{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T19:29:50Z","timestamp":1788550190942,"version":"build-2803163510"},"reference-count":16,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2016,2,4]],"date-time":"2016-02-04T00:00:00Z","timestamp":1454544000000},"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>This Special Issue gathers sixteen papers focusing on applying various remote sensing techniques to crop growth monitoring. The studies span observations from multiple scales, a combination of model simulations and experimental measurements, and a range of topics on crop monitoring and mapping. This preface provides a brief overview of the contributed papers.<\/jats:p>","DOI":"10.3390\/rs8020116","type":"journal-article","created":{"date-parts":[[2016,2,4]],"date-time":"2016-02-04T10:26:22Z","timestamp":1454581582000},"page":"116","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Preface: Recent Advances in Remote Sensing for Crop Growth Monitoring"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4184-0730","authenticated-orcid":false,"given":"Tao","family":"Cheng","sequence":"first","affiliation":[{"name":"National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China"},{"name":"Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengwei","family":"Yang","sequence":"additional","affiliation":[{"name":"USDA National Agricultural Statistics Service, Research and Development Division, 3251 Old Iee Highway, Room 305, Fairfax, VA 22030, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yoshio","family":"Inoue","sequence":"additional","affiliation":[{"name":"National Institute for Agro-Environmental Sciences (NIAES), Tsukuba, Ibaraki 305-8604, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1884-2404","authenticated-orcid":false,"given":"Yan","family":"Zhu","sequence":"additional","affiliation":[{"name":"National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China"},{"name":"Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weixing","family":"Cao","sequence":"additional","affiliation":[{"name":"National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China"},{"name":"Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2016,2,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"14179","DOI":"10.3390\/rs71014179","article-title":"Quantitative estimation of fluorescence parameters for crop leaves with bayesian inversion","volume":"7","author":"Zhao","year":"2015","journal-title":"Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"14939","DOI":"10.3390\/rs71114939","article-title":"Evaluation of six algorithms to monitor wheat leaf nitrogen concentration","volume":"7","author":"Yao","year":"2015","journal-title":"Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5329","DOI":"10.3390\/rs70505329","article-title":"Spectral index for quantifying leaf area index of winter wheat by field hyperspectral measurements: A case study in Gifu prefecture, central Japan","volume":"7","author":"Tanaka","year":"2015","journal-title":"Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"10646","DOI":"10.3390\/rs70810646","article-title":"Satellite remote sensing-based in-season diagnosis of rice nitrogen status in Northeast China","volume":"7","author":"Huang","year":"2015","journal-title":"Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"9886","DOI":"10.3390\/rs70809886","article-title":"Temporal dependency of yield and quality estimation through spectral vegetation indices in pear orchards","volume":"7","author":"Tits","year":"2015","journal-title":"Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"11326","DOI":"10.3390\/rs70911326","article-title":"Comparison of NDVIs from GOCI and MODIS data towards improved assessment of crop temporal dynamics in the case of paddy rice","volume":"7","author":"Yeom","year":"2015","journal-title":"Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"5203","DOI":"10.3390\/rs70505203","article-title":"Exploring the vertical distribution of structural parameters and light radiation in rice canopies by the coupling model and remote sensing","volume":"7","author":"Guo","year":"2015","journal-title":"Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"14079","DOI":"10.3390\/rs71014079","article-title":"The impact of sunlight conditions on the consistency of vegetation indices in croplands\u2014Effective usage of vegetation indices from continuous ground-based spectral measurements","volume":"7","author":"Ishihara","year":"2015","journal-title":"Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"8883","DOI":"10.3390\/rs70708883","article-title":"Monitoring spatio-temporal distribution of rice planting area in the Yangtze River Delta region using MODIS images","volume":"7","author":"Shi","year":"2015","journal-title":"Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3467","DOI":"10.3390\/rs70403467","article-title":"Rice fields mapping in fragmented area using multi-temporal HJ-1A\/B CCD images","volume":"7","author":"Wang","year":"2015","journal-title":"Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3633","DOI":"10.3390\/rs70403633","article-title":"A hidden markov models approach for crop classification: Linking crop phenology to time series of multi-sensor remote sensing data","volume":"7","author":"Siachalou","year":"2015","journal-title":"Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"512","DOI":"10.3390\/rs70100512","article-title":"MODIS-based fractional crop mapping in the U.S. Midwest with spatially constrained phenological mixture analysis","volume":"7","author":"Zhong","year":"2015","journal-title":"Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5347","DOI":"10.3390\/rs70505347","article-title":"Feature selection of time series MODIS data for early crop classification using random forest: A case study in Kansas, USA","volume":"7","author":"Hao","year":"2015","journal-title":"Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"12859","DOI":"10.3390\/rs71012859","article-title":"In-season mapping of crop type with optical and X-band SAR data: A classification tree approach using synoptic seasonal features","volume":"7","author":"Villa","year":"2015","journal-title":"Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"6535","DOI":"10.3390\/rs70606535","article-title":"Rapid assessment of crop status: An application of MODIS and SAR data to rice areas in Leyte, Philippines affected by Typhoon Haiyan","volume":"7","author":"Boschetti","year":"2015","journal-title":"Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"8728","DOI":"10.3390\/rs70708728","article-title":"Mapping of daily mean air temperature in agricultural regions using daytime and nighttime land surface temperatures derived from Terra and Aqua MODIS data","volume":"7","author":"Huang","year":"2015","journal-title":"Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/2\/116\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:18:47Z","timestamp":1760210327000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/2\/116"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,2,4]]},"references-count":16,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2016,2]]}},"alternative-id":["rs8020116"],"URL":"https:\/\/doi.org\/10.3390\/rs8020116","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,2,4]]}}}