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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
[1] 吴炳方. 中国农情遥感速报系统. 遥感学报, 2004, 8: 481-497
doi: 10.11834/jrs.20040601
Wu B F.China crop watch system with remote sensing.J Remote Sens, 2004, 8: 481-497 (in Chinese with English abstract)
doi: 10.11834/jrs.20040601
[2] 中华人民共和国统计局. 中国统计年鉴2017 (12-8 农作物播种面积). 北京: 中国统计出版社, 2017
National Bureau of Statistics of the People’s Republic of China. China Statistical Yearbook 2017 (12-8 Sown areas of farm crops). Beijing: China Statistics Press, 2017 (in Chinese with English abstract)
[3] 杨邦杰, 裴志远. 国家级农情遥感监测系统的研究开发与运行. 农业工程学报, 2003, 19(增刊): 11-14
Yang B J, Pei Z Y.National agricultural condition monitoring system based on satellite remote sensing: development, application and improvement.Trans CSAE, 2003, 19(suppl): 11-14 (in Chinese with English abstract)
[4] Tarko A, De Bruin S, Fasbender D, Bregt A.Users’ assessment of orthoimage photometric quality for visual interpretation of agricultural fields. Remote Sens, 2015, 7: 4919-4936
doi: 10.3390/rs70404919
[5] Khatami R, mountrakis G, Stehman S V. A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: general guidelines for practitioners and future research.Remote Sens Environ, 2016, 177: 89-100
doi: 10.1016/j.rse.2016.02.028
[6] Löw F, Duveiller G.Determining suitable image resolutions for accurate supervised crop classification using remote sensing data. In: Proceedings of the SPIE, Vol. 8893: Earth Resources and Environmental Remote Sensing/GIS Applications IV, Dresden, Germany, 2013. pp 909-927
[7] Lewiński S A.Object-oriented classification of Landsat ETM+ satellite image. J Water & Land Develop, 2007, 10: 91-106
doi: 10.2478/v10025-007-0008-4
[8] Kumar P, Gupta D K, Mishra V N, Prasad R.Comparison of support vector machine, artificial neural network, and spectral angle mapper algorithms for crop classification using LISS IV data.Int J Remote Sens, 2015, 36: 1604-1617
doi: 10.1080/2150704X.2015.1019015
[9] Li J, Zhao G X, Yang P G.Knowledge-based land use/cover classification of remote sensing image in Kenli, Shandong Province, China.Chin Sci Bull, 2006, 51: 218-224
doi: 10.1007/s11434-006-8210-3
[10] 李霞, 王飞, 徐德斌, 刘清旺. 基于混合像元分解提取大豆种植面积的应用探讨. 农业工程学报, 2008, 24(1): 213-217
Li X, Wang F, Xu D B, Liu Q W.Application research on the method for extracting soybean covered areas based on the pixel unmixing.Trans CSAE, 2008, 24(1): 213-217 (in Chinese with English abstract)
[11] 张建国, 李宪文, 吴延磊. 面向对象的冬小麦种植面积遥感估算研究. 农业工程学报, 2008, 24(5): 156-160
Zhang J G, Li X W, Wu Y L.Object oriented estimation of winter wheat planting area using remote sensing data.Trans CSAE, 2008, 24(5): 156-160 (in Chinese with English abstract)
[12] Lennington R K, Sorensen C T, Heydorn R P.A mixture model approach for estimating crop areas from Landsat data.Remote Sens Environ, 1984, 14: 197-206
doi: 10.1016/0034-4257(84)90015-4
[13] Chellasamy M, Ferré T P A, Greeve M H. Automatic training sample selection for a multi-evidence based crop classification approach. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 7th edn., Vol. 40, 2014. pp 63-69
[14] 王利民, 刘佳, 高建孟, 杨玲波, 杨福刚, 王小龙. 冬小麦面积遥感识别精度与空间分辨率关系. 农业工程学报, 2016, 32(23): 152-160
doi: 10.11975/j.issn.1002-6819.2016.23.021
Wang L M, Liu J, Gao J M, Yang L B, Yang F G, Wang X L.Relationship between accuracy of winter wheat area remote sensing identification and spatial resolution. Trans CSAE, 2016, 32(23): 152-160 (in Chinese with English abstract)
doi: 10.11975/j.issn.1002-6819.2016.23.021
[15] 李颖, 陈秀万, 段红伟, 沈阳. 多源多时相遥感数据在冬小麦识别中的应用研究. 地理与地理信息科学, 2010, 26(4): 47-49
Li Y, Chen X W, Duan H W, Shen Y.Application of multi-source and multi-temporal remote sensing data in winter wheat identification.Geog Geo-Inf Sci, 2010, 26(4): 47-49 (in Chinese with English abstract)
[16] Conese C, Maselli F.Use of multi-temporal information to improve classification performance of TM scenes in complex terrain.ISPRS J Photogram Remote Sens, 1991, 46: 187-197
doi: 10.1016/0924-2716(91)90052-W
[17] 张喜旺, 秦耀辰, 秦奋. 综合季相节律和特征光谱的冬小麦种植面积遥感估算. 农业工程学报, 2013, 29(8): 154-163
doi: 10.3969/j.issn.1002-6819.2013.08.018
Zhang X W, Qin Y C, Qin F.Remote sensing estimation of planting area for winter wheat by integrating seasonal rhythms and spectral characteristics.Trans CSAE, 2013, 29(8): 154-163 (in Chinese with English abstract)
doi: 10.3969/j.issn.1002-6819.2013.08.018
[18] Wardlow B D, Egbert S L.Large-area crop mapping using time- series MODIS 250m NDVI data: an assessment for the U.S. Central Great Plains.Remote Sens Environ, 2008, 112: 1096-1116
doi: 10.1016/j.rse.2007.07.019
[19] Zheng B, Myint S W, Thenkabail P S, Aggarwal R M.A support vector machine to identify irrigated crop types using time-series Landsat NDVI data.Int J Appl Earth Observ & Geoinf, 2015, 34: 103-112
doi: 10.1016/j.jag.2014.07.002
[20] 刘佳, 王利民, 杨福刚, 杨玲波, 王小龙. 基于HJ时间序列数据的农作物种植面积估算. 农业工程学报, 2015, 31(3): 199-206
doi: 10.3969/j.issn.1002-6819.2015.03.026
Liu J, Wang L M, Yang F G, Yang L B, Wang X L.Remote sensing estimation of crop planting area based on HJ time-series images.Trans CSAE, 2015, 31(3): 199-206 (in Chinese with English abstract)
doi: 10.3969/j.issn.1002-6819.2015.03.026
[21] Suzuki K, Takeuchi T.Classification of upland crops using multi-temporal RapidEye data.J Jpn Agric Syst Soc, 2015, 31: 1-10
doi: 10.14962/jass.31.1_1
[22] Carfagna E, Gallego F J.Using remote sensing for agricultural statistics.Int Statistical Rev, 2005, 73: 389-404
doi: 10.1111/j.1751-5823.2005.tb00155.x
[23] 焦险峰, 杨邦杰, 裴志远. 基于分层抽样的中国水稻种植面积遥感调查方法研究. 农业工程学报, 2006, 22(5): 105-110
Jiao X F, Yang B J, Pei Z Y.Paddy rice area estimation using a stratified sampling method with remote sensing in China.Trans CSAE, 2006, 22(5): 105-110 (in Chinese with English abstract)
[24] 王增林, 朱大明. 基于遥感影像的最大似然分类算法的探讨. 河南科学, 2010, 28: 1458-1461
doi: 10.3969/j.issn.1004-3918.2010.11.024
Wang Z L, Zhu D M.A study of maximum likelihood classification algorithm based on remote sensing image.Henan Sci, 2010, 28: 1458-1461 (in Chinese with English abstract)
doi: 10.3969/j.issn.1004-3918.2010.11.024
[25] Mosleh M K, Hassan Q K, Chowdhury E H.Application of remote sensors in mapping rice area and forecasting its production: a review.Sensors, 2015, 15: 769-791
doi: 10.3390/s150100769 pmid: 4327048
[26] 王利民, 刘佳, 杨玲波, 杨福刚, 滕飞, 王小龙. 基于NDVI 加权指数的冬小麦种植面积遥感监测. 农业工程学报, 2016, 32(17): 127-135
Wang L M, Liu J, Yang L B, Yang F G, Teng F, Wang X L.Remote sensing monitoring winter wheat area based on weighted NDVI index.Trans CSAE, 2016, 32(17): 127-135 (in Chinese with English abstract)
[27] Bargiel D.A new method for crop classification combining time series of radar images and crop phenology information.Remote Sens Environ, 2017, 198: 369-383
doi: 10.1016/j.rse.2017.06.022
[28] Massey R, Sankey T T, Congalton R G, Yadav K, Thenkabail P S, Ozdogan M, Meador A J S. MODIS phenology-derived, multi-year distribution of conterminous U.S. crop types.Remote Sens Environ, 2017, 198: 490-503
doi: 10.1016/j.rse.2017.06.033
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