{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T19:18:46Z","timestamp":1790882326445,"version":"4.1.0"},"reference-count":24,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2017,9,8]],"date-time":"2017-09-08T00:00:00Z","timestamp":1504828800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000200","name":"United States Agency for International Development","doi-asserted-by":"publisher","award":["AID-OAA-L-14-00006"],"award-info":[{"award-number":["AID-OAA-L-14-00006"]}],"id":[{"id":"10.13039\/100000200","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate measurements of crop production in smallholder farming systems are critical to the understanding of yield constraints and, thus, setting the appropriate agronomic investments and policies for improving food security and reducing poverty. Nevertheless, mapping the yields of smallholder farms is challenging because of factors such as small field sizes and heterogeneous landscapes. Recent advances in fine-resolution satellite sensors offer promise for monitoring and characterizing the production of smallholder farms. In this study, we investigated the utility of different sensors, including the commercial Skysat and RapidEye satellites and the publicly accessible Sentinel-2, for tracking smallholder maize yield variation throughout a ~40,000 km2 western Kenya region. We tested the potential of two types of multiple regression models for predicting yield: (i) a \u201ccalibrated model\u201d, which required ground-measured yield and weather data for calibration, and (ii) an \u201cuncalibrated model\u201d, which used a process-based crop model to generate daily vegetation index and end-of-season biomass and\/or yield as pseudo training samples. Model performance was evaluated at the field, division, and district scales using a combination of farmer surveys and crop cuts across thousands of smallholder plots in western Kenya. Results show that the \u201ccalibrated\u201d approach captured a significant fraction (R2 between 0.3 and 0.6) of yield variations at aggregated administrative units (e.g., districts and divisions), while the \u201cuncalibrated\u201d approach performed only slightly worse. For both approaches, we found that predictions using the MERIS Terrestrial Chlorophyll Index (MTCI), which included the red edge band available in RapidEye and Sentinel-2, were superior to those made using other commonly used vegetation indices. We also found that multiple refinements to the crop simulation procedures led to improvements in the \u201cuncalibrated\u201d approach. We identified the prevalence of small field sizes, intercropping management, and cloudy satellite images as major challenges to improve the model performance. Overall, this study suggested that high-resolution satellite imagery can be used to map yields of smallholder farming systems, and the methodology presented in this study could serve as a good foundation for other smallholder farming systems in the world.<\/jats:p>","DOI":"10.3390\/rs9090931","type":"journal-article","created":{"date-parts":[[2017,9,8]],"date-time":"2017-09-08T11:34:52Z","timestamp":1504870492000},"page":"931","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":90,"title":["Mapping Smallholder Yield Heterogeneity at Multiple Scales in Eastern Africa"],"prefix":"10.3390","volume":"9","author":[{"given":"Zhenong","family":"Jin","sequence":"first","affiliation":[{"name":"Department of Earth System Science and Center on Food Security and the Environment, Stanford University, Stanford, CA 94305, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"George","family":"Azzari","sequence":"additional","affiliation":[{"name":"Department of Earth System Science and Center on Food Security and the Environment, Stanford University, Stanford, CA 94305, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marshall","family":"Burke","sequence":"additional","affiliation":[{"name":"Department of Earth System Science and Center on Food Security and the Environment, Stanford University, Stanford, CA 94305, USA"},{"name":"National Bureau of Economic Research, Cambridge, MA 02138, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephen","family":"Aston","sequence":"additional","affiliation":[{"name":"One Acre Fund, Kigali, Rwanda"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Lobell","sequence":"additional","affiliation":[{"name":"Department of Earth System Science and Center on Food Security and the Environment, Stanford University, Stanford, CA 94305, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,9,8]]},"reference":[{"key":"ref_1","unstructured":"(2017, September 05). World Bank World Development Report 2008\u2014Agriculture for Development. Available online: https:\/\/openknowledge.worldbank.org\/handle\/10986\/5990."},{"key":"ref_2","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_3","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_4","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 Crop. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/feart.2017.00017","article-title":"Exploring Google Earth Engine Platform for Big Data Processing: Classification of Multi-Temporal Satellite Imagery for Crop Mapping","volume":"5","author":"Shelestov","year":"2017","journal-title":"Front. Earth Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1016\/j.rse.2015.04.021","article-title":"A scalable satellite-based crop yield mapper","volume":"164","author":"Lobell","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_7","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_8","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.agrformet.2017.08.001","article-title":"Improving the accuracy of satellite-based high-resolution yield estimation: A test of multiple scalable approaches","volume":"247","author":"Jin","year":"2017","journal-title":"Agric. For. Meteorol."},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1016\/j.rse.2009.10.002","article-title":"Evaluation of MODIS land surface temperature data to estimate air temperature in different ecosystems over Africa","volume":"114","author":"Vancutsem","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_11","unstructured":"Freden, S.C., and Mercanti, E.P. (1973, January 10\u201314). Monitoring Vegetation Systems in the Great Okains with ERTS. Proceedings of the Third Earth Resources Technology Satellite-1 Symposium, Washington, DC, USA."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1016\/S0034-4257(96)00112-5","article-title":"A comparison of vegetation indices over a global set of TM images for EOS-MODIS","volume":"59","author":"Huete","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1078\/0176-1617-01176","article-title":"Wide Dynamic Range Vegetation Index for remote quantification of biophysical characteristics of vegetation","volume":"161","author":"Gitelson","year":"2004","journal-title":"J. Plant Physiol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1078\/0176-1617-00887","article-title":"Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves","volume":"160","author":"Gitelson","year":"2003","journal-title":"J. Plant Physiol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"5403","DOI":"10.1080\/0143116042000274015","article-title":"The MERIS terrestrial chlorophyll index","volume":"2523","author":"Dash","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1016\/j.rse.2013.08.014","article-title":"Monitoring conterminous United States (CONUS) land cover change with Web-Enabled Landsat Data (WELD)","volume":"140","author":"Hansen","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Azzari, G., and Lobell, D.B. (2017). Landsat-based classification in the cloud: An opportunity for a paradigm shift in land cover monitoring. Remote Sens. Environ.","DOI":"10.1016\/j.rse.2017.05.025"},{"key":"ref_18","first-page":"47","article-title":"Remote estimation of nitrogen and chlorophyll contents in maize at leaf and canopy levels","volume":"25","author":"Schlemmera","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2301","DOI":"10.1111\/gcb.12520","article-title":"How do various maize crop models vary in their responses to climate change factors?","volume":"20","author":"Bassu","year":"2014","journal-title":"Glob. Chang. Biol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2087","DOI":"10.2134\/agronj14.0200","article-title":"Predicting maize phenology: Intercomparison of functions for developmental response to temperature","volume":"106","author":"Kumudini","year":"2014","journal-title":"Agron. J."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1025","DOI":"10.2134\/agronj2013.0421","article-title":"Evaluating APSIM Maize, Soil Water, Soil Nitrogen, Manure, and Soil Temperature Modules in the Midwestern United States","volume":"106","author":"Archontoulis","year":"2014","journal-title":"Agron. J."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3112","DOI":"10.1111\/gcb.13376","article-title":"Do maize models capture the impacts of heat and drought stresses on yield? Using algorithm ensembles to identify successful approaches","volume":"22","author":"Jin","year":"2016","journal-title":"Glob. Chang. Biol."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","unstructured":"Peng, Y., Nguy-Robertson, A., Arkebauer, T., and Gitelson, A.A. (2017). Assessment of canopy chlorophyll content retrieval in maize and soybean: Implications of hysteresis on the development of generic algorithms. Remote Sens., 9.","DOI":"10.3390\/rs9030226"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/9\/931\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:44:28Z","timestamp":1760208268000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/9\/931"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,9,8]]},"references-count":24,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2017,9]]}},"alternative-id":["rs9090931"],"URL":"https:\/\/doi.org\/10.3390\/rs9090931","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,9,8]]}}}