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遥感技术与应用  2023, Vol. 38 Issue (3): 544-557    DOI: 10.11873/j.issn.1004-0323.2023.3.0544
农业遥感专栏     
基于Sentinel-2影像的冬小麦收获面积测算
刘胜威1,2(),彭代亮2(),陈俊杰1,胡锦康2,3,楼子杭2,3,冯旭祥4,程恩惠2,3
1.河南理工大学 测绘与国土信息工程学院,河南 焦作 454003
2.中国科学院空天信息创新研究院 数字地球重点实验室,北京 100094
3.中国科学院大学,北京 100049
4.中国科学院空天信息创新研究院 中国遥感卫星地面站,北京 100094
Estimation of Winter Wheat Harvesting Area based on Sentinel-2 Images
Shengwei LIU1,2(),Dailiang PENG2(),Junjie CHEN1,Jinkang HU2,3,Zihang LOU2,3,Xuxiang FENG4,Enhui CHENG2,3
1.School of Surveying and Land Information Engineering,Henan Polytechnic University,Jiaozuo,454003,China
2.Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China
3.University of Chinese Academy of Science,Beijing 100049,China
4.China Remote Sensing Satellite Ground Station,Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China
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摘要:

准确获取冬小麦空间分布和收获面积信息,对产量准确估算、保障粮食安全等具有重要意义。当前,绝大多数研究及统计数据集中于冬小麦种植面积,很少关注其收获面积。以濮阳县为研究区,基于2019年成熟期Sentinel-2遥感影像和随机森林相结合的方法,进行冬小麦收获面积测算研究。首先,根据特征筛选获得最佳特征子集,然后,基于最佳特征子集的J-M距离分析冬小麦与其他地物的可分性,识别提取冬小麦收获面积和种植面积,并实现冬小麦收获面积制图。最后,进一步分析冬小麦收获面积和种植面积差异以及收获面积的影响因素。结果发现:Sentinel-2影像最佳特征子集测算冬小麦收获面积总体精度和Kappa系数分别为94.62%和0.93。2019年提取濮阳县冬小麦种植面积为79.47 khm2,收获面积为76.74 khm2,相较于种植面积,数量上减少了2.73 khm2。研究结果表明:人为活动会造成收获面积少于种植面积,及时监测冬小麦收获面积可以为冬小麦产量预测等相关研究和决策提供一定的科学参考价值。

关键词: Sentinel?2影像成熟期冬小麦收获面积最佳特征子集    
Abstract:

The spatial distribution and winter wheat harvesting area is of great significance to accurately estimate production and ensure food security. However, the vast majority of study and statistical data is based on planting area of winter wheat, and few studies have been done on the winter wheat harvesting areas. In this study, Puyang County was selected as the study area, and the harvesting area of winter wheat was estimated by combining Sentinel-2 remote sensing imagery at the maturing period in 2019 and random forest model. Firstly, best feature subsets were obtained through feature selection. And then, the separability between winter wheat and other land types was analyzed by the J-M distance of these best feature subsets, the harvesting area and planting area of winter wheat were identified and extracted, and the harvesting area of winter wheat was mapped. Finally, the differences in harvesting area and planting area of winter wheat and the influencing factors of harvested area were further analyzed. The results found that the overall accuracy and Kappa coefficient of winter wheat harvested area estimated by the best feature subset of Sentinel-2 images were 94.62% and 0.93, respectively. The planting area of winter wheat in Puyang County in 2019 was 79.47 thousand hectares, and the extracted harvesting area was 76.74 thousand hectares, their difference (2.73 thousand hectares) was largely attributed to human activities, and timely monitoring of the harvesting area of winter wheat can provide a certain scientific reference value for related research and decision-making such as winter wheat yield prediction.

Key words: Sentinel-2    Maturing period    Winter wheat    Harvesting area    Best feature subset
收稿日期: 2022-07-30 出版日期: 2023-07-11
ZTFLH:  S512  
基金资助: 国家自然科学基金项目(42030111)
通讯作者: 彭代亮     E-mail: lswlbt@163.com;pengdl@aircas.ac.cn
作者简介: 刘胜威(1998-),男,湖南娄底人,硕士研究生,主要从事植被遥感、作物分类方面的研究。E?mail:lswlbt@163.com
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引用本文:

刘胜威,彭代亮,陈俊杰,胡锦康,楼子杭,冯旭祥,程恩惠. 基于Sentinel-2影像的冬小麦收获面积测算[J]. 遥感技术与应用, 2023, 38(3): 544-557.

Shengwei LIU,Dailiang PENG,Junjie CHEN,Jinkang HU,Zihang LOU,Xuxiang FENG,Enhui CHENG. Estimation of Winter Wheat Harvesting Area based on Sentinel-2 Images. Remote Sensing Technology and Application, 2023, 38(3): 544-557.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2023.3.0544        http://www.rsta.ac.cn/CN/Y2023/V38/I3/544

图1  研究区地理位置,背景是2020年全球30 m精细地表覆盖产品(GLC_FCS30-2020)
年份数据集提取类型数据日期影像数量
2019Sentinel-2 1C级种植面积2018.10.13~2019.04.0131
2018.12.092
收获面积2019.05.232
表1  遥感影像参数信息
生育期时间
播种?出苗期2018.10.13~11.14
分蘖期2018.11.15~12.10
越冬期2018.12.11~2019.03.05
返青期2019.03.06~04.01
拔节?抽穗期2019.04.02~04.28
开花?乳熟期2019.04.29~05.19
成熟期2019.05.20~06.04
表2  濮阳县冬小麦物候日历
图2  技术流程图
指数名称计算公式参考文献
NDVIB8-B4B8+B4

Tucker C J,

1979[17]

EVI2.5×B8-B4B8+6×B4-7.5×B2+1

Huete A R,等,

1997[18]

SAVI1.5×B8-B4B8+B4+0.5

Huete A R,等,

1988[19]

MNDWIB3-B11B3+B11

Xu Hanqiu,

2005[20]

NDBIB11-B8B11+B8

Zha Y,等,

2003[21]

NDVIre1B8-B5B8+B5

Gitelson A A,等,

2010[22]

NDVIre2B8-B6B8+B6

Gitelson A A,等,

2010[22]

REP705+35×0.5×B4+B7-B5B6-B5

Guyot G,等,

1988[23]

GNDVIB8-B3B8+B3

Gitelson A A,等,

1996[24]

PSRIB4-B2B6

Anderegg J,等,

2019[25]

BSIB11+B4-B8+B2B11+B4+B8+B2

Ni R,等,

2021[26]

表3  Sentinel-2影像所用指数特征描述
图3  基于序列前向选择方法的特征数量与分类精度之间的关系,红点和虚线表示最佳特征子集的数量
图4  冬小麦收获面积和种植面积提取精度评定结果
图5  冬小麦收获面积空间分布图以及冬小麦样方识别结果(样方子图由左侧的2019年5月23日Sentinel-2真彩色影像(bands 4/3/2)和右侧对应的冬小麦识别结果构成,1~6分别代表样方的具体位置,在图中用红色方框标记)
图6  冬小麦收获面积与种植面积差异图(柱状图代表整体的结果差异,卫星影像选用的是2019年4月18的Google earth影像,影像右侧是具体的空间分布差异情况)
图7  人为活动下冬小麦收获面积缺失图(Ⅰ-Ⅴ分别代表2019年3月14号Sentinel-2 10 m真彩色影像、冬小麦种植面积分类结果、2019年5月23号Sentinel-2 10 m真彩色影像、冬小麦收获面积分类结果、收获面积相较于种植面积的减少值;1~5则分别代表不同位置的子图)
图8  美国2011~2021年种植面积和收获面积
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