{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T11:23:39Z","timestamp":1771068219973,"version":"3.50.1"},"reference-count":63,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2018,11,17]],"date-time":"2018-11-17T00:00:00Z","timestamp":1542412800000},"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>Sound water resources planning and management requires adequate data with sufficient spatial and temporal resolution. This is especially true in the context of irrigated agriculture, which is one of the main consumptive users of the world\u2019s freshwater resources. Existing remote sensing methods for the management of irrigated agricultural systems are often based on empirical cropland data that are difficult to obtain, and that put into question the transferability of mapping algorithms in space and time. Here we implement an automatic irrigation mapping procedure in Google Earth Engine that uses surface reflectance satellite imagery from different sensors. The method is based on unsupervised training of a pixel-by-pixel classification algorithm within image regions identified through unsupervised object-based segmentation, followed by multi-temporal image analysis to distinguish productive irrigated fields from non-productive and non-irrigated areas. Ground-based data are not required. The final output of the mapping algorithm are monthly and annual irrigation maps (30 m resolution). The novel method is applied to the Central Asian Chu and Talas River Basins that are shared between upstream Kyrgyzstan and downstream Kazakhstan. We calculate the development of irrigated areas from 2000 to 2017 and assess the classification results in terms of robustness and accuracy. Based on seven available validation scenes (in total more than 2.5 million pixels) the classification accuracy is 77\u201396%. We show that on the Kyrgyz side of the Talas basin, the identified increasing trends over the years are highly significant (23% area increase between 2000 and 2017). In the Kazakh parts of the basins the irrigated acreages are relatively stable over time, but the average irrigation frequency within Soviet-era irrigation perimeters is very low, which points to a poor physical condition of the irrigation infrastructure and inadequate water supply.<\/jats:p>","DOI":"10.3390\/rs10111823","type":"journal-article","created":{"date-parts":[[2018,11,21]],"date-time":"2018-11-21T11:23:27Z","timestamp":1542799407000},"page":"1823","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["An Unsupervised Classification Algorithm for Multi-Temporal Irrigated Area Mapping in Central Asia"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9817-8541","authenticated-orcid":false,"given":"Silvan","family":"Ragettli","sequence":"first","affiliation":[{"name":"Hydrosolutions Ltd., 8006 Zurich, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Timo","family":"Herberz","sequence":"additional","affiliation":[{"name":"Hydrosolutions Ltd., 8006 Zurich, Switzerland"},{"name":"Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tobias","family":"Siegfried","sequence":"additional","affiliation":[{"name":"Hydrosolutions Ltd., 8006 Zurich, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,11,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/S0378-3774(00)00080-9","article-title":"Remote sensing for irrigated agriculture: Examples from research and possible applications","volume":"46","author":"Bastiaanssen","year":"2000","journal-title":"Agric. Water Manag."},{"key":"ref_2","unstructured":"Seckler, D.W. (1998). World Water Demand and Supply, 1990 to 2025: Scenarios and Issues, IWMI."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.rse.2004.12.018","article-title":"Ganges and Indus river basin land use\/land cover (LULC) and irrigated area mapping using continuous streams of MODIS data","volume":"95","author":"Thenkabail","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_4","unstructured":"Bruinsma, J. (2003). World Agriculture: Towards 2015\/2030: An FAO Perspective, Earthscan."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1119","DOI":"10.5194\/hess-22-1119-2018","article-title":"A global approach to estimate irrigated areas\u2014A comparison between different data and statistics","volume":"22","author":"Meier","year":"2018","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2890","DOI":"10.3390\/rs4102890","article-title":"An automated cropland classification algorithm (ACCA) for Tajikistan by combining landsat, MODIS, and secondary data","volume":"4","author":"Thenkabail","year":"2012","journal-title":"Remote Sens."},{"key":"ref_7","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_8","doi-asserted-by":"crossref","unstructured":"Gao, Q., Zribi, M., Escorihuela, M., Baghdadi, N., and Segui, P. (2018). Irrigation Mapping Using Sentinel-1 Time Series at Field Scale. Remote Sens., 10.","DOI":"10.3390\/rs10091495"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1016\/j.rse.2015.03.028","article-title":"Mapping farmland abandonment and recultivation across Europe using MODIS NDVI time series","volume":"163","author":"Estel","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Sharma, A.K., Hubert-Moy, L., Buvaneshwari, S., Sekhar, M., Ruiz, L., Bandyopadhyay, S., and Corgne, S. (2018). Irrigation history estimation using multitemporal landsat satellite images: Application to an intensive groundwater irrigated agricultural watershed in India. Remote Sens., 10.","DOI":"10.3390\/rs10060893"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/sdata.2016.118","article-title":"Remotely sensed high resolution irrigated area mapping in India for 2000 to 2015","volume":"3","author":"Ambika","year":"2016","journal-title":"Sci. Data"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Conrad, C., Sch\u00f6nbrodt-Stitt, S., L\u00f6w, F., Sorokin, D., and Paeth, H. (2016). Cropping intensity in the Aral Sea Basin and its dependency from the runoffformation 2000\u20132012. Remote Sens., 8.","DOI":"10.3390\/rs8080630"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.compag.2008.04.001","article-title":"Mapping irrigated area in Mediterranean basins using low cost satellite Earth Observation","volume":"64","author":"Alexandridis","year":"2008","journal-title":"Comput. Electron. Agric."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4245","DOI":"10.1080\/01431160600851801","article-title":"Irrigated area mapping in heterogeneous landscapes with MODIS time series, ground truth and census data, Krishna Basin, India","volume":"27","author":"Biggs","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","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_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3390\/rs8030207","article-title":"Mapping irrigated and rainfed wheat areas using multi-temporal satellite data","volume":"8","author":"Jin","year":"2016","journal-title":"Remote Sens."},{"key":"ref_17","first-page":"72","article-title":"Object-based delineation of homogeneous landscape units at regional scale based on modis time series","volume":"37","author":"Bisquert","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"738","DOI":"10.1080\/01431161.2013.873151","article-title":"Combining per-pixel and object-based classifications for mapping land cover over large areas","volume":"35","author":"Costa","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_19","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_20","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_21","doi-asserted-by":"crossref","first-page":"3520","DOI":"10.1016\/j.rse.2008.04.010","article-title":"A new methodology to map irrigated areas using multi-temporal MODIS and ancillary data: An application example in the continental US","volume":"112","author":"Ozdogan","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1016\/j.rse.2004.12.009","article-title":"Mapping paddy rice agriculture in southern China using multi-temporal MODIS images","volume":"95","author":"Xiao","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"535","DOI":"10.5194\/hess-9-535-2005","article-title":"Development and validation of the global map of irrigation areas","volume":"9","author":"Siebert","year":"2005","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1127\/0941-2948\/2006\/0130","article-title":"World map of the K\u00f6ppen-Geiger climate classification updated","volume":"15","author":"Kottek","year":"2006","journal-title":"Meteorol. Z."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Funk, C.C., Peterson, P.J., Landsfeld, M.F., Pedreros, D.H., Verdin, J.P., Rowland, J.D., Romero, B.E., Husak, G.J., Michaelsen, J.C., and Verdin, A.P. (2014). A Quasi-Global Precipitation Time Series for Drought Monitoring, Earth Resources Observation and Science (EROS) Center. Technical Report, US Geological Survey.","DOI":"10.3133\/ds832"},{"key":"ref_27","unstructured":"Demydenko, A. (2005, January 6\u20138). The evolution of bilateral agreements in the face of changing geo-politics in the Chu-Talas basin. Proceedings of the International Conference \u2018WATER: A Catalyst for Peace\u2019, Zaragoza, Spain."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1023\/A:1017558921173","article-title":"Similarities and Differences in Reservoirs of Kyrgyzstan","volume":"35","author":"Alekseevskii","year":"2001","journal-title":"Power Technol. Eng. (Former. Hydrotech. Construct.)"},{"key":"ref_29","unstructured":"Bucknall, J., Klytchnikova, I., Lampietti, J., Lundell, M., Scatasta, M., and Thurman, M. (2003). Irrigation in Central Asia. Social, Economic and Environmental Considerations, The World Bank. Technical Report February."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1016\/j.rse.2010.12.010","article-title":"A simple and effective method for filling gaps in Landsat ETM+ SLC-off images","volume":"115","author":"Chen","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2691","DOI":"10.1109\/TGRS.2004.840720","article-title":"Landsat sensor performance: History and current status","volume":"42","author":"Markham","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","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_33","doi-asserted-by":"crossref","first-page":"3112","DOI":"10.1016\/j.rse.2008.03.009","article-title":"Multi-temporal MODIS-Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data","volume":"112","author":"Roy","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.rse.2017.10.030","article-title":"Detecting irrigation extent, frequency, and timing in a heterogeneous arid agricultural region using MODIS time series, Landsat imagery, and ancillary data","volume":"204","author":"Chen","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_35","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_36","doi-asserted-by":"crossref","first-page":"2388","DOI":"10.3390\/rs2102388","article-title":"Mapping irrigated lands at 250-m scale by merging MODIS data and National Agricultural Statistics","volume":"2","author":"Pervez","year":"2010","journal-title":"Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2005RG000183","article-title":"The shuttle radar topography mission","volume":"45","author":"Farr","year":"2007","journal-title":"Rev. Geophys."},{"key":"ref_38","unstructured":"Jarvis, A., Reuter, H.I., Nelson, A., and Guevara, E. (2018, June 01). Hole-Filled SRTM for the Globe Version 4. Available online: http:\/\/srtm.csi.cgiar.org."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1079","DOI":"10.1109\/83.701170","article-title":"Region growing: A new approach","volume":"7","author":"Hojjatoleslami","year":"1998","journal-title":"IEEE Trans. Image Process."},{"key":"ref_40","first-page":"27","article-title":"Image Segmentation by Clustering Methods: Performance Analysis","volume":"29","author":"Sathya","year":"2011","journal-title":"Int. J. Comput. Appl."},{"key":"ref_41","first-page":"323","article-title":"Landscape analysis using multiscale segmentation and object orientated classification","volume":"8","author":"Clark","year":"2009","journal-title":"Recent Adv. Remote Sens. Geoinf. Process. Land Degrad. Assess."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1254","DOI":"10.21273\/HORTSCI.27.12.1254","article-title":"Reporting of objective color measurements","volume":"27","author":"McGuire","year":"1992","journal-title":"HortScience"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1007\/BF00138369","article-title":"A comparison of the vegetation response to rainfall in the Sahel and East Africa, using normalized difference vegetation index from NOAA AVHRR","volume":"17","author":"Nicholson","year":"1990","journal-title":"Clim. Chang."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"3595","DOI":"10.1080\/01431160110115799","article-title":"Quantitative relationships between field-measured leaf area index and vegetation index derived from VEGETATION images for paddy rice fields","volume":"23","author":"Xiao","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1080\/01431169108929727","article-title":"Satellite remote sensing of primary production: Comparison of results for Sahelian grasslands 1981\u20131988","volume":"12","author":"Prince","year":"1991","journal-title":"Int. J. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1533","DOI":"10.1080\/014311698215333","article-title":"The derivation of the green vegetation fraction from NOAA\/AVHRR data for use in numerical weather prediction models","volume":"19","author":"Gutman","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"704","DOI":"10.1016\/j.rse.2013.10.008","article-title":"A near real-time water surface detection method based on HSV transformation of MODIS multi-Spectral time series data","volume":"140","author":"Pekel","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_48","first-page":"100","article-title":"Algorithm AS 136: A k-means clustering algorithm","volume":"28","author":"Hartigan","year":"1979","journal-title":"J. R. Stat. Soc. Ser. C"},{"key":"ref_49","unstructured":"MacQueen, J. (July, January 21). Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Oakland, CA, USA."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Portmann, F.T., Siebert, S., and D\u00f6ll, P. (2010). MIRCA2000-Global monthly irrigated and rainfed crop areas around the year 2000: A new high-resolution data set for agricultural and hydrological modeling. Glob. Biogeochem. Cycles, 24.","DOI":"10.1029\/2008GB003435"},{"key":"ref_51","unstructured":"Zhao, H., Yang, Z., Di, L., Li, L., and Zhu, H. (2009, January 12\u201314). Crop phenology date estimation based on NDVI derived from the reconstructed MODIS daily surface reflectance data. Proceedings of the 17th International Conference on Geoinformatics, Fairfax, VA, USA."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.isprsjprs.2011.11.002","article-title":"An assessment of the effectiveness of a random forest classifier for land-cover classification","volume":"67","author":"Ghimire","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Perner, P. (2012). How Many Trees in a Random Forest. Machine Learning and Data Mining in Pattern Recognition, Springer.","DOI":"10.1007\/978-3-642-31537-4"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Pelletier, C., Valero, S., Inglada, J., Champion, N., Sicre, C.M., and Dedieu, G. (2017). Effect of training class label noise on classification performances for land cover mapping with satellite image time series. Remote Sens., 9.","DOI":"10.3390\/rs9020173"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Milisavljevic, N. (2009). Multi-Sensor & Temporal Data Fusion for Cloud-Free Vegetation Index Composites. Sensor and Data Fusion, InTech.","DOI":"10.5772\/102"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Gao, Q., Zribi, M., Escorihuela, M.J., and Baghdadi, N. (2017). Synergetic use of sentinel-1 and sentinel-2 data for soil moisture mapping at 100 m resolution. Sensors, 17.","DOI":"10.3390\/s17091966"},{"key":"ref_57","unstructured":"Google Inc. (2018). Google Earth Engine API, Google Inc."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/S0034-4257(01)00295-4","article-title":"Status of land cover classification accuracy assessment","volume":"80","author":"Foody","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.rse.2014.02.015","article-title":"Good practices for estimating area and assessing accuracy of land change","volume":"148","author":"Olofsson","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1379","DOI":"10.1080\/01621459.1968.10480934","article-title":"Estimates of the regression coefficient based on Kendall\u2019s tau","volume":"63","author":"Sen","year":"1968","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_61","unstructured":"Rahaman, M., and Varis, O. (2008). Passing over the conflict. The Chu Talas basin agreement as a model for Central Asia. Central Asian Waters: Social, Economic, Environmental and Governance Puzzle, Water & Development Publications-Helsinki University of Technology."},{"key":"ref_62","first-page":"107","article-title":"Transboundary Issues of Wildlife Management in the Talas River Basin","volume":"11","author":"Kireycheva","year":"2015","journal-title":"Int. Res. J."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"2359","DOI":"10.1080\/0143116042000298261","article-title":"Remote sensing and GIS for estimation of irrigation crop water demand","volume":"26","author":"Tanton","year":"2005","journal-title":"Int. J. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/11\/1823\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:30:25Z","timestamp":1760196625000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/11\/1823"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,17]]},"references-count":63,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2018,11]]}},"alternative-id":["rs10111823"],"URL":"https:\/\/doi.org\/10.3390\/rs10111823","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,11,17]]}}}