Scientia Agricultura Sinica ›› 2021, Vol. 54 ›› Issue (11): 2302-2318.doi: 10.3864/j.issn.0578-1752.2021.11.005

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY·AGRICULTURE INFORMATION TECHNOLOGY • Previous Articles     Next Articles

Area Extraction and Growth Monitoring of Winter Wheat in Henan Province Supported by Google Earth Engine

ZHOU Ke1,3(),LIU Le1,3,ZHANG YanNa2(),MIAO Ru1,3,YANG Yang1,3   

  1. 1School of Computer and Information Engineering, Henan University, Kaifeng 475004, Henan
    2Department of Laboratory and Equipment Management, Henan University, Kaifeng 475004, Henan
    3Henan Key Laboratory of Big Data Analysis and Processing/ Henan University, Kaifeng 475004, Henan
  • Received:2020-08-01 Accepted:2020-09-27 Online:2021-06-01 Published:2021-06-09
  • Contact: YanNa ZHANG E-mail:zhouke@radi.ac.cn;zyn@henu.edu.cn

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Abstract:

【Objective】 The aim of this study was to use remote sensing technology to extract the spatial distribution information of winter wheat in Henan province from 2017 to 2020, and then to monitor the growth of winter wheat in 2020 with high frequency and to analyze the meteorological conditions. 【Method】 Based on the cloud platform of Google Earth engine (GEE), the selected Landsat 8 image data were synthesized according to the maximum value of NDVI, and then the features were constructed to add terrain features, texture features, NDVI and a new feature NDVI amplification. Random forest classification method was used to train the sample data according to the constructed features to extract the winter wheat planting area in Henan province from 2017 to 2020. The accuracy of the extracted winter wheat sown area was verified by confusion matrix and Henan statistical yearbook data. After accuracy verification, a mask was generated for the extracted winter wheat planting area in Henan province in 2020. In the mask area (winter wheat planting area) combined with MODIS high time resolution image data, the NDVI synchronization difference method was used to monitor the winter wheat growth from February to April in 2020. 【Result】 The GEE cloud platform could be used to quickly map the spatial distribution information of winter wheat planting areas in Henan province. Using random forest method to add terrain feature, texture feature, NDVI and new feature NDVI could effectively improve the extraction accuracy of winter wheat and reduce the relative error with statistical data. Based on confusion matrix, the average overall classification accuracy was 95.2%, the average kappa coefficient was 0.909, and the average classification accuracy of winter wheat was 95.3%. Compared with the statistical yearbook data of Henan province, the relative errors of winter wheat sown area extracted by this method in Henan province from 2017 to 2019 were all less than 3%. The average relative error of winter wheat sown area in the main planting areas of winter wheat in Henan province was less than 6%. MODIS image data combined with NDVI difference model could be used to monitor the growth of winter wheat in Henan province in 2020. The growth of winter wheat in Henan province was better than that of previous years and 2019 during the return to green period. In the later growth stage of winter wheat, the growth of most areas was the same as that of previous years and 2019. On the whole, the growth of winter wheat in 2020 was better than that of previous years and 2019. 【Conclusion】 The method proposed in this paper could carry out high-precision extraction and high-frequency growth monitoring of winter wheat in Henan province, and could provide a scientific basis for local governments or some agricultural departments in arranging and guiding agricultural activities.

Key words: winter wheat, growth monitoring, Google Earth Engine, random forests, NDVI, Landsat, MODIS

Fig. 1

Topographic map of the study area"

Table 1

The growth cycle of winter wheat in Henan province"

月份Month 09 10 11 12 01 02 03 04 05 06
旬
Ten days		 上
First		 中Middle 下
Last		 上First 中Middle 下Last 上First 中Middle 下Last 上First 中Middle 下Last 上First 中Middle 下Last 上First 中
Middle		 下
Last		 上
First		 中Middle 下Last 上
First		 中Middle 下
Last		 上
First		 中
Middle		 下
Last		 上
First		 中Middle 下
Last
冬小麦生
育期
Winter wheat growth period		 播种期
Sowing stage		 出苗-三叶期
Regreening stage		 分蘖期
Tillering stage		 越冬期
Over-wintering stage		 返青期
Re-greening stage		 起身拔节期
Rising stage		 孕穗-抽穗期
Heading stage		 开花期
Flowering stage		 灌浆乳熟期
Mature stage

Table 2

The details of selected Landsat 8 image"

年份 Year 日期 Date (M-D) 选用影像数据年份Year of image data selected 筛选云量 Cloud covered 数量 Count
2017-2018 09-15—11-15 2015-2017 <20% 70
12-01—03-25 2015-2018 <20% 133
2018-2019 09-15—11-15 2016-2018 <20% 83
12-01—03-25 2016-2019 <20% 125
2019-2020 09-15—11-15 2017-2019 <20% 94
12-01—03-25 2019-2020 <10% 36

Table 3

Basis for sample selection"

样本种类 Sample type 解译标志 Interpretation mark 描述 Description
冬小麦 Winter wheat 研究区域冬小麦主要分布在平原地带的农村周边,在Google earth纹理较为清晰,成片出现,有较为规则的形状（矩形）,颜色为绿色或深绿色
Winter wheat in the study area is mainly distributed in the rural periphery of the plain area, the texture of Google earth is relatively clear, appears in pieces, has a more regular shape (rectangle), and the color is green or dark green
水体 Water 研究区域的水体区域主要由水库、湖泊、河流等组成,纹理上边缘明显,在颜色上水体区域在颜色上的表现为青色、淡蓝以及土黄色（黄河）
The water area of the study area is mainly composed of reservoirs, lakes, rivers and so on, and the upper edge of the texture is obvious. In color, the water area is cyan, light blue and earth yellow
不透水面Town 研究区域内的不透水面有城镇建筑以及城镇周边道路组成,在Google earth上纹理信息较明显,也是成片出现,能够清晰的识别
The impervious water surface in the study area is composed of urban buildings and roads around towns, and the texture information is more obvious on Google earth, and it also appears in pieces, which can be clearly identified
其他植被
Other vegetation		 其他植被由山体植被、城镇中的景观植物以及裸地植被等组成。在Google earth上此类地物具有清晰的特征,山体植被海拔较高成山体状;景观植物分布在城镇生活区域中;裸地植被也分布于城镇各处,表面较为稀疏
Other vegetation is composed of mountain vegetation, urban landscape plants and bare land vegetation. On Google earth, such features have clear characteristics, mountain vegetation is mountain-shaped at high altitude; Landscape plants are distributed in urban living areas; Bare land vegetation is also distributed throughout cities and towns, and the surface is relatively sparse

Fig. 2

Monitoring process of winter wheat growth"

Fig. 3

Time series NDVI changes of various features in the study area"

Table 4

Comparison of sown area extracted by remote sensing and statistical sown area"

特征
Feature		 年份
Year		 提取面积
Extraction area (hm2)		 统计面积
Statistical area (hm2)		 绝对误差
Absolute error (hm2)		 相对误差
Relative error (%)
光谱+地形+纹理+NDVI
Spectrum + terrain + texture + NDVI		 2017-2018 5930815 5714640 216175 3.78
2018-2019 6043211 5739850 303361 5.28
光谱+地形+纹理+NDVI+NDVI增幅
Spectrum+topography+texture+NDVI+ NDVIincrease		 2017-2018 5548529 5714640 166111 2.91
2018-2019 5604970 5739850 134670 2.35
2019-2020 5843632

Table 5

Comparison between the sown area extracted by remote sensing and the statistical sown area in the main planting areas of winter wheat in Henan province"

特征 Feature 2017-2018 2018-2019
光谱+地形+纹理+NDVI
Spectrum + topography + texture + NDVI		 市
City		 提取面积
Extraction area (hm2)		 统计面积
Statistical area (hm2)		 绝对误差
Absolute error (hm2)		 相对误差
Relative error (%)		 提取面积
Extraction area (hm2)		 统计面积
Statistical area (hm2)		 绝对误差
Absolute error (hm2)		 相对误差
Relative error (%)
南阳 Nanyang 803278 709020 94258 13.29 771926 724660 47266 4.09
驻马店 Zhumadian 929103 750520 178583 23.79 914174 781670 132504 16.95
周口 Zhoukou 846381 718350 128031 17.82 893891 734380 159511 21.72
商丘 Shangqiu 611644 588870 22774 3.87 705349 603380 101969 16.90
开封 Kaifeng 330408 299920 30488 10.16 322177 305200 16977 5.50
许昌 Xuchang 238458 228960 9498 4.15 226581 233010 6429 2.76
新乡 Xinxiang 442132 379410 62722 16.53 437147 387670 49477 12.76
安阳 Anyang 307150 320380 13230 7.22 305831 325730 19539 6.00
光谱+地形+纹理+ NDVI+NDVI增幅
Spectrum + topography +texture+NDVI+
NDVIincrease		 南阳 Nanyang 721819 709020 12799 1.81 695001 724660 29659 4.09
驻马店 Zhumadian 849960 750520 9944 13.25 834591 781670 52921 6.77
周口 Zhoukou 809734 718350 91384 12.72 808415 734380 74035 10.08
商丘 Shangqiu 575660 588870 13210 2.24 629586 603380 26206 4.34
开封 Kaifeng 300274 299920 354 0.12 316275 305200 11075 3.63
许昌 Xuchang 230127 228960 1167 0.51 224240 233010 8770 3.76
新乡 Xinxiang 427995 379410 485.85 12.80 41784 387670 30170 7.78
安阳 Anyang 313655 320380 6725 2.10 304353 325730 11377 3.49

Table 6

Comparison of extraction accuracy of winter wheat based on confusion matrix in the study area"

特征
Feature		 年份
Year		 总体精度
OA (%)		 Kappa系数
Kappa		 冬小麦分类精度
Classification accuracy of winter wheat (%)
光谱+地形+纹理+NDVI
Spectrum + topography + texture + NDVI		 2017-2018 93.3 0.862 93.5
2018-2019 94.8 0.891 95.1
2019-2020 94.7 0.892 94.5
光谱+地形+纹理+NDVI+NDVI增幅
Spectrum + topography + texture + NDVI+NDVIincrease		 2017-2018 94.8 0.893 94.6
2018-2019 95.6 0.921 95.3
2019-2020 95.8 0.913 95.9

Fig. 4

Distribution map of sown area of winter wheat in the study area from 2017 to 2020"

Fig. 5

NDVI changes of winter wheat planting areas in 2020 compared with previous years"

Fig. 6

The growth situation of winter wheat in the study area from February to April in 2020 compared with the usual year"

Fig. 7

The growth of winter wheat in the study area from February to April in 2020 compared with previous years"

Fig. 8

The growth distribution of winter wheat in the study area from February to April in 2020 compared with previous years"

Fig. 9

Mean temperature and sunshine from February to April in 2017-2020"

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