Correction: Wang, J., et al. Dynamic Mapping of Rice Growth Parameters Using HJ-1 CCD Time Series Data. Remote Sens. 2016, 8, 931

Jing Wang 1, Jingfeng Huang 1,*, Ping Gao 2, Chuanwen Wei 1 and Lamin R. Mansaray 1,3 1 Institute of Remote Sensing and Information Application, Zhejiang University, Hangzhou 310058, China; wjnj1108@zju.edu.cn (J.W.); weichuanwen@zju.edu.cn (C.W.); l.mansaray@slari.gov.sl (L.R.M.) 2 Jiangsu Meteorological Bureau, Nanjing 210008, China; gaoping5268@126.com 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 * Correspondence: hjf@zju.edu.cn; Tel./Fax: +86-571-8898-2830

The authors wish to make the following corrections to their paper [1].Due to miscalculating, please replace: Please also replace:  These changes have no material impact on the conclusions of our paper.We apologize to our readers for the inconvenience.The manuscript will be updated and the original will remain online on the article webpage.These changes have no material impact on the conclusions of our paper.We apologize to our readers for the inconvenience.The manuscript will be updated and the original will remain online on the article webpage.

Figure 4 .
Figure 4. Relationships between measured rice leaf area index (m 2 /m 2 ) and dry aboveground biomass (g/m 2 ) at different rice growth stages with VIs.(a) Before heading LAI estimation using EVI2-BPNN regression; (b) after heading LAI estimation using NDVI-SVM regression; (c) all-growth stage AGB estimation using daily cumulative NDVI and based on the quadratic polynomial fit function; (d) all-growth stage AGB estimation using 10-day composite data and based on the cumulative NDVI quadratic polynomial fit function.The black dash lines are the 45° lines, and the red solid lines are the linear regression trend lines.

Figure 4 .withFigure 4 .
Figure 4. Relationships between measured rice leaf area index (m 2 /m 2 ) and dry aboveground biomass (g/m 2 ) at different rice growth stages with VIs.(a) Before heading LAI estimation using EVI2-BPNN regression; (b) after heading LAI estimation using NDVI-SVM regression; (c) all-growth stage AGB estimation using daily cumulative NDVI and based on the quadratic polynomial fit function; (d) all-growth stage AGB estimation using 10-day composite data and based on the cumulative NDVI quadratic polynomial fit function.The black dash lines are the 45 • lines, and the red solid lines are the linear regression trend lines.

Figure 4 .
Figure 4. Relationships between measured rice leaf area index (m 2 /m 2 ) and dry aboveground biomass (g/m 2 ) at different rice growth stages with VIs.(a) Before heading LAI estimation using EVI2-BPNN regression; (b) after heading LAI estimation using NDVI-SVM regression; (c) all-growth stage AGB estimation using daily cumulative NDVI and based on the quadratic polynomial fit function; (d) all-growth stage AGB estimation using 10-day composite data and based on the cumulative NDVI quadratic polynomial fit function.The black dash lines are the 45 • lines, and the red solid lines are the linear regression trend lines.

Table 4 .
Results of regression models at different single-cropped rice (SCR) growth stages.

Table 4 .
Results of regression models at different single-cropped rice (SCR) growth stages.

Growth Stages LAI AGB VI Model R 2 CV RRMSE CV VI Model R 2 CV RRMSE CV
E, P, and Q denote exponential, power, and quadratic polynomial fit of the traditional regression methods, respectively; B, S denote BPNN and SVM regression methods, respectively.Please also replace:

Table 4 .
Results of regression models at different single-cropped rice (SCR) growth stages.
E, P, and Q denote exponential, power, and quadratic polynomial fit of the traditional regression methods, respectively; B, S denote BPNN and SVM regression methods, respectively.