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Acta Agronomica Sinica ›› 2022, Vol. 48 ›› Issue (9): 2409-2420.doi: 10.3724/SP.J.1006.2022.12066

• RESEARCH NOTES • Previous Articles    

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 Online:2022-09-12 Published:2022-07-15
  • Contact: TANG Zhi-Guang E-mail:sgq@mail.hnust.edu.cn;tangzhg11@hnust.edu.cn
  • 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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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

Fig. 1

Field samples and verification regions in the study area The above vector map is from the National Geographic Information Resource Directory Service System (https://www.webmap.cn/)."

Fig. 2

Workflow of remote sensing extraction of paddy rice based on GEE platform"

Table 1

Number of samples"

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

Fig. 3

Variation and standard deviation of EVI for typical land covers"

Fig. 4

Variation and standard deviation of VH polarization backscatter coefficient for typical land covers"

Fig. 5

Decision tree model for extracting paddy rice in this study EVImean(90,130) represent the mean of EVI during day 90-130."

Table 2

Accuracy assessment confusion matrix of paddy rice in Hunan province"

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

Table 3

Accuracy of paddy rice for verification sample areas"

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

Fig. 6

Comparison of extraction results for paddy rice in 2019 a, d: Sentinel-2 composite image (bands: Red/Green/Blue); b, e: paddy rice map of visual interpretation from Google Earth image; c, f: paddy rice map extracted from the model."

Fig. 7

Classification results of paddy rice in Hunan province during 2017-2020 The above vector maps are from the National Geographic Information Resource Directory Service System (https://www.webmap.cn/)."

Fig. 8

Paddy rice area of single and double cropping rice in prefecture-level cities of Hunan province during 2017-2020"

Fig. 9

Distribution index of paddy rice in Hunan province"

Table 4

F1-scores of paddy rice in different methods"

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

Fig. 10

Number of Sentinel-1/2 images (a) and percentage of available observation pixels (b) in Hunan province Available observation pixels rate indicates cloudless cover pixels number divided by the total number of Sentinel-2 images."

Fig. 11

County-level comparisons in the paddy rice-planted areas between agricultural census reports and the extraction results"

Table 5

Overall accuracy and Kappa coefficient of different classification methods"

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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