{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T12:58:39Z","timestamp":1781269119779,"version":"3.54.1"},"reference-count":58,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2021,2,1]],"date-time":"2021-02-01T00:00:00Z","timestamp":1612137600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2021,2,1]],"date-time":"2021-02-01T00:00:00Z","timestamp":1612137600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.elsevier.com\/legal\/tdmrep-license"}],"funder":[{"DOI":"10.13039\/501100003819","name":"Natural Science Foundation of Hubei Province","doi-asserted-by":"publisher","award":["2020CFA053"],"award-info":[{"award-number":["2020CFA053"]}],"id":[{"id":"10.13039\/501100003819","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003819","name":"Natural Science Foundation of Hubei Province","doi-asserted-by":"publisher","award":["58817"],"award-info":[{"award-number":["58817"]}],"id":[{"id":"10.13039\/501100003819","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61871298"],"award-info":[{"award-number":["61871298"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42071322"],"award-info":[{"award-number":["42071322"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers and Electronics in Agriculture"],"published-print":{"date-parts":[[2021,2]]},"DOI":"10.1016\/j.compag.2020.105978","type":"journal-article","created":{"date-parts":[[2021,1,18]],"date-time":"2021-01-18T00:28:54Z","timestamp":1610929734000},"page":"105978","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":45,"special_numbering":"C","title":["Spatial domain bridge transfer : An automated paddy rice mapping method with no training data required and decreased image inputs for the large cloudy area"],"prefix":"10.1016","volume":"181","author":[{"given":"Chengkang","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongyan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liangpei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.compag.2020.105978_b0005","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.isprsjprs.2014.05.013","article-title":"Evaluating the impact of red-edge band from Rapideye image for classifying insect defoliation levels","volume":"95","author":"Adelabu","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"10.1016\/j.compag.2020.105978_b0010","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":"10.1016\/j.compag.2020.105978_b0015","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"10.1016\/j.compag.2020.105978_b0020","doi-asserted-by":"crossref","first-page":"1090","DOI":"10.1016\/j.rse.2010.12.014","article-title":"Use of ENVISAT\/ASAR wide-swath data for timely rice fields mapping in the Mekong River Delta","volume":"115","author":"Bouvet","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.compag.2020.105978_b0025","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Machine Learning"},{"key":"10.1016\/j.compag.2020.105978_b0030","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.rse.2018.02.045","article-title":"A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach","volume":"210","author":"Cai","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.compag.2020.105978_b0035","doi-asserted-by":"crossref","first-page":"1367","DOI":"10.1080\/01431160500421507","article-title":"A neural network integrated approach for rice crop monitoring","volume":"27","author":"Chen","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"10.1016\/j.compag.2020.105978_b0040","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(91)90048-B","article-title":"A review of assessing the accuracy of classifications of remotely sensed data","volume":"37","author":"Congalton","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.compag.2020.105978_b0045","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.isprsjprs.2016.05.010","article-title":"Evolution of regional to global paddy rice mapping methods: A review","volume":"119","author":"Dong","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"10.1016\/j.compag.2020.105978_b0050","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.rse.2016.02.016","article-title":"Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine","volume":"185","author":"Dong","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.compag.2020.105978_b0055","first-page":"170","article-title":"SENTINEL-2A red-edge spectral indices suitability for discriminating burn severity","volume":"50","author":"Fern\u00e1ndez-Manso","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"10.1016\/j.compag.2020.105978_b0060","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":"10.1016\/j.compag.2020.105978_b0065","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.rse.2009.08.016","article-title":"MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets","volume":"114","author":"Friedl","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.compag.2020.105978_b0070","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.1007\/s00254-006-0544-2","article-title":"Designing of the perpendicular drought index","volume":"52","author":"Ghulam","year":"2007","journal-title":"Environ. Geol."},{"key":"10.1016\/j.compag.2020.105978_b0075","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.patrec.2005.08.011","article-title":"Random Forests for land cover classification","volume":"27","author":"Gislason","year":"2006","journal-title":"Pattern Recogn. Lett."},{"key":"10.1016\/j.compag.2020.105978_b0080","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.scib.2019.03.002","article-title":"Stable classification with limited sample: transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017","volume":"64","author":"Gong","year":"2019","journal-title":"Science Bulletin"},{"key":"10.1016\/j.compag.2020.105978_b0085","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.agwat.2012.08.012","article-title":"Development of Variable Threshold Models for detection of irrigated paddy rice fields and irrigation timing in heterogeneous land cover","volume":"115","author":"Jeong","year":"2012","journal-title":"Agric. Water Manag."},{"key":"10.1016\/j.compag.2020.105978_b0090","doi-asserted-by":"crossref","unstructured":"Jiang, M., Xin, L., Li, X., Tan, M., & Wang, R. (2018). Decreasing Rice Cropping Intensity in Southern China from 1990 to 2015. Remote Sensing, 11.","DOI":"10.3390\/rs11010035"},{"key":"10.1016\/j.compag.2020.105978_b0095","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.compag.2018.02.016","article-title":"Deep learning in agriculture: A survey","volume":"147","author":"Kamilaris","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2020.105978_b0100","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.rse.2015.08.004","article-title":"Mapping rice paddy extent and intensification in the Vietnamese Mekong River Delta with dense time stacks of Landsat data","volume":"169","author":"Kontgis","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.compag.2020.105978_b0105","doi-asserted-by":"crossref","first-page":"2101","DOI":"10.1080\/01431161.2012.738946","article-title":"Remote sensing of rice crop areas","volume":"34","author":"Kuenzer","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"10.1016\/j.compag.2020.105978_b0110","doi-asserted-by":"crossref","first-page":"9034","DOI":"10.3390\/rs6099034","article-title":"Defining the Spatial Resolution Requirements for Crop Identification Using Optical Remote Sensing","volume":"6","author":"L\u00f6w","year":"2014","journal-title":"Remote Sensing"},{"key":"10.1016\/j.compag.2020.105978_b0115","series-title":"A map of lowland rice extent in the major rice growing countries of Asia","first-page":"37","author":"Nelson","year":"2015"},{"key":"10.1016\/j.compag.2020.105978_b0120","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1007\/s11442-018-1490-0","article-title":"Spatiotemporal patterns and characteristics of land-use change in China during 2010\u20132015","volume":"28","author":"Ning","year":"2018","journal-title":"J. Geog. Sci."},{"key":"10.1016\/j.compag.2020.105978_b0125","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.rse.2012.10.031","article-title":"Making better use of accuracy data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation","volume":"129","author":"Olofsson","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.compag.2020.105978_b0130","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":"10.1016\/j.compag.2020.105978_b0135","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.isprsjprs.2015.04.008","article-title":"Mapping paddy rice planting area in cold temperate climate region through analysis of time series Landsat 8 (OLI), Landsat 7 (ETM+) and MODIS imagery","volume":"105","author":"Qin","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"10.1016\/j.compag.2020.105978_b0140","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.ecolind.2015.03.039","article-title":"Mapping paddy rice areas based on vegetation phenology and surface moisture conditions","volume":"56","author":"Qiu","year":"2015","journal-title":"Ecol. Ind."},{"key":"10.1016\/j.compag.2020.105978_b0145","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1016\/j.scitotenv.2017.03.221","article-title":"Automatic and adaptive paddy rice mapping using Landsat images: Case study in Songnen Plain in Northeast China","volume":"598","author":"Qiu","year":"2017","journal-title":"Sci. Total Environ."},{"key":"10.1016\/j.compag.2020.105978_b0150","doi-asserted-by":"crossref","first-page":"1471","DOI":"10.1109\/JSTARS.2019.2906684","article-title":"Developing an Automatic Phenology-Based Algorithm for Rice Detection Using Sentinel-2 Time-Series Data","volume":"12","author":"Rad","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"10.1016\/j.compag.2020.105978_b0155","doi-asserted-by":"crossref","DOI":"10.3390\/rs6053965","article-title":"Automated Training Sample Extraction for Global Land Cover Mapping","volume":"6","author":"Radoux","year":"2014","journal-title":"Remote Sensing"},{"key":"10.1016\/j.compag.2020.105978_b0160","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.rse.2009.08.011","article-title":"Web-enabled Landsat Data (WELD): Landsat ETM+ composited mosaics of the conterminous United States","volume":"114","author":"Roy","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.compag.2020.105978_b0165","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.rse.2016.01.023","article-title":"A general method to normalize Landsat reflectance data to nadir BRDF adjusted reflectance","volume":"176","author":"Roy","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.compag.2020.105978_b0170","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.isprsjprs.2012.04.001","article-title":"Comparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data points","volume":"70","author":"Shao","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"10.1016\/j.compag.2020.105978_b0175","doi-asserted-by":"crossref","DOI":"10.3390\/rs11101235","article-title":"Identifying Dry-Season Rice-Planting Patterns in Bangladesh Using the Landsat Archive","volume":"11","author":"Shew","year":"2019","journal-title":"Remote Sensing"},{"key":"10.1016\/j.compag.2020.105978_b0180","first-page":"63","article-title":"Assessment of atmospheric correction methods for Sentinel-2 images in Mediterranean landscapes","volume":"73","author":"Sola","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"10.1016\/j.compag.2020.105978_b0185","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.compag.2015.05.001","article-title":"Crop classification of upland fields using Random forest of time-series Landsat 7 ETM+ data","volume":"115","author":"Tatsumi","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2020.105978_b0190","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":"10.1016\/j.compag.2020.105978_b0195","doi-asserted-by":"crossref","first-page":"234","DOI":"10.2307\/143141","article-title":"A Computer Movie Simulating Urban Growth in the Detroit Region","volume":"46","author":"Tobler","year":"1970","journal-title":"Economic Geography"},{"key":"10.1016\/j.compag.2020.105978_b0200","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1016\/S0167-8809(02)00182-2","article-title":"Land use change in rice, wheat and maize production in China (1961\u20131998)","volume":"95","author":"Tong","year":"2003","journal-title":"Agric. Ecosyst. Environ."},{"key":"10.1016\/j.compag.2020.105978_b0205","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.isprsjprs.2014.12.006","article-title":"Assessment of MODIS spectral indices for determining rice paddy agricultural practices and hydroperiod","volume":"101","author":"Tornos","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"10.1016\/j.compag.2020.105978_b0210","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":"10.1016\/j.compag.2020.105978_b0215","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":"10.1016\/j.compag.2020.105978_b0220","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.rse.2018.12.026","article-title":"Crop type mapping without field-level labels: Random forest transfer and unsupervised clustering techniques","volume":"222","author":"Wang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.compag.2020.105978_b0225","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":"10.1016\/j.compag.2020.105978_b0230","doi-asserted-by":"crossref","first-page":"3009","DOI":"10.1080\/01431160110107734","article-title":"Observation of flooding and rice transplanting of paddy rice fields at the site to landscape scales in China using VEGETATION sensor data","volume":"23","author":"Xiao","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"10.1016\/j.compag.2020.105978_b0235","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":"10.1016\/j.compag.2020.105978_b0240","doi-asserted-by":"crossref","first-page":"3025","DOI":"10.1080\/01431160600589179","article-title":"Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery","volume":"27","author":"Xu","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"10.1016\/j.compag.2020.105978_b0245","doi-asserted-by":"crossref","first-page":"545","DOI":"10.3390\/rs2020545","article-title":"Soil line influences on two-band vegetation indices and vegetation isolines: a numerical study","volume":"2","author":"Yoshioka","year":"2010","journal-title":"Remote Sensing"},{"key":"10.1016\/j.compag.2020.105978_b0250","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.rse.2018.11.014","article-title":"Joint Deep Learning for land cover and land use classification","volume":"221","author":"Zhang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.compag.2020.105978_b0255","doi-asserted-by":"crossref","unstructured":"Zhang, C., Zhang, H., Du, J., & Zhang, L. (2018). Automated Paddy Rice Extent Extraction with Time Stacks of Sentinel Data: A Case Study in Jianghan Plain, Hubei, China. In, 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics) (pp. 1-6).","DOI":"10.1109\/Agro-Geoinformatics.2018.8476119"},{"key":"10.1016\/j.compag.2020.105978_b0260","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.scitotenv.2016.10.223","article-title":"Spatiotemporal patterns of paddy rice croplands in China and India from 2000 to 2015","volume":"579","author":"Zhang","year":"2017","journal-title":"Sci. Total Environ."},{"key":"10.1016\/j.compag.2020.105978_b0265","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2020.105618","article-title":"Accessing the temporal and spectral features in crop type mapping using multi-temporal Sentinel-2 imagery: A case study of Yi\u2019an County, Heilongjiang province China","volume":"176","author":"Zhang","year":"2020","journal-title":"Computers and Electronics in Agriculture"},{"key":"10.1016\/j.compag.2020.105978_b0270","doi-asserted-by":"crossref","first-page":"3071","DOI":"10.1109\/TGRS.2019.2947333","article-title":"Hyperspectral Image Denoising With Total Variation Regularization and Nonlocal Low-Rank Tensor Decomposition","volume":"58","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.compag.2020.105978_b0275","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2020.3038405","article-title":"Remote Sensing Image Spatiotemporal Fusion Using a Generative Adversarial Network","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.compag.2020.105978_b0280","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.rse.2017.05.024","article-title":"Using the 500m MODIS land cover product to derive a consistent continental scale 30m Landsat land cover classification","volume":"197","author":"Zhang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.compag.2020.105978_b0285","doi-asserted-by":"crossref","DOI":"10.1029\/2003JD004457","article-title":"Calculation of radiative fluxes from the surface to top of atmosphere based on ISCCP and other global data sets: Refinements of the radiative transfer model and the input data","volume":"109","author":"Zhang","year":"2004","journal-title":"J. Geophysical Research: Atmospheres"},{"key":"10.1016\/j.compag.2020.105978_b0290","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.rse.2014.12.014","article-title":"Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4\u20137, 8, and Sentinel 2 images","volume":"159","author":"Zhu","year":"2015","journal-title":"Remote Sens. Environ."}],"container-title":["Computers and Electronics in Agriculture"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/api.elsevier.com\/content\/article\/PII:S0168169920331835?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/api.elsevier.com\/content\/article\/PII:S0168169920331835?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T12:01:49Z","timestamp":1778673709000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/linkinghub.elsevier.com\/retrieve\/pii\/S0168169920331835"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2]]},"references-count":58,"alternative-id":["S0168169920331835"],"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/j.compag.2020.105978","relation":{},"ISSN":["0168-1699"],"issn-type":[{"value":"0168-1699","type":"print"}],"subject":[],"published":{"date-parts":[[2021,2]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Spatial domain bridge transfer : An automated paddy rice mapping method with no training data required and decreased image inputs for the large cloudy area","name":"articletitle","label":"Article Title"},{"value":"Computers and Electronics in Agriculture","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/j.compag.2020.105978","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2020 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"105978"}}