Please wait a minute...
 
Remote Sensing for Land & Resources    2020, Vol. 32 Issue (1) : 191-199     DOI: 10.6046/gtzyyg.2020.01.26
|
Extraction of early paddy rice area in Lingao County based on Sentinel-1A data
Jingjian LIU1,2, Hongzhong LI1(), Cui HUA3, Yuman SUN4, Jinsong CHEN1, Yu HAN1
1. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
2. Land Resources Surveying and Mapping Institute of Guangxi Zhuang Autonomous Region, Nanning 530023, China
3. School of Natural Resources and Surveying, Nanning Normal University, Nanning 530001, China
4. College of Land and Resources Engineering, Kunming University of Science and Technology, Kunming 650500, China
Download: PDF(4618 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

With the purpose of exploring the extraction of early paddy rice area distribution information from bipolar Sentinel-1A Radar image data recognition and on the basis of an analysis of backscattering coefficients of typical terrain objects, the authors employed the idea that polarization differential SAR images and polarization ratio SAR images play an important role in the classification of typical terrain objects and proposed the utilization of the normalized parameters of water body. Then, the support vector machine (SVM) classification method and the threshold classification method were used to extract the area of early paddy rice from the normalized polarimetric SAR data of single-phase and multi-temporal water body on March 10, March 22, April 3, April 15 and 15 April 15 in 2017. The results show that the threshold classification method is better than the SVM classification method. The overall accuracy of the former method is 89.01%, Kappa coefficient is 0.823 1, mapping accuracy and user accuracy of early paddy rice are 92.68% and 82.26%, respectively. The planting area is 129,000 hectares, which is basically consistent with the spatial distribution of the main early paddy rice production bases in Lingao County. It can be concluded that multi-parameter polarimetric SAR data can improve the accuracy of recognition and extraction of terrain objects. The best monitoring data for extracting early paddy rice area are multi-temporal NDVH polarimetric SAR data.
Keywords early paddy rice area      Sentinel-1A      backscatter coefficient      normalized parameters of water body     
:  TP79  
Corresponding Authors: Hongzhong LI     E-mail: hz.li@siat.ac.cn
Issue Date: 14 March 2020
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Jingjian LIU
Hongzhong LI
Cui HUA
Yuman SUN
Jinsong CHEN
Yu HAN
Cite this article:   
Jingjian LIU,Hongzhong LI,Cui HUA, et al. Extraction of early paddy rice area in Lingao County based on Sentinel-1A data[J]. Remote Sensing for Land & Resources, 2020, 32(1): 191-199.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020.01.26     OR     https://www.gtzyyg.com/EN/Y2020/V32/I1/191
Fig.1  Pretreatment process on Sentinel-1A data
Fig.2  Spatial distribution of field samples in the study area
Fig.3  Field photographs of early paddy rice growing periods in the study area
Fig.4  Scatter distribution of backscattering coefficients in time domain on typical objects
J-M距离 早稻
3月10日 3月22日 4月3日 4月15日 4月27日 3月10日—4月27日
旱地作物 1.56/1.08 1.44/0.90 1.16/1.13 0.21/1.32 1.42/1.49 1.86/1.78
橡胶园地 0.87/0.62 0.99/0.71 0.99/0.75 0.13/0.89 0.44/0.80 1.58/1.57
香蕉园地 1.82/1.71 1.80/1.70 1.74/1.66 1.12/1.67 1.86/1.84 1.98/1.98
建筑物 1.82/0.94 1.87/1.12 1.88/1.06 1.35/1.09 1.87/1.24 1.98/1.71
水域 1.63/1.64 0.61/1.46 1.22/1.58 1.48/1.39 0.86/0.40 1.94/1.95
其他 0.90/0.50 1.03/0.50 0.76/0.65 0.79/0.79 0.95/0.88 1.49/1.36
Tab.1  J-M distance of the SAR data with different temporal about early paddy rice and other typical objects
J-M距离 早稻
3月10日 3月22日 4月3日 4月15日 4月27日 3月10日—4月27日
旱地作物 0.79/0.68 0.58/0.65 0.53/0.91 1.18/1.06 1.18/1.03 1.80/1.89
橡胶园地 0.94/1.11 0.88/1.08 0.81/1.33 0.45/1.43 0.49/1.13 1.72/2.00
香蕉园地 1.73/1.64 1.63/1.60 1.73/1.76 1.74/1.77 1.88/1.74 2.00/2.00
建筑物 1.07/1.02 0.87/1.00 0.85/1.12 0.92/1.13 0.91/0.97 1.82/1.96
水域 1.97/1.72 0.86/0.78 1.24/1.32 1.30/1.10 1.14/0.75 1.73/1.95
其他 0.86/1.07 0.88/1.15 0.65/1.24 0.79/1.18 0.99/1.03 2.00/2.00
Tab.2  J-M distance of the normalized SAR data of water body about early paddy rice and other typical objects
时间 制图精度/% 用户精度/% 总体精度/% Kappa系数 相对误差/%
早稻 其他 早稻 其他
3月10日 35.63/62.07 75.16/82.61 52.56/54.14 58.15/78.95 50.67/48.65 0.287 8/0.270 1 -21.74/-11.04
3月22日 40.26/29.31 78.69/98.55 54.36/55.74 46.39/29.44 45.63/35.81 0.195 6/0.122 7 -34.26/-25.77
4月3日 51.69/36.21 77.54/86.96 46.26/49.41 56.89/29.27 48.56/34.80 0.156 9/0.100 5 -30.43/-21.72
4月15日 53.89/91.38 78.49/36.78 58.33/47.75 59.39/40.13 49.36/46.96 0.213 6/0.188 1 21.74/13.24
4月27日 58.62/73.28 84.06/84.06 57.63/64.89 32.58/36.48 42.57/50.00 0.183 7/0.284 8 15.26/18.7
3月10日—
4月27日		 70.34/78.03 82.61/83.36 60.38/68.73 62.14/75.26 62.45/79.23 0.526 3/0.638 5 47.82/23.79
Tab.3  Accuracy verification analysis based on SVM algorithms
Fig.5  Scatter distribution of normalized backscattering coefficient of water in time domain on typical objects
Fig.6  Characteristic of backscattering coefficient of the NDVV and NDVH with typical objects
Fig.7  Spatial distribution map of early rice in Lingao County
时间 制图精度/% 用户精度/% 总体精度/% Kappa系数 相对误差/%
早稻 其他 早稻 其他
3月10日 4.88/26.83 98.16/97.2 80.00/84.62 58.15/63.80 62.20/68.38 0.287 8/0.435 8 -89.44/-67.74
3月22日 21.95/50.00 96.26/98.13 78.26/93.18 62.05/72.41 66.99/78.95 0.389 8/0.619 3 -79.77/-54.41
4月3日 28.05/31.71 92.46/93.46 74.19/76.47 31.37/64.52 67.94/69.38 0.412 5/0.440 5 -70.74/-64.31
4月15日 17.07/42.68 90.59/95.33 58.33/85.37 59.39/68.92 62.68/74.64 0.310 9/0.540 0 -81.11/-57.64
4月27日 23.17/63.41 96.26/94.39 79.17/89.66 62.05/77.10 66.99/81.82 0.388 2/0.675 9 -78.37/-43.04
3月10日—
4月27日		 76.83/92.68 80.37/85.05 74.12/82.26 82.69/94.79 80.38/89.01 0.659 7/0.823 1 -33.31/12.17
Tab.4  Accuracy verification analysis based on the threshold classification method
[1] 吴炳方 . 中国农情遥感速报系统[J]. 遥感学报, 2004,8(6):481-497.
[1] Wu B F . China crop watch system with remote sensing[J]. Journal of Remote Sensing, 2004,8(6):481-497.
[2] 邵芸, 郭华东, 范湘涛 , 等. 水稻时域散射特征分析及其应用研究[J]. 遥感学报, 2001,5(5):340-345.
[2] Shao Y, Guo H D, Fan X T , et al. Studies on rice backscatter signatures in time domain and its applications[J]. Journal of Remote Sensing, 2001,5(5):340-345.
[3] 刘浩, 邵芸, 王翠珍 , 等. 多时相RADARSAT数据在广东肇庆地区稻田分类中的应用[J]. 国土资源遥感, 1997,9(4):1-6,13.doi: 10.6046/gtzyyg.1997.04.01.
[3] Liu H, Shao Y, Wang C Z , et al. The application of muti-temopral RADARSAT data to paddy field classification in Zhaoqing area,Guangdong Province,China[J]. Remote Sensing for Land and Resources, 1997,9(4):1-6,13.doi: 10.6046/gtzyyg.1997.04.01.
[4] 杨沈斌, 李秉柏, 申双和 , 等. 基于多时相多极化差值图的稻田识别研究[J]. 遥感学报, 2008,12(4):613-619.
[4] Yang S B, Li B B, Shen S H , et al. Rice mapping research based on multi-temporal,multi-polarization backscattering differences[J]. Journal of Remote Sensing, 2008,12(4):613-619.
[5] 国贤玉, 李坤, 王志勇 , 等. 基于SVM+SFS策略的多时相紧致极化SAR水稻精细分类[J]. 国土资源遥感, 2018,30(4):20-27.doi: 10.6046/gtzyyg.2018.04.04.
[5] Guo X Y, Li K, Wang Z Y , et al. Fine classification of rice with multi-temporal compact polarimetric SAR based on SVM + SFS strategy[J]. Remote Sensing for Land and Resources, 2018,30(4):20-27.doi: 10.6046/gtzyyg.2018.04.04.
[6] 王迪, 周清波, 陈仲新 , 等. 基于合成孔径雷达的农作物识别研究进展[J]. 农业工程学报, 2014,30(16):203-212.
[6] Wang D, Zhou Q B, Chen Z X , et al. Research advances on crop identification using synthetic aperture Radar[J]. Transactions of the Chinese Society of Agricultural Engineering, 2014,30(16):203-212.
[7] 严小微, 岑新杰, 唐清杰 , 等. 海南省西北部临高及周边地区早造杂交水稻制种适宜播种期研究[J]. 杂交水稻, 2016,31(5):25-27.
[7] Yan X W, Cen X J, Tang Q J , et al. Studies on suitable sowing time for hybrid rice seed production in the early-cropping season in Lingao County and its surrounding area in northwestern Hainan[J]. Hybrid Rice, 2016,31(5):25-27.
[8] 化国强, 李晨, 杨沈斌 , 等. 利用RADARSAT-2数据基于比值检测的水稻制图[J]. 江苏农业学报, 2012,28(6):1451-1458.
[8] Hua G Q, Li C, Yang S B , et al. Rice mapping based on ratio method using RADARSAT-2 SAR data[J]. Jiangsu Journal of Agricultural Sciences, 2012,28(6):1451-1458.
[9] 李俐, 孔庆玲, 王鹏新 , 等. 基于时间序列Sentinel-1A数据的玉米种植面积监测研究[J]. 资源科学, 2018,40(8):1608-1621.
[9] Li L, Kong Q L, Wang P X , et al. Monitoring of maize planting area based on time-series Sentinel-1A SAR data[J]. Resources Science, 2018,40(8):1608-1621.
[10] 周北燕 . 中国分省系列地图册:海南省地图册[M/CD]. 北京: 中国地图出版社, 2017: 76-79.
[10] Zhou B Y. China Provincial Atlas Series:Atlas of Hainan[M/CD]. Beijing: China Cartographic Publishing House, 2017: 76-79.
[11] 杨魁, 杨建兵, 江冰茹 . Sentinel-1卫星综述[J]. 城市勘测, 2015(2):24-27.
[11] Yang K, Yang J B, Jiang B R . Sentinel-1 satellite overview[J]. Urban Geotechnical Investigation and Surveying, 2015(2):24-27.
[12] 韩东, 杨浩, 杨贵军 , 等. 基于Sentinel-1雷达影像的玉米倒伏监测模型[J]. 农业工程学报, 2018,34(3):166-172.
[12] Han D, Yang H, Yang G J , et al. Monitoring model of maize lodging based on Sentinel-1 Radar image[J]. Transactions of the Chinese Society of Agricultural Engineering, 2018,34(3):166-172.
[13] 陈劲松, 林珲, 邵芸 . 微波遥感农业应用研究——水稻生长监测[M]. 北京: 科学出版社, 2010: 82-95.
[13] Chen J S, Lin H, Shao Y. Application of Microwave Remote Sensing in Agriculture:Paddy Rice Growth Monitoring[M]. Beijing: Science Press, 2010: 82-95.
[14] 张猛, 曾永年 . 基于多时相Landsat数据融合的洞庭湖区水稻面积提取[J]. 农业工程学报, 2015,31(13):178-185.
[14] Zhang M, Zeng Y N . Mapping paddy fields of Dongting Lake area by fusing Landsat and MODIS data[J]. Transactions of the Chinese Society of Agricultural Engineering, 2015,31(13):178-185.
[15] 邵芸, 廖静娟, 范湘涛 , 等. 水稻时域后向散射特性分析:雷达卫星观测与模型模拟结果对比[J]. 遥感学报, 2002,6(6):440-450.
[15] Shao Y, Liao J J, Fan X T , et al. Analysis on rice backscatter signatures in time domain:Comparison between RADARSAT-SAR observation and simulated model results[J]. Journal of Remote Sensing, 2002,6(6):440-450.
[16] Richards J A, Jia X P . Remote Sensing Digital Image Analysis[M]. Berlin:Springer-Verlag, 1999.
[17] Loosvelt L, Peters J, Skriver H , et al. Random forests as a tool for estimating uncertainty at pixel-level in SAR image classification[J]. International Journal of Applied Earth Observations and Geoinformation, 2012,19(1):173-184.
[18] 李楠, 朱秀芳, 潘耀忠 , 等. 人工蜂群算法优化的SVM遥感影像分类[J]. 遥感学报, 2018,22(4):559-569.
[18] Li N, Zhu X F, Pan Y Z , et al. Optimized SVM based on artificial bee colony algorithm for remote sensing image classification[J]. Journal of Remote Sensing, 2018,22(4):559-569.
[19] 汪小钦, 王钦敏, 史晓明 , 等. 基于主成分变换的ASAR数据水稻种植面积提取[J]. 农业工程学报, 2008,24(10):122-126.
[19] Wang X Q, Wang Q M, Shi X M , et al. Rice field mapping and monitoring using ASAR data based on principal component analysis[J]. Transactions of the Chinese Society of Agricultural Engineering, 2008,24(10):122-126.
[20] 孔金玲, 李菁菁, 甄珮珮 , 等. 微波与光学遥感协同反演旱区地表土壤水分研究[J]. 地球信息科学学报, 2016,18(6):857-863.
[20] Kong J L, Li J J, Zhen P P , et al. Inversion of soil moisture in arid area based on microwave and optical remote sensing data[J]. Journal of Geo-Information Science, 2016,18(6):857-863.
[1] SHI Min, GONG Huili, CHEN Beibei, GAO Mingliang, ZHANG Shunkang. Monitoring of land subsidence in Beijing-Tianjin-Hebei plain during 2016—2018 based on InSAR and Sentinel-1A data[J]. Remote Sensing for Natural Resources, 2021, 33(4): 55-63.
[2] SUN Chao, CHEN Zhenjie, WANG Beibei. Expansion monitoring of construction land based on SAR time series: A case study of Xinbei District, Changzhou[J]. Remote Sensing for Land & Resources, 2020, 32(4): 154-162.
[3] Zechao BAI, Baocun WANG, Guowang JIN, Qing XU, Hongmin ZHANG, Hui LIU. Applicability analysis of ground deformation monitoring in mining area by Sentinel - 1A data[J]. Remote Sensing for Land & Resources, 2019, 31(2): 210-217.
[4] CHEN Jiwei, ZENG Qiming, JIAO Jian, ZHAO Binchen. SBAS time series analysis technique based on Sentinel-1A TOPS SAR images: A case study of Yellow River Delta[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(4): 82-87.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech