{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T05:36:24Z","timestamp":1774416984499,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2019,1,13]],"date-time":"2019-01-13T00:00:00Z","timestamp":1547337600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100013283","name":"EIT Climate-KIC","doi-asserted-by":"publisher","award":["ARED0004_2013-1.1-008_P001-06"],"award-info":[{"award-number":["ARED0004_2013-1.1-008_P001-06"]}],"id":[{"id":"10.13039\/100013283","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Irrigated agriculture practiced by smallholders is essential for food security in East Africa. Insight in the spatio-temporal distribution of irrigated agriculture is required to optimize irrigation water use. Irrigation-mapping efforts in the complex smallholder-dominated agricultural landscape in the Horn of Africa so far are generally too coarse and often the extent of smallholder irrigated agriculture is underestimated. The arrival of Sentinel-2 (10-m resolution) considerably enhanced the prospect of analyzing agriculture at field level. The objective of this study is to demonstrate the feasibility to map spatio-temporal patterns of smallholder irrigated agriculture in the Horn of Africa using a novel method based on object-based image analysis and Sentinel-2 imagery. The method includes segmentation at field level and smart process-based rules on neighbouring objects and NDVI time series to distinguish irrigated agriculture from rainfed agriculture. The assumption is that irrigation is applied at field level, while a rainfall event is not restricted to field borders and that this information on the local context of irrigated agriculture can be exploited in an object-based approach. Monthly land-use maps on irrigated agriculture were produced for September 2016 to August 2017 at 10-m resolution field level (objects). Three different spatial-heterogeneity thresholds were used to describe the vegetation development of neighbouring objects and to assign crop growth to either rainfall or irrigation. This method is unique as it can discriminate irrigation- and rainfall-induced crop growth, even in the rainy season. The estimates of irrigated agriculture in the Horn of Africa range from 27.96 Mha to 37.13 Mha. This is 2.8 to 3.7 times higher than the current highest estimate, the Global Irrigated Area Map at 1000 m resolution, and 1.2 to 1.7 times higher than the Irrigated Area Map Asia (2000\u20132010) and Africa (2010) when including water-managed non-irrigated croplands. For the dry season (October\u2013March), the estimates of irrigated agriculture range from 17.67 Mha to 23.72 Mha. The irrigation frequency, the number of time steps (months) with irrigation events in the studied year, varies strongly. Irrigated area with an irrigation frequency of 1 to 2 events has a mapped surface area of 22.57 Mha to 23.13 Mha. Irrigated area with an irrigation frequency of 3 or more events has a mapped surface area of 4.83 Mha to 14.56 Mha. The produced maps will provide valuable information for the development of irrigated agriculture and optimization of irrigation water use in the Horn of Africa. In addition, the portability of this method to other (semi-)arid regions seems feasible as the local context of irrigated agriculture, used in this study for irrigation classification, describes universal characteristics regarding irrigated agriculture. This is especially valuable in the context of food security and water availability for other large data-poor regions in low- and middle-income countries.<\/jats:p>","DOI":"10.3390\/rs11020143","type":"journal-article","created":{"date-parts":[[2019,1,14]],"date-time":"2019-01-14T12:20:07Z","timestamp":1547468407000},"page":"143","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Spatio-Temporal Patterns of Smallholder Irrigated Agriculture in the Horn of Africa Using GEOBIA and Sentinel-2 Imagery"],"prefix":"10.3390","volume":"11","author":[{"given":"Marjolein F.A.","family":"Vogels","sequence":"first","affiliation":[{"name":"Department of Physical Geography, Utrecht University, P.O. box 80115, 3508 TC Utrecht, The Netherlands"}]},{"given":"Steven M.","family":"de Jong","sequence":"additional","affiliation":[{"name":"Department of Physical Geography, Utrecht University, P.O. box 80115, 3508 TC Utrecht, The Netherlands"}]},{"given":"Geert","family":"Sterk","sequence":"additional","affiliation":[{"name":"Department of Physical Geography, Utrecht University, P.O. box 80115, 3508 TC Utrecht, The Netherlands"}]},{"given":"Harke","family":"Douma","sequence":"additional","affiliation":[{"name":"Department of Physical Geography, Utrecht University, P.O. box 80115, 3508 TC Utrecht, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0919-6498","authenticated-orcid":false,"given":"Elisabeth A.","family":"Addink","sequence":"additional","affiliation":[{"name":"Department of Physical Geography, Utrecht University, P.O. box 80115, 3508 TC Utrecht, The Netherlands"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1525","DOI":"10.1016\/j.gloenvcha.2013.10.002","article-title":"The climate-population nexus in the East African Horn: Emerging degradation trends in rangeland and pastoral livelihood zones","volume":"23","author":"Pricope","year":"2013","journal-title":"Glob. Environ. Chang."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Gaur, M., and Squires, V. (2018). Recent trends in drylands and future scope for advancement. Climate Variability Impacts on Land Use and Livelihoods in Drylands, Springer International Publishing AG.","DOI":"10.1007\/978-3-319-56681-8"},{"key":"ref_3","unstructured":"FAO (2017). Horn of Africa Cross-Border Drought Action Plan 2017, Food and Agriculture Organization of the United Nations."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1007\/s12571-009-0026-y","article-title":"Declining global per capita agricultural production and warming oceans threaten food security","volume":"1","author":"Funk","year":"2009","journal-title":"Food Secur."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.gloenvcha.2008.08.005","article-title":"Spatial variation of crop yield response to climate change in East Africa","volume":"19","author":"Thornton","year":"2009","journal-title":"Glob. Environ. Chang."},{"key":"ref_6","unstructured":"Salami, A., Kamara, A.B., and Brixiova, Z. (2010). Smallholder Agriculture in East Africa: Trends, Constraints and Opportunities, African Development Bank. Technical Report."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/S1464-1909(00)00015-0","article-title":"Water resources management in smallholder farms in Eastern and Southern Africa: An overview","volume":"25","year":"2000","journal-title":"Phys. Chem. Earth Part B Hydrol. Oceans Atmos."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1146\/annurev.ento.45.1.631","article-title":"Pest management strategies in traditional agriculture: An African perspective","volume":"45","author":"Abate","year":"2000","journal-title":"Annu. Rev. Entomol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1017\/S0014479710000876","article-title":"Review of seasonal climate forecasting for agriculture in sub-saharan Africa","volume":"47","author":"Hansen","year":"2011","journal-title":"Exp. Agric."},{"key":"ref_10","first-page":"211","article-title":"Trial on supplemental irrigation technology during rainy season in semi-arid area of Ethiopia","volume":"22","author":"Oya","year":"2012","journal-title":"J. Arid Land Stud."},{"key":"ref_11","unstructured":"Lebdi, F. (2016). Irrigation for Agricultural Transformation, African Center for Economic Transformation (ACET) and Japan International Cooperation Agency Research Institute (JICA-RI). Technical Report."},{"key":"ref_12","unstructured":"USGS (2017, September 12). Irrigation Water Use: Surface Irrigation, Available online: https:\/\/water.usgs.gov\/edu\/irfurrow.html."},{"key":"ref_13","unstructured":"FAO (2004). Economic Valuation of Water Resources in Agriculture: From the Sectoral to a Functional Perspective of Natural Resource Management, Food and Agriculture Organization of the United Nations. Water Reports No. 27."},{"key":"ref_14","unstructured":"IAC (2004). Realizing the Promise and Potential of African Agriculture: Implementation of Recommendations and Action Agenda, InterAcademy Council."},{"key":"ref_15","unstructured":"World Bank (2008). World Development Report 2008: Agriculture for Development, World Bank."},{"key":"ref_16","unstructured":"Tschirley, D. (2011). What Is the Scope for Horticulture to Drive Smallholder Poverty Reduction in Africa? Policy Synthesis, USAID, University of Michigan. Technical Report."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.worlddev.2011.05.007","article-title":"Smallholder Irrigation as a Poverty Alleviation Tool in Sub-Saharan Africa","volume":"40","author":"Burney","year":"2012","journal-title":"World Dev."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.pce.2014.10.002","article-title":"Identifying the potential for irrigation development in Mozambique: Capitalizing on the drivers behind farmer-led irrigation expansion","volume":"76\u201378","author":"Beekman","year":"2014","journal-title":"Phys. Chem. Earth Parts A\/B\/C"},{"key":"ref_19","unstructured":"Siddiqui, S., Cai, X., and Chandrasekharan, K. (2018, May 08). Irrigated Area Map Asia and Africa (International Water Management Institute). Available online: http:\/\/waterdata.iwmi.org\/applications\/irri{_}area\/."},{"key":"ref_20","unstructured":"Siebert, S., Henrich, V., Frenken, K., and Burke, J. (2013). Update of the Digital Global Map of Irrigation Areas to Version 5, Institute of Crop Science and Resource Conservation Rheinische Friedrich- Wilhelms-Universit\u00e4t. Technical Report."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3679","DOI":"10.1080\/01431160802698919","article-title":"Global irrigated area map (GIAM), derived from remote sensing, for the end of the last millennium","volume":"30","author":"Thenkabail","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"B\u00e9gu\u00e9, A., Arvor, D., Bellon, B., Betbeder, J., De Abelleyra, D., Ferraz, R.P.D., Lebourgeois, V., Lelong, C., Sim\u00f5es, M., and Ver\u00f3n, S.R. (2018). Remote sensing and cropping practices: A review. Remote Sens., 10.","DOI":"10.3390\/rs10010099"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.3390\/rs2092274","article-title":"Remote sensing of irrigated agriculture: Opportunities and challenges","volume":"2","author":"Ozdogan","year":"2010","journal-title":"Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1383","DOI":"10.14358\/PERS.75.12.1383","article-title":"Influence of resolution in irrigated area mapping and area estimation","volume":"75","author":"Velpuri","year":"2009","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.isprsjprs.2013.09.014","article-title":"Geographic Object-Based Image Analysis\u2014Towards a new paradigm","volume":"87","author":"Blaschke","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Xiong, J., Thenkabail, P.S., Tilton, J.C., Gumma, M.K., Teluguntla, P., Oliphant, A., Congalton, R.G., Yadav, K., and Gorelick, N. (2017). Nominal 30-m cropland extent map of continental Africa by integrating pixel-based and object-based algorithms using Sentinel-2 and Landsat-8 data on Google Earth Engine. Remote Sens., 9.","DOI":"10.3390\/rs9101065"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Lebourgeois, V., Dupuy, S., Vintrou, \u00c9., Ameline, M., Butler, S., and B\u00e9gu\u00e9, A. (2017). A combined random forest and OBIA classification scheme for mapping smallholder agriculture at different nomenclature levels using multisource data (simulated Sentinel-2 time series, VHRS and DEM). Remote Sens., 9.","DOI":"10.3390\/rs9030259"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1016\/j.rse.2018.06.036","article-title":"Remote Sensing of Environment 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_29","first-page":"118","article-title":"Mapping irrigated agriculture in complex landscapes using SPOT6 imagery and object-based image analysis\u2014A case study in the Central Rift Valley, Ethiopia","volume":"19","author":"Vogels","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3897","DOI":"10.1080\/01431160310001654428","article-title":"NDVI response to rainfall in different ecological zones in Jordan","volume":"25","author":"Suleiman","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_31","unstructured":"FEWSNET (2018, December 29). Illustrating the Extent and Severity of the 2016\/17 Horn of Africa Drought; Technical Report; Famine Early Warning Systems Network. Available online: https:\/\/fews.net\/sites\/default\/files\/documents\/reports\/FEWS_NET_Horn_of_Africa_June%202017_Drought_Map_Book.pdf."},{"key":"ref_32","unstructured":"Google Earth Engine Team (2017). Google Earth Engine: A Planetary-Scale Geo-Spatial Analysis Platform."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1002\/2016RG000544","article-title":"Climate and climatic variability of rainfall over eastern Africa","volume":"55","author":"Nicholson","year":"2017","journal-title":"Rev. Geophys."},{"key":"ref_34","unstructured":"Trimble (2017). eCognition Developer 9.3 Reference Book, Trimble Germany Documentation."},{"key":"ref_35","unstructured":"Baatz, M., and Sch\u00e4pe, A. (2000). Multiresolution Segmentation: An Optimization Approach for High Quality Multi-Scale Image Segmentation, Wichmann-Verlag. Angew. Geogr. Info. Verarbeitung."},{"key":"ref_36","unstructured":"ESRI (2018, February 07). ArcGIS Online Standard Service: World Imagery Collection, Map Server. Maps Throughout This Book Were Created Using ArcGIS\u00ae Software by ESRI. ArcGIS\u00ae and ArcMapTM Are the Intellectual Property of ESRI and Are Used Herein under License, Copyright \u00a9ESRI. Available online: https:\/\/www.arcgis.com\/index.html."},{"key":"ref_37","unstructured":"(2018, June 07). Google Maps. Available online: https:\/\/www.google.nl\/maps\/place\/Metehara."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_39","first-page":"18","article-title":"Classification and Regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_40","unstructured":"R Development Core Team (2017). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing."},{"key":"ref_41","unstructured":"Lillesand, T., Kiefer, R., and Chipman, J. (2015). Remote Sensing and Image Interpretation, John Wiley & Sons. [6th ed.]."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., and Michaelsen, J. (2015). The climate hazards infrared precipitation with stations\u2014A new environmental record for monitoring extremes. Sci. Data, 2.","DOI":"10.1038\/sdata.2015.66"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.isprsjprs.2014.09.002","article-title":"Global land cover mapping at 30 m resolution: A POK-based operational approach","volume":"103","author":"Chen","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.isprsjprs.2017.01.019","article-title":"Automated cropland mapping of continental Africa using Google Earth Engine cloud computing","volume":"126","author":"Xiong","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Arino, O., Gross, D., Ranera, F., Leroy, M., Bicheron, P., Brockman, C., Defourny, P., Vancutsem, C., and Achard, F. (2007, January 23\u201327). GlobCover: ESA service for global land cover from MERIS. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Barcelona, Spain.","DOI":"10.1109\/IGARSS.2007.4423328"},{"key":"ref_46","first-page":"321","article-title":"Global rain-fed, irrigated, and paddy croplands: A new high resolution map derived from remote sensing, crop inventories and climate data","volume":"38","author":"Salmon","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_47","unstructured":"Thenkabail, P. (2015). Global Food Security Support Analysis Data at nominal 1 km (GFSAD1km) derived from remote sensing in support of food security in the twenty-first century: current achievements and future possibilities. Remote Sensing Handbook (Volume II): Land Resources Monitoring, Modeling, and Mapping with Remote Sensing, CRC Press."},{"key":"ref_48","unstructured":"FAO (2018, October 10). AQUASTAT Main Database, Food and Agriculture Organization of the United Nations. Available online: http:\/\/www.fao.org\/nr\/water\/aquastat\/main\/index.stm."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"229","DOI":"10.14358\/PERS.81.3.229-238","article-title":"Mapping irrigated farmlands using vegetation and thermal thresholds derived from Landsat and ASTER data in an irrigation district of Australia","volume":"81","author":"Abuzar","year":"2015","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1899","DOI":"10.1007\/s11269-011-9780-7","article-title":"Status and potential of spate irrigation in Ethiopia","volume":"25","author":"Steenbergen","year":"2011","journal-title":"Water Resour. Manag."},{"key":"ref_51","unstructured":"Dejen, Z.A. (2014). Hydraulic and Operational Performance of Irrigation System in View of Interventions for Water Saving and Sustainability. [Ph.D. Thesis, UNESCO-IHE Institute for Water Education]."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.1080\/014311600210191","article-title":"Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data","volume":"21","author":"Loveland","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1091","DOI":"10.1007\/s00271-012-0390-9","article-title":"Optimum irrigation and pond operation to move away from exclusively rainfed agriculture: the Boru Dodota spate irrigation scheme, Ethiopia","volume":"31","author":"Demissie","year":"2013","journal-title":"Irrig. Sci."},{"key":"ref_54","unstructured":"DCP (2018, October 15). Degree Confluence Project: Organized Sampling of the Entire World at Every 1u Latitude and 1u Longitude Intersection. Available online: http:\/\/confluence.org\/."},{"key":"ref_55","unstructured":"GFSAD (2018, August 01). Global Food Security Support Analysis Data (GFSAD) Project Validation Dataset. Available online: https:\/\/croplands.org\/app\/data\/search."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/2\/143\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:25:36Z","timestamp":1760185536000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/2\/143"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,13]]},"references-count":55,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2019,1]]}},"alternative-id":["rs11020143"],"URL":"https:\/\/doi.org\/10.3390\/rs11020143","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,1,13]]}}}