{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T17:52:00Z","timestamp":1770486720617,"version":"3.49.0"},"reference-count":62,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2016,3,3]],"date-time":"2016-03-03T00:00:00Z","timestamp":1456963200000},"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>Irrigation is crucial to agriculture in arid and semi-arid areas and significantly contributes to crop development, food diversity and the sustainability of agro-ecosystems. For a specific crop, the separation of its irrigated and rainfed areas is difficult, because their phenology is similar and therefore less distinguishable, especially when there are phenology shifts due to various factors, such as elevation and latitude. In this study, we present a simple, but robust method to map irrigated and rainfed wheat areas in a semi-arid region of China. We used the Normalized Difference Vegetation Index (NDVI) at a 30 \u00d7 30 m spatial resolution derived from the Chinese HJ-1A\/B (HuanJing(HJ) means environment in Chinese) satellite to create a time series spanning the whole growth period of wheat from September 2010 to July 2011. The maximum NDVI and time-integrated NDVI (TIN) that usually exhibit significant differences between irrigated and rainfed wheat were selected to establish a classification model using a support vector machine (SVM) algorithm. The overall accuracy of the Google-Earth testing samples was 96.0%, indicating that the classification results are accurate. The estimated irrigated-to-rainfed ratio was 4.4:5.6, close to the estimates provided by the agricultural sector in Shanxi Province. Our results illustrate that the SVM classification model can effectively avoid empirical thresholds in supervised classification and realistically capture the magnitude and spatial patterns of rainfed and irrigated wheat areas. The approach in this study can be applied to map irrigated\/rainfed areas in other regions when field observational data are available.<\/jats:p>","DOI":"10.3390\/rs8030207","type":"journal-article","created":{"date-parts":[[2016,3,3]],"date-time":"2016-03-03T10:30:47Z","timestamp":1457001047000},"page":"207","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":67,"title":["Mapping Irrigated and Rainfed Wheat Areas Using Multi-Temporal Satellite Data"],"prefix":"10.3390","volume":"8","author":[{"given":"Ning","family":"Jin","sequence":"first","affiliation":[{"name":"Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Shanxi Climate Center, Taiyuan 030002, China"}]},{"given":"Bo","family":"Tao","sequence":"additional","affiliation":[{"name":"Department of Plant and Soil Sciences, College of Agriculture, Food and Environment, University of Kentucky, KY 40506, USA"}]},{"given":"Wei","family":"Ren","sequence":"additional","affiliation":[{"name":"Department of Plant and Soil Sciences, College of Agriculture, Food and Environment, University of Kentucky, KY 40506, USA"}]},{"given":"Meichen","family":"Feng","sequence":"additional","affiliation":[{"name":"Institute of Dry Farming Engineering, Shanxi Agricultural University, Shanxi Taigu 030801, China"}]},{"given":"Rui","family":"Sun","sequence":"additional","affiliation":[{"name":"Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Beijing 100101, China"}]},{"given":"Liang","family":"He","sequence":"additional","affiliation":[{"name":"National Meteorological Center, Beijing 100081, China"}]},{"given":"Wei","family":"Zhuang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Beijing 100101, China"}]},{"given":"Qiang","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Life Sciences, University of Technology Sydney, P.O. Box 123, Broadway NSW 2007, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2016,3,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.scitotenv.2014.11.076","article-title":"Quantifying the link between crop production and mined groundwater irrigation in China","volume":"511","author":"Grogan","year":"2015","journal-title":"Sci. Total Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1080\/02508060008686794","article-title":"Appraisal and assessment of world water resources","volume":"25","author":"Shiklomanov","year":"2000","journal-title":"Water Int."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.agwat.2009.08.011","article-title":"An improved water use efficiency of cereals under temporal and spatial deficit irrigation in north China","volume":"97","author":"Du","year":"2010","journal-title":"Agric. Water Manag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1007\/s00382-004-0402-4","article-title":"Direct human influence of irrigation on atmospheric water vapour and climate","volume":"22","author":"Boucher","year":"2004","journal-title":"Clim. Dyn."},{"key":"ref_5","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_6","doi-asserted-by":"crossref","first-page":"7612","DOI":"10.1073\/pnas.0500208102","article-title":"Human modification of global water vapor flows from the land surface","volume":"102","author":"Gordon","year":"2005","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.agwat.2005.01.006","article-title":"Optimizing irrigation scheduling for winter wheat in the north china plain","volume":"76","author":"Li","year":"2005","journal-title":"Agric. Water Manag."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1579\/0044-7447-34.3.230","article-title":"Geospatial indicators of emerging water stress: An application to africa","volume":"34","author":"Douglas","year":"2005","journal-title":"AMBIO: A J. Hum. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1175\/JHM485.1","article-title":"Examination of the bouchet-morton complementary relationship using a mesoscale climate model and observations under a progressive irrigation scenario","volume":"7","author":"Ozdogan","year":"2006","journal-title":"J. Hydrometeorol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.agwat.2015.03.021","article-title":"Effects of ridge and furrow rainwater harvesting system combined with irrigation on improving water use efficiency of maize (Zea mays L.) in semi-humid area of China","volume":"158","author":"Wu","year":"2015","journal-title":"Agric. Water Manag."},{"key":"ref_11","first-page":"55","article-title":"A digital global map of irrigated areas","volume":"49","author":"Siebert","year":"2000","journal-title":"ICID J."},{"key":"ref_12","first-page":"1299","article-title":"Development and validation of the global map of irrigation areas","volume":"2","author":"Siebert","year":"2005","journal-title":"Hydrol. Earth Syst. Sci. Discuss."},{"key":"ref_13","unstructured":"Food and Agriculture Organization of the United Nations (2010). Provincial Land Cover Atlas of Islamic Republic of Afghanistan, FAO."},{"key":"ref_14","unstructured":"Droogers, P. (2002). Global Irrigated Area Mapping: Overview and Recommendations, IWMI."},{"key":"ref_15","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_16","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_17","unstructured":"Thenkabail, P.S. (2006). An Irrigated Area Map of the World (1999), Derived from Remote Sensing, IWMI."},{"key":"ref_18","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_19","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_20","doi-asserted-by":"crossref","first-page":"2527","DOI":"10.1080\/01431160500104335","article-title":"Discrimination of irrigated and rainfed rice in a tropical agricultural system using SPOT vegetation NDVI and rainfall data","volume":"26","author":"Kamthonkiat","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_21","first-page":"103","article-title":"A support vector machine to identify irrigated crop types using time-series landsat NDVI data","volume":"34","author":"Zheng","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.rse.2014.04.008","article-title":"Mapping irrigated areas in afghanistan over the past decade using MODIS NDVI","volume":"149","author":"Pervez","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_23","first-page":"428","article-title":"Water body mapping method with HJ-1A\/B satellite imagery","volume":"13","author":"Lu","year":"2011","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_24","unstructured":"Vapnik, V.N. (1998). Statistical Learning Theory, Wiley."},{"key":"ref_25","unstructured":"Ben-Hur, A., and Weston, J. (2010). Data Mining Techniques for the Life Sciences, Springer."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An Introduction to Statistical Learning, Springer.","DOI":"10.1007\/978-1-4614-7138-7"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.rse.2006.04.001","article-title":"The use of small training sets containing mixed pixels for accurate hard image classification: Training on mixed spectral responses for classification by a SVM","volume":"103","author":"Foody","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1016\/j.rse.2012.05.019","article-title":"Mapping abandoned agriculture with multi-temporal MODIS satellite data","volume":"124","author":"Alcantara","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.rse.2014.05.018","article-title":"Assessment of multi-temporal, multi-sensor radar and ancillary spatial data for grasslands monitoring in ireland using machine learning approaches","volume":"152","author":"Barrett","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1016\/S0301-4215(03)00156-3","article-title":"Co-benefits of climate policy\u2014Lessons learned from a study in Shanxi, China","volume":"32","author":"Aunan","year":"2004","journal-title":"Energy Policy"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.agrformet.2014.01.013","article-title":"Responses of wheat growth and yield to climate change in different climate zones of China, 1981\u20132009","volume":"189","author":"Tao","year":"2014","journal-title":"Agric. For. Meteorol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"518","DOI":"10.1002\/joc.3704","article-title":"Year patterns of climate impact on wheat yields","volume":"34","author":"Yu","year":"2014","journal-title":"Int. J. Climatol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.agrformet.2014.09.023","article-title":"Spatial analysis of the sensitivity of wheat yields to temperature in India","volume":"200","author":"Rao","year":"2015","journal-title":"Agric. For. Meteorol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1007\/BF00188445","article-title":"Crop water stress index relationships with crop productivity","volume":"11","author":"Wanjura","year":"1990","journal-title":"Irrig. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1016\/j.rse.2004.08.002","article-title":"Cropland distributions from temporal unmixing of MODIS data","volume":"93","author":"Lobell","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"576","DOI":"10.1016\/j.rse.2007.05.017","article-title":"Wavelet analysis of MODIS time series to detect expansion and intensification of row-crop agriculture in Brazil","volume":"112","author":"Galford","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3637","DOI":"10.1109\/TGRS.2013.2274431","article-title":"Crop type classification by simultaneous use of satellite images of different resolutions","volume":"52","author":"Liu","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1111\/gcb.12077","article-title":"Changes in satellite\u2014Derived spring vegetation green\u2014Up date and its linkage to climate in China from 1982 to 2010: A multimethod analysis","volume":"19","author":"Cong","year":"2013","journal-title":"Glob. Change Biol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3190","DOI":"10.3390\/rs5073190","article-title":"Remote sensing based detection of crop phenology for agricultural zones in china using a new threshold method","volume":"5","author":"You","year":"2013","journal-title":"Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2335","DOI":"10.1111\/j.1365-2486.2009.01910.x","article-title":"Intercomparison, interpretation, and assessment of spring phenology in north america estimated from remote sensing for 1982\u20132006","volume":"15","author":"White","year":"2009","journal-title":"Glob. Chang. Biol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2504","DOI":"10.1111\/j.1365-2486.2010.02189.x","article-title":"Remote sensing of larch phenological cycle and analysis of relationships with climate in the alpine region","volume":"16","author":"Busetto","year":"2010","journal-title":"Glob. Change Biol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2385","DOI":"10.1111\/j.1365-2486.2011.02397.x","article-title":"Phenology shifts at start vs. End of growing season in temperate vegetation over the northern hemisphere for the period 1982\u20132008","volume":"17","author":"JEONG","year":"2011","journal-title":"Glob. Change Biol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.rse.2013.11.020","article-title":"Remotely sensed trends in the phenology of northern high latitude terrestrial vegetation, controlling for land cover change and vegetation type","volume":"143","author":"Jeganathan","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1016\/j.cageo.2004.05.006","article-title":"Timesat\u2014A program for analyzing time-series of satellite sensor data","volume":"30","author":"Eklundh","year":"2004","journal-title":"Comput. Geosci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1016\/j.rse.2012.04.001","article-title":"Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology","volume":"123","author":"Atkinson","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"3367","DOI":"10.1016\/j.rse.2011.08.001","article-title":"Use of MODIS NDVI to evaluate changing latitudinal gradients of rangeland phenology in Sudano-Sahelian West Africa","volume":"115","author":"Butt","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1029\/97GB00330","article-title":"A continental phenology model for monitoring vegetation responses to interannual climatic variability","volume":"11","author":"White","year":"1997","journal-title":"Glob. Biogeochem. Cycles"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1564","DOI":"10.1016\/j.rse.2011.02.015","article-title":"The effect of the temporal resolution of NDVI data on season onset dates and trends across Canadian broadleaf forests","volume":"115","author":"Kross","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.agrformet.2012.06.009","article-title":"Spring vegetation green-up date in China inferred from SPOT NDVI data: A multiple model analysis","volume":"165","author":"Cong","year":"2012","journal-title":"Agric. For. Meteorol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.agsy.2012.09.003","article-title":"Satellite detection of earlier wheat sowing in India and implications for yield trends","volume":"115","author":"Lobell","year":"2013","journal-title":"Agric. Syst."},{"key":"ref_51","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":"Photogramm. Eng. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"5535","DOI":"10.1080\/01431160500300297","article-title":"Classifying rangeland vegetation type and coverage from NDVI time series using fourier filtered cycle similarity","volume":"26","author":"Geerken","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_53","first-page":"486","article-title":"Sub-pixel classification of spot-vegetation time series for the assessment of regional crop areas in Belgium","volume":"10","author":"Verbeiren","year":"2008","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"83","DOI":"10.2134\/agronj2000.92183x","article-title":"Spectral vegetation indices as nondestructive tools for determining durum wheat yield","volume":"92","author":"Aparicio","year":"2000","journal-title":"Agron. J."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1016\/j.rse.2007.07.019","article-title":"Large-area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the U.S. Central Great Plains","volume":"112","author":"Wardlow","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1080\/01431160110040323","article-title":"An assessment of support vector machines for land cover classification","volume":"23","author":"Huang","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"2877","DOI":"10.1080\/01431160500242515","article-title":"Support vector machine\u2014based feature selection for land cover classification: A case study with DAIS hyperspectral data","volume":"27","author":"Pal","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_58","unstructured":"Tso, B., and Mather, P. (2009). Classification Methods for Remotely Sensed Data, CRC Press."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/S0169-7439(02)00046-1","article-title":"A flexible classification approach with optimal generalisation performance: Support vector machines","volume":"64","author":"Belousov","year":"2002","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1145\/1961189.1961199","article-title":"LibSVM: A library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.eja.2012.07.005","article-title":"Spatiotemporal changes of wheat phenology in China under the effects of temperature, day length and cultivar thermal characteristics","volume":"43","author":"Tao","year":"2012","journal-title":"Eur. J. Agron."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.eja.2013.09.020","article-title":"Contributions of cultivars, management and climate change to winter wheat yield in the North China plain in the past three decades","volume":"52","author":"Xiao","year":"2014","journal-title":"Eur. J. Agron."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/3\/207\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:20:06Z","timestamp":1760210406000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/3\/207"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,3,3]]},"references-count":62,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2016,3]]}},"alternative-id":["rs8030207"],"URL":"https:\/\/doi.org\/10.3390\/rs8030207","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,3,3]]}}}