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

The authors wish to make the following corrections to their paper [1]. Due to miscalculating, please replace:

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

Growth Stages	LAI				AGB
	VI	Model	${\mathit{R}}_{\mathit{C}\mathit{V}}^2$	RRMSECV	VI	Model	${\mathit{R}}_{\mathit{C}\mathit{V}}^2$	RRMSECV
All stages	EVI2	E	0.358	10.210	cu EVI2	Q	0.923	59.912
		B	0.362	10.193		B	0.918	60.586
		S	0.444	9.968		S	0.921	107.08
	NDVI	E	0.275	10.798	cu NDVI	Q	0.929	57.856
		B	0.334	10.460		B	0.922	58.984
		S	0.467	10.185		S	0.927	105.371
Before heading	EVI2	E	0.831	6.074	cu EVI2	Q	0.909	54.037
		B	0.926	6.152		B	0.901	57.484
		S	0.900	6.776		S	0.884	96.317
	NDVI	P	0.644	8.960	cu NDVI	Q	0.922	50.150
		B	0.615	9.023		B	0.902	53.759
		S	0.629	10.363		S	0.920	86.899
After heading	EVI2	E	0.421	8.036	cu EVI2	Q	0.481	61.331
		B	0.474	8.019		B	0.474	65.122
		S	0.416	8.205		S	0.571	60.499
	NDVI	E	0.496	7.607	cu NDVI	Q	0.516	59.562
		B	0.610	8.630		B	0.426	53.759
		S	0.657	7.076		S	0.573	59.378

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.

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

Growth Stages	LAI				AGB
	VI	Model	${\mathit{R}}_{\mathit{C}\mathit{V}}^2$	RRMSECV	VI	Model	${\mathit{R}}_{\mathit{C}\mathit{V}}^2$	RRMSECV
All stages	EVI2	E	0.358	10.210	cu EVI2	Q	0.923	59.912
		B	0.362	10.193		B	0.918	60.586
		S	0.444	9.968		S	0.921	107.08
	NDVI	E	0.275	10.798	cu NDVI	Q	0.929	57.856
		B	0.334	10.460		B	0.922	58.984
		S	0.467	10.185		S	0.927	105.371
Before heading	EVI2	E	0.831	6.074	cu EVI2	Q	0.909	54.037
		B	0.926	6.152		B	0.901	57.484
		S	0.900	6.776		S	0.884	96.317
	NDVI	P	0.644	8.960	cu NDVI	Q	0.922	50.150
		B	0.615	9.023		B	0.902	53.759
		S	0.629	10.363		S	0.920	86.899
After heading	EVI2	E	0.421	8.036	cu EVI2	Q	0.481	61.331
		B	0.474	8.019		B	0.474	65.122
		S	0.416	8.205		S	0.571	60.499
	NDVI	E	0.496	7.607	cu NDVI	Q	0.516	59.562
		B	0.610	8.630		B	0.426	53.759
		S	0.657	7.076		S	0.573	59.378

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.

with

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

Growth Stages	LAI				AGB
	VI	Model	${\mathit{R}}_{\mathit{C}\mathit{V}}^2$	RRMSECV	VI	Model	${\mathit{R}}_{\mathit{C}\mathit{V}}^2$	RRMSECV
All stages	EVI2	E	0.358	10.210	cu EVI2	Q	0.923	18.247
		B	0.362	10.193		B	0.918	18.452
		S	0.444	9.968		S	0.921	32.613
	NDVI	E	0.275	10.798	cu NDVI	Q	0.929	17.621
		B	0.334	10.460		B	0.922	17.964
		S	0.467	10.185		S	0.927	32.092
Before heading	EVI2	E	0.831	6.074	cu EVI2	Q	0.909	25.317
		B	0.926	6.152		B	0.901	26.932
		S	0.900	6.776		S	0.884	45.126
	NDVI	P	0.644	8.960	cu NDVI	Q	0.922	23.496
		B	0.615	9.023		B	0.902	25.187
		S	0.629	10.363		S	0.920	40.714
After heading	EVI2	E	0.421	8.036	cu EVI2	Q	0.481	15.067
		B	0.474	8.019		B	0.474	15.998
		S	0.416	8.205		S	0.571	14.862
	NDVI	E	0.496	7.607	cu NDVI	Q	0.516	14.632
		B	0.610	8.630		B	0.426	13.207
		S	0.657	7.076		S	0.573	14.587

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.

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

Growth Stages	LAI				AGB
	VI	Model	${\mathit{R}}_{\mathit{C}\mathit{V}}^2$	RRMSECV	VI	Model	${\mathit{R}}_{\mathit{C}\mathit{V}}^2$	RRMSECV
All stages	EVI2	E	0.358	10.210	cu EVI2	Q	0.923	18.247
		B	0.362	10.193		B	0.918	18.452
		S	0.444	9.968		S	0.921	32.613
	NDVI	E	0.275	10.798	cu NDVI	Q	0.929	17.621
		B	0.334	10.460		B	0.922	17.964
		S	0.467	10.185		S	0.927	32.092
Before heading	EVI2	E	0.831	6.074	cu EVI2	Q	0.909	25.317
		B	0.926	6.152		B	0.901	26.932
		S	0.900	6.776		S	0.884	45.126
	NDVI	P	0.644	8.960	cu NDVI	Q	0.922	23.496
		B	0.615	9.023		B	0.902	25.187
		S	0.629	10.363		S	0.920	40.714
After heading	EVI2	E	0.421	8.036	cu EVI2	Q	0.481	15.067
		B	0.474	8.019		B	0.474	15.998
		S	0.416	8.205		S	0.571	14.862
	NDVI	E	0.496	7.607	cu NDVI	Q	0.516	14.632
		B	0.610	8.630		B	0.426	13.207
		S	0.657	7.076		S	0.573	14.587

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:

Figure 4. Relationships between measured rice leaf area index (m²/m²) and dry aboveground biomass (g/m²) 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. Relationships between measured rice leaf area index (m²/m²) and dry aboveground biomass (g/m²) 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.

with

Figure 4. Relationships between measured rice leaf area index (m²/m²) and dry aboveground biomass (g/m²) 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. Relationships between measured rice leaf area index (m²/m²) and dry aboveground biomass (g/m²) 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.

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.