{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T14:14:46Z","timestamp":1769696086789,"version":"3.49.0"},"reference-count":45,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,3,29]],"date-time":"2021-03-29T00:00:00Z","timestamp":1616976000000},"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>Accurate irrigated area maps remain difficult to generate, as smallholder irrigation schemes often escape detection. Efforts to map smallholder irrigation have often relied on complex classification models fitted to temporal image stacks. The use of high-dimensional geometric median composites (geomedians) and high-dimensional statistics of time-series may simplify classification models and enhance accuracy. High-dimensional statistics for temporal variation, such as the spectral median absolute deviation, indicate spectral variability within a period contributing to a geomedian. The Ord River Irrigation Area was used to validate Digital Earth Australia\u2019s annual geomedian and temporal variation products. Geomedian composites and the spectral median absolute deviation were then calculated on Sentinel-2 images for three smallholder irrigation schemes in Matabeleland, Zimbabwe, none of which were classified as areas equipped for irrigation in AQUASTAT\u2019s Global Map of Irrigated Areas. Supervised random forest classification was applied to all sites. For the three Matabeleland sites, the average Kappa coefficient was 0.87 and overall accuracy was 95.9% on validation data. This compared with 0.12 and 77.2%, respectively, for the Food and Agriculture Organisation\u2019s Water Productivity through Open access of Remotely sensed derived data (WaPOR) land use classification map. The spectral median absolute deviation was ranked among the most important variables across all models based on mean decrease in accuracy. Change detection capacity also means the spectral median absolute deviation has some advantages for cropland mapping over indices such as the Normalized Difference Vegetation Index. The method demonstrated shows potential to be deployed across countries and regions where smallholder irrigation schemes account for large proportions of irrigated area.<\/jats:p>","DOI":"10.3390\/rs13071300","type":"journal-article","created":{"date-parts":[[2021,3,29]],"date-time":"2021-03-29T16:01:57Z","timestamp":1617033717000},"page":"1300","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["High-Dimensional Satellite Image Compositing and Statistics for Enhanced Irrigated Crop Mapping"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6585-6120","authenticated-orcid":false,"given":"Michael J.","family":"Wellington","sequence":"first","affiliation":[{"name":"Fenner School of Environment and Society, The Australian National University, Canberra, ACT 2601, Australia"}]},{"given":"Luigi J.","family":"Renzullo","sequence":"additional","affiliation":[{"name":"Fenner School of Environment and Society, The Australian National University, Canberra, ACT 2601, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Vogels, M.F., De Jong, S.M., Sterk, G., Douma, H., and Addink, E.A. (2019). Spatio-Temporal Patterns of Smallholder Irrigated Agriculture in the Horn of Africa Using GEOBIA and Sentinel-2 Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11020143"},{"key":"ref_2","unstructured":"Zawe, C., Madyiwa, S., and Matete, M. (2015). Trends and Outlook: Agricultural Water Management in Southern Africa-Country Report Zimbabwe, International Water Management Institute."},{"key":"ref_3","unstructured":"AQUASTAT (2020, September 01). Agricultural Water Use Statistics: Zimbabwe. Available online: http:\/\/www.fao.org\/nr\/water\/aquastat\/data\/query\/index.html?lang=en."},{"key":"ref_4","unstructured":"Food and Agriculture Organisation (2020, December 10). Zimbabwe. Available online: http:\/\/www.fao.org\/aquastat\/en\/geospatial-information\/global-maps-irrigated-areas\/irrigation-by-country\/country\/ZWE."},{"key":"ref_5","unstructured":"Pittock, J., Ramshaw, P., Bjornlund, H., Kimaro, E., Mdemu, M.V., Moyo, M., Ndema, S., van Rooyen, A., Stirzaker, R., and de Sousa, W. (2018). Transforming Smallholder Irrigation Schemes in Africa. A Guide to Help Farmers Become More Profitable and Sustainable."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"S20","DOI":"10.1080\/07900627.2020.1739512","article-title":"Why agricultural production in sub-Saharan Africa remains low compared to the rest of the world\u2014A historical perspective","volume":"36","author":"Bjornlund","year":"2020","journal-title":"Int. J. Water Resour. Dev."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"824","DOI":"10.1080\/07900627.2017.1321531","article-title":"Irrigating Africa: Policy barriers and opportunities for enhanced productivity of smallholder farmers","volume":"33","author":"Mwamakamba","year":"2017","journal-title":"Int. J. Water Resour. Dev."},{"key":"ref_8","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_9","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_10","doi-asserted-by":"crossref","first-page":"111402","DOI":"10.1016\/j.rse.2019.111402","article-title":"Remote sensing for agricultural applications: A meta-review","volume":"236","author":"Weiss","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Bousbih, S., Zribi, M., El Hajj, M., Baghdadi, N., Lili-Chabaane, Z., Gao, Q., and Fanise, P. (2018). Soil Moisture and Irrigation Mapping in A Semi-Arid Region, Based on the Synergetic Use of Sentinel-1 and Sentinel-2 Data. Remote Sens., 10.","DOI":"10.3390\/rs10121953"},{"key":"ref_12","unstructured":"Hollander, V.R. (2018). Mapping of Farmer-Led Irrigated Agriculture with Remote Sensing: A Case Study in Central Mozambique, Delft University of Technology."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"6527","DOI":"10.1080\/01431161.2020.1739355","article-title":"Crops monitoring and yield estimation using sentinel products in semi-arid smallholder irrigation schemes","volume":"41","author":"Ouattara","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"6254","DOI":"10.1109\/TGRS.2017.2723896","article-title":"High-Dimensional Pixel Composites from Earth Observation Time Series","volume":"55","author":"Roberts","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1038","DOI":"10.1080\/2150704X.2019.1648901","article-title":"Optimizing harmonics from Landsat time series data: The case of mapping rainfed and irrigated agriculture in Zimbabwe","volume":"10","author":"Landmann","year":"2019","journal-title":"Remote Sens. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1016\/j.rse.2017.10.005","article-title":"Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis","volume":"204","author":"Belgiu","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_17","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_18","doi-asserted-by":"crossref","unstructured":"Roberts, D., Dunn, B., and Mueller, N. (2018, January 22\u201327). Open Data Cube Products Using High-Dimensional Statistics of Time Series. Proceedings of the IGARSS 2018\u20142018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518312"},{"key":"ref_19","unstructured":"Geoscience Australia (2020, September 01). DEA Surface Reflectance Geomedian (Landsat), Available online: https:\/\/cmi.ga.gov.au\/data-products\/dea\/140\/dea-surface-reflectance-geomedian-landsat."},{"key":"ref_20","unstructured":"Geoscience Australia (2020, September 01). DEA Surface Reflectance Median Absolute Deviation (Landsat), Available online: https:\/\/cmi.ga.gov.au\/data-products\/dea\/346\/dea-surface-reflectance-median-absolute-deviation-landsat."},{"key":"ref_21","unstructured":"Davies, A., Strickland, G., Moulden, J., and Yeates, S. (2020, September 01). NORpak Ord River Irrigation Area Cotton Production and Management Guidelines for the Ord River Irrigation Area (ORIA) 2007. Available online: http:\/\/www.insidecotton.com\/xmlui\/handle\/1\/204."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.agsy.2017.04.010","article-title":"Irrigated agricultural development in northern Australia: Value-chain challenges and opportunities","volume":"155","author":"Ash","year":"2017","journal-title":"Agric. Syst."},{"key":"ref_23","unstructured":"Wellington, M. (2020). Locations of TISA Irrigation Schemes in Zimbabwe, Australian National University."},{"key":"ref_24","unstructured":"DAFWA (2021, February 12). Ord River Development and Irrigated Agriculture, Available online: https:\/\/www.agric.wa.gov.au\/assessment-agricultural-expansion\/ord-river-development-and-irrigated-agriculture."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1080\/07900627.2016.1175339","article-title":"Irrigation development in Zimbabwe: Understanding productivity barriers and opportunities at Mkoba and Silalatshani irrigation schemes","volume":"33","author":"Moyo","year":"2017","journal-title":"Int. J. Water Resour. Dev."},{"key":"ref_26","unstructured":"Digital Earth Australia (2020, September 01). Calculating Band Indices, Available online: https:\/\/docs.dea.ga.gov.au\/notebooks\/Frequently_used_code\/Calculating_band_indices.html."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Rouse, J., Haas, R., Deering, D., Schell, J.A., and Harlan, J. (1973, January 10\u201314). Monitoring vegetation systems in the Great Plains with ERTS. Proceedings of the 3rd ERTS Symposium, Washington, DC, USA.","DOI":"10.1109\/TGE.1973.294284"},{"key":"ref_28","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_29","first-page":"39","article-title":"Tropical forest cover density mapping","volume":"43","author":"Rikimaru","year":"2002","journal-title":"Trop. Ecol."},{"key":"ref_30","unstructured":"Digital Earth Africa (2020, September 01). s2_l2a, Available online: https:\/\/explorer.digitalearth.africa\/products\/s2_l2a."},{"key":"ref_31","unstructured":"Kirill Kouzoubov (2020, September 01). _geomedian.py. Available online: https:\/\/github.com\/opendatacube\/odc-tools\/blob\/develop\/libs\/algo\/odc\/algo\/_geomedian.py."},{"key":"ref_32","unstructured":"Digital Earth Africa (2020, September 01). Band Indices, Available online: https:\/\/training.digitalearthafrica.org\/en\/latest\/session_4\/01_band_indices.html."},{"key":"ref_33","first-page":"1","article-title":"Classes and methods for spatial data in R","volume":"Volume 5","author":"Pebesma","year":"2005","journal-title":"R News"},{"key":"ref_34","unstructured":"Kuhn, M. (R News, 2020). Caret: Classification and regression training. R package version 6.0-86, R News."},{"key":"ref_35","first-page":"1","article-title":"Classification and regression by randomForest","volume":"Volume 2","author":"Liaw","year":"2001","journal-title":"R News"},{"key":"ref_36","unstructured":"Food and Agriculture Organisation (2020, January 15). AQUASTAT Maps. Available online: https:\/\/data.apps.fao.org\/aquamaps\/."},{"key":"ref_37","unstructured":"Food and Agriculture Organisation (2021, February 14). Global Map of Irrigation Areas (GMIA). Available online: http:\/\/www.fao.org\/aquastat\/en\/geospatial-information\/global-maps-irrigated-areas\/."},{"key":"ref_38","unstructured":"Siebert, S., Henrich, V., Frenken, K., and Burke, J. (2013). Global Map of Irrigation Areas Version 5, Rheinische Friedrich-Wilhelms-University."},{"key":"ref_39","unstructured":"Food and Agriculture Organisation (2018). WaPOR Database Methodology: Level 1, Food and Agriculture Organisation. Remote Sensing for Water Productivity Technical Report."},{"key":"ref_40","unstructured":"Food and Agriculture Organisation (2021, February 12). WaPOR 2.1. Available online: https:\/\/wapor.apps.fao.org\/home\/WAPOR_2\/1."},{"key":"ref_41","unstructured":"Wellington, M. (2021). Distribution of Crop Types in the Ord River Irrigation Area, Australian National University."},{"key":"ref_42","unstructured":"Savva, A.P., and Frenken, K. (2002). Planning, Development, Monitoring and Evaluation of Irrigated Agriculture with Farmer Participation, Food and Agriculture Organisation."},{"key":"ref_43","unstructured":"Quirke, G. (2019). Assessing Smallholder Irrigated Plot-Use in Zimbabwe, Australian National University."},{"key":"ref_44","unstructured":"Bell, M., Faulkner, R., Hotchkiss, P., Lambert, R., Roberts, N., and Windram, A. (1987). The Use of Dambos in Rural Development, with Reference to Zimbabwe, Loughborough University, University of Zimbabwe."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1080\/14735903.2013.863450","article-title":"Optimizing dambo (seasonal wetland) cultivation for climate change adaptation and sustainable crop production in the smallholder farming areas of Zimbabwe","volume":"13","author":"Nyamadzawo","year":"2014","journal-title":"Int. J. Agric. Sustain."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/7\/1300\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:53:22Z","timestamp":1760363602000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/7\/1300"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,29]]},"references-count":45,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["rs13071300"],"URL":"https:\/\/doi.org\/10.3390\/rs13071300","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,29]]}}}