{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T08:38:57Z","timestamp":1771922337754,"version":"3.50.1"},"reference-count":62,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,5,18]],"date-time":"2021-05-18T00:00:00Z","timestamp":1621296000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Prof. Muddu Sekhar","award":["IFCPAR\/CEFIPRA AICHA (2013-2016)"],"award-info":[{"award-number":["IFCPAR\/CEFIPRA AICHA (2013-2016)"]}]},{"name":"Dr. Laurent Ruiz","award":["ANR ATCHA (2017-2020)"],"award-info":[{"award-number":["ANR ATCHA (2017-2020)"]}]},{"name":"Prof. Samuel Corgne","award":["CNES\/TOSCA (Irriga-Detection project (2017-2019))"],"award-info":[{"award-number":["CNES\/TOSCA (Irriga-Detection project (2017-2019))"]}]},{"name":"Universit\u00e9 Bretagne Loire","award":["UBL Ph.D. student 554 grant for mobility (2017)"],"award-info":[{"award-number":["UBL Ph.D. student 554 grant for mobility (2017)"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Groundwater has become a major source of irrigation in the past few decades in India, but as it comes from millions of individual borewells owned by smallholders irrigating small fields, it is difficult to quantify the actual irrigated area across seasons and years. This study\u2019s main goal was to monitor seasonal irrigated cropland using multiple optical satellite images. The proposed research was performed over the Berambadi watershed, an experimental site in southern peninsular India. While cloud cover during crop growth is the greatest obstacle to optical remote sensing in tropical regions, the cloud-free images from multiple optical satellite platforms (Landsat-8 (OLI), EO1 (ALI), IRS-P6 (LISS3 and LISS4), and Spot5Take5 (HRG2)) were used to fill data gaps during crop growth periods. The seasonal cumulative normalized difference vegetation index (NDVI) was calculated and resampled at 5 m spatial resolution for various cropping seasons. The support vector machine (SVM) classification was applied to seasonal cumulative NDVI images for irrigated cropland area classification. Validation of the classified irrigated cropland was performed by calculating kappa coefficients for three cropping seasons (summer, kharif, and rabi) from 2014\u20132016 using ground observations. Kappa coefficients ranged from 0.81\u20130.96 for 2014\u20132015 and 0.62\u20130.89 for 2015\u20132016, except for summer 2016, when it was 1.00. Groundwater irrigation in the watershed ranged from 4.6% to 16.5% of total cropland during these cropping seasons. These results showed that multi-source optical satellite data are relevant for quantifying areas under groundwater irrigation in tropical regions.<\/jats:p>","DOI":"10.3390\/rs13101960","type":"journal-article","created":{"date-parts":[[2021,5,18]],"date-time":"2021-05-18T12:17:16Z","timestamp":1621340236000},"page":"1960","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Identifying Seasonal Groundwater-Irrigated Cropland Using Multi-Source NDVI Time-Series Images"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6964-999X","authenticated-orcid":false,"given":"Amit Kumar","family":"Sharma","sequence":"first","affiliation":[{"name":"L\u2019Unit\u00e9 Mixte de Recherche Littoral, Environnement, G\u00e9omatique, T\u00e9l\u00e9d\u00e9tection, le Centre National de la Recherche Scientifique, University of Rennes, 35043 Rennes, France"},{"name":"Indo-French Cell for Water Science, Indian Institute of Science, Bangalore 560012, India"}]},{"given":"Laurence","family":"Hubert-Moy","sequence":"additional","affiliation":[{"name":"L\u2019Unit\u00e9 Mixte de Recherche Littoral, Environnement, G\u00e9omatique, T\u00e9l\u00e9d\u00e9tection, le Centre National de la Recherche Scientifique, University of Rennes, 35043 Rennes, France"}]},{"given":"Sriramulu","family":"Buvaneshwari","sequence":"additional","affiliation":[{"name":"Indo-French Cell for Water Science, Indian Institute of Science, Bangalore 560012, India"},{"name":"Department of Civil Engineering, Indian Institute of Science, Bangalore 560012, India"}]},{"given":"Muddu","family":"Sekhar","sequence":"additional","affiliation":[{"name":"Indo-French Cell for Water Science, Indian Institute of Science, Bangalore 560012, India"},{"name":"Department of Civil Engineering, Indian Institute of Science, Bangalore 560012, India"}]},{"given":"Laurent","family":"Ruiz","sequence":"additional","affiliation":[{"name":"Indo-French Cell for Water Science, Indian Institute of Science, Bangalore 560012, India"},{"name":"Institut de Recherche Pour le D\u00e9veloppement, Centre National de la Recherche Scientifique, Universit\u00e9 Toulouse III-Paul Sabatier, l\u2019Unit\u00e9 Mixte de Recherche Littoral, G\u00e9osciences Environnement Toulouse\u2013La Terre, 31401 Toulouse, France"},{"name":"L\u2019Institut National de Recherche pour l\u2019Agriculture, l\u2019alimentation et l\u2019Eenvironnement, AgroCampus Ouest, l\u2019Unit\u00e9 Mixte de Recherche Littoral, Sol Agro et hydrosyst\u00e8me Spatialisation, 35043 Rennes, France"}]},{"given":"Hemanth","family":"Moger","sequence":"additional","affiliation":[{"name":"Indo-French Cell for Water Science, Indian Institute of Science, Bangalore 560012, India"}]},{"given":"Soumya","family":"Bandyopadhyay","sequence":"additional","affiliation":[{"name":"Earth Observation and Disaster Management Programme Office, Indian Space Research Organisation, Headquarter, Bangalore 560094, India"}]},{"given":"Samuel","family":"Corgne","sequence":"additional","affiliation":[{"name":"L\u2019Unit\u00e9 Mixte de Recherche Littoral, Environnement, G\u00e9omatique, T\u00e9l\u00e9d\u00e9tection, le Centre National de la Recherche Scientifique, University of Rennes, 35043 Rennes, France"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1863","DOI":"10.5194\/hess-14-1863-2010","article-title":"Groundwater use for irrigation\u2014A global inventory","volume":"14","author":"Siebert","year":"2010","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Thenkabail, P., States, U., Survey, G., Turral, H., and Biradar, C.M. (2009). Remote Sensing of Global Croplands for Food Security, CRC Press.","DOI":"10.1201\/9781420090109"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Thenkabail, P.S., Biradar, C.M., Noojipady, P., Dheeravath, V., Li, Y.J., Velpuri, M., Reddy, G.P.O., Cai, X., Gumma, M.K., and Turral, H. (2008). A Global Irrigated Area Map (GIAM) using remote sensing at the end of the last millennium. A Global Irrigated Area Map (GIAM) Using Remote Sensing at the End of the Last Millennium, International Water Management Institute (IWMI).","DOI":"10.5337\/2011.0024"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1016\/j.worlddev.2008.05.008","article-title":"Is Irrigation Water Free? A Reality Check in the Indo-Gangetic Basin","volume":"37","author":"Shah","year":"2009","journal-title":"World Dev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1038\/516179a","article-title":"When wells run dry","volume":"516","author":"Taylor","year":"2014","journal-title":"Nature"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"035006","DOI":"10.1088\/1748-9326\/4\/3\/035006","article-title":"Vulnerability to the impact of climate change on renewable groundwater resources: A global-scale assessment","volume":"4","author":"Petra","year":"2009","journal-title":"Environ. Res. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1625","DOI":"10.3390\/rs2071625","article-title":"Global Patterns of Cropland Use Intensity","volume":"2","author":"Siebert","year":"2010","journal-title":"Remote Sens."},{"key":"ref_8","first-page":"4002","article-title":"Crop per Drop of Diesel? Energy Squeeze on India\u2019s Smallholder Irrigation","volume":"42","author":"Shah","year":"2007","journal-title":"Econ. Polit. Wkly."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1002\/ird.593","article-title":"Development and composition of irrigation in India: Temporal trends and regional patterns","volume":"60","author":"Narayanamoorthy","year":"2010","journal-title":"Irrig. Drain."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"838","DOI":"10.1016\/j.scitotenv.2016.11.017","article-title":"Groundwater resource vulnerability and spatial variability of nitrate contamination: Insights from high density tubewell monitoring in a hard rock aquifer","volume":"579","author":"Sriramulu","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3691","DOI":"10.1038\/s41598-020-60365-z","article-title":"Potash fertilizer promotes incipient salinization in groundwater irrigated semi-arid agriculture","volume":"10","author":"Buvaneshwari","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_12","first-page":"EGU2016","article-title":"High spatial variability of nitrate in the hard rock aquifer of an irrigated catchment: Implications for water resource assessment and vulnerability","volume":"18","author":"Sriramulu","year":"2016","journal-title":"Gen. Assem. Conf. Abstr."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"160118","DOI":"10.1038\/sdata.2016.118","article-title":"Data Descriptor: 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_14","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1007\/s10584-014-1216-y","article-title":"Winter crop sensitivity to inter-annual climate variability in central India","volume":"126","author":"Mondal","year":"2014","journal-title":"Clim. Chang."},{"key":"ref_15","first-page":"999","article-title":"Satellite-based estimates of groundwater depletion in India","volume":"460","author":"Rodell","year":"2009","journal-title":"Nat. Cell Biol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Jain, M., Srivastava, A.K., Joon, R.K., McDonald, A., Royal, K., Lisaius, M.C., and Lobell, D.B. (2016). Mapping smallholder wheat yields and sowing dates using micro-satellite data. Remote Sens., 8.","DOI":"10.3390\/rs8100860"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1007\/s10113-016-1068-2","article-title":"Dynamics and determinants of land change in India: Integrating satellite data with village socioeconomics","volume":"17","author":"Meiyappan","year":"2016","journal-title":"Reg. Environ. Chang."},{"key":"ref_18","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_19","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_20","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_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","first-page":"159","article-title":"Multiscale object-based drought monitoring and comparison in rainfed and irrigated agriculture from Landsat 8 OLI imagery","volume":"44","author":"Ozelkan","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_23","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_24","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_25","doi-asserted-by":"crossref","first-page":"113","DOI":"10.3390\/w3010113","article-title":"Mapping Irrigated Areas Using MODIS 250 Meter Time-Series Data: A Study on Krishna River Basin (India)","volume":"3","author":"Gumma","year":"2011","journal-title":"Water"},{"key":"ref_26","first-page":"103","article-title":"International Journal of Applied Earth Observation and Geoinformation 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_27","doi-asserted-by":"crossref","first-page":"13005","DOI":"10.3390\/rs71013005","article-title":"Monitoring Irrigation Consumption Using High Resolution NDVI Image Time Series: Calibration and Validation in the Kairouan Plain (Tunisia)","volume":"7","author":"Saadi","year":"2015","journal-title":"Remote Sens."},{"key":"ref_28","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_29","first-page":"1299","article-title":"Development and validation of the global map of irrigation areas Development and validation of the global map of irrigation areas","volume":"2","author":"Siebert","year":"2005","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_30","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_31","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.isprsjprs.2018.07.017","article-title":"A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform","volume":"144","author":"Teluguntla","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ferrant, S., Selles, A., Le Page, M., Herrault, P.-A., Pelletier, C., Al-Bitar, A., Mermoz, S., Gascoin, S., Bouvet, A., and Saqalli, M. (2017). Detection of Irrigated Crops from Sentinel-1 and Sentinel-2 Data to Estimate Seasonal Groundwater Use in South India. Remote Sens., 9.","DOI":"10.3390\/rs9111119"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1277","DOI":"10.1109\/TGRS.2003.812909","article-title":"Comparison of earth observing-1 ali and landsat etm+ for crop identification and yield prediction in mexico","volume":"41","author":"Lobell","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","first-page":"19","article-title":"Spatially detailed retrievals of spring phenology from single-season high-resolution image time series","volume":"59","author":"Vrieling","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_35","unstructured":"Sekhar, M., and Ruiz, L. (2010). IFCPAR\/CEFIPRA PROJECT-Adaptation of Irrigated Agriculture to Climate Change (AICHA): Project Proposal, IFCPAR\/CEFIPRA."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"833","DOI":"10.16943\/ptinsa\/2016\/48488","article-title":"Influences of Climate and Agriculture on Water and Biogeochemical Cycles: Kabini Critical Zone Observatory","volume":"82","author":"Sekhar","year":"2016","journal-title":"Proc. Indian Natl. Sci. Acad."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"180067","DOI":"10.2136\/vzj2018.04.0067","article-title":"OZCAR: The French Network of Critical Zone Observatories","volume":"17","author":"Gaillardet","year":"2018","journal-title":"Vadose Zone J."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1177","DOI":"10.1080\/17538947.2019.1604834","article-title":"Evaluation of Radarsat-2 quad-pol SAR time-series images for monitoring groundwater irrigation","volume":"12","author":"Sharma","year":"2019","journal-title":"Int. J. Digit. Earth"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"8128","DOI":"10.3390\/rs70608128","article-title":"Retrieval and multi-scale validation of Soil Moisture from multi-temporal SAR Data in a semi-arid tropical region","volume":"7","author":"Tomer","year":"2015","journal-title":"Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.1080\/01431161.2016.1145363","article-title":"Disaggregation of LST over India: Comparative analysis of different vegetation indices","volume":"37","author":"Eswar","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1016\/j.chaos.2017.12.003","article-title":"Can the global modeling technique be used for crop classification?","volume":"106","author":"Mangiarotti","year":"2018","journal-title":"Chaos Solitons Fractals"},{"key":"ref_42","first-page":"17445","article-title":"Irrigated area estimation using Landsat satellite images in the Berambadi watershed","volume":"20","author":"Sharma","year":"2018","journal-title":"EGU Gen. Assem. Conf. Abstr."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1109\/36.581987","article-title":"Second Simulation of the Satellite Signal in the Solar Spectrum, 6S: An overview","volume":"35","author":"Vermote","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","unstructured":"U.S. Geological Survey (2020). Landsat 8 Collection 1 (C1) Land Surface Reflectance Code (LaSRC) Product Guide."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1016\/j.rse.2015.08.030","article-title":"Evaluation of the Landsat-5 TM and Landsat-7 ETM+ surface reflectance products","volume":"169","author":"Claverie","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Robert, M., Thomas, A., Sekhar, M., Badiger, S., Ruiz, L., Willaume, M., Leenhardt, D., and Bergez, J.-E. (2017). Farm Typology in the Berambadi Watershed (India): Farming Systems Are Determined by Farm Size and Access to Groundwater. Water, 9.","DOI":"10.3390\/w9010051"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"8906","DOI":"10.3390\/rs70708906","article-title":"A Temporal-Spatial Iteration Method to Reconstruct NDVI Time Series Datasets","volume":"7","author":"Xu","year":"2015","journal-title":"Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.rse.2014.01.006","article-title":"Remote Sensing of Environment Automated crop field extraction from multi-temporal Web Enabled Landsat Data","volume":"144","author":"Yan","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1192","DOI":"10.1016\/j.jenvman.2017.10.015","article-title":"Remote sensing based deforestation analysis in Mahanadi and Brahmaputra river basin in India since 1985","volume":"206","author":"Behera","year":"2018","journal-title":"J. Environ. Manag."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.rse.2015.12.024","article-title":"Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity","volume":"185","author":"Roy","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1016\/j.rse.2015.04.004","article-title":"Characteristics of Landsat 8 OLI-derived NDVI by comparison with multiple satellite sensors and in-situ observations","volume":"164","author":"Ke","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2499","DOI":"10.1016\/j.rse.2011.05.010","article-title":"Downscaling real-time vegetation dynamics by fusing multi-temporal MODIS and Landsat NDVI in topographically complex terrain","volume":"115","author":"Hwang","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1109\/LGRS.2008.915597","article-title":"Multiclass and Binary SVM Classification: Implications for Training and Classification Users","volume":"5","author":"Mathur","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Guidici, D., and Clark, M.L. (2017). One-Dimensional Convolutional Neural Network Land-Cover Classification of Multi-Seasonal Hyperspectral Imagery in the San Francisco Bay Area, California. Remote Sens., 9.","DOI":"10.3390\/rs9060629"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"6163","DOI":"10.3390\/rs6076163","article-title":"Combined Use of Multi-Temporal Optical and Radar Satellite Images for Grassland Monitoring","volume":"6","author":"Dusseux","year":"2014","journal-title":"Remote Sens."},{"key":"ref_56","unstructured":"L\u00f6w, F. (2013). Agricultural Crop Mapping from Multi-Scale Remote Sensing Data-Concepts and Applications in Heterogeneous Middle Asian Agricultural Landscapes. [Ph.D. Thesis, Universit\u00e4t W\u00fcrzburg]."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.1080\/01431160512331314083","article-title":"Support vector machines for classification in remote sensing","volume":"26","author":"Pal","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"964","DOI":"10.1016\/j.patcog.2006.06.016","article-title":"An improved incremental training algorithm for support vector machines using active query","volume":"40","author":"Cheng","year":"2007","journal-title":"Pattern Recognit."},{"key":"ref_59","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1541","DOI":"10.1016\/j.agwat.2010.05.009","article-title":"Land use and land cover classification in the irrigated Indus Basin using growth phenology information from satellite data to support water management analysis","volume":"97","author":"Cheema","year":"2010","journal-title":"Agric. Water Manag."},{"key":"ref_61","first-page":"397","article-title":"Remote Sensing Brief Accuracy Assessment: A User\u2019s Perspective","volume":"52","author":"Story","year":"1986","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.agsy.2016.08.001","article-title":"Adaptive and dynamic decision-making processes: A conceptual model of production systems on Indian farms","volume":"157","author":"Robert","year":"2017","journal-title":"Agric. Syst."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/10\/1960\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:03:12Z","timestamp":1760162592000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/10\/1960"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,18]]},"references-count":62,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2021,5]]}},"alternative-id":["rs13101960"],"URL":"https:\/\/doi.org\/10.3390\/rs13101960","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,18]]}}}