{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T13:46:50Z","timestamp":1779889610069,"version":"3.53.1"},"reference-count":43,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2020,7,10]],"date-time":"2020-07-10T00:00:00Z","timestamp":1594339200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"ERA.Net RUS Plus Initiative","award":["ID 166"],"award-info":[{"award-number":["ID 166"]}]},{"DOI":"10.13039\/501100003130","name":"Fonds Wetenschappelijk Onderzoek","doi-asserted-by":"publisher","award":["RUS PLUS JTC 2017"],"award-info":[{"award-number":["RUS PLUS JTC 2017"]}],"id":[{"id":"10.13039\/501100003130","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002261","name":"Russian Foundation for Basic Research","doi-asserted-by":"publisher","award":["18-55-76004"],"award-info":[{"award-number":["18-55-76004"]}],"id":[{"id":"10.13039\/501100002261","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/rs12142206","type":"journal-article","created":{"date-parts":[[2020,7,10]],"date-time":"2020-07-10T09:25:28Z","timestamp":1594373128000},"page":"2206","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Wheat Yield Estimation from NDVI and Regional Climate Models in Latvia"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5140-832X","authenticated-orcid":false,"given":"Astrid","family":"Vannoppen","sequence":"first","affiliation":[{"name":"Vlaamse Instelling voor Technologisch Onderzoek NV, 2400 Mol, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3742-7062","authenticated-orcid":false,"given":"Anne","family":"Gobin","sequence":"additional","affiliation":[{"name":"Vlaamse Instelling voor Technologisch Onderzoek NV, 2400 Mol, Belgium"},{"name":"Faculty of BioScience Engineering, Department of Earth and Environmental Sciences, University of Leuven, 3001 Leuven, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lola","family":"Kotova","sequence":"additional","affiliation":[{"name":"Climate Service Center Germany (GERICS), Helmholtz-Zentrum Geesthacht (HZG), Fischertwiete 1, 20095 Hamburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sara","family":"Top","sequence":"additional","affiliation":[{"name":"Department of Physics and Astronomy, Ghent University, 9000 Ghent, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4458-8953","authenticated-orcid":false,"given":"Lesley","family":"De Cruz","sequence":"additional","affiliation":[{"name":"Royal Meteorological Institute, 1180 Brussels, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andris","family":"V\u012bksna","sequence":"additional","affiliation":[{"name":"Latvian Environment, Geology and Meteorology Centre, Maskavas Street 165, LV-1019 Riga, Latvia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Svetlana","family":"Aniskevich","sequence":"additional","affiliation":[{"name":"Latvian Environment, Geology and Meteorology Centre, Maskavas Street 165, LV-1019 Riga, Latvia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leonid","family":"Bobylev","sequence":"additional","affiliation":[{"name":"Nansen International Environmental and Remote Sensing Centre (NIERSC), St. Petersburg 199034, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0849-2404","authenticated-orcid":false,"given":"Lars","family":"Buntemeyer","sequence":"additional","affiliation":[{"name":"Climate Service Center Germany (GERICS), Helmholtz-Zentrum Geesthacht (HZG), Fischertwiete 1, 20095 Hamburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Steven","family":"Caluwaerts","sequence":"additional","affiliation":[{"name":"Department of Physics and Astronomy, Ghent University, 9000 Ghent, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rozemien","family":"De Troch","sequence":"additional","affiliation":[{"name":"Royal Meteorological Institute, 1180 Brussels, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Natalia","family":"Gnatiuk","sequence":"additional","affiliation":[{"name":"Nansen International Environmental and Remote Sensing Centre (NIERSC), St. Petersburg 199034, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4419-5757","authenticated-orcid":false,"given":"Rafiq","family":"Hamdi","sequence":"additional","affiliation":[{"name":"Royal Meteorological Institute, 1180 Brussels, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5236-5026","authenticated-orcid":false,"given":"Armelle","family":"Reca Remedio","sequence":"additional","affiliation":[{"name":"Climate Service Center Germany (GERICS), Helmholtz-Zentrum Geesthacht (HZG), Fischertwiete 1, 20095 Hamburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdulla","family":"Sakalli","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Faculty of Engineering and Natural Sciences, Iskenderun Technical University, Iskenderun 31200, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hans","family":"Van De Vyver","sequence":"additional","affiliation":[{"name":"Royal Meteorological Institute, 1180 Brussels, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9507-7929","authenticated-orcid":false,"given":"Bert","family":"Van Schaeybroeck","sequence":"additional","affiliation":[{"name":"Royal Meteorological Institute, 1180 Brussels, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Piet","family":"Termonia","sequence":"additional","affiliation":[{"name":"Department of Physics and Astronomy, Ghent University, 9000 Ghent, Belgium"},{"name":"Royal Meteorological Institute, 1180 Brussels, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1073\/pnas.1804387115","article-title":"Decline in climate resilience of European wheat","volume":"116","author":"Kahiluoto","year":"2019","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.fcr.2017.11.008","article-title":"Sensitivity of European wheat to extreme weather","volume":"222","author":"Kaseva","year":"2018","journal-title":"Field Crop. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2670","DOI":"10.1073\/pnas.1409606112","article-title":"The fingerprint of climate trends on European crop yields","volume":"112","author":"Moore","year":"2015","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_4","unstructured":"Porter, J.R., Xie, L., Challinor, A.J., Cochrane, K., Howden, S.W., Iqbal, M.M., Lobell, D.B., and Travasso, M.I. (2014). Food security and food production systems. Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1002\/2017EF000629","article-title":"Impacts and Uncertainties of +2 \u00b0C of Climate Change and Soil Degradation on European Crop Calorie Supply","volume":"6","author":"Folberth","year":"2018","journal-title":"Earth\u2019s Future"},{"key":"ref_6","unstructured":"(2019, April 12). European Commission Short-term Outlook for EU Agricultural Markets in 2018 and 2019 Report nr 22. Available online: https:\/\/ec.europa.eu\/info\/sites\/info\/files\/food-farming-fisheries\/farming\/documents\/agri-short-term-outlook-reports_2018.zip."},{"key":"ref_7","unstructured":"Jacobs, C., Berglund, M., Kurnik, B., Dworak, T., Marras, S., Mereu, V., and Michetti, M. (2019). Climate Change Adaptation in the Agriculture Sector in Europe, European Environment Agency."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1038\/nature16467","article-title":"Influence of extreme weather disasters on global crop production","volume":"529","author":"Lesk","year":"2016","journal-title":"Nature"},{"key":"ref_9","unstructured":"Doraiswamy, P.C., Akhmedov, B., Beard, L., Stern, A., and Mueller, R. (December, January 30). Operational prediction of crop yields using MODIS data and products. Proceedings of the International Achives of Photogrametry, Remote Sensing and Spatial Information Sciences, International Society for Photogrammetry and Remote Sensing (ISPRS) working group VIII\/10 Workshop, Stresa, Italy."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"758","DOI":"10.3390\/rs2030758","article-title":"Decadal Variations in NDVI and Food Production in India","volume":"2","author":"Milesi","year":"2010","journal-title":"Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1704","DOI":"10.3390\/rs5041704","article-title":"Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection","volume":"5","author":"Rembold","year":"2013","journal-title":"Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1312","DOI":"10.1016\/j.rse.2010.01.010","article-title":"A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data","volume":"114","author":"Vermote","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.agrformet.2013.01.007","article-title":"Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics","volume":"173","author":"Bolton","year":"2013","journal-title":"Agric. For. Meteorol."},{"key":"ref_14","first-page":"101988","article-title":"A study on trade-offs between spatial resolution and temporal sampling density for wheat yield estimation using both thermal and calendar time","volume":"86","author":"Durgun","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Durgun, Y.\u00d6., Gobin, A., Gilliams, S., Duveiller, G., and Tychon, B. (2016). Testing the Contribution of Stress Factors to Improve Wheat and Maize Yield Estimations Derived from Remotely-Sensed Dry Matter Productivity. Remote Sens., 8.","DOI":"10.3390\/rs8030170"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4113","DOI":"10.1080\/01431160410001698870","article-title":"Crop yield estimation by satellite remote sensing","volume":"25","author":"Ferencz","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"111460","DOI":"10.1016\/j.rse.2019.111460","article-title":"Synergistic integration of optical and microwave satellite data for crop yield estimation","volume":"234","author":"Piles","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1051\/agro:2001111","article-title":"A methodology for a combined use of normalised difference vegetation index and CORINE land cover data for crop yield monitoring and forecasting. A case study on Spain","volume":"21","author":"Genovese","year":"2001","journal-title":"Agronomie"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3775","DOI":"10.1080\/01431160601075608","article-title":"Operational maize yield model development and validation based on remote sensing and agro-meteorological data in Kenya","volume":"28","author":"Rojas","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"111402","DOI":"10.1016\/j.rse.2019.111402","article-title":"Remote sensing for agricultural applications: A meta-review","volume":"236","author":"Weiss","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Zhang, M., Zhang, X., Zeng, H., and Wu, B. (2016). Mapping Winter Wheat Biomass and Yield Using Time Series Data Blended from PROBA-V 100- and 300-m S1 Products. Remote Sens., 8.","DOI":"10.3390\/rs8100824"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1109\/TGRS.2015.2466438","article-title":"Evaluating NDVI Data Continuity Between SPOT-VEGETATION and PROBA-V Missions for Operational Yield Forecasting in North African Countries","volume":"54","author":"Meroni","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","unstructured":"FAOSTAT (2019, April 12). Production Share of Wheat by Region. Average 1994\u20132017. Available online: http:\/\/www.fao.org\/faostat\/en\/#data\/QC\/visualize."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1038\/nclimate2470","article-title":"Rising temperatures reduce global wheat production","volume":"5","author":"Asseng","year":"2015","journal-title":"Nat. Clim. Chang."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.eja.2018.09.003","article-title":"Cereal yield gaps across Europe","volume":"101","author":"Schils","year":"2018","journal-title":"Eur. J. Agron."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1038\/nclimate2242","article-title":"Adverse weather conditions for European wheat production will become more frequent with climate change","volume":"4","author":"Trnka","year":"2014","journal-title":"Nat. Clim. Chang."},{"key":"ref_27","unstructured":"FAO (2014). World Reference Base for Soil Resources 2014: International Soil Classification System for Naming Soils and Creating Legends for Soil Maps, FAO."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1109\/TAC.1974.1100705","article-title":"A new look at the statistical model identification","volume":"19","author":"Akaike","year":"1974","journal-title":"IEEE Trans. Autom. Control"},{"key":"ref_29","first-page":"18","article-title":"Classification and Regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"181","DOI":"10.3390\/atmos3010181","article-title":"Assessing the Transferability of the Regional Climate Model REMO to Different COordinated Regional Climate Downscaling EXperiment (CORDEX) Regions","volume":"3","author":"Jacob","year":"2012","journal-title":"Atmosphere"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Remedio, A.R., Teichmann, C., Buntemeyer, L., Sieck, K., Weber, T., Rechid, D., Hoffmann, P., Nam, C., Kotova, L., and Jacob, D. (2019). Evaluation of New CORDEX Simulations Using an Updated K\u00f6ppen\u2013Trewartha Climate Classification. Atmosphere, 10.","DOI":"10.3390\/atmos10110726"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1143","DOI":"10.5194\/gmd-9-1143-2016","article-title":"Validation of the ALARO-0 model within the EURO-CORDEX framework","volume":"9","author":"Giot","year":"2016","journal-title":"Geosci. Model Dev."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"257","DOI":"10.5194\/gmd-11-257-2018","article-title":"The ALADIN System and its canonical model configurations AROME CY41T1 and ALARO CY40T1","volume":"11","author":"Termonia","year":"2018","journal-title":"Geosci. Model Dev."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1002\/qj.828","article-title":"The ERA-Interim reanalysis: Configuration and performance of the data assimilation system","volume":"137","author":"Dee","year":"2011","journal-title":"Q. J. Royal Meteorol. Soc."},{"key":"ref_35","first-page":"405","article-title":"A lateral boundary formulation for multi-level prediction models","volume":"102","author":"Davies","year":"1976","journal-title":"Q. J. Royal Meteorol. Soc."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.cliser.2018.11.003","article-title":"A new project AFTER investigates the impacts of climate change in the Europe-Russia-Turkey region","volume":"12","author":"Kotova","year":"2018","journal-title":"Clim. Serv."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Top, S., Kotova, L., Cruz, L.D., Aniskevich, S., Bobylev, L., Troch, R.D., Gnatiuk, N., Gobin, A., Hamdi, R., and Kriegsmann, A. (2020). Evaluation of regional climate models ALARO-0 and REMO2015 at 0.22\u00b0 resolution over the CORDEX Central Asia domain. Geosci. Model Dev. Discuss. Rev., 1\u201338.","DOI":"10.5194\/gmd-2019-368"},{"key":"ref_38","unstructured":"European Commission (2014). Crop Monitoring in Europe. Monitoring Agricultural Resources (MARS) Bulletins, Publications Office of the European Union Luxembourg."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1911","DOI":"10.5194\/nhess-12-1911-2012","article-title":"Impact of heat and drought stress on arable crop production in Belgium","volume":"12","author":"Gobin","year":"2012","journal-title":"Natl. Hazards Earth Syst. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/srep00066","article-title":"Modelling predicts that heat stress, not drought, will increase vulnerability of wheat in Europe","volume":"1","author":"Semenov","year":"2011","journal-title":"Sci. Rep."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.agsy.2017.06.009","article-title":"Weather related risks in Belgian arable agriculture","volume":"159","author":"Gobin","year":"2018","journal-title":"Agric. Syst."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"673","DOI":"10.4141\/cjps76-107","article-title":"Effect of temperature on dehardening and rehardening of winter cereals","volume":"56","author":"Gusta","year":"1976","journal-title":"Can. J. Plant Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/s10725-015-0029-y","article-title":"Wheat plants exposed to winter warming are more susceptible to low temperature stress in the spring","volume":"77","author":"Li","year":"2015","journal-title":"Plant Growth Regul."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/14\/2206\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:49:45Z","timestamp":1760176185000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/14\/2206"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,10]]},"references-count":43,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2020,7]]}},"alternative-id":["rs12142206"],"URL":"https:\/\/doi.org\/10.3390\/rs12142206","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,10]]}}}