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Acta Agronomica Sinica ›› 2020, Vol. 46 ›› Issue (7): 1099-1111.doi: 10.3724/SP.J.1006.2020.94134

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY • Previous Articles     Next Articles

Extraction of crop acreage based on multi-temporal and dual-polarization SAR data

Gulnur ISAK,Mamat SAWUT(),MA Chun-Yue   

  1. College of Resources and Environmental Sciences, Xinjiang University / Ministry of Education Key Laboratory of Oasis Ecology, Xinjiang University / Key Laboratory for Wisdom City and Environmental Modeling, Xinjiang University, Urumqi 830046, Xinjiang, China
  • Received:2019-09-10 Accepted:2020-03-24 Online:2020-07-12 Published:2020-04-13
  • Contact: SAWUT Mamat E-mail:korxat@xju.edu.cn
  • Supported by:
    National Natural Science Foundation of China(41361016);National Natural Science Foundation of China(41461051)

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Abstract:

It plays a vital role in agricultural production management and agricultural policy formulation to acquire spatial distribution information and planting area of crops timely and accurately. In this paper, according to the phenological characteristics of crops, the back scattering coefficients (Sigma) and normalized back scattering coefficients (Gamma) of cotton, corn and orchard in different growth periods were extracted by using multi-temporal Sentinel-1A images (April 17, May 5, June 16, July 22, August 27, and September 2). The time-series change curves of the scattering characteristics were established and the characteristics were analyzed by using backscattering coefficients of crops with different polarizations and different time phases. Artificial neural network (ANN), support vector machine (SVM), and random forest (RF) were used to classify and identify cotton, corn and orchard. On this basis, the classification results were compared and analyzed, and the accuracy was verified. The backscattering coefficient of cotton increased significantly in June and July with the highest value in August, of which the changes were most obvious and easily distinguished from other crops. The backscattering coefficients of corn and fruit trees were significantly different from those of other land objects in September. The optimal classification was obtained by using random forest (the overall accuracy was up to 88.97%) than by using neural network and support vector machine. The classification accuracy for cotton and orchard was 90.88% and 93.17%, and the classification effect of corn is the worst, only 71.6%. In conclusion, multi-temporal and dual-polarization SAR data has certain application potential in the identification and area extraction of different crops.

Key words: multi-temporal, Sentinel-1A, backscattering coefficient, crop area extraction

Fig. 1

Map of study area and sample distribution"

Table 1

Main parameters of Sentinel-1A SAR data"

获取时间
Acquisition time		 入射角
Incidence angle		 极化方式
Polarization		 产品类型
Product type		 分辨率
Resolution (m)
2018/4/17 39.2° VV, VH GRD 5×10
2018/5/05 39.2° VV, VH GRD 5×10
2018/6/16 39.2° VV, VH GRD 5×10
2018/7/22 39.2° VV, VH GRD 5×10
2018/8/27 39.2° VV, VH GRD 5×10
2018/9/02 39.2° VV, VH GRD 5×10

Table 2

Jeffries-Matusita distance of typical crops with different phases"

类型
Type		 2018/04/17 2018/05/05 2018/06/16 2018/07/22 2018/08/27 2018/09/02
Sigma Gamma Sigma Gamma Sigma Gamma Sigma Gamma Sigma Gamma Sigma Gamma
棉花-玉米Cotton-corn 0.01 0.01 0.09 0.09 0.53 0.62 0.58 0.59 0.81 0.81 0.12 0.12
棉花-果园Cotton-orchard 0.19 0.18 0.06 0.06 0.29 0.26 0.06 0.06 0.07 0.07 0.40 0.40
棉花-水体Cotton-water 0.97 0.98 1.52 1.51 1.98 1.96 1.76 1.77 1.99 1.99 1.93 1.92
棉花-建筑Cotton-building 1.65 1.65 1.84 1.83 1.70 1.67 1.66 1.65 1.67 1.67 1.64 1.64
玉米-果园Corn-orchard 0.14 0.14 0.04 0.04 1.22 1.22 0.93 0.94 1.15 1.15 0.12 0.12
玉米-水体Corn-water 1.04 1.05 1.49 1.49 1.98 1.94 1.34 1.33 1.99 1.99 1.97 1.97
玉米-建筑Corn-building 1.65 1.65 1.80 1.80 1.89 1.87 1.86 1.86 1.91 1.91 1.50 1.50
果园-水体Orchard-water 1.40 1.40 1.61 1.61 1.99 1.99 1.88 1.88 1.99 1.99 1.99 1.99
果园-建筑Orchard-building 1.55 1.55 1.77 1.77 1.70 1.63 1.66 1.65 1.74 1.74 1.54 1.54
建筑-水体Building-water 1.90 1.90 1.99 1.99 1.99 1.99 1.96 1.96 1.99 1.99 1.98 1.99

Table 3

Jeffries-Matusita distance of typical crops with different band combinations"

类型
Type		 6/16 06/16+08/27 06/16+09/02 08/27+09/02 6/16+08/27+09/02
Sigma Gamma Sigma Gamma Sigma Gamma Sigma Gamma Sigma Gamma
棉花-玉米Cotton-corn 0.53 0.62 0.88 0.93 0.83 0.81 1.02 0.93 1.50 1.49
棉花-果园Cotton-orchard 0.29 0.26 0.55 0.54 1.10 1.09 0.85 0.86 1.47 1.45
棉花-水体Cotton-water 1.98 1.96 1.99 1.98 1.98 1.99 1.99 1.97 1.99 1.99
棉花-建筑Cotton-building 1.70 1.67 1.78 1.77 1.77 1.80 1.80 1.78 1.96 1.92
玉米-果园Corn-orchard 1.22 1.22 1.35 1.34 1.35 1.37 1.40 1.38 1.52 1.49
玉米-水体Corn-water 1.98 1.94 1.99 1.96 1.99 1.98 1.98 1.97 1.99 1.98
玉米-建筑Corn-building 1.89 1.87 1.84 1.83 1.78 1.74 1.73 1.70 1.81 1.81
果园-水体Orchard-water 1.99 1.99 1.99 1.98 1.99 1.99 1.98 1.99 1.99 1.99
果园-建筑Orchard-building 1.70 1.63 1.78 1.76 1.65 1.63 1.67 1.66 1.73 1.73
建筑-水体Building-water 1.99 1.99 1.99 1.99 1.99 1.98 1.99 1.99 1.99 1.99

Fig. 2

Technology road map"

Fig. 3

Histograms of backscattering of typical ground objects in multi-temporal SAR data VV: the incident wave is vertically polarized and scatters vertically polarized; VH: the incident wave is vertically polarized and scatters horizontally polarized."

Fig. 4

Time series variation of typical ground objects backscattering features VV: the incident wave is vertically polarized and scatters vertically polarized; VH: the incident wave is vertically polarized and scatters horizontally polarized."

Fig. 5

Classification result of multi-temporal and dual-polarization SAR images ANN: Artificial neural network; SVM: Support vector machine; RF: Random forest."

Table 4

Planting area of different types of crop"

类型
Type		 面积
Area (hm2)		 占全图比例
Proportion of total graph (%)
棉花Cotton 228842 30.23
玉米Corn 45183 9.47
果园Orchard 71665 5.97

Table 5

Accuracy verification of different classification methods for time series data"

类型
Type		 人工神经网络 ANN 支持向量机 SVM 随机森林 RF
Sigma Gamma Sigma Gamma Sigma Gamma
Prod.
Acc		 User.
Acc		 Prod.
Acc		 User.
Acc		 Prod.
Acc		 User.
Acc		 Prod.
Acc		 User.
Acc		 Prod.
Acc		 User.
Acc		 Prod.
Acc		 User.
Acc
果园Orchard 90.92 73.86 90.78 81.01 89.03 84.55 92.23 84.67 93.17 90.93 91.50 85.42
玉米Corn 17.31 93.66 46.35 88.01 56.45 88.54 48.51 91.65 71.60 93.19 53.38 89.70
棉花Cotton 85.96 86.72 79.06 94.85 89.53 82.11 81.34 99.89 90.88 88.43 86.52 92.61
建筑Building 84.11 98.74 88.57 93.85 84.02 98.74 85.65 90.45 87.14 97.99 82.32 99.57
水体Water 82.07 99.32 44.60 83.42 86.57 97.88 73.52 97.39 92.68 98.01 75.49 99.38
其他Other 97.30 58.18 98.09 52.71 95.74 70.50 98.23 59.06 95.46 85.62 98.79 80.72
Overall. Acc 77.85% 76.42% 84.36% 81.3% 88.97% 82.57%
Kappa 0.73 0.71 0.81 0.77 0.87 0.79

Table 6

Accuracy verification of cotton planting area"

编号
No.		 棉花样方面积
Sample area of
cotton (hm2)		 棉花分类面积
Classified area of
cotton (hm2)		 精度
Accuracy
(%)		 编号
No.		 非棉花样方面积
Sample area of
non-cotton (hm2)		 非棉花分类面积Classified area of non-cotton (hm2) 精度
Accuracy
(%)
1 43.37 43.58 99.53 21 1.40 1.39 98.76
2 102.52 104.98 97.60 22 1.74 1.77 98.35
3 37.12 35.69 96.14 23 2.19 2.26 96.97
4 34.69 33.20 95.69 24 1.24 0.88 95.40
5 31.38 32.88 95.23 25 2.08 1.98 95.14
6 8.44 8.87 94.90 26 32.20 33.82 94.98
7 54.79 51.59 94.15 27 31.13 33.02 93.95
8 8.24 7.51 91.20 28 24.27 21.51 88.62
9 106.06 116.13 90.51 29 16.46 14.44 87.75
10 117.49 129.85 89.48 30 1.71 1.43 83.84
11 131.91 114.47 86.78 31 36.74 29.42 80.08
12 28.15 24.25 86.13 32 72.66 52.31 72.00
13 36.63 31.53 86.08 33 1.18 0.85 71.94
14 105.81 90.80 85.81 34 4.28 5.60 69.09
15 94.85 80.10 84.44 35 21.44 13.70 63.89
16 26.81 21.82 81.40 36 47.88 30.51 63.72
17 18.27 11.80 64.60 37 47.44 26.79 56.47
18 38.16 17.76 46.54 38 3.69 5.42 53.42
19 18.51 8.59 46.40 39 30.36 7.85 25.87
20 28.68 12.81 44.67 40 6.67 1.19 17.86
棉花样方总面积Total sample area (hm2) 1147.85 非棉花样方总面积Total sample area (hm2) 434.81
棉花分类总面积Total classified area (hm2) 1058.03 非棉花分类总面积Total classified area (hm2) 334.89
总精度 Overall accuracy: 92.17% 总精度 Overall accuracy: 73.64%
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