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作物学报 ›› 2022, Vol. 48 ›› Issue (9): 2409-2420.doi: 10.3724/SP.J.1006.2022.12066

• 研究简报 • 上一篇    

基于GEE云平台与Sentinel数据的高分辨率水稻种植范围提取——以湖南省为例

桑国庆1,2(), 唐志光1,2,*(), 毛克彪3, 邓刚1,2, 王靖文1,2, 李佳1,2   

  1. 1.湖南科技大学 / 测绘遥感信息工程湖南省重点实验室, 湖南湘潭 411201
    2.湖南科技大学 / 地理空间信息技术国家地方联合工程实验室, 湖南湘潭 411201
    3.中国农业科学院农业资源与农业区划研究所, 北京 100081
  • 收稿日期:2021-09-15 接受日期:2022-01-06 出版日期:2022-09-12 网络出版日期:2022-07-15
  • 通讯作者: 唐志光
  • 作者简介:E-mail: sgq@mail.hnust.edu.cn
  • 基金资助:
    湖南省自然科学基金创新研究群体项目(2020JJ1003);湖南省自然科学基金项目(2022JJ30245);湖南省教育厅科研项目(20B227);国家自然科学基金项目(41871058)

High-resolution paddy rice mapping using Sentinel data based on GEE platform: a case study of Hunan province, China

SANG Guo-Qing1,2(), TANG Zhi-Guang1,2,*(), MAO Ke-Biao3, DENG Gang1,2, WANG Jing-Wen1,2, LI Jia1,2   

  1. 1. Hunan Provincial Key Laboratory of Geo-Information Engineering in Surveying, Mapping and Remote Sensing, Hunan University of Science and Technology, Xiangtan 411201, Hunan, China
    2. National-Local Joint Engineering Laboratory of Geo-spatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, Hunan, China
    3. Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
  • Received:2021-09-15 Accepted:2022-01-06 Published:2022-09-12 Published online:2022-07-15
  • Contact: TANG Zhi-Guang
  • Supported by:
    Foundation for Innovative Research Groups of the Natural Science Foundation of Hunan Province, China(2020JJ1003);Natural Science Foundation of Hunan Province, China(2022JJ30245);Scientific Research Foundation of Hunan Education Department, China(20B227);National Natural Science Foundation of China(41871058)

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摘要：

准确获取大范围的水稻种植空间分布信息对调整农业生产结构和保障粮食安全至关重要。本文以湖南省为研究区, 基于谷歌地球引擎(Google Earth Engine, GEE)云平台, 协同Sentinel-1 SAR和Sentinel-2 MSI数据, 根据水稻物候期极化(vertical transmit/horizontal receive, VH)后向散射系数、增强型植被指数(enhanced vegetation index, EVI)的变化特征构建水稻提取决策树模型, 开展高分辨率水稻种植范围遥感提取, 并进行精度验证。结果表明: 本模型能够准确实现多云多雨地区的水稻种植范围遥感制图; 基于混淆矩阵计算水稻总体分类精度为93.97%, Kappa系数为0.908, 单、双季稻F1-score均超过91%, 可为亚热带多云雨且稻田破碎分布区的水稻种植范围遥感提取提供参考。湖南省水稻分布受地形和气温的影响明显, 主要分布在海拔200 m以下, 坡度小于6°, 年均气温大于17℃的区域; 双季稻集中分布在岳阳、常德和益阳市, 而单季稻种植分布相对零散。

关键词: 水稻, 遥感提取, Sentinel-1/2, Google Earth Engine, 决策树, 物候特征

Abstract:

Accurate acquisition of large-scale paddy rice cultivation spatial distribution is essential for adjusting the agricultural production structure and ensuring food security. Selecting Hunan Province as the study area, on the basis of the prior knowledge of spectral and polarization characteristics of rice growing period, a high-resolution remote sensing extraction model of rice planting area has been developed using decision tree algorithm based on Google Earth Engine cloud computing platform and Sentinel-1 SAR and Sentinel-2 MSI data. The results showed that the developed decision tree algorithm could accurately map the rice planting area in cloudy and rainy regions. The overall accuracy was 93.97%, the kappa coefficient was 0.908, and the F1-score of both single cropping rice and double cropping rice exceeded 91%. This model can provide a reference for mapping paddy rice planting area in cloudy and rainy hilly region. Moreover, the paddy rice distribution was significantly affected by topography and temperature. It was mainly distributed in the area with the elevation below 200 m, slope less than 6° and annual average temperature greater than 17℃. The double cropping rice was concentrated in Yueyang, Changde, and Yiyang cities, while the single cropping rice is sparsely distributed relatively.

Key words: paddy rice, remote sensing extraction, Sentinel-1/2, Google Earth Engine, decision tree model, phenological characteristics

图1

研究区实测样点及验证区空间分布 矢量地图数据来源: 全国地理信息资源目录服务系统(https://www.webmap.cn/)。"

图2

基于GEE平台的水稻种植范围遥感提取技术流程"

表1

样本点数量"

类别
Classification		 单季稻
Single cropping rice		 双季稻
Double cropping rice		 林地
Forest		 草地
Grass		 建设用地
Construction		 水体
Water		 总数
Total
在线选取Select samples online 1986 1544 1351 528 852 1193 7454
实地考察Field samples 350 260

图3

典型地物EVI时序曲线及标准差"

图4

典型地物VH极化后向散射系数时序曲线及标准差"

图5

水稻种植范围决策树提取模型 EVImean(90, 130)表示第90~130天EVI的均值。"

表2

基于样点的水稻提取精度评价"

验证样本
Validation samples		 分类结果Classified results 生产者精度Producer accuracy (%) 用户精度User accuracy (%) F1得分
F1-score (%)		 总体精度
Overall accuracy (%)		 Kappa系数 Kappa
coefficient
其他
Other		 单季稻
Single cropping rice		 双季稻
Double cropping rice
单季稻
Single cropping rice		 42 619 23 90.49 92.38 91.43
双季稻
Double cropping rice		 16 30 518 91.84 94.01 92.91
其他
Other		 1077 21 10 97.20 94.88 96.03 93.97 0.908

表3

典型样区水稻提取精度评价"

目视解译结果
Visual interpretation		 分类结果(像元数)
Classified results (number of pixels)		 生产者精度Producer accuracy (%) 用户精度
User accuracy (%)		 F1得分
F1-score (%)		 总体精度
Overall
accuracy (%)		 Kappa系数 Kappa coefficient
其他Other 水稻Rice
水稻Rice 80,633 1,114,232 93.25 91.51 92.37
其他Other 2,146,011 103,334 95.40 96.37 95.88 94.65 0.889

图6

以2019年验证样区2、6为例的水稻提取结果对比 a、d: Sentinel-2 B4 (Red)、B3 (Green)、B2 (Blue)合成影像; b、e: Google Earth目视解译结果; c、f: 模型水稻提取结果。"

图7

2017-2020年湖南省水稻种植信息提取结果 矢量地图数据来源: 全国地理信息资源目录服务系统(https://www.webmap.cn/)。"

图8

2017-2020年湖南省各地区单、双季水稻种植面积"

图9

湖南省水稻分布指数"

表4

模型中单纯使用方法一和方法二的水稻提取精度分析"

方法
Method		 地形条件
Terrain		 F1得分
F1-score (%)		 水稻识别率
Rice recognition rate (%)
EVI光谱特征
EVI spectral characteristics		 坡度Slope≤ 3° 92.39 91.98 82.04
坡度Slope> 3° 88.49
VH极化特征
VH polarization characteristics		 坡度Slope≤ 3° 87.51 85.38 100.00
坡度Slope> 3° 77.99

图10

研究区Sentinel-1/2影像数量(a)及Sentinel-2影像有效观测像元占比(b) 有效观测像元占比: 无云覆盖像元数/总像元数。"

图11

水稻种植面积提取结果与农业统计数据的比较(按各县域统计)"

表5

不同分类方法的总体精度和Kappa系数"

分类方法
Classification method		 总体精度
Overall accuracy (%)		 Kappa系数
Kappa coefficient
最大似然分类方法Maximum likelihood 88.1 0.783
支持向量机分类方法Support vector machine 90.51 0.822
本文分类方法This study 94.65 0.889
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