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Correction published on 24 January 2017, see Remote Sens. 2017, 9(2), 94.

Open AccessArticle
Remote Sens. 2016, 8(11), 931; doi:10.3390/rs8110931

Dynamic Mapping of Rice Growth Parameters Using HJ-1 CCD Time Series Data

1
Institute of Remote Sensing and Information Application, Zhejiang University, Hangzhou 310058, China
2
Jiangsu Meteorological Bureau, Nanjing 210008, China
3
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
*
Author to whom correspondence should be addressed.
Academic Editors: Guijun Yang, Zhenhong Li, Yoshio Inoue and Prasad S. Thenkabail
Received: 25 June 2016 / Revised: 31 October 2016 / Accepted: 3 November 2016 / Published: 9 November 2016
(This article belongs to the Special Issue Earth Observations for Precision Farming in China (EO4PFiC))
View Full-Text   |   Download PDF [37155 KB, uploaded 24 January 2017]   |  

Abstract

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. View Full-Text
Keywords: dynamic mapping; rice growth monitoring; leaf area index (LAI); aboveground biomass (AGB); HJ-1 charge-coupled device (CCD) dynamic mapping; rice growth monitoring; leaf area index (LAI); aboveground biomass (AGB); HJ-1 charge-coupled device (CCD)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Wang, J.; Huang, J.; Gao, P.; Wei, C.; Mansaray, L.R. Dynamic Mapping of Rice Growth Parameters Using HJ-1 CCD Time Series Data. Remote Sens. 2016, 8, 931.

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