Oilseed Rape Yield Estimation Based on the WOFOST Model and Remote Sensing Data

tao Guo; jingbo Wei; wenchao Tang

doi:10.3788/LOP213003

Journals >Laser & Optoelectronics Progress >Volume 60 >Issue 10 >Page 1028009 > Article

Laser & Optoelectronics Progress
Vol. 60, Issue 10, 1028009 (2023)

Oilseed Rape Yield Estimation Based on the WOFOST Model and Remote Sensing Data

tao Guo^1,2, jingbo Wei², and wenchao Tang^1,*

Author Affiliations

¹Institute of Space Science and Technology, Nanchang University, Nanchang 330031, Jiangxi, China

²School of Information Engineering, Nanchang University, Nanchang 330031, Jiangxi, China

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DOI: 10.3788/LOP213003 Cite this Article Set citation alerts

tao Guo, jingbo Wei, wenchao Tang. Oilseed Rape Yield Estimation Based on the WOFOST Model and Remote Sensing Data[J]. Laser & Optoelectronics Progress, 2023, 60(10): 1028009 Copy Citation Text

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Fig. 1. Ground test plot

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Fig. 2. LAI assimilation results of Jingzhou city in 2015

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Fig. 3. Classification results of GF-1 WFV image in Yangxin county in 2019.(a) GF-1 WFV image of Yangxin county in April 2019; (b) classification results of GF-1 WFV images based on pyramidal bottleneck residual network

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Fig. 4. Comparison of rape extraction results and statistical yearbook

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Fig. 5. Comparison of rapeseed yield estimation results and statistical yearbook

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Study area	Weather station location
Zhongxiang	N31°6′00″，E112°20′24″
Macheng	N31°6′36″，E115°0′36″
Jianli	N29°30′00″，E112°32′24″
Jiayu	N29°35′24″，E113°33′00″
Jingzhou	N30°12′36″，E112°5′23″
Yangxin	N29°30′36″，E115°7′11″

Table 1. Study area and weather station locations

Coverage area	Number of images
Jingzhou	4
Macheng	2
Zhongxiang	2
Wuxue	2
Jiayu	4

Table 2. GF-1 WFV image information for model construction

Get time	Growth period
2015-01-22	bolting stage
2015-03-12	flowering stage
2016-02-03	bolting stage
2016-03-19	flowering stage
2018-02-06	bolting stage
2018-03-28	flowering stage
2019-01-17	bolting stage
2019-04-01	flowering stage
2020-01-29	bolting stage
2020-03-18	flowering stage

Table 3. GF-1 WFV image information of Yangxin County for model validation

Year	Day	Minimum temperature /℃	Maximum temperature /℃	Average air pressure /Pa	Sunshine time /h	Rainfall /mm	Average wind speed /（m·s^-1）
2017	1	5.2	9.8	10152	1.9	0	15
2017	2	6.7	8.6	10158	2.5	3.7	14
2017	3	3.3	6.7	10202	3.2	13.5	13
2017	4	-0.3	3.3	10187	3.6	35.1	16
2017	5	-1.2	1.3	10192	2.3	0	29
2017	6	1.1	2.5	10140	3.2	4.4	24
2017	7	-0.7	1.7	10152	2.8	1.3	24
2017	8	-0.5	7	10210	3.4	0.1	22
2017	9	-2	10.1	10217	4.2	0	22
2017	10	-1.3	10.7	10237	2.2	0	27
2017	11	-1.2	10.8	10253	2.8	0	20
2017	12	-2	9.2	10257	3	0	16
2017	13	0.5	8.8	10212	2.6	0	23
2017	14	2	13.8	10165	2	0	28
2017	15	3.4	10.4	10126	1.5	0	23
2017	16	5.8	9	10115	1.7	2.2	25
2017	17	4.9	12	10115	2.1	0	9
2017	18	4	9	10152	2.6	0	17
2017	19	4.8	7	10175	1.8	2.8	20
2017	20	5.2	7.7	10148	0.7	3.8	25
2017	21	6.1	7.4	10130	2.1	12.1	15
2017	22	3.7	12.5	10122	2.8	0	27
2017	23	3.9	8.5	10139	4	0	26
2017	24	2.2	4	10146	4.3	7.4	27
2017	25	-1.8	2.2	10177	4.8	14	17
2017	26	-2.5	-0.9	10232	3.7	3.4	23
2017	27	-2.8	-0.8	10185	3.6	11.5	18
2017	28	-3.8	-1.3	10225	2.9	0.8	23
2017	29	-5.6	1.2	10248	1.6	0	32
2017	30	-5.4	2.8	10215	2.2	0	28

Table 4. Data information of weather station in Jiayu county in the first 30 days of 2017

Index	Seedling stage	Bolting stage	Flowering stage	Pod stage	Bolting stage+flowering stage
R²	0.21	0.70	0.59	0.84	0.72
SE /（kg·hm^-2）	764	475	561	352	424

Table 5. Coefficient of determination R² and standard error (SE) of estimated yield of rapeseed LAI in the four growing periods

Vegetation index	Formula
NDVI	$(ρ_{N I R} - ρ_{r e d}) / (ρ_{N I R} + ρ_{r e d})$
VARI_green	$(ρ_{g r e e n} - ρ_{r e d}) / (ρ_{g r e e n} + ρ_{r e d})$
MSAVI	$[2 R_{N I R} + 1 - \sqrt[]{(2 R_{N I R} {+ 1)}^{2} - 8 (R_{N I R} - R_{r e d})}] / 2$
EVI2	$2.5 \times (ρ_{N I R} - ρ_{r e d}) / (1 + ρ_{N I R} + 2.4 \times ρ_{r e d})$
SR	$ρ_{N I R} / ρ_{r e d}$

Table 6. Vegetation index used in study

Inversion model	Vegetation index	Decisive factor R²	Period
NDVI	$y = - 2.1076 x^{2} + 4.6991 x + 1.4882$	0.7498	bolting stage
VARI_green	$y = 3.6302 x + 2.9215$	0.6917	bolting stage
MSAVI	$y = 2.8876 x + 1.3787$	0.746	bolting stage
EVI	$y = - 1.0104 x^{2} + 2.9926 x + 1.4482$	0.7602	bolting stage
SR	$y = - 0.2229 x^{2} + 1.7857 x + 0.1191$	0.8209	bolting stage
NDVI	$y = 5.3612 x^{2} - 4.5728 x + 4.3207$	0.1384	flowering stage
VARI_green	$y = - 61.405 x^{2} + 19.333 x + 2.7307$	0.7708	flowering stage
MSAVI	$y = 14.342 x^{2} - 18.852 x + 9.5592$	0.154	flowering stage
EVI	$y = 0.7645 x + 2.6508$	0.1282	flowering stage
SR	$y = - 0.0384 x^{2} + 0.5389 x + 2.0373$	0.1508	flowering stage

Table 7. Vegetation index and LAI

Year	Illustrated data /km²	Classification data /km²	Illustrated output /t	Forecast output /t	Error rate total output /%
2015	16.82	17.36	32393	34142.53	5.4
2016	16.44	15.67	33372	31420.02	-5.8
2018	15	15.48	35397	36037.11	1.7
2019	17.66	18.16	37086	36064.74	2.7
2020	18.3	17.67	39622	41210.17	4.0

Table 8. Comparison of rapeseed yield estimated results and statistical yearbook

tao Guo, jingbo Wei, wenchao Tang. Oilseed Rape Yield Estimation Based on the WOFOST Model and Remote Sensing Data[J]. Laser & Optoelectronics Progress, 2023, 60(10): 1028009

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