{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T16:10:18Z","timestamp":1769098218242,"version":"3.49.0"},"reference-count":76,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2020,5,27]],"date-time":"2020-05-27T00:00:00Z","timestamp":1590537600000},"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>Climate change has an increasing impact on food security and child nutrition, particularly among rural smallholder farmers in sub-Saharan Africa. Their limited resources and rainfall dependent farming practices make them sensitive to climate change-related effects. Data and research linking yield, human health, and nutrition are scarce but can provide a basis for adaptation and risk management strategies. In support of studies on child undernutrition in Burkina Faso, this study analyzed the potential of remote sensing-based yield estimates at household level. Multi-temporal Sentinel-2 data from the growing season 2018 were used to model yield of household fields (median 1.4 hectares (ha), min 0.01 ha, max 12.6 ha) for the five most prominent crops in the Nouna Health and Demographic Surveillance (HDSS) area in Burkina Faso. Based on monthly metrics of vegetation indices (VIs) and in-situ harvest measurements from an extensive field survey, yield prediction models for different crops of high dietary importance (millet, sorghum, maize, and beans) were successfully generated producing R\u00b2 between 0.4 and 0.54 (adj. R\u00b2 between 0.32 and 0.5). The models were spatially applied and resulted in a yield estimation map at household level, enabling predictions of up to 2 months prior to harvest. The map links yield on a 10-m spatial resolution to households and consequently can display potential food insecurity. The results highlight the potential for satellite imagery to provide yield predictions of smallholder fields and are discussed in the context of health-related studies such as child undernutrition and food security in rural Africa under climate change.<\/jats:p>","DOI":"10.3390\/rs12111717","type":"journal-article","created":{"date-parts":[[2020,5,28]],"date-time":"2020-05-28T12:36:58Z","timestamp":1590669418000},"page":"1717","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Estimating Yields of Household Fields in Rural Subsistence Farming Systems to Study Food Security in Burkina Faso"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4084-6954","authenticated-orcid":false,"given":"Isabel G.","family":"Karst","sequence":"first","affiliation":[{"name":"Remote Sensing Solutions (RSS) GmbH, Dingolfingerstr. 9, 81673 Munich, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1321-1731","authenticated-orcid":false,"given":"Isabel","family":"Mank","sequence":"additional","affiliation":[{"name":"Heidelberg Institute of Global Health (HIGH), Faculty of Medicine, Heidelberg University, Im Neuenheimer Feld 130.3, 69120 Heidelberg, Germany"}]},{"given":"Issouf","family":"Traor\u00e9","sequence":"additional","affiliation":[{"name":"Centre de Recherche en Sant\u00e9 de Nouna (CRSN), Institut National de Sant\u00e9 Publique (INSP), Rue Namory Keita, PO Box 02, Nouna, Boucle du Mouhoun, Burkina Faso"},{"name":"Institut Universitaire de Formations Initiale et Continue (IUFIC), Universit\u00e9 Ouaga II, 12 BP 417 Ouagadougou 12, Burkina Faso"}]},{"given":"Raissa","family":"Sorgho","sequence":"additional","affiliation":[{"name":"Heidelberg Institute of Global Health (HIGH), Faculty of Medicine, Heidelberg University, Im Neuenheimer Feld 130.3, 69120 Heidelberg, Germany"}]},{"given":"Kim-Jana","family":"St\u00fcckemann","sequence":"additional","affiliation":[{"name":"Institute of Computer Science, University of Osnabr\u00fcck, Wachsbleiche 27, 49090 Osnabr\u00fcck, Germany"}]},{"given":"S\u00e9raphin","family":"Simboro","sequence":"additional","affiliation":[{"name":"Centre de Recherche en Sant\u00e9 de Nouna (CRSN), Institut National de Sant\u00e9 Publique (INSP), Rue Namory Keita, PO Box 02, Nouna, Boucle du Mouhoun, Burkina Faso"}]},{"given":"Ali","family":"Si\u00e9","sequence":"additional","affiliation":[{"name":"Centre de Recherche en Sant\u00e9 de Nouna (CRSN), Institut National de Sant\u00e9 Publique (INSP), Rue Namory Keita, PO Box 02, Nouna, Boucle du Mouhoun, Burkina Faso"}]},{"given":"Jonas","family":"Franke","sequence":"additional","affiliation":[{"name":"Remote Sensing Solutions (RSS) GmbH, Dingolfingerstr. 9, 81673 Munich, Germany"}]},{"given":"Rainer","family":"Sauerborn","sequence":"additional","affiliation":[{"name":"Heidelberg Institute of Global Health (HIGH), Faculty of Medicine, Heidelberg University, Im Neuenheimer Feld 130.3, 69120 Heidelberg, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1146\/annurev-publhealth-031816-044356","article-title":"Climate Change and Global Food Systems: Potential Impacts on Food Security and Undernutrition","volume":"38","author":"Myers","year":"2017","journal-title":"Ann. Rev. Public Health"},{"key":"ref_2","unstructured":"Grolleaud, M. (2020, January 31). Post-harvest Losses: Discovering the full story: Overview of the Phenomenon of Losses During the Post-harvest System. Available online: http:\/\/www.fao.org\/3\/AC301E\/AC301e00.htm."},{"key":"ref_3","unstructured":"FAO, IFAD, UNICEF, WFP, and WHO (2019). The State of Food Security and Nutrition in the World 2019. Safeguarding against Economic Slowdowns and Downturns, FAO."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"19680","DOI":"10.1073\/pnas.0701855104","article-title":"The impact of climate change on smallholder and subsistence agriculture","volume":"104","author":"Morton","year":"2007","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"B\u00e9gu\u00e9, A., Arvor, D., Bellon, B., Betbeder, J., Abelleyra, D.D., Ferraz, P.D.R., Lebourgeois, V., Lelong, C., Sim\u00f5es, M.R., and Ver\u00f3n, S. (2018). Remote Sensing and Cropping Practices: A Review. Remote Sens., 10.","DOI":"10.3390\/rs10010099"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"160020","DOI":"10.1038\/sdata.2016.20","article-title":"An agricultural survey for more than 9500 African households","volume":"3","author":"Waha","year":"2016","journal-title":"Sci. Data"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"256","DOI":"10.13189\/ujph.2017.050508","article-title":"Linking Weather Data, Satellite Imagery and Field Observations to Household Food Production and Child Undernutrition: An Exploratory Study in Burkina Faso","volume":"5","author":"Sorgho","year":"2017","journal-title":"Univers. J. Public Health"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4393","DOI":"10.1002\/joc.4640","article-title":"Long-term analysis of rainfall and temperature data in Burkina Faso (1950-2013)","volume":"36","author":"Longueville","year":"2016","journal-title":"Int. J. Climatol."},{"key":"ref_9","unstructured":"IPCC (2014). Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, IPCC."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"538","DOI":"10.1016\/j.scitotenv.2019.07.027","article-title":"Mortality impact of low annual crop yields in a subsistence farming population of Burkina Faso under the current and a 1.5 \u00b0C warmer climate in 2100","volume":"691","author":"Belesova","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"74","DOI":"10.3402\/gha.v5i0.19083","article-title":"Past, present, and future climate at select INDEPTH member Health and Demographic Surveillance Systems in Africa and Asia","volume":"5","author":"Hondula","year":"2012","journal-title":"Glob. Health Action"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1038\/nclimate1043","article-title":"Nonlinear heat effects on African maize as evidenced by historical yield trials","volume":"1","author":"Lobell","year":"2011","journal-title":"Nat. Clim. Chang."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1016\/j.agrformet.2010.12.002","article-title":"Climate variability and crop production in Tanzania","volume":"151","author":"Rowhani","year":"2011","journal-title":"Agric. For. Meteorol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1146\/annurev-publhealth-032315-021740","article-title":"Heat, Human Performance, and Occupational Health: A Key Issue for the Assessment of Global Climate Change Impacts","volume":"37","author":"Kjellstrom","year":"2016","journal-title":"Ann. Rev. Public Health"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"424","DOI":"10.2486\/indhealth.2013-0006","article-title":"Heat exposure, cardiovascular stress and work productivity in rice harvesters in India: Implications for a climate change future","volume":"51","author":"Sahu","year":"2013","journal-title":"Ind. Health"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1007\/s11111-013-0201-0","article-title":"Using satellite remote sensing and household survey data to assess human health and nutrition response to environmental change","volume":"36","author":"Brown","year":"2014","journal-title":"Popul. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.agsy.2016.09.005","article-title":"Assessment of food self-sufficiency in smallholder farming systems of south-western Madagascar using survey and remote sensing data","volume":"149","author":"Noromiarilanto","year":"2016","journal-title":"Agric. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.foodpol.2014.01.010","article-title":"Examining the link between food prices and food insecurity: A multi-level analysis of maize price and birthweight in Kenya","volume":"46","author":"Grace","year":"2014","journal-title":"Food Policy"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"E4522","DOI":"10.1073\/pnas.1409769112","article-title":"Systematic review of current efforts to quantify the impacts of climate change on undernutrition","volume":"112","author":"Phalkey","year":"2015","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_20","unstructured":"Mank, I., Vandormael, A., Traor\u00e9, I., Ou\u00e9draogo, A., Sauerborn, R., and Danquah, I. Dietary habits associated with growth development among children aged <5 years in the Nouna Health and Demographic Surveillance System, Burkina Faso. Nutr. J., (under review)."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1111\/j.1365-3156.2012.02985.x","article-title":"Malnutrition in young children of rural Burkina Faso: Comparison of survey data from 1999 with 2009","volume":"17","author":"Beiersmann","year":"2012","journal-title":"Trop. Med. Int. Health"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1093\/aje\/kwx241","article-title":"Annual Crop-Yield Variation, Child Survival, and Nutrition Among Subsistence Farmers in Burkina Faso","volume":"187","author":"Belesova","year":"2018","journal-title":"Am. J. Epidemiol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2719","DOI":"10.3390\/s90402719","article-title":"Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors","volume":"9","author":"Zheng","year":"2009","journal-title":"Sensors (Basel)"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.fcr.2007.03.023","article-title":"Remote sensing of nitrogen and water stress in wheat","volume":"104","author":"Tilling","year":"2007","journal-title":"Field Crops Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.1080\/01431160500486732","article-title":"The potential and challenge of remote sensing-based biomass estimation","volume":"27","author":"Lu","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Oerke, E.-C., Gerhards, R., Menz, G., and Sikora, R.A. (2010). Remote Sensing for Precision Crop Protection\u2014A Matter of Scale. Precision Crop Protection\u2014The Challenge and Use of Heterogeneity, Springer.","DOI":"10.1007\/978-90-481-9277-9"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1495","DOI":"10.1080\/01431169308953983","article-title":"NDVI\u2014Crop monitoring and early yield assessment of Burkina Faso","volume":"14","author":"Groten","year":"1993","journal-title":"Int. J. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"665","DOI":"10.14358\/PERS.69.6.665","article-title":"Crop Yield Assessment from Remote Sensing","volume":"69","author":"Doraiswamy","year":"2003","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_29","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_30","doi-asserted-by":"crossref","first-page":"107609","DOI":"10.1016\/j.agrformet.2019.06.008","article-title":"Assimilation of remote sensing into crop growth models: Current status and perspectives","volume":"276\u2013277","author":"Huang","year":"2019","journal-title":"Agric.For. Meteorol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.rse.2015.03.029","article-title":"Multi-sensor mapping of West African land cover using MODIS, ASAR and TanDEM-X\/TerraSAR-X data","volume":"164","author":"Gessner","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Knauer, K., Gessner, U., Fensholt, R., Forkuor, G., and Kuenzer, C. (2017). Monitoring Agricultural Expansion in Burkina Faso over 14 Years with 30 m Resolution Time Series: The Role of Population Growth and Implications for the Environment. Remote Sens., 9.","DOI":"10.3390\/rs9020132"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Jin, Z., Azzari, G., Burke, M., Aston, S., and Lobell, D. (2017). Mapping Smallholder Yield Heterogeneity at Multiple Scales in Eastern Africa. Remote Sens., 9.","DOI":"10.3390\/rs9090931"},{"key":"ref_34","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. Natl. Acad. Sci. USA"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Jain, M., Srivastava, A., Balwinder, S., Joon, R., McDonald, A., Royal, K., Lisaius, M., and Lobell, D. (2016). Mapping Smallholder Wheat Yields and Sowing Dates Using Micro-Satellite Data. Remote Sens., 8.","DOI":"10.3390\/rs8100860"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1016\/j.rse.2018.06.036","article-title":"Estimating smallholder crops production at village level from Sentinel-2 time series in Mali\u2019s cotton belt","volume":"216","author":"Lambert","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"539","DOI":"10.3390\/rs5020539","article-title":"Remote Sensing Based Yield Estimation in a Stochastic Framework\u2014Case Study of Durum Wheat in Tunisia","volume":"5","author":"Meroni","year":"2013","journal-title":"Remote Sens."},{"key":"ref_38","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. For. Meteorol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.biosystemseng.2018.04.020","article-title":"Forecasting maize yield at field scale based on high-resolution satellite imagery","volume":"171","author":"Schwalbert","year":"2018","journal-title":"Biosyst. Eng."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Skakun, S., Franch, B., Vermote, E., Roger, J.-C., Justice, C., Masek, J., and Murphy, E. (2018). Winter Wheat Yield Assessment Using Landsat 8 and Sentinel-2 Data. IGARSS 2018\u20142018 IEEE International Geoscience and Remote Sens. Symposium, IEEE.","DOI":"10.1109\/IGARSS.2018.8519134"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Lambert, M.-J., Blaes, X., Traore, P.S., and Defourny, P. (2017). Estimate yield at parcel level from S2 time serie in sub-Saharan smallholder farming systems. IEEE, 1\u20137.","DOI":"10.1109\/Multi-Temp.2017.8035204"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.healthplace.2015.06.008","article-title":"Environmental variability and child growth in Nepal","volume":"35","author":"Shively","year":"2015","journal-title":"Health Place"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.apgeog.2014.08.007","article-title":"Environmental risk factors and child nutritional status and survival in a context of climate variability and change","volume":"54","author":"Johnson","year":"2014","journal-title":"Appl. Geogr."},{"key":"ref_44","first-page":"322","article-title":"Investigating rural poverty and marginality in Burkina Faso using remote sensing-based products","volume":"26","author":"Imran","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1186\/s12940-017-0258-9","article-title":"Household cereal crop harvest and children\u2019s nutritional status in rural Burkina Faso","volume":"16","author":"Belesova","year":"2017","journal-title":"Environ. Health"},{"key":"ref_46","unstructured":"Dixon, S., and Holt, J. (2010). Livelihood Zoning and Profiling Report: Burkina Faso. A Special Report by the Famine Early Warning Systems Network (FEWS NET)."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"16","DOI":"10.5897\/JAERD14.0595","article-title":"Adapting to climate variability and change in smallholder farming communities: A case study from Burkina Faso, Chad and Niger","volume":"7","author":"Sarr","year":"2015","journal-title":"J. Agric. Ext. Rural Dev."},{"key":"ref_48","unstructured":"Direction de la Prospective et des Statistiques Agricoles et Alimentaires (DPSAA) (2011). Rapport General du Module Pluvial. Phase 2\u00b0, Minist\u00e8re de l\u2019Agriculture et de l\u2019Hydraulique."},{"key":"ref_49","unstructured":"Fermont, A., and Benson, T. (2011). Estimating Yield of Food Crops Grown by Smallholder Farmers. A Review in the Uganda Context, IFPRI."},{"key":"ref_50","unstructured":"ESA (2020, January 13). Sentinel-2-Missions, Available online: https:\/\/sentinels.copernicus.eu\/web\/sentinel\/missions\/sentinel-2."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2861","DOI":"10.1080\/01431161.2019.1697004","article-title":"Estimating natural grassland biomass by vegetation indices using Sentinel 2 remote sensing data","volume":"41","author":"Kuplich","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_52","first-page":"235","article-title":"Assessment of RapidEye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops","volume":"34","author":"Kross","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_53","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. of Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"27832","DOI":"10.3390\/s151127832","article-title":"Active-Optical Sensors Using Red NDVI Compared to Red Edge NDVI for Prediction of Corn Grain Yield in North Dakota, U.S.A","volume":"15","author":"Sharma","year":"2015","journal-title":"Sensors (Basel)"},{"key":"ref_55","unstructured":"Rouse, J., Haas, R., Schell, J., and Deering, D. (1973, January 10\u201314). Monitoring Vegetation Systems in the Great Plains with ERTS. Proceedings of the Third Earth Resources Technology Satellite-1 Symposium, Washington, DC, USA."},{"key":"ref_56","unstructured":"Barnes, E., Clarke, T.R., Richards, S.E., Colaizzi, P., Haberland, J., Kostrzewski, M., Waller, P., Choi, C., Riley, E., and Thompson, T.L. (2000, January 16\u201319). Coincident detection of crop water stress, nitrogen status, and canopy density using ground based multispectral data. Proceedings of the Fifth International Conference on Precision Agriculture, Bloomington, MN, USA."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/S0167-8809(02)00034-8","article-title":"A new crop yield forecasting model based on satellite measurements applied across the Indus Basin, Pakistan","volume":"94","author":"Bastiaanssen","year":"2003","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_58","unstructured":"Pearson New International (2014). Probability and Statistics, Pearson."},{"key":"ref_59","unstructured":"Chakravarti, I.M., Laha, R.G., and Roy, J. (1967). Handbook of Methods of Applied Statistics, John Wiley & Sons."},{"key":"ref_60","unstructured":"Brownlee, J. (2020, February 13). How to Transform Data to Better Fit the Normal Distribution. Available online: https:\/\/machinelearningmastery.com\/how-to-transform-data-to-fit-the-normal-distribution\/."},{"key":"ref_61","first-page":"82","article-title":"Applied Regression Analysis: A Research Tool","volume":"41","author":"Ziegel","year":"1999","journal-title":"Technometrics"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Cohen, I., Huang, Y., Chen, J., and Benesty, J. (2009). Noise Reduction in Speech Processing, Springer.","DOI":"10.1007\/978-3-642-00296-0"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1111\/j.1600-0587.2012.07348.x","article-title":"Collinearity: A review of methods to deal with it and a simulation study evaluating their performance","volume":"36","author":"Dormann","year":"2013","journal-title":"Ecography"},{"key":"ref_64","first-page":"97","article-title":"Regression diagnostics: Identifying influential data and sources of collinearity","volume":"4","author":"Belsley","year":"1989","journal-title":"J. Appl. Econ."},{"key":"ref_65","unstructured":"Belsley, D.A. (1991). Conditioning Diagnostics. Collinearity and Weak Data in Regression, Wiley."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"15","DOI":"10.3354\/cr024015","article-title":"Test for harmful collinearity among predictor variables used in modeling global temperature","volume":"24","author":"Douglass","year":"2003","journal-title":"Clim. Res."},{"key":"ref_67","unstructured":"James, G., Witten, D., Hastie, T., and Tibshirani, R. (2017). An Introduction to Statistical Learning. With Applications in R, Springer. Corrected at 8th printing edition."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.1016\/j.envsoft.2004.09.001","article-title":"Principal component and multiple regression analysis in modelling of ground-level ozone and factors affecting its concentrations","volume":"20","author":"Bakheit","year":"2005","journal-title":"Environ. Model. Softw."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1111\/j.1751-5823.2005.tb00155.x","article-title":"Using Remote Sensing for Agricultural Statistics","volume":"73","author":"Carfagna","year":"2005","journal-title":"Int. Stat. Rev."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1080\/014311698216468","article-title":"Developing simple, operational, consistent NDVI-vegetation models by applying environmental and climatic information. Part II: Crop yield assessment","volume":"19","author":"Rasmussen","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_71","unstructured":"Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences, Erlbaum. [2nd ed.]."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"6620","DOI":"10.3390\/rs6076620","article-title":"Toward a Satellite-Based System of Sugarcane Yield Estimation and Forecasting in Smallholder Farming Conditions: A Case Study on Reunion Island","volume":"6","author":"Morel","year":"2014","journal-title":"Remote Sens."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.ehb.2016.10.010","article-title":"Effects of drought on child health in Marsabit District, Northern Kenya","volume":"24","author":"Bauer","year":"2017","journal-title":"Econ. Hum. Biol."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Si\u00e9, A., Louis, V.R., Gbangou, A., M\u00fcller, O., Niamba, L., Stieglbauer, G., Y\u00e9, M., Kouyat\u00e9, B., Sauerborn, R., and Becher, H. (2010). The Health and Demographic Surveillance System (HDSS) in Nouna, Burkina Faso, 1993\u20132007. Glob. Health Action, 3.","DOI":"10.3402\/gha.v3i0.5284"},{"key":"ref_75","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_76","unstructured":"Nelson, A.D., Rogers, D.J., and Robinson, T.P. (2012). Poverty Mapping in Uganda. Extrapolating Household Expenditure Data Using Environmental Data and Regression Techniques, Food and Agriculture Organization of the United Nations (FAO)."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/11\/1717\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:32:59Z","timestamp":1760175179000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/11\/1717"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,27]]},"references-count":76,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2020,6]]}},"alternative-id":["rs12111717"],"URL":"https:\/\/doi.org\/10.3390\/rs12111717","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,27]]}}}