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Acta Agronomica Sinica ›› 2018, Vol. 44 ›› Issue (05): 762-773.doi: 10.3724/SP.J.1006.2018.00762

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

Acguisition of Winter Wheat Area in the Beijing-Tianjin-Hebei Region with GF-1 Satellite Data

Li-Min WANG*(), Jia LIU, Fu-Gang YANG, Ling-Bo YANG, Bao-Min YAO, Xiao-Long WANG   

  1. Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
  • Received:2017-06-14 Accepted:2018-01-08 Online:2018-05-20 Published:2018-01-26
  • Contact: Li-Min WANG E-mail:wanglimin01@caas.cn
  • Supported by:
    This study was supported by the National Key Research and Development Program of China (2016YFD0300603).

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Abstract:

Winter wheat area accurate acquisition at provincial scale is an important aspect in remote-sensing monitoring of crop area. This study aimed at estimating winter wheat area in Beijing, Tianjin, and Hebei at provincial scale using classification map units from the national standard topographic map and the winter wheat area index (WWAI) created from the wide field view (WFV) data of GF-1 satellite. A total of 984 satellite monitoring images between October 1, 2013 and June 30, 2014 were used as data sources. The major process steps were data preprocessing, NDVI synthesis of standard map units, selection of samples, creation of winter wheat area index, confirmation of winter wheat crop type, provincial scale mapping, and accuracy verification. Multi-temporal GF-1/WFV data were preprocessed and NDVI value of images were calculated by using block adjustment and 6S atmospheric correction algorithm. By means of the 1:100 000 national standard topographic map framings of China, as the classification map units, WWAI was equally divided at a proportion of 1 percent into 101 extraction nodes from 0 to 100%. The NDVI values of extraction nodes were compared with type confirmation samples, and the most accurate NDVI was adopted as the extraction threshold value of winter wheat area. This threshold was then used into WWAI image in the map units to obtain winter wheat planting distribution. The identification result showed that, by taking standard map framing as calculation units and based on the creation of WWAI of GF-1 images, we remarkably improved the wave spectrum difference between winter wheat and other ground objects, with the overall accuracy of 89.6%, user accuracy of 89.8%, mapping accuracy of 96.5%, and Kappa coefficient of 0.72. In a typical region, the algorithm proposed had a similar accuracy with supervised classification algorithm. Except for 4.77% of the difference in mapping accuracy, the differences in overall accuracy and user accuracy were less than 1.00%. These results indicate that the used in this study is of algorithm high accuracy, efficiency and consistency in classification unit identification and applicable in agricultural monitoring at provincial level.

Key words: GF-1 satellite, region scale, winter wheat, area index, remote sensing

Fig. 1

Wheat regionalization in Beijing-Tianjin-Hebei region"

Fig. 2

Winter wheat remote sensing monitoring map unit distribution in the study area and high resolution image examples A: Beijing-Tianjin-Hebei WFV clear sky image and image unit distribution; B: WFV clear sky images of Langfang city; C: NDVI maximum value composition of Langfang image unit of April."

Fig. 3

Distribution of sample points in study area and sample maps A: weights build samples; B: type confirmation samples; C: rule verification samples; D: random verification samples."

Fig. 4

Supervised classification implementation regions (map No. J50D006006) and sample distribution"

Fig. 5

Winter wheat area extraction and processing steps"

Fig. 6

NDVI curves of winter wheat and other ground objects based on image data (A) and ground observation data (B)"

Table 1

Comparison between NDVI mean value result and winter wheat area index"

地表物
Ground objective		 NDVI平均值
Average NDVI		 冬小麦面积指数
Winter wheat area index
冬小麦 Winter wheat 0.4250 0.3489
其他地表物 Other ground objectives 0.2624 0.1298
差异绝对值 Difference value 0.1626 0.2191
差异倍数 Difference multiple 1.62 2.69

Fig. 7

Winter wheat area indexes (A) of different locations and their classification results (B) A1B1: Baoding city (J50D003004); A2B2: Cangzhou city (J50D006006); A3B3: Handan city (J50D011002); A4B4: Tangshan city (J50D002010); A5B5: Xingtai city (J50D008002)."

Table 2

Confusion matrix of validation sample points of national scale winter wheat extraction result"

地表物
Ground objective		 冬小麦
Winter wheat		 其他地表物
Other ground objectives		 总计
Total		 用户精度
User accuracy (%)		 制图精度
Mapping
accuracy (%)		 总体精度
Overall
accuracy (%)		 Kappa系数
Kappa
coefficient
冬小麦
Winter wheat		 1033 37 1070 89.8 96.5 89.6 0.72
其他地表物
Other ground objectives		 117 292 409 88.8 71.4
总计
Total		 1150 329 1479

Fig. 8

Spatial distribution of annual winter wheat area (2013-2014 growing season) in this study"

Fig. 9

Winter wheat monitoring results in the map No. J50D006006 region"

Table 3

Comparison on accuracy between supervised classification method and WWAI algorithm"

指标
Index		 最大似然监督分类 Maximum likelihood 冬小麦面积指数 Winter wheat area index
冬小麦
Winter wheat		 其他地表物
Other ground objectives		 冬小麦
Winter wheat		 其他地表物
Other ground objectives
用户精度 User accuracy (%) 87.88 95.24 87.35 93.08
制图精度 Mapping accuracy (%) 95.11 88.18 90.34 90.85
总体精度 Overall accuracy (%) 91.46 90.64
Kappa系数 Kappa coefficient 0.83 0.81
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