{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T06:32:28Z","timestamp":1773901948857,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2018,5,5]],"date-time":"2018-05-05T00:00:00Z","timestamp":1525478400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Department of Agriculture, Fisheries and Forestry, South Africa"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Although advances in remote sensing have enhanced mapping and monitoring of irrigated areas, producing accurate cropping information through satellite image classification remains elusive due to the complexity of landscapes, changes in reflectance of different land-covers, the remote sensing data selected, and image processing methods used, among others. This study extracted agricultural fields in the former homelands of Venda and Gazankulu in Limpopo Province, South Africa. Landsat 8 imageries for 2015 were used, applying the maximum likelihood supervised classifier to delineate the agricultural fields. The normalized difference vegetation index (NDVI) applied on Landsat imageries on the mapped fields during the dry season (July to August) was used to identify irrigated areas, because years of satellite data analysis suggest that healthy crop conditions during dry seasons are only possible with irrigation. Ground truth points totaling 137 were collected during fieldwork for pre-processing and accuracy assessment. An accuracy of 96% was achieved on the mapped agricultural fields, yet the irrigated area map produced an initial accuracy of only 71%. This study explains and improves the 29% error margin from the irrigated areas. Accuracy was enhanced through post-classification correction (PCC) using 74 post-classification points randomly selected from the 2015 irrigated area map. High resolution aerial photographs of the 74 sample fields were acquired by an unmanned aerial vehicle (UAV) to give a clearer picture of the irrigated fields. The analysis shows that mapped irrigated fields that presented anomalies included abandoned croplands that had green invasive alien species or abandoned fruit plantations that had high NDVI values. The PCC analysis improved irrigated area mapping accuracy from 71% to 95%.<\/jats:p>","DOI":"10.3390\/rs10050712","type":"journal-article","created":{"date-parts":[[2018,5,7]],"date-time":"2018-05-07T03:12:21Z","timestamp":1525662741000},"page":"712","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Improving the Accuracy of Remotely Sensed Irrigated Areas Using Post-Classification Enhancement Through UAV Capability"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2944-1769","authenticated-orcid":false,"given":"Luxon","family":"Nhamo","sequence":"first","affiliation":[{"name":"International Water Management Institute, Southern Africa Regional Office (IWMI-SA), 141 Cresswell St., Weavind Park, Silverton, Pretoria 0184, South Africa"}]},{"given":"Ruben","family":"Van Dijk","sequence":"additional","affiliation":[{"name":"University of Wageningen, Droevendaalsesteeg 2, 6708 PB Wageningen, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5887-0904","authenticated-orcid":false,"given":"James","family":"Magidi","sequence":"additional","affiliation":[{"name":"Geomatics Department, Tshwane University of Technology, Staatsartillerie Road, Pretoria 0001, South Africa"}]},{"given":"David","family":"Wiberg","sequence":"additional","affiliation":[{"name":"International Water Management Institute (IWMI), 127, Sunil Mawatha, Pelawatte, Battaramulla 10120, Sri Lanka"}]},{"given":"Khathu","family":"Tshikolomo","sequence":"additional","affiliation":[{"name":"Limpopo Department of Agriculture &amp; Rural Development, 69 Biccard Street, Polokwane 0700, South Africa"}]}],"member":"1968","published-online":{"date-parts":[[2018,5,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Cai, X., Magidi, J., Nhamo, L., and van Koppen, B. (2017). Mapping Irrigated Areas in the Limpopo Province, South Africa, International Water Management Institute (IWMI). Working Paper 172.","DOI":"10.5337\/2017.205"},{"key":"ref_2","first-page":"13","article-title":"Detection and estimation of mixed paddy rice cropping patterns with MODIS data","volume":"13","author":"Peng","year":"2011","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1080\/03066150.2016.1219719","article-title":"African farmer-led irrigation development: Re-framing agricultural policy and investment?","volume":"44","author":"Woodhouse","year":"2017","journal-title":"J. Peasant Stud."},{"key":"ref_4","first-page":"247","article-title":"Calculation of the irrigation water needs spatial and temporal distribution in Greece","volume":"59","author":"Soulis","year":"2017","journal-title":"Eur. Water"},{"key":"ref_5","unstructured":"Kimenyi, M.S., Routman, B., Westbury, A., Omiti, J., and Akande, T. (2013). CAADP at 10: Progress towards Agricultural Prosperity, Africa Growth Initiative at Brookings."},{"key":"ref_6","unstructured":"NEPAD (2014). Implementation Strategy and Roadmap to Achieve the 2025 Vision on CAADP, African Union. Operationalizing the 2014 Malabo Declaration on Accelerated African Agricultural Growth and Transformation for Shared Prosperity and Improved Livelihood."},{"key":"ref_7","unstructured":"Awulachew, S.B., Yilma, A.D., Loulseged, M., Loiskandl, W., Ayana, M., and Alamirew, T. (2007). Water Resources and Irrigation Development in Ethiopia, International Water Management Institute (IWMI). IWMI Working Paper 123."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"50","DOI":"10.3390\/rs1020050","article-title":"Irrigated area maps and statistics of India using remote sensing and national statistics","volume":"1","author":"Thenkabail","year":"2009","journal-title":"Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"15715","DOI":"10.1073\/pnas.0506467102","article-title":"The role of science in solving the world\u2019s emerging water problems","volume":"102","author":"Jury","year":"2005","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_10","unstructured":"Corvalan, C., Hales, S., and McMichael, A.J. (2005). Ecosystems and Human Well-Being: Health Synthesis, World Health Organization (WHO)."},{"key":"ref_11","unstructured":"Forslund, A., Ren\u00f6f\u00e4lt, B.M., Barchiesi, S., Cross, K., Davidson, S., Farrell, T., Korsgaard, L., Krchnak, K., McClain, M., and Meijer, K. (2009). Securing water for ecosystems and human well-being: The importance of environmental flows. World Water Week, Swedish Water House."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1146\/annurev-environ-021810-094524","article-title":"State of the world's freshwater ecosystems: Physical, chemical, and biological changes","volume":"36","author":"Carpenter","year":"2011","journal-title":"Annu. Rev. Environ. Resour."},{"key":"ref_13","unstructured":"Gleick, P.H. (2014). The World\u2019s Water Volume 8: The Biennial Report on Freshwater Resources, Island Press."},{"key":"ref_14","first-page":"476","article-title":"The right irrigation? Policy directions for agricultural water management in sub-Saharan Africa","volume":"2","author":"Lankford","year":"2009","journal-title":"Water Altern."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"816","DOI":"10.3390\/rs3040816","article-title":"Mapping irrigated areas of Ghana using fusion of 30 m and 250 m resolution remote-sensing data","volume":"3","author":"Gumma","year":"2011","journal-title":"Remote Sens."},{"key":"ref_16","first-page":"1029","article-title":"Spectral matching techniques to determine historical land-use\/land-cover (LULC) and irrigated areas using time-series 0.1-degree AVHRR Pathfinder Datasets","volume":"73","author":"Thenkabail","year":"2007","journal-title":"Photogr. Eng. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.rse.2005.10.004","article-title":"Mapping paddy rice agriculture in south and southeast Asia using multi-temporal MODIS images","volume":"100","author":"Xiao","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/j.agwat.2009.09.021","article-title":"Integrating remote sensing, census and weather data for an assessment of rice yield, water consumption and water productivity in the Indo-Gangetic River Basin","volume":"97","author":"Cai","year":"2010","journal-title":"Agric. Water Manag."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Van Koppen, B., Nhamo, L., Cai, X., Gabriel, M., Sekgala, M., Shikwambana, S., Tshikolomo, K., Nevhutanda, S., Matlala, B., and Manyama, D. (2017). Smallholder Irrigation Schemes in the Limpopo Province, South Africa, International Water Management Institute (IWMI). IWMI Working Paper 174.","DOI":"10.5337\/2017.206"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"330","DOI":"10.3390\/rs1030330","article-title":"Improving the accuracy of land use and land cover classification of Landsat data using post-classification enhancement","volume":"1","author":"Manandhar","year":"2009","journal-title":"Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.rse.2006.02.023","article-title":"Correspondence analysis for detecting land cover change","volume":"102","author":"Cakir","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4365","DOI":"10.1080\/01431161.2010.486806","article-title":"Pre-classification and post-classification change-detection techniques to monitor land-cover and land-use change using multi-temporal Landsat imagery: A case study on Pisa Province in Italy","volume":"32","author":"Peiman","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","unstructured":"DAFF (2016). Abstract of Agricultural Statistics 2016."},{"key":"ref_24","unstructured":"Dankelman, I. (2010). Gendered vulnerability to climate change in Limpopo Province, South Africa. Gender and Climate Change: An Introduction, Earthscan."},{"key":"ref_25","unstructured":"DAFF (2003). Limpopo Water Management Area. Overview of Water Resources Availability and Utilization."},{"key":"ref_26","unstructured":"DAFF (2004). Luvuvhu-Letaba Water Management Area. Overview of Water Resources Availability and Utilization."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"32","DOI":"10.4102\/koedoe.v50i1.125","article-title":"Major vegetation types of the Soutpansberg Conservancy and the Blouberg Nature Reserve, South Africa","volume":"50","author":"Mostert","year":"2008","journal-title":"Koedoe"},{"key":"ref_28","unstructured":"NASA (2015). Landsat 8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensot)."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"673","DOI":"10.3390\/rs2030673","article-title":"Application of vegetation indices for agricultural crop yield prediction using neural network techniques","volume":"2","author":"Panda","year":"2010","journal-title":"Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared linear combinations for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1016\/j.protcy.2012.10.074","article-title":"Feature extraction using normalized difference vegetation index (NDVI): A case study of jabalpur city","volume":"6","author":"Bhandari","year":"2012","journal-title":"Procedia Technol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.worlddev.2015.05.012","article-title":"The State of Family Farms in the World","volume":"87","author":"Graeub","year":"2016","journal-title":"World Dev."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1177\/001316446002000104","article-title":"A Coefficient of Agreement for Nominal Scales","volume":"20","author":"Cohen","year":"1960","journal-title":"Educ. Psychol. Meas."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/5\/712\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:03:22Z","timestamp":1760195002000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/5\/712"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,5,5]]},"references-count":33,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2018,5]]}},"alternative-id":["rs10050712"],"URL":"https:\/\/doi.org\/10.3390\/rs10050712","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,5,5]]}}}