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Remote Sensing for Natural Resources    2023, Vol. 35 Issue (4) : 71-80     DOI: 10.6046/zrzyyg.2023141
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Remote sensing monitoring method for flooded farmland based on domestic GF-3 radar images
YANG Chiyi1,2(), GUAN Haixiang1,2, WU Wei3, LIU Meiyu3, LI Ying4,5, SU Wei1,2()
1. College of Land Science and Technology, China Agricultural University, Beijing 100083,China
2. Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture, Beijing 100083, China
3. National Disaster Reduction Center of China, Ministry of Emergency Management of the People’s Republic of China, Beijing 100124,China
4. Henan Agrometeorological Support and Applied Technique Key Laboratory, China Meteorological Administration, Zhengzhou 450003, China
5. Henan Institute of Meteorological Sciences, Zhengzhou 450003, China
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Abstract  

Against the backdrop of global warming, increasingly frequent floods become a primary agricultural disaster that causes reduced crop production in China. Radar remote sensing technology, possessing all-weather earth observation capabilities, serves as a critical means for rapid monitoring of regional flood information. With the advancement in artificial intelligence, machine learning methods have been extensively applied in the remote sensing-based monitoring of floods. Despite the high accuracy of their algorithms, their training processes often entail extensive field investigations or numerous samples for remote sensing image interpretation. This study aims to overcome sample labeling limitations and improve regional flood monitoring accuracy. Based on the catastrophic flood that occurred in northern Henan on July 20, 2021, this study constructed a flooded crop monitoring method based on the weakly supervised Gaussian mixture model (GMM) using domestic GF-3 HH-HV radar images. Then this method was applied to extract the flooding range of farmland in some areas of northern Henan. Compared to four typical machine learning methods, i.e., random forest (RF), support vector machine (SVM), K-nearest neighbor classification, and parallelepiped classification, the weakly supervised GMM in this study enjoyed the highest accuracy, with overall precision of 0.95 and a Kappa coefficient of 0.90. This study holds great significance for enhancing the accuracy and universality of regional crop flooding monitoring based on remote sensing technology and synthetic aperture radars (SARs).
Keywords GF-3 radar image      flood      farmland flooding      Gaussian mixture model     
ZTFLH:  TP79  
Issue Date: 21 December 2023
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Chiyi YANG
Haixiang GUAN
Wei WU
Meiyu LIU
Ying LI
Wei SU
Cite this article:   
Chiyi YANG,Haixiang GUAN,Wei WU, et al. Remote sensing monitoring method for flooded farmland based on domestic GF-3 radar images[J]. Remote Sensing for Natural Resources, 2023, 35(4): 71-80.
URL:  
https://www.gtzyyg.com/EN/10.6046/zrzyyg.2023141     OR     https://www.gtzyyg.com/EN/Y2023/V35/I4/71
Fig.1  Study area location,study area elevation,Sentinel-2 image and field investigation of “July 20” rainstorm in Henan Province
Fig.2  Precipitation of some meteorological observation stations in Northern Henan from July 15 to 31
Fig.3  Technique process
Fig.4  Backscatter coefficients of HH and HV images of flood-affected farmland and non-disaster-stricken farmland
Fig.5  Partial plot of Sentinel-2 optical imagery and classification results
Fig.6  Accuracy verification of five machine learning methods
Fig.7  Global view of Sentinel 2 optical imagery and classification results
市区县 被淹农田
面积/km2		 总农田面
积/km2		 被淹农田占总农田
面积比例/%
新乡县 22.61 268.46 8
凤泉区 13.44 53.70 25
卫滨区 3.72 23.77 16
汤阴县 24.92 473.67 5
北关区 0.80 18.81 4
浚县 115.57 732.32 16
淇县 40.54 255.82 16
红旗区 3.65 56.32 6
淇滨区 4.32 148.73 3
殷都区 0.78 25.76 3
牧野区 9.01 26.50 34
龙安区 4.68 146.00 3
文峰区 10.11 86.56 12
延津县 22.41 713.63 3
卫辉市 100.13 459.42 22
山城区 1.73 75.60 2
鹤山区 0.96 41.70 2
林州市 18.05 609.47 3
Tab.1  The area of farmland affected by the flood disaster in each urban,district and county
[1] Tong X H, Luo X, Liu S G, et al. An approach for flood monitoring by the combined use of Landsat 8 optical imagery and COSMO-SkyMed radar imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 136:144-153.
doi: 10.1016/j.isprsjprs.2017.11.006 url: https://linkinghub.elsevier.com/retrieve/pii/S0924271616304142
[2] 徐宗学, 刘琳, 杨晓静. 极端气候事件与旱涝灾害研究回顾与展望[J]. 中国防汛抗旱, 2017, 27(1):66-74.
[2] Xu Z X, Liu L, Yang X J. Study on extreme climate events and drought/flood disasters:Review and prospect[J]. China Flood & Drought Management, 2017, 27(1):66-74.
[3] 曹罗丹, 李加林. 基于遥感与GIS的浙江省洪涝灾害综合风险评估研究[J]. 自然灾害学报, 2015, 24(4):111-119.
[3] Cao L D, Li J L. Integrated risk assessment of flood disaster in Zhejiang Province based on RS and GIS[J]. Journal of Natural Disasters, 2015, 24(4): 111-119.
[4] 王国杰, 胡一凡, 张森, 等. 深度卷积神经网络的遥感影像水体识别[J]. 遥感学报, 2022, 26(11):2304-2316.
[4] Wang G J, Hu Y F, Zhang S, et al. Water identification from the GF-1 satellite image based on the deep convolutional neural networks[J]. National Remote Sensing Bulletin, 2022, 26(11): 2304-2316.
doi: 10.11834/jrs.20210175 url: http://www.ygxb.ac.cn/zh/article/doi/10.11834/jrs.20210175/
[5] 刘宇晨, 高永年. Sentinel时序影像的长江流域地表水体提取[J]. 遥感学报, 2022, 26(2):358-372.
[5] Liu Y C, Gao Y N. Surface water extraction in Yangtze River basin based on sentinel time series image[J]. National Remote Sensing Bulletin, 2022, 26(2):358-372.
doi: 10.11834/jrs.20211287 url: http://www.ygxb.ac.cn/zh/article/doi/10.11834/jrs.20211287/
[6] Shao J L, Gao H, Wang X, et al. Application of Fengyun-4 satellite to flood disaster monitoring through a rapid multi-temporal synthesis approach[J]. Journal of Meteorological Research, 2020, 34(4):720-731.
doi: 10.1007/s13351-020-9184-9
[7] Cohen J, Riihimäki H, Pulliainen J, et al. Implications of boreal forest stand characteristics for X-band SAR flood mapping accuracy[J]. Remote Sensing of Environment, 2016, 186: 47-63.
doi: 10.1016/j.rse.2016.08.016 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425716303194
[8] Jedlovec G. Advances in geoscience and remote sensing[M]. London:Intech, 2009:679-708.
[9] Brisco B, Touzi R, Van der Sanden J J, et al. Water resource applications with RADARSAT-2:A preview[J]. International Journal of Digital Earth, 2008, 1(1):130-147.
doi: 10.1080/17538940701782577 url: http://www.tandfonline.com/doi/abs/10.1080/17538940701782577
[10] Hess L L, Melack J M. Remote sensing of vegetation and flooding on Magela Creek Floodplain (Northern Territory,Australia) with the SIR-C synthetic aperture radar[J] .Hydrobiologia, 2003, 500:65-82.
doi: 10.1023/A:1024665017985 url: http://link.springer.com/10.1023/A:1024665017985
[11] 张庆君. 高分三号卫星总体设计与关键技术[J]. 测绘学报, 2017, 46(3):269-277.
doi: 10.11947/j.AGCS.2017.20170049
[11] Zhang Q J. System design and key technologies of the GF-3 satellite[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(3): 269-277.
doi: 10.11947/j.AGCS.2017.20170049
[12] 郭山川, 杜培军, 蒙亚平, 等. 时序Sentinel-1A数据支持的长江中下游汛情动态监测[J]. 遥感学报, 2021, 25(10):2127-2141.
[12] Guo S C, Du P J, Meng Y P, et al. Dynamic monitoring on flooding situation in the middle and lower reaches of Yangtze River region using Sentinel-1A time series[J]. National Remote Sensing Bulletin, 2021, 25(10): 2127-2141.
doi: 10.11834/jrs.20210547 url: http://www.ygxb.ac.cn/zh/article/doi/10.11834/jrs.20210547/
[13] 郁宗桥, 王育红, 刘文宋, 等. 顾及GF-3影像纹理特征的洪涝范围提取方法[J]. 测绘通报, 2022, 542(5):26-31.
[13] Yu Z Q, Wang Y H, Liu W S, et al. Flood extent extraction method based on the texture features of GF-3 images[J]. Bulletin of Surveying and Mapping, 2022, 542(5): 26-31.
[14] Nemni E, Aylett B J, Belabbes S, et al. Fully convolutional neural network for rapid flood segmentation in synthetic aperture radar Imagery[J]. Remote Sensing, 2020(12):2532.
[15] Lyu W T, Yu Q Z, Yu W X. Water extraction in SAR images using GLCM and Support Vector Machine[C]// IEEE 10th International Conference on Signal Processing Proceedings,IEEE, 2010: 740-743.
[16] Zhang S C, Ma Z M, Li Z H, et al. Using CYGNSS data to map flood inundation during the 2021 extreme precipitation in Henan Province,China[J]. Remote Sensing, 2021, 13 (24):5181.
doi: 10.3390/rs13245181 url: https://www.mdpi.com/2072-4292/13/24/5181
[17] Li Z P, Liu Y L, Cheng Y F, et al. Sentinel-2A MSI and Landsat 8 OLI provide complementary information for estimating chlorophyll-a concentration in a turbid and eutrophic reservoir[J]. Remote Sensing of Environment, 2016, 173:236-247.
[18] 李艳玲, 王加俊. 基于高斯混合模型的纹理图像的分割[J]. 微电子学与计算机, 2004(4):63-65,68.
[18] Li Y L, Wang J J. Texture image segmentation based on Gaussian Mixture Models[J]. Microelectronics & Computer, 2004(4):63-65,68.
[19] Vapnik V. Pattern recognition using generalized portrait method[J]. Automation and Remote Control, 1963(24): 774-780.
[20] Cortes C, Vapnik V N. Support-vector networks[J]. Machine Learning, 1995(20):273-297.
[21] Cover T, Hart P. Nearest neighbor pattern classification[J]. IEEE Transactions on Information Theory, 1967, 13(1):21-27.
doi: 10.1109/TIT.1967.1053964 url: http://ieeexplore.ieee.org/document/1053964/
[22] Schowengerdt R A. Remote sensing:Models and methods for image processing[M]. San Diego: Academic Press, 1997:411-412.
[23] 黄敏敏. 基于Seminel-1/2数据的寿光市洪涝灾害监测与预测研究[D]. 南京: 南京信息工程大学, 2022.
[23] Huang M M. Research on flood disaster monitoring and prediction in Shouguang City based on Seminel-1/2 data[D]. Nanjing: Nanjing University of information Science & Technology, 2022.
[24] Wang L H, Ma H, Li J L, et al. An automated extraction of small- and middle-sized rice fields under complex terrain based on SAR time series:A case study of Chongqing[J]. Computers and Electronics in Agriculture, 2022(200):107232.
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