基于Sentinel-2时序数据的新疆焉耆盆地农作物遥感识别与评估

doi:10.12118/j.issn.1000－6060.2023.262

Abstract

Abstract:

To obtain timely and accurate information about crop cultivation in arid zones, this study used the PIE-Engine Studio platform to extract 14 vegetation indices in the Yanqi Basin, Xinjiang, China based on 2022 Sentinel-2 images and 1948 field location sampling data during the crop reproduction period. Crop planting information was extracted using the See5.0 decision tree, random forest (RF), and multiple regression (MR) models to select feature parameters. Each model was combined with support vector machine (SVM) algorithms to construct five classification models and five sample segmentation schemes. The best classification scheme was determined by visual interpretation and confusion matrix comparison. The results are as follows: (1) The overall accuracy (OA) and Kappa coefficients of all classification models are above 92.20% and 0.9037, respectively, indicating that it is feasible to extract crop information using the SVM algorithm in the PIE platform. (2) The mean OA and Kappa coefficients of SVM-with-red-edge are 93.77% and 0.9236, which are 0.96% and 0.0120, respectively. (3) The introduction of vegetation index improved the OA and Kappa coefficients of SVM-RF, SVM-MR, and SVM-See5.0 compared with the SVM-with-red-edge method. (4) The mean OA and Kappa coefficient relationships for the five classification models were SVM-RF>SVM-MR>SVM-See5.0>SVM-with-red-edge>SVM-without-red-edge, showing that the inclusion of the red-edge band and vegetation index significantly improved the accuracy of crop identification, with SVM-RF (8:2) being the best classification model with OA and Kappa coefficients of 98.72% and 0.9866, respectively. These results provide new ideas and references for accurate and rapid access to large-scale arid zone crop information.

Key words: crop, Sentinel-2, support vector machines, PIE-Engine Studio, Yanqi Basin

ZHANG Xuhui, Yusufujiang RUSULI, QIU Zhongli, Yaxiaer AISIKEER, Abudureheman WUSIMAN. Remote sensing identification and assessment of crops in the Yanqi Basin, Xinjiang, China based on Sentinel-2 time series data[J].Arid Land Geography, 2024, 47(4): 672-683.

Figures/Tables 10

Fig. 2

Tab. 1

Tab. 2

Tab. 3

Vegetation index calculation formula"

序号	植被指数	计算公式
1	NDVI	$(B 8 - B 4) / (B 8 + B 4)$
2	NDVI₇₀₅	$(B 5 - B 4) / (B 5 + B 4)$
3	NDVI₇₄₀	$(B 6 - B 4) / (B 6 + B 4)$
4	NDVI₇₈₃	$(B 7 - B 4) / (B 7 + B 4)$
5	GI	$B 3 / B 4$
6	VIgreen	$(B 3 - B 4) / (B 3 + B 4)$
7	GNDVI	$(B 8 - B 3) / (B 8 + B 3)$
8	RDVI	$(B 8 - B 4) / B 8 + B 4$
9	TVI	$0.5 [120 B 8 - B 3 - 200 (B 4 - B 3)]$
10	EVI	$G (B 8 - B 4 B 8 + C 1 × B 4 - C 2 × B 2 + L)$
11	SRI	$B 8 / B 4$
12	MSR	$[(B 8 / B 4) - 1] / [(B 8 / B 4) 0.5 + 1]$
13	MCARI	$[B 8 - B 4 - 0.2 B 8 - B 3] × (B 8 / B 4)$
14	TCARI	$3 [B 8 - B 4 - 0.2 B 8 - B 3 × B 8 / B 4]$

Tab. 3

Tab. 4

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

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序号	传感器类型及产品序号	影像获取时间（年-月-日）	光谱波段	空间分辨率/m	云量/%
1	S2_L2A_45TUG_20220304	2022-03-04	B2、B3、B4、B5、B6、B7、B8、B8a、B11、B12	10、20	<10
2	S2_L2A_45TUG_20220403	2022-04-03	B2、B3、B4、B5、B6、B7、B8、B8a、B11、B12	10、20	<10
3	S2_L2A_45TUG_20220503	2022-05-03	B2、B3、B4、B5、B6、B7、B8、B8a、B11、B12	10、20	<10
4	S2_L2A_45TUG_20220605	2022-06-05	B2、B3、B4、B5、B6、B7、B8、B8a、B11、B12	10、20	<10
5	S2_L2A_45TUG_20220705	2022-07-05	B2、B3、B4、B5、B6、B7、B8、B8a、B11、B12	10、20	<10
6	S2_L2A_45TUG_20220804	2022-08-04	B2、B3、B4、B5、B6、B7、B8、B8a、B11、B12	10、20	<10
7	S2_L2A_45TUG_20220903	2022-09-03	B2、B3、B4、B5、B6、B7、B8、B8a、B11、B12	10、20	<10
8	S2_L2A_45TUG_20221003	2022-10-03	B2、B3、B4、B5、B6、B7、B8、B8a、B11、B12	10、20	<10
9	S2_L2A_45TUG_20221102	2022-11-02	B2、B3、B4、B5、B6、B7、B8、B8a、B11、B12	10、20	<10

分类模型	特征参数	波段数目	样本分割方案
SVM-无红边	B2、B3、B4、B8、B8a、B11、B12	7	5:5、6:4、7:3、8:2、9:1
SVM-有红边	B2、B3、B4、B5、B6、B7、B8、B8a、B11、B12	10	5:5、6:4、7:3、 8:2、9:1
SVM-See5.0	B8、EVI、B5、B6、TVI、B7、TCARI、NDVI₇₀₅、GI、B3、B2、VIgreen、B4、NDVI₇₄₀、NDWI、RNDVI、NDVI、NDVI₇₈₃、MCARI、B11、B8a、SRI	22	5:5、6:4、7:3、 8:2、9:1
SVM-RF	B8、NDVI₇₀₅、B3、B6、TVI、B11、B2、B4、EVI、B7、MCARI、RNDVI、B8a、VIgreen、GI、B5、NDVI₇₄₀、TCARI、GNDVI、NDWI	20	5:5、6:4、7:3、8:2、9:1
SVM-MR	MCARI、NDVI、TCARI、SRI、TVI、VIgreen、NDVI₇₈₃、MSR、B5、B4、B8a、EVI	12	5:5、6:4、7:3、8:2、9:1

Remote sensing identification and assessment of crops in the Yanqi Basin, Xinjiang, China based on Sentinel-2 time series data

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样本类别	样本总数	5:5		6:4		7:3		8:2		9:1
样本类别	样本总数	训练数	检验数	训练数	检验数	训练数	检验数	训练数	检验数	训练数	检验数
番茄	190	95	95	114	76	133	57	152	38	171	19
甘草	70	35	35	42	28	49	21	56	14	63	7
葵花	180	90	90	108	72	126	54	144	36	162	18
辣椒	289	145	144	173	116	202	87	231	58	260	29
芦苇	120	60	60	72	48	84	36	96	24	108	12
棉花	125	63	62	75	50	88	37	100	25	113	12
葡萄	190	95	95	114	76	133	57	152	38	171	19
果林	70	35	35	42	28	49	21	56	14	63	7
水稻	180	90	90	108	72	126	54	144	36	162	18
甜菜	289	145	144	173	116	202	87	231	58	260	29
小麦	120	60	60	72	48	84	36	96	24	108	12
玉米	125	63	62	75	50	88	37	100	25	113	12

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