Wheat Yield Estimation from NDVI and Regional Climate Models in Latvia

被引:30
作者
Vannoppen, Astrid [1 ]
Gobin, Anne [1 ,2 ]
Kotova, Lola [3 ]
Top, Sara [4 ]
De Cruz, Lesley [5 ]
Viksna, Andris [6 ]
Aniskevich, Svetlana [6 ]
Bobylev, Leonid [7 ]
Buntemeyer, Lars [3 ]
Caluwaerts, Steven [4 ]
De Troch, Rozemien [5 ]
Gnatiuk, Natalia [7 ]
Hamdi, Rafiq [5 ]
Reca Remedio, Armelle [3 ]
Sakalli, Abdulla [8 ]
Van de Vyver, Hans [5 ]
Van Schaeybroeck, Bert [5 ]
Termonia, Piet [4 ,5 ]
机构
[1] Vlaamse Instelling voor Technol Onderzoek NV, B-2400 Mol, Belgium
[2] Univ Leuven, Fac BioSci Engn, Dept Earth & Environm Sci, B-3001 Leuven, Belgium
[3] Helmholtz Zentrum Geesthacht HZG, Climate Serv Ctr Germany GERICS, Fischertwiete 1, D-20095 Hamburg, Germany
[4] Univ Ghent, Dept Phys & Astron, B-9000 Ghent, Belgium
[5] Royal Meteorol Inst, B-1180 Brussels, Belgium
[6] Latvian Environm Geol & Meteorol Ctr, Maskavas St 165, LV-1019 Riga, Latvia
[7] Nansen Int Environm & Remote Sensing Ctr NIERSC, St Petersburg 199034, Russia
[8] Iskenderun Tech Univ, Fac Engn & Nat Sci, Dept Ind Engn, TR-31200 Iskenderun, Turkey
基金
俄罗斯基础研究基金会;
关键词
yield estimation; NDVI; regional climate model; winter wheat; spring wheat; Latvia; PROBA-V; ALARO-0; REMO; weather impact; EUROPEAN WHEAT; VEGETATION; TEMPERATURE; VALIDATION; PREDICTION; STRESS;
D O I
10.3390/rs12142206
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Wheat yield variability will increase in the future due to the projected increase in extreme weather events and long-term climate change effects. Currently, regional agricultural statistics are used to monitor wheat yield. Remotely sensed vegetation indices have a higher spatio-temporal resolution and could give more insight into crop yield. In this paper, we (i) evaluate the possibility to use Normalized Difference Vegetation Index (NDVI) time series to estimate wheat yield in Latvia and (ii) determine which weather variables impact wheat yield changes using both ALARO-0 and REMO Regional Climate Models (RCM) output. The integral from NDVI series (aNDVI) for winter and spring wheat fields is used as a predictor to model regional wheat yield from 2014 to 2018. A correlation analysis between weather variables, wheat yield and aNDVI was used to elucidate which weather variables impact wheat yield changes in Latvia. Our results indicate that high temperatures in June for spring wheat and in July for winter wheat had a negative correlation with yield. A linear regression yield model explained 71% of the variability with a residual standard error of 0.55 Mg/ha. When RCM data were added as predictor variables to the wheat yield empirical model a random forest approach resulted in better results compared to a linear regression approach, the explained variance increased up to 97% and the residual standard error decreased to 0.17 Mg/ha. We conclude that NDVI time series and RCM output enabled regional crop yield and weather impact monitoring at higher spatio-temporal resolutions than regional statistics.
引用
收藏
页数:20
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