{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T07:36:54Z","timestamp":1776411414055,"version":"3.51.2"},"reference-count":67,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2018,11,1]],"date-time":"2018-11-01T00:00:00Z","timestamp":1541030400000},"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>Developing countries often have poor monitoring and reporting of weather and crop health, leading to slow responses to droughts and food shortages. Here, I develop satellite analysis methods and software tools to predict crop yields two to four months before the harvest. This method measures relative vegetation health based on pixel-level monthly anomalies of NDVI, EVI and NDWI indices. Because no crop mask, tuning, or subnational ground truth data are required, this method can be applied to any location, crop, or climate, making it ideal for African countries with small fields and poor ground observations. Testing began in Illinois where there is reliable county-level crop data. Correlations were computed between corn, soybean, and sorghum yields and monthly vegetation health anomalies for every county and year. A multivariate regression using every index and month (up to 1600 values) produced a correlation of 0.86 with corn, 0.74 for soybeans, and 0.65 for sorghum, all with p-values less than     10  \u2212 6     . The high correlations in Illinois show that this model has good forecasting skill for crop yields. Next, the method was applied to every country in Africa for each country\u2019s main crops. Crop production was then predicted for the 2018 harvest and compared to actual production values. Twenty percent of the predictions had less than 2% error, and 40% had less than 5% error. This method is unique because of its simplicity and versatility: it shows that a single user on a laptop computer can produce reasonable real-time estimates of crop yields across an entire continent.<\/jats:p>","DOI":"10.3390\/rs10111726","type":"journal-article","created":{"date-parts":[[2018,11,1]],"date-time":"2018-11-01T11:31:47Z","timestamp":1541071907000},"page":"1726","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["Real-Time Prediction of Crop Yields From MODIS Relative Vegetation Health: A Continent-Wide Analysis of Africa"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8816-8657","authenticated-orcid":false,"given":"Lillian Kay","family":"Petersen","sequence":"first","affiliation":[{"name":"Los Alamos High School, Los Alamos, NM 87544, USA"}]}],"member":"1968","published-online":{"date-parts":[[2018,11,1]]},"reference":[{"key":"ref_1","unstructured":"Hamer, H., Picanso, R., Prusacki, J.J., Rater, B., Johnson, J., Barnes, K., Parsons, J., and Young, D.L. (2018, October 30). USDA\/NASS QuickStats US Crop Data, Available online: https:\/\/quickstats.nass.usda.gov."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"897","DOI":"10.1175\/JTECH-D-11-00103.1","article-title":"An Overview of the Global Historical Climatology Network-Daily Database","volume":"29","author":"Menne","year":"2012","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_3","unstructured":"Petersen, L.K. (2018). America\u2019s Farming Future: The Impact of Climate Change on Crop Yields. AMS."},{"key":"ref_4","unstructured":"McKinnon, K. (2018, October 30). GHCN-D: Global Historical Climatology Network Daily Temperatures NCAR\u2014Climate Data Guide. Available online: https:\/\/climatedataguide.ucar.edu\/climate-data\/ghcn-d-global-historical-climatology-network-daily-temperatures."},{"key":"ref_5","unstructured":"Carletto, G., Beegle, K., Himelein, K., Kilic, T., Murray, S., Oseni, M., Scott, K., and Steele, D. (2008, January 8\u20139). Improving the Availability, Quality and Policy-Relevance of Agricultural Data: The Living Standards Measurement Study Integrated Surveys on Agriculture. Proceedings of the Third Wye City Group Global Conference on Agricultural and Rural Household Statistic, Washington, DC, USA."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1080\/00220388.2014.968140","article-title":"From Tragedy to Renaissance: Improving Agricultural Data for Better Policies","volume":"51","author":"Carletto","year":"2015","journal-title":"J. Dev. Stud."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1007\/s10584-007-9249-0","article-title":"Assessing the vulnerability of food crop systems in Africa to climate change","volume":"83","author":"Challinor","year":"2007","journal-title":"Clim. Chang."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.foodpol.2016.02.003","article-title":"Toward a food secure future: Ensuring food security for sustainable human development in Sub-Saharan Africa","volume":"60","author":"Conceicao","year":"2016","journal-title":"Food Policy"},{"key":"ref_9","unstructured":"The World Bank (2008). World Development Report 2008: Agriculture for Development, The World Bank."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1002\/fes3.15","article-title":"Prospects of doubling global wheat yields","volume":"2","author":"Hawkesford","year":"2013","journal-title":"Food Energy Secur."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.fcr.2016.10.014","article-title":"Ethiopian wheat yield and yield gap estimation: A spatially explicit small area integrated data approach","volume":"201","author":"Mann","year":"2017","journal-title":"Field Crops Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"655","DOI":"10.2134\/agronj1988.00021962008000040021x","article-title":"Using Satellite Data to Improve Model Estimates of Crop Yield","volume":"80","author":"Maas","year":"1988","journal-title":"Agron. J."},{"key":"ref_13","unstructured":"Hellden, U., and Eklundh, L. (1988). National Drought Impact Monitoring\u2014A NOAA NDVI and Precipitation Data Study of Ethiopia, Lund University Press. Technical Report."},{"key":"ref_14","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_15","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.fcr.2012.08.008","article-title":"The use of satellite data for crop yield gap analysis","volume":"143","author":"Lobell","year":"2013","journal-title":"Field Crops Res."},{"key":"ref_16","first-page":"65","article-title":"A comprehensive assessment of the correlations between field crop yields and commonly used MODIS products","volume":"52","author":"Johnson","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.rse.2016.11.004","article-title":"Toward mapping crop progress at field scales through fusion of Landsat and MODIS imagery","volume":"188","author":"Gao","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_18","first-page":"122","article-title":"How much does multi-temporal Sentinel-2 data improve crop type classification?","volume":"72","author":"Vuolo","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Jin, Z., Azzari, G., Burke, M., Aston, S., and Lobell, D.B. (2017). Mapping Smallholder Yield Heterogeneity at Multiple Scales in Eastern Africa. Remote Sens., 9.","DOI":"10.3390\/rs9090931"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1093\/jae\/ejv011","article-title":"From Guesstimates to GPStimates: Land Area Measurement and Implications for Agricultural Analysis","volume":"24","author":"Carletto","year":"2015","journal-title":"J. Afr. Econ."},{"key":"ref_21","unstructured":"United States Department of Agriculture, National Agricultural Statistics Service (2018, October 30). Farms and Land in Farms: 2017 Summary, Available online: http:\/\/usda.mannlib.cornell.edu\/usda\/current\/FarmLandIn\/FarmLandIn-02-16-2018.pdf."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2189","DOI":"10.1073\/pnas.1616919114","article-title":"Satellite-based assessment of yield variation and its determinants in smallholder African systems","volume":"114","author":"Burke","year":"2017","journal-title":"Proc. Nat. Acad. Sci. USA"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Fritz, S., You, L., Bun, A., See, L., McCallum, I., Schill, C., Perger, C., Liu, J., Hansen, M., and Obersteiner, M. (2011). Cropland for sub-Saharan Africa: A synergistic approach using five land cover data sets. Geophys. Res. Lett., 38.","DOI":"10.1029\/2010GL046213"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Vancutsem, C., Pekel, J., and Kayitakire, F. (2011, January 12\u201314). Dynamic mapping of cropland areas in Sub-Saharan Africa using MODIS time series. Proceedings of the 2011 6th International Workshop on the Analysis of Multi-Temporal Remote Sensing Images (Multi-Temp), Trento, Italy.","DOI":"10.1109\/Multi-Temp.2011.6005038"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"19","DOI":"10.3390\/rs5010019","article-title":"Harmonizing and Combining Existing Land Cover\/Land Use Datasets for Cropland Area Monitoring at the African Continental Scale","volume":"5","author":"Vancutsem","year":"2012","journal-title":"Remote Sens."},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.isprsjprs.2016.02.010","article-title":"Monitoring interannual variation in global crop yield using long-term AVHRR and MODIS observations","volume":"114","author":"Zhang","year":"2016","journal-title":"ISPRS J. Photogram. Remote Sens."},{"key":"ref_28","first-page":"39","article-title":"Evaluating a satellite-based seasonal evapotranspiration product and identifying its relationship with other satellite-derived products and crop yield: A case study for Ethiopia","volume":"40","author":"Tadesse","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2615","DOI":"10.1080\/01431161.2014.883106","article-title":"Obtaining crop-specific time profiles of NDVI: The use of unmixing approaches for serving the continuity between SPOT-VGT and PROBA-V time series","volume":"35","author":"Atzberger","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"690","DOI":"10.1016\/j.rse.2017.09.031","article-title":"Fractional cover mapping of spruce and pine at 1ha resolution combining very high and medium spatial resolution satellite imagery","volume":"204","author":"Immitzer","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1335","DOI":"10.3390\/rs5031335","article-title":"Mapping the Spatial Distribution of Winter Crops at Sub-Pixel Level Using AVHRR NDVI Time Series and Neural Nets","volume":"5","author":"Atzberger","year":"2013","journal-title":"Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1002\/joc.1078","article-title":"Seasonal forecasting of the Ethiopian summer rains","volume":"24","author":"Gissila","year":"2004","journal-title":"Int. J. Climatol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2176","DOI":"10.1002\/2013WR014281","article-title":"Satellite-based hybrid drought monitoring tool for prediction of vegetation condition in Eastern Africa: A case study for Ethiopia","volume":"50","author":"Tadesse","year":"2014","journal-title":"Water Resour. Res."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Klisch, A., and Atzberger, C. (2016). Operational Drought Monitoring in Kenya Using MODIS NDVI Time Series. Remote Sens., 8.","DOI":"10.3390\/rs8040267"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Enenkel, M., Steiner, C., Mistelbauer, T., Dorigo, W., Wagner, W., See, L., Atzberger, C., Schneider, S., and Rogenhofer, E. (2016). A Combined Satellite-Derived Drought Indicator to Support Humanitarian Aid Organizations. Remote Sens., 8.","DOI":"10.3390\/rs8040340"},{"key":"ref_36","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_37","doi-asserted-by":"crossref","first-page":"1589","DOI":"10.3390\/rs2061589","article-title":"Monitoring Global Croplands with Coarse Resolution Earth Observations: The Global Agriculture Monitoring (GLAM) Project","volume":"2","author":"Justice","year":"2010","journal-title":"Remote Sens."},{"key":"ref_38","unstructured":"Shroder, J.F., Paron, P., and Baldassarre, G.D. (2015). Chapter 9\u2014Drought Monitoring and Assessment: Remote Sensing and Modeling Approaches for the Famine Early Warning Systems Network. Hydro-Meteorological Hazards, Risks and Disasters, Elsevier."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Funk, C., and Verdin, J.P. (2010). Real-Time Decision Support Systems: The Famine Early Warning System Network. Satellite Rainfall Applications for Surface Hydrology, Springer.","DOI":"10.1007\/978-90-481-2915-7_17"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Molly, E., and Brown, E.B.B. (2012). Evaluating the use of remote sensing data in the U.S. Agency for International Development Famine Early Warning Systems Network. J. Appl. Remote Sens., 6.","DOI":"10.1117\/1.JRS.6.063511"},{"key":"ref_41","unstructured":"(2018, October 30). GIEWS\u2014Global Information and Early Warning System Food and Agriculture Organization of the United Nations. Available online: http:\/\/www.fao.org\/giews."},{"key":"ref_42","unstructured":"Baruth, B., Royer, A., Klisch, A., and Genovese, G. (2008, January 3\u201311). The Use of Remote Sensing Within the Mars Crop Yield Monitoring System of the European Commission. Proceedings of the 21st Congress of the International Society for Photogrammetry and Remote Sensing\u2014ISPRS, Beijing, China."},{"key":"ref_43","unstructured":"(2018, October 30). Monitoring Agricultural ResourceS (MARS). Available online: https:\/\/www.eea.europa.eu\/data-and-maps\/data\/external\/monitoring-agricultural-resources-mars."},{"key":"ref_44","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. Digit. Earth"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2807","DOI":"10.1080\/01431160310001632729","article-title":"Early cotton yield assessment by the use of the NOAA\/AVHRR derived Vegetation Condition Index (VCI) in Greece","volume":"25","author":"Domenikiotis","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1080\/01431160802108497","article-title":"Assessing potential of MODIS derived temperature\/vegetation condition index (TVDI) to infer soil moisture status","volume":"30","author":"Patel","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_47","unstructured":"Labs, D. (2018, October 30). Descartes Labs: Platform. Available online: https:\/\/www.descarteslabs.com\/platform.html."},{"key":"ref_48","unstructured":"Petersen, L.K. (2018, October 30). MODIS Crop Prediction Code Repository. Available online: https:\/\/github.com\/lillianpetersen\/CropPredictionFromSatellite2018."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1080\/01431169608948714","article-title":"The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features","volume":"17","author":"McFeeters","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_50","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_51","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1007\/s10661-005-9006-7","article-title":"Assessment of MODIS-EVI, MODIS-NDVI and VEGETATION-NDVI Composite Data Using Agricultural Measurements: An Example at Corn Fields in Western Mexico","volume":"119","author":"Chen","year":"2006","journal-title":"Environ. Monit. Assess."},{"key":"ref_52","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_53","doi-asserted-by":"crossref","first-page":"2636","DOI":"10.3390\/s7112636","article-title":"Sensitivity of the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) to Topographic Effects: A Case Study in High-density Cypress Forest","volume":"7","author":"Matsushita","year":"2007","journal-title":"Sensors"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"954","DOI":"10.1890\/04-0470","article-title":"Modeling Gross Primary Production of an Evergreen Needleleaf Forest Using Modis and Climate Data","volume":"15","author":"Xiao","year":"2005","journal-title":"Ecol. Appl."},{"key":"ref_55","unstructured":"United States Department of Agriculture, National Agricultural Statistics Service (2014). Farms and Farmland: Numbers, Acreage, Ownership, and Use."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.agee.2008.01.007","article-title":"Coping better with current climatic variability in the rain-fed farming systems of sub-Saharan Africa: An essential first step in adapting to future climate change?","volume":"126","author":"Cooper","year":"2008","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_57","unstructured":"United States Department of Agriculture, National Agricultural Statistics Service (2017). Illinois Corn County Estimates: Corn Area Planted And Harvested, Yield, and Production by County\u2014Illinois."},{"key":"ref_58","unstructured":"United States Department of Agriculture, National Agricultural Statistics Service (2017). Illinois Corn County Estimates: Soybean Area Planted And Harvested, Yield, and Production by County\u2014Illinois."},{"key":"ref_59","unstructured":"United States Department of Agriculture, National Agricultural Statistics Service (2016). Illinois Corn County Estimates: Sorghum Area Planted and Harvested, Yield, and Production by County\u2014Illinois."},{"key":"ref_60","unstructured":"Mundi, I. (2018, October 30). Agricultural Production Statistics by Country. Available online: https:\/\/www.indexmundi.com\/agriculture."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.agsy.2014.01.002","article-title":"Generating global crop distribution maps: From census to grid","volume":"127","author":"You","year":"2014","journal-title":"Agric. Syst."},{"key":"ref_62","unstructured":"Petersen, L.K. (2018, October 30). Dense Farming Regions in Each African Country. Available online: https:\/\/gist.github.com\/lillianpetersen\/6b2227bad0c44d0a9565c717e6f178d3."},{"key":"ref_63","unstructured":"(2018, October 30). FAO GIEWS Country Briefs-Home. Available online: http:\/\/www.fao.org\/giews\/countrybrief\/."},{"key":"ref_64","unstructured":"United States Department of Agriculture (2010). Field Crops Usual Planting and Harvesting Dates, Technical Report."},{"key":"ref_65","unstructured":"GraphPadSoftware (2018, October 30). p-Value Calculator. Available online: https:\/\/www.graphpad.com\/quickcalcs\/pvalue1.cfm."},{"key":"ref_66","unstructured":"Taffesse, A.S. (2012). Crop production in Ethiopia. Food and Agriculture in Ethiopia Progress and Policy Challenges, University of Pennsylvania Press."},{"key":"ref_67","unstructured":"Petersen, L.K. (2018, October 30). Predicting Food Shortages in Africa from Satellite Imagery. Available online: https:\/\/lillianpetersen.github.io\/africa_satellite."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/11\/1726\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:27:29Z","timestamp":1760196449000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/11\/1726"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,1]]},"references-count":67,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2018,11]]}},"alternative-id":["rs10111726"],"URL":"https:\/\/doi.org\/10.3390\/rs10111726","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,11,1]]}}}