{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T20:43:34Z","timestamp":1784839414406,"version":"3.55.0"},"reference-count":57,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2014,10,23]],"date-time":"2014-10-23T00:00:00Z","timestamp":1414022400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Crop yield forecasting plays a vital role in coping with the challenges of the impacts of climate change on agriculture. Improvements in the timeliness and accuracy of yield forecasting by incorporating near real-time remote sensing data and the use of sophisticated statistical methods can improve our capacity to respond effectively to these challenges. The objectives of this study were (i) to investigate the use of derived vegetation indices for the yield forecasting of spring wheat (Triticum aestivum L.) from the Moderate resolution Imaging Spectroradiometer (MODIS) at the ecodistrict scale across Western Canada with the Integrated Canadian Crop Yield Forecaster (ICCYF); and (ii) to compare the ICCYF-model based forecasts and their accuracy across two spatial scales-the ecodistrict and Census Agricultural Region (CAR), namely in CAR with previously reported ICCYF weak performance. Ecodistricts are areas with distinct climate, soil, landscape and ecological aspects, whereas CARs are census-based\/statistically-delineated areas. Agroclimate variables combined respectively with MODIS-NDVI and MODIS-EVI indices were used as inputs for the in-season yield forecasting of spring wheat during the 2000\u20132010 period. Regression models were built based on a procedure of a leave-one-year-out. The results showed that both agroclimate + MODIS-NDVI and agroclimate + MODIS-EVI performed equally well predicting spring wheat yield at the ECD scale. The mean absolute error percentages (MAPE) of the models selected from both the two data sets ranged from 2% to 33% over the study period. The model efficiency index (MEI) varied between \u22121.1 and 0.99 and \u22121.8 and 0.99, respectively for the agroclimate + MODIS-NDVI and agroclimate + MODIS-EVI data sets. Moreover, significant improvement in forecasting skill (with decreasing MAPE of 40% and 5 times increasing MEI, on average) was obtained at the finer, ecodistrict spatial scale, compared to the coarser CAR scale. Forecast models need to consider the distribution of extreme values of predictor variables to improve the selection of remote sensing indices. Our findings indicate that statistical-based forecasting error could be significantly reduced by making use of MODIS-EVI and NDVI indices at different times in the crop growing season and within different sub-regions.<\/jats:p>","DOI":"10.3390\/rs61010193","type":"journal-article","created":{"date-parts":[[2014,10,23]],"date-time":"2014-10-23T12:28:28Z","timestamp":1414067308000},"page":"10193-10214","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":120,"title":["Assessing the Performance of MODIS NDVI and EVI for Seasonal Crop Yield Forecasting at the Ecodistrict Scale"],"prefix":"10.3390","volume":"6","author":[{"given":"Louis","family":"Kouadio","sequence":"first","affiliation":[{"name":"Science and Technology Branch (S & T), Agriculture and Agri-Food Canada (AAFC), Lethbridge Research Centre, 5403 1st Avenue South, P.O. Box 3000, Lethbridge,AB T1J 4B1, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nathaniel","family":"Newlands","sequence":"additional","affiliation":[{"name":"Science and Technology Branch (S & T), Agriculture and Agri-Food Canada (AAFC), Lethbridge Research Centre, 5403 1st Avenue South, P.O. Box 3000, Lethbridge,AB T1J 4B1, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Davidson","sequence":"additional","affiliation":[{"name":"AgroClimate, Geomatics, and Earth Observations Division (ACGEO), S & T, AAFC, 960 Carling Avenue, Ottawa, ON K1A 0C6, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yinsuo","family":"Zhang","sequence":"additional","affiliation":[{"name":"AgroClimate, Geomatics, and Earth Observations Division (ACGEO), S & T, AAFC, 960 Carling Avenue, Ottawa, ON K1A 0C6, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aston","family":"Chipanshi","sequence":"additional","affiliation":[{"name":"ACGEO, S & T, AAFC, 300-2010 12th Avenue, Regina, SK S4P OM3, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2014,10,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/0304-3800(88)90031-2","article-title":"Use of remotely-sensed information in agricultural crop growth models","volume":"41","author":"Maas","year":"1988","journal-title":"Ecol. Model"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4155","DOI":"10.1080\/014311602320567955","article-title":"Improving an operational wheat yield model using phenological phase-based Normalized Difference Vegetation Index","volume":"23","author":"Boken","year":"2002","journal-title":"Int. J. Remote Sens"},{"key":"ref_3","unstructured":"Reichert, G., and Caissy, D. (2002, January 8\u201312). Reliable Crop Condition Assessment Program (CCAP) incorporating NOAA AVHRR data, a geographical information system, and the Internet. San Diego, CA, USA."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"562","DOI":"10.3390\/rs2020562","article-title":"Value of using different vegetative indices to quantify agricultural crop characteristics at different growth stages under varying management practices","volume":"2","author":"Hatfield","year":"2010","journal-title":"Remote Sens"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"949","DOI":"10.3390\/rs5020949","article-title":"Advances in remote sensing of agriculture: Context description, existing operational monitoring systems and major information needs","volume":"5","author":"Atzberger","year":"2013","journal-title":"Remote Sens"},{"key":"ref_6","unstructured":"Basso, B., Cammarano, D., and Carfagna, E. (2013, January 18\u201319). Review of crop yield forecasting methods and early warning systems. FAO Headquarters, Rome, Italy."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1016\/j.rse.2004.05.017","article-title":"Crop condition and yield simulations using Landsat and MODIS","volume":"92","author":"Doraiswamy","year":"2004","journal-title":"Remote Sens. Environ"},{"key":"ref_8","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_9","doi-asserted-by":"crossref","first-page":"4281","DOI":"10.1080\/01431161.2010.486415","article-title":"Estimating winter crop area across seasons and regions using time-sequential MODIS imagery","volume":"32","author":"Potgieter","year":"2011","journal-title":"Int. J. Remote Sens"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/j.agrformet.2010.11.012","article-title":"Crop yield forecasting on the Canadian Prairies using MODIS NDVI data","volume":"151","author":"Mkhabela","year":"2011","journal-title":"Agric. Forest Meteorol"},{"key":"ref_11","first-page":"111","article-title":"Estimating regional wheat yield from the shape of decreasing curves of green area index temporal profiles retrieved from MODIS data","volume":"18","author":"Kouadio","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf"},{"key":"ref_12","first-page":"83","article-title":"Crop area mapping in West Africa using landscape stratification of MODIS time series and comparison with existing global land products","volume":"14","author":"Vintrou","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.rse.2013.10.027","article-title":"An assessment of pre- and within-season remotely sensed variables for forecasting corn and soybean yields in the United States","volume":"141","author":"Johnson","year":"2014","journal-title":"Remote Sens. Environ"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1938","DOI":"10.3390\/rs6031938","article-title":"Development of a remote sensing-based \u201cBoro\u201d rice mapping system","volume":"6","author":"Mosleh","year":"2014","journal-title":"Remote Sens"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Whitcraft, A.K., Becker-Reshef, I., and Justice, C.O. (2014). Agricultural growing season calendars derived from MODIS surface reflectance. Int. J. Dig. Earth.","DOI":"10.1080\/17538947.2014.894147"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/0034-4257(93)90113-C","article-title":"On the use of NDVI profiles as a tool for agricultural statistics: The case study of wheat yield estimate and forecast in Emilia Romagna","volume":"45","author":"Benedetti","year":"1993","journal-title":"Remote Sens. Environ"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysical performance of the MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2561","DOI":"10.1080\/01431160500033724","article-title":"Evaluation of MODIS and NOAA AVHRR vegetation indices with in situ measurements in a semi-arid environment","volume":"26","author":"Fensholt","year":"2005","journal-title":"Int. J. Remote Sens"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/0034-4257(94)90018-3","article-title":"Development of vegetation and soil indices for MODIS-EOS","volume":"49","author":"Huete","year":"1994","journal-title":"Remote Sens. Environ"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3833","DOI":"10.1016\/j.rse.2008.06.006","article-title":"Development of a two-band enhanced vegetation index without a blue band","volume":"112","author":"Jiang","year":"2008","journal-title":"Remote Sens. Environ"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1560","DOI":"10.1016\/j.agrformet.2009.03.016","article-title":"Advantages of a two band EVI calculated from solar and photosynthetically active radiation fluxes","volume":"149","author":"Rocha","year":"2009","journal-title":"Agric. For. Meteorol"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.rse.2005.03.015","article-title":"Application of MODIS derived parameters for regional crop yield assessment","volume":"97","author":"Doraiswamy","year":"2005","journal-title":"Remote Sens. Environ"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1071\/AR06279","article-title":"Estimating crop area using seasonal time series of Enhanced Vegetation Index from MODIS satellite imagery","volume":"58","author":"Potgieter","year":"2007","journal-title":"Aust. J. Agric. Res"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2492","DOI":"10.3390\/rs4092492","article-title":"Monitoring biennial bearing effect on coffee yield using MODIS remote sensing imagery","volume":"4","author":"Bernardes","year":"2012","journal-title":"Remote Sens"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.agrformet.2012.07.014","article-title":"Remotely sensed green area index for winter wheat crop monitoring: 10-Year assessment at regional scale over a fragmented landscape","volume":"1","author":"Duveiller","year":"2012","journal-title":"Agric. Forest Meteorol"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lawless, J.F. (2014). Statistics in Action: A Canadian Outlook, CRC Press (Taylor & Francis Group).","DOI":"10.1201\/b16597"},{"key":"ref_27","unstructured":"Estimated Areas, Yield, Production and Average Farm Price of Principal Field Crops, in Metric Units, Annual. Avaliable online: http:\/\/www.statcan.gc.ca\/pub\/22-007-x\/2012004\/related-connexes-eng.htm."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Newlands, N.K., Zamar, D.S., Kouadio, L.A., Zhang, Y., Chipanshi, A., Potgieter, A., Toure, S., and Hill, H.S. (2014). An integrated, probabilistic model for improved seasonal forecasting of agricultural crop yield under environmental uncertainty. Front. Environ. Sci, 2.","DOI":"10.3389\/fenvs.2014.00017"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/S0168-1923(97)00037-3","article-title":"Predicting national wheat yields using a crop simulation and trend models","volume":"88","author":"Supit","year":"1997","journal-title":"Agric. For. Meteorol"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1080\/17538947.2013.821185","article-title":"Remote sensing-based global crop monitoring: Experiences with China\u2019s CropWatch system","volume":"7","author":"Wu","year":"2014","journal-title":"Int. J. Dig. Earth"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.agrformet.2004.12.006","article-title":"Early maize yield forecasting in the four agro-ecological regions of Swaziland using NDVI data derived from NOAA\u2019s-AVHRR","volume":"129","author":"Mkhabela","year":"2005","journal-title":"Agric. For. Meteorol"},{"key":"ref_32","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_33","unstructured":"Census Agricultural Regions Boundary Files for the 2006 Census of Agriculture-Reference Guide. Available online: http:\/\/www5.statcan.gc.ca\/olc-cel\/olc.action?objId=92-174-G&objType=2&lang=en&limit=0."},{"key":"ref_34","unstructured":"A National Ecological Framework for Canada. Report and National Map at 1:7,500,000 Scale. Avaliable online: http:\/\/sis.agr.gc.ca\/cansis\/publications\/ecostrat\/index.html."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1080\/07055900.2013.835253","article-title":"Use of the National Drought Model (NDM) in monitoring selected agroclimatic risks across the agricultural landscape of Canada","volume":"51","author":"Chipanshi","year":"2013","journal-title":"Atmos.\u2013Ocean"},{"key":"ref_36","unstructured":"Chipanshi, A., Zhang, Y., Kouadio, L., Newlands, N., Davidson, A., Hill, H., Warren, R., Qian, B., Daneshfar, B., and Bedard, F. (2014). Agric. For. Meteorol, under review."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1214\/009053604000000067","article-title":"Least angle regression","volume":"32","author":"Efron","year":"2004","journal-title":"Ann. Stat"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1198\/016214507000000950","article-title":"Robust linear model selection based on least angle regression","volume":"102","author":"Khan","year":"2007","journal-title":"J. Am. Statist. Assoc"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3121","DOI":"10.1016\/j.csda.2010.01.031","article-title":"Fast robust estimation of prediction error based on resampling","volume":"54","author":"Khan","year":"2010","journal-title":"Comput. Stat. Data An"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.agrformet.2011.09.013","article-title":"Efficient stabilization of crop yield prediction in the Canadian Prairies","volume":"153","author":"Bornn","year":"2012","journal-title":"Agric. For. Meteorol"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1002\/env.780","article-title":"A sequential Monte Carlo approach for marine ecological prediction","volume":"17","author":"Dowd","year":"2006","journal-title":"Environmetrics"},{"key":"ref_42","first-page":"18","article-title":"Classification and regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R. News"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"45","DOI":"10.4141\/S01-046","article-title":"Production of annual crops on the Canadian prairies: Trends during 1976\u20131998","volume":"82","author":"Campbell","year":"2002","journal-title":"Can. J. Soil Sci"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"251","DOI":"10.4141\/cjss96-032","article-title":"Soil moisture modelling\u2014Conception and evolution of the VSMB","volume":"76","author":"Baier","year":"1996","journal-title":"Can. J. Soil Sci"},{"key":"ref_45","unstructured":"Canadian Soil Information Service. Available online: http:\/\/sis.agr.gc.ca\/cansis\/nsdb\/index.html."},{"key":"ref_46","unstructured":"Drought Watch, Agriculture and Agri-Food Canada. Available online: http:\/\/www.agr.gc.ca\/pfra\/drought\/index_e.htm."},{"key":"ref_47","unstructured":"NASA Land Processes Distributed Active Archive Center. Available online: https:\/\/lpdaac.usgs.gov."},{"key":"ref_48","unstructured":"Land Cover for Agricultural Regions of Canada, Circa 2000. Available online: http:\/\/data.gc.ca\/data\/en\/dataset\/16d2f828-96bb-468d-9b7d-1307c81e17b8."},{"key":"ref_49","unstructured":"Definitions, Data Sources and Methods of Field Crop Reporting Series. Available online: http:\/\/www23.statcan.gc.ca\/imdb\/p2SV.pl?Function=getSurvey&SDDS=3401."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1175\/1520-0434(1993)008<0281:WIAGFA>2.0.CO;2","article-title":"What is a good forecast? An essay on the nature of goodness in weather forecasting","volume":"8","author":"Murphy","year":"1993","journal-title":"Weather Forecast"},{"key":"ref_51","unstructured":"A Language and Environment for Statistical Computing. Available online: http:\/\/cran.case.edu\/web\/packages\/dplR\/vignettes\/timeseries-dplR.pdf."},{"key":"ref_52","unstructured":"Arcgis Desktop: Release 10. Available online: http:\/\/www.esri.com\/software\/arcgis\/arcgis-for-desktop."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"97","DOI":"10.4141\/P03-070","article-title":"Optimal time for remote sensing to relate to crop grain yield on the Canadian prairies","volume":"84","author":"Basnyat","year":"2004","journal-title":"Can. J. Plant Sci"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Shorthouse, J.D., and Floate, K.D. (2010). Arthropods of Canadian Grasslands: Ecology and Interactions in Grassland Habitats, Biological Survey of Canada.","DOI":"10.3752\/9780968932148"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/S0034-4257(02)00128-1","article-title":"Estimating spatio-temporal patterns of agricultural productivity in fragmented landscapes using AVHRR NDVI time series","volume":"84","author":"Hill","year":"2003","journal-title":"Remote Sens. Environ"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/S0034-4257(96)00067-3","article-title":"NDWI\u2014A normalized difference water index for remote sensing of vegetation liquid water from space","volume":"58","author":"Gao","year":"1996","journal-title":"Remote Sens. Environ"},{"key":"ref_57","unstructured":"NASA Clouds and the Earth\u2019s Radiant Energy System (CERES). Available online: http:\/\/ceres.larc.nasa.gov\/index.php."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/6\/10\/10193\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:17:19Z","timestamp":1760217439000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/6\/10\/10193"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,10,23]]},"references-count":57,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2014,10]]}},"alternative-id":["rs61010193"],"URL":"https:\/\/doi.org\/10.3390\/rs61010193","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,10,23]]}}}