Monitoring canopy growth and grain yield of paddy rice in South Korea by using the GRAMI model and high spatial resolution imagery

Figures & data

Figure 1. (a) Locations of the two study sites. (b) True-color images of the two study sites: An unmanned aerial vehicle image of a paddy rice field at Chonnam National University (CNU), Gwangju, and a RapidEye image of TaeAn, South Chungcheong Province, Korea. (c) A digitized rice paddy cover map of TaeAn.

Table 1. GRAMI-rice model parameters for simulation at both the field and regional scales.

Parameter	Symbol	Unit	Value
Radiation use efficiency (RUE)	ε	g MJ^−1	3.49
Light extinction coefficient	k	–	0.6
Specific leaf area	SLA	m^2 g^−1	0.016
Harvest index	HI	–	0.4
Base temperature	Tb	°C	12.0
Leaf area index at transplanting	L0	m^2 m^−2	0.2
Leaf partitioning parameter, A	a	–	0.325
Leaf partitioning parameter, B	b	–	0.00125
Leaf senescence parameter	c	–	0.00125

Table 2. Pearson’s correlation coefficients (r) and linear regression equations between unmanned aerial vehicle (UAV)-based digital numbers (y) and CROPSCAN reflectance values (x) for wavelengths of interest at different dates during the growing season in 2013.

Day of year	Wavelength (nm)	r	Linear regression
172	450	0.96^ns	y = 0.226x − 4.411
	550	0.94^ns	y = 0.201x − 4.373
	650	0.98^ns	y = 0.224x − 0.047
	800	0.99**	y = 0.427x − 7.863
	880	0.99**	y = 0.578x + 6.285
192	450	0.77^ns	y = 0.191x − 0.351
	550	0.82^ns	y = 0.184x − 1.605
	650	0.92^ns	y = 0.211x − 0.660
	800	0.98^ns	y = 0.311x − 3.616
	880	0.98^ns	y = 0.507x + 1.082
206	450	0.99***	y = 0.433x − 7.315
	550	0.99**	y = 0.429x − 10.996
	650	0.99***	y = 0.438x − 6.492
	800	0.99***	y = 0.576x − 14.43
	880	0.99***	y = 0.887x − 6.821
220	450	0.99**	y = 0.004x − 0.060
	550	0.99**	y = 0.004x − 0.101
	650	0.99***	y = 0.004x − 0.057
	800	0.99***	y = 0.005x − 0.087
	880	0.99***	y = 0.007x − 0.050
233	450	0.99**	y = 0.492x − 2.330
	550	0.99**	y = 0.511x − 4.555
	650	0.99***	y = 0.630x − 3.180
	800	0.99***	y = 0.975x − 8.533
	880	0.99***	y = 1.587x − 2.061

The symbols ** and *** represent significance at the 95% and the 99% probability levels, respectively, and “ns” indicates no significance (n = 3). Criteria for correlations : 0.1–0.3: small; 0.3–0.5: medium; and 0.5–1.0: large.

Table 3. Specifications of the RapidEye satellite system.

Mission characteristic	Information
Number of satellites	5
Spacecraft lifetime	7 years
Orbit altitude	630 km in sun-synchronous orbit
Sensor type	Multispectral push-broom imager
Spectral bands (nm)	Blue (440–510)
	Green (520–590)
	Red (630–685)
	Red edge (690–730)
	Near-infrared (760–850)
Ground sampling distance (nadir)	6.5 m
Pixel size (ortho-rectified)	5 m
Swath width	77 km
Onboard data storage	Up to 1500 km of image data per orbit
Revisit time	Daily (off nadir), 5.5 days (at nadir)
Image capture capacity	4 million km^2 per day
Dynamic range	Up to 12 bits

Figure 2. Schematic representation of crop growth mapping using the GRAMI-rice model. NDVI, LAI, and AGDM represent normalized difference vegetation index, leaf area index, and above-ground dry mass, respectively.

Figure 3. Time series maps (a–e) of the normalized difference vegetation index (NDVI) of paddy rice fields at Chonnam National University, Gwangju, Korea, in 2013. The NDVI maps were generated for several days of the year (DOY): (a) DOY 172, (b) DOY 192, (c) DOY 206, (d) DOY 220, and (e) DOY 233.

Figure 4. Time series maps (a–d) of the normalized difference vegetation index (NDVI) of classified paddy rice fields at TaeAn, South Chungcheong Province, Korea, in 2010. The NDVI maps were generated for several days of the year (DOY): (a) DOY 152, (b) DOY 174, (c) DOY 220, and (d) DOY 250.

Table 4. Error statistics (root mean square error, RMSE, mean absolute error, MAE, and model efficiency, ME) for comparisons between simulated and measured normalized difference vegetation index (NDVI), leaf area index (LAI), and above-ground dry mass (AGDM) values under nitrogen applications of 0, 50, and 115 kg ha⁻¹ at Chonnam National University, Gwangju, Korea, in 2013.

Nitrogen application (kg ha^−1)	NDVI			LAI			AGDM
	RMSE	MAE	ME	RMSE (m^2 m^−2)	MAE (m^2 m^−2)	ME	RMSE (kg ha^−1)	MAE (kg ha^−1)	ME
0	0.030	0.018	0.972	0.259	0.149	0.948	18.126	6.751	0.861
50	0.066	0.024	0.885	0.331	0.248	0.913	9.671	2.714	0.958
115	0.065	0.026	0.889	0.375	0.259	0.947	2.653	2.437	0.995

Figure 5. Simulated and measured values of normalized difference vegetation index (NDVI), leaf area index (LAI), and above-ground dry mass (AGDM) of paddy rice during the growing season in 2013 at Chonnam National University, Gwangju, Korea, under nitrogen (N) applications of 0, 50, and 115 kg ha⁻¹. The data points and vertical error bars represent the mean measured values ±1 SE (n = 4).

Figure 6. Comparison of simulated and measured grain yields of paddy rice during the growing season in 2013 at Chonnam National University, Gwangju, Korea, under nitrogen (N) applications of 0, 50, and 115 kg ha⁻¹. The p values were obtained from paired t-tests (α = 0.05). The vertical error bars represent ±1 SE of the simulated and measured mean values (n = 3).

Figure 7. Maps of (a) grain yield; (b) above-ground dry mass, AGDM; (c) leaf area index, LAI; and (d) normalized difference vegetation index, NDVI, of classified paddy fields in TaeAn, South Chungcheong Province, Korea, at 57 days after transplanting in 2010.