Estimation of Rice Evapotranspiration Using Reflective Images of Landsat Satellite in Sefidrood Irrigation and Drainage Network

doi:10.1016/j.rsci.2018.02.003

Abstract

Abstract:

More accurate estimation of crop evapotranspiration (ET_c) in a regional scale has always been one of the most important challenges. Temporal and spatial monitoring of ET_cusing satellite images can help to enhance accuracy of estimations. In this study, the (ET_c)_rice maps were produced by using statistical/experimental methods based on crop coefficient (K_c) maps derived from vegetation index (VI). K_c was estimated using four methods, including linear relationship between K_c and VI (K_c-VI), calibrated model of K_c-VI, linear relationship between K_cb (the basal crop coefficient) and VI (K_cb-VI), and calibrated model of K_cb-VI. The results showed that calibrated model of K_c-VI had a better performance compared to the other methods, with normalized root mean square errors (NRMSE), mean absolute error and root mean square error being 5.7%, 0.05 mm/d and 0.06 mm/d, respectively. (ET_c)_rice maps were produced by using calibrated model of K_c-VI and reference evapotranspiration (ET₀) from FAO Penman-Monteith method. The NRMSE was 21.3% for using FAO Penman-Monteith method. Therefore, calibrated K_c-VI model in combining with ET₀ based on the Landsat 7 ETM+ images could be provided a good estimation of (ET_c)_rice in regional scale, and can be applied to estimate water requirement due to the free and facilitate access.

Key words: vegetation index, lysimeter, satellite data, evapotranspiration, crop coefficient, Landsat image

Taherparvar Maryam, Pirmoradian Nader. Estimation of Rice Evapotranspiration Using Reflective Images of Landsat Satellite in Sefidrood Irrigation and Drainage Network[J]. Rice Science, 2018, 25(2): 111-116.

Figures/Tables 5

References 36

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Date	Period	Model type	Linear relationship for K_c-VI model				Linear relationship for K_cb-VI model
(Year-Month-Day)			RMSE (mm/d)	NRMSE	MAE	ME	RMSE (mm/d)	NRMSE	MAE	ME
			RMSE (mm/d)	(%)	(mm/d)	(mm/d)	RMSE (mm/d)	(%)	(mm/d)	(mm/d)
2012/5/14	Initial	Linear	0.29	29.9	0.28	0.28	0.33	32.8	0.32	0.32
2012/5/30	Initial	Linear	0.36	37.9	0.3	0.04	0.24	25.6	0.23	0
2012/6/15	Middle	Linear	0.19	16.2	0.17	-0.15	0.2	18.3	0.19	-0.19
2012/7/17	Middle	Linear	0.07	6.2	0.06	-0.03	0.13	9.7	0.12	-0.12
2012/8/2	Late	Linear	0.16	15.4	0.12	0.11	0.2	10.1	0.17	0.15
Total (2012)			0.24	22.5	0.19	0.05	0.23	20.6	0.21	0.03
2013/5/17	Initial	Calibrated	0.03	3.2	0.02	-0.02	0.18	18.7	0.3	0.3
2013/6/18	Middle	Calibrated	0.08	7.2	0.06	0.06	0.07	5.9	0.1	0.04
Total (2013)			0.06	5.7	0.05	0.02	0.13	12.3	0.19	0.16

Date	Period	Model type	Linear relationship for K_c-VI model				Linear relationship for K_cb-VI model
(Year-Month-Day)			RMSE (mm/d)	NRMSE	MAE	ME	RMSE (mm/d)	NRMSE	MAE	ME
			RMSE (mm/d)	(%)	(mm/d)	(mm/d)	RMSE (mm/d)	(%)	(mm/d)	(mm/d)
2012/5/14	Initial	Linear	0.29	29.9	0.28	0.28	0.33	32.8	0.32	0.32
2012/5/30	Initial	Linear	0.36	37.9	0.3	0.04	0.24	25.6	0.23	0
2012/6/15	Middle	Linear	0.19	16.2	0.17	-0.15	0.2	18.3	0.19	-0.19
2012/7/17	Middle	Linear	0.07	6.2	0.06	-0.03	0.13	9.7	0.12	-0.12
2012/8/2	Late	Linear	0.16	15.4	0.12	0.11	0.2	10.1	0.17	0.15
Total (2012)			0.24	22.5	0.19	0.05	0.23	20.6	0.21	0.03
2013/5/17	Initial	Calibrated	0.03	3.2	0.02	-0.02	0.18	18.7	0.3	0.3
2013/6/18	Middle	Calibrated	0.08	7.2	0.06	0.06	0.07	5.9	0.1	0.04
Total (2013)			0.06	5.7	0.05	0.02	0.13	12.3	0.19	0.16

Date	Average temperature (ºC)	Average humidity (%)
(Year-Month-Day)	Average temperature (ºC)	Average humidity (%)
2012/5/14	24.4	75
2012/5/30	26	79
2012/6/15	27.4	67
2012/7/17	25.8	77
2012/8/2	26.5	74
2013/5/17	19.8	75
2013/6/18	25.7	53

Date	Average temperature (ºC)	Average humidity (%)
(Year-Month-Day)	Average temperature (ºC)	Average humidity (%)
2012/5/14	24.4	75
2012/5/30	26	79
2012/6/15	27.4	67
2012/7/17	25.8	77
2012/8/2	26.5	74
2013/5/17	19.8	75
2013/6/18	25.7	53