of Satellite Imagery and Ground‐Based Active Optical Sensors as Yield Predictors in Sugar Beet, Spring Wheat, Corn, and Sunflower

Mid‐season county‐level corn yield forecast for US Corn Belt integrating satellite imagery and weather variables

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