{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T17:43:43Z","timestamp":1770486223955,"version":"3.49.0"},"reference-count":57,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2016,11,9]],"date-time":"2016-11-09T00:00:00Z","timestamp":1478649600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The National High Technology Research and Development Program of China","award":["2012AA12A30703"],"award-info":[{"award-number":["2012AA12A30703"]}]},{"name":"China Meteorological Standardization Project","award":["QX\/T-2015-07"],"award-info":[{"award-number":["QX\/T-2015-07"]}]}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The high temporal resolution (4-day) charge-coupled device (CCD) cameras onboard small environment and disaster monitoring and forecasting satellites (HJ-1A\/B) with 30 m spatial resolution and large swath (700 km) have substantially increased the availability of regional clear sky optical remote sensing data. For the application of dynamic mapping of rice growth parameters, leaf area index (LAI) and aboveground biomass (AGB) were considered as plant growth indicators. The HJ-1 CCD-derived vegetation indices (VIs) showed robust relationships with rice growth parameters. Cumulative VIs showed strong performance for the estimation of total dry AGB. The cross-validation coefficient of determination (     R  C V  2     ) was increased by using two machine learning methods, i.e., a back propagation neural network (BPNN) and a support vector machine (SVM) compared with traditional regression equations of LAI retrieval. The LAI inversion accuracy was further improved by dividing the rice growth period into before and after heading stages. This study demonstrated that continuous rice growth monitoring over time and space at field level can be implemented effectively with HJ-1 CCD 10-day composite data using a combination of proper VIs and regression models.<\/jats:p>","DOI":"10.3390\/rs8110931","type":"journal-article","created":{"date-parts":[[2016,11,9]],"date-time":"2016-11-09T10:09:39Z","timestamp":1478686179000},"page":"931","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Dynamic Mapping of Rice Growth Parameters Using HJ-1 CCD Time Series Data"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6506-7984","authenticated-orcid":false,"given":"Jing","family":"Wang","sequence":"first","affiliation":[{"name":"Institute of Remote Sensing and Information Application, Zhejiang University, Hangzhou 310058, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4627-6021","authenticated-orcid":false,"given":"Jingfeng","family":"Huang","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Information Application, Zhejiang University, Hangzhou 310058, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ping","family":"Gao","sequence":"additional","affiliation":[{"name":"Jiangsu Meteorological Bureau, Nanjing 210008, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuanwen","family":"Wei","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Information Application, Zhejiang University, Hangzhou 310058, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9250-3657","authenticated-orcid":false,"given":"Lamin","family":"Mansaray","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Information Application, Zhejiang University, Hangzhou 310058, China"},{"name":"Department of Agro-meteorology and Geo-informatics, Magbosi Land, Water and Environment Research Center (MLWERC), Sierra Leone Agricultural Research Institute (SLARI), Freetown PMB 1313, Sierra Leone"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2016,11,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1007\/s12571-012-0168-1","article-title":"Crops that feed the world 7: Rice","volume":"4","author":"Seck","year":"2012","journal-title":"Food Secur."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1111\/j.1365-3040.1992.tb00992.x","article-title":"Defining Leaf-Area Index for Non-Flat Leaves","volume":"15","author":"Chen","year":"1992","journal-title":"Plant Cell Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.rse.2010.08.009","article-title":"Real-time retrieval of Leaf Area Index from MODIS time series data","volume":"115","author":"Xiao","year":"2011","journal-title":"Remote Sens. 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