Phenological analysis and yield estimation of rice based on multi-spectral and SAR data in Maha Sarakham, Thailand

Figures & data

Figure 1. The geographical location of the study area.

Figure 2. The research flowchart. VV and VH represented the polarisation of Sentinel-1 data. VV represented vertical transmitting and vertical receiving and VH represented vertical transmitting and horizontal receiving. The OAE is the office of agricultural economics of Thailand. MLP-BP is the multilayer perceptron BP neural network regression model.

Figure 3. The phenological analysis results of main rice. (a) Result of the cumulative NDVI based logistic regression curves method; (b) Result of the cumulative EVI based logistic regression curves method.

Figure 4. The phenological analysis results of second rice. (a) Result of the cumulative NDVI based logistic regression curves method; (b) Result of the cumulative EVI based logistic regression curves method.

Table 1. The explanatory variables of two season-rice yield estimation regression model. The lower right number is the day of year in 2018.

Explanatory Variables	Rice
	Second Rice	Main Rice
1	NDVI65	NDVI257
2	NDVI81	NDVI273
3	EVI65	EVI257
4	EVI81	EVI273
5	VV76	VV256
6	VH76	VH256
7	VV88	VV268
8	VH88	VH268
9	VV76 – VH76	VV256 – VH256
10	VV88 – VH88	VV268 – VH268

Table 2. The accuracy of six linear and nonlinear regression models in the training and validation samples.

Regression Models		R^2
		Training	Validation
Linear Regression	Multiple Linear Regression	0.90	0.88
	Ridge Regression	0.90	0.88
	ANN Regression	0.84	0.85
Nonlinear Regression	RF Regression	0.96	0.95
	DT Regression	0.93	0.92
	MLP-BP Regression	0.96	0.92

Figure 5. The accuracy evaluation results of three linear regression models. (a, d) Multiple linear regression model; (b, e) Ridge regression model; (c, f) ANN regression model. The unit of RMSE, the true and predicted yield is ton/ha.

Figure 6. The accuracy of ridge regression model with different alpha parameters and the value of accuracy is the coefficient of determination, R².

Figure 7. The accuracy evaluation results of three nonlinear regression models. (a, d) RF regression model; (b, e) DT regression model; (c, f) MLP-BP regression model. The unit of the true yield, predicted yield and RMSE is ton/ha.