DOI: 10.1016/j.jag.2013.01.002
Scopus记录号: 2-s2.0-84880317742
论文题名: Winter wheat yield forecasting in Ukraine based on Earth observation, meteorologicaldata and biophysical models
作者: Kogan F ; , Kussul N ; , Adamenko T ; , Skakun S ; , Kravchenko O ; , Kryvobok O ; , Shelestov A ; , Kolotii A ; , Kussul O ; , Lavrenyuk A
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2013
卷: 23, 期: 1 起始页码: 192
结束页码: 203
语种: 英语
英文关键词: Agriculture
; Remote sensing
; Ukraine
; Wheat
; Yield
Scopus关键词: crop yield
; forecasting method
; MODIS
; NDVI
; remote sensing
; satellite data
; wheat
; winter
; Ukraine
英文摘要: Ukraine is one of the most developed agriculture countries and one of the biggest crop producers in the world. Timely and accurate crop yield forecasts for Ukraine at regional level become a key element in providing support to policy makers in food security. In this paper, feasibility and relative efficiency of using moderate resolution satellite data to winter wheat forecasting in Ukraine at oblast level is assessed. Oblast is a sub-national administrative unit that corresponds to the NUTS2 level of the Nomenclature of Territorial Units for Statistics (NUTS) of the European Union. NDVI values were derived fromthe MODIS sensor at the 250 m spatial resolution. For each oblast NDVI values were averaged for a cropland map (Rainfed croplands class) derived from the ESA GlobCover map, and were used as predictors in the regression models. Using a leave-one-out cross-validation procedure, the best time for making reliable yield forecasts in terms of root mean square error was identified. For most oblasts, NDVI values taken in April-May provided the minimum RMSE value when comparing to the official statistics, thus enabling forecasts 2-3 months prior to harvest. The NDVI-based approach was compared to the following approaches: empirical model based on meteorological observations (with forecasts in April-May that provide minimum RMSE value) and WOFOST crop growth simulation model implemented in the CGMS system (with forecasts in June that provide minimum RMSE value). All three approaches wererun to produce winter wheat yield forecasts for independent datasets for 2010 and 2011, i.e. on data that were not used within model calibration process. The most accurate predictions for 2010 were achieved using the CGMS system with the RMSE value of 0.3 t ha-1 in June and 0.4 t ha-1 in April, while performance of three approaches for 2011 was almost the same (0.5-0.6 t ha-1 in April). Both NDVI-based approach and CGMS system overestimated winter wheat yield comparing to official statistics in 2010, and underestimated it in 2011. Therefore, we can conclude that performance of empirical NDVI-based regression model was similar to meteorological and CGMS models when producing winter wheat yield forecasts at oblast level in Ukraine 2-3 months prior to harvest, while providing minimum requirements to input datasets. © 2012 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79833
Appears in Collections: 气候变化事实与影响
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作者单位: National Oceanic and Atmospheric Administration, National Environmental Satellite Data and Information Services, 5200 Auth Rd, Camp Springs MD 20746, United States; Space Research Institute NASU-NSAU, Glushkov Ave, 40, build. 4/1, Kyiv 03680, Ukraine; Ukrainian Hydrometeorological Center, Zolotovoritskaya str., 6-B, Kyiv 01034, Ukraine; Ukrainian Hydrometeorological Institute, Nayku Ave, 37, Kyiv 03650, Ukraine; National University of Life and Environmental Sciences of Ukraine, Heroyiv Oborony str., 15, Kyiv 03680, Ukraine; National Technical University of Ukraine Kyiv Polytechnic Institute, Peremogy Ave, 37, Kyiv 03056, Ukraine
Recommended Citation:
Kogan F,, Kussul N,, Adamenko T,et al. Winter wheat yield forecasting in Ukraine based on Earth observation, meteorologicaldata and biophysical models[J]. International Journal of Applied Earth Observation and Geoinformation,2013-01-01,23(1)