| 基于Sentinel-1/2数据的中国南方单双季稻识别结果一致性分析 |
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| 引用本文： | 杨靖雅,胡琼,魏浩东,蔡志文,张馨予,宋茜,徐保东.基于Sentinel-1/2数据的中国南方单双季稻识别结果一致性分析[J].中国农业科学,2022,55(16):3093-3109. |
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| 作者姓名： | 杨靖雅 胡琼 魏浩东 蔡志文 张馨予 宋茜 徐保东 |
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| 作者单位： | 1华中农业大学资源与环境学院/华中农业大学宏观农业研究院,武汉 4300702华中师范大学城市与环境科学学院,武汉 4300793中国农业科学院农业资源与农业区划研究所/农业农村部农业遥感重点实验室,北京 100081 |
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| 基金项目： | 国家自然科学基金(42001303);国家自然科学基金(41901380);国家自然科学基金(41801371);国家重点研发计划(2019YFE0126700);中国科协青年人才托举工程项目(2020QNRC001);中央高校基本科研业务费专项基金(2662021JC013);中央高校基本科研业务费专项基金(CCNU20QN032) |
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| 摘 要： | 【目的】微波遥感因具有全天时、全天候数据获取的特点,在多云雨的中国南方水稻识别研究中表现出巨大潜力。本研究通过对比Sentinel-1SAR遥感数据和Sentinel-2光学遥感数据用于水稻遥感制图的效果,分析光学和SAR遥感数据对于单双季稻识别结果的一致性,并探索水稻识别的最优SAR影像特征。【方法】本研究使用Sentinel-1/2卫星数据,基于面向对象的随机森林分类算法和Google Earth Engine平台,提取洞庭湖平原4个典型水稻种植区的单双季稻空间分布。通过比较9种不同传感器和特征组合场景的分类精度和分类结果统计指标,并计算NDVI和SAR特征时序（VH、VV、VH/VV）的R2和DTW距离,分析识别单双季稻的最优SAR特征,评估光学和SAR遥感数据对于单双季稻识别结果的一致性。【结果】VH、VV和VH/VV时序识别单双季的总体精度分别为90.42%、82.08%和88.33%,而联合VH和VH/VV时序识别单双季稻的总体精度可达91.67%。VH（VH/VV、VV）时序与单双季稻NDVI时序的R2和DTW距离分别为0.870（0.915、0.986）、4.715（1.896、5.506）（单季稻）和0.597（0.783、0.673）、2.396（1.839、3.441）（双季稻）。较高的R2和较低的DTW距离说明单双季稻的VH/VV时序与NDVI时序相关度更高,可以较好地反映单双季稻的生长周期规律。同时,VH可以较好地反映单双季稻移栽期的淹水特征。基于光学数据和SAR数据在6个时间窗口的特征（S-2：NDVI、EVI、LSWI;S-1：VH、VH/VV）识别单双季稻的总体精度分别为91.25%和90.00%,识别结果面积相关性可达95.70%。【结论】SAR遥感数据与光学遥感数据水稻识别结果一致性较高。应用Sentinel-1在多云雨区识别单双季稻具有巨大潜力,VH和VH/VV后向散射系数时序是识别水稻的优质特征。研究结果为多云多雨区使用SAR数据进行特征优选以高精度识别单双季稻提供了重要技术支撑。
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| 关 键 词： | 单双季稻识别 时序特征 Sentinel-1/2 一致性分析 GoogleEarthEngine |
| 收稿时间： | 2021-10-28 |
Consistency Analysis of Classification Results for Single and Double Cropping Rice in Southern China Based on Sentinel-1/2 Imagery |
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| YANG JingYa,HU Qiong,WEI HaoDong,CAI ZhiWen,ZHANG XinYu,SONG Qian,XU BaoDong.Consistency Analysis of Classification Results for Single and Double Cropping Rice in Southern China Based on Sentinel-1/2 Imagery[J].Scientia Agricultura Sinica,2022,55(16):3093-3109. |
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| Authors: | YANG JingYa HU Qiong WEI HaoDong CAI ZhiWen ZHANG XinYu SONG Qian XU BaoDong |
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| Affiliation: | 1College of Resources and Environment/Macro Agriculture Research Institute, Huazhong Agricultural University, Wuhan 4300702College of Urban and Environmental Sciences, Central China Normal University, Wuhan 4300793Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences/Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs, Beijing 100081 |
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| Abstract: | 【Objective】Due to the abilities of all-time and all-weather data acquisition, the microwave remote sensing holds great potentials to identify rice in regions dominated by cloudy and rainy weather. The aim of this study was to analyze the consistency of classification results for single and double cropping rice by using optical and SAR remote sensing data, and then to explore the optimal SAR imagery features for rice classification. 【Method】In this study, using the object-based random forest classifier on the Google Earth Engine platform, Sentinel-1 and Sentinel-2 images were adopted to extract the single and double cropping rice from four typical rice growing areas in the Dongting Lake Plain. To analyze the optimal SAR features for the single and double cropping rice identification and the consistency of classification results based on Sentinel-1 and Sentinel-2 images, nine scenarios were established by the combination of different sensors and features and compared the performances of different scenarios. Furthermore, the R2 and DTW distance between the NDVI time series and the SAR backscatter coefficient time series (VH, VH/VV) were calculated, respectively. 【Result】 The overall accuracy of single and double rice cropping identification by using VH, VV and VH/VV time series was 90.42%, 82.08% and 88.33%, respectively. Moreover, the combination of VH and VH/VV time series could achieve a better performance (91.67%) for mapping single and double cropping rice. The derived R2 and DTW distance between VH (VH/VV, VV) time series and NDVI time series were 0.870 (0.915, 0.986) and 4.715 (1.896, 5.506) for single cropping rice, as well as 0.597 (0.783, 0.673) and 2.396 (1.839, 3.441) for double cropping rice, respectively. Higher R2 and lower DTW distance suggested that VH/VV time series, like NDVI, was more sensitive to the rice growth cycle. Furthermore, the flooding signals in rice transplanting phase could be well captured by VH time series. Additionally, the overall accuracy of single and double cropping rice classification based on optical and SAR features (S-2: NDVI, EVI, LSWI; S-1: VH, VH/VV) in six time windows was 91.25% and 90.00%, respectively, and their consistency was high, with the area correlation of 95.70%.【Conclusion】There was high consistency of classification results for single and double cropping rice based on optical and SAR imagery. Thus, Sentinel-1 imagery held great potentials to identify rice area in cloudy and rainy regions. Specifically, VH and VH/VV backscatter coefficient were optimal features for mapping rice. This study provided vital technical supports for feature optimization by using SAR imagery in cloudy and rainy regions to identify single and double cropping rice accurately. |
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| Keywords: | single and double cropping rice identification feature time series Sentinel-1/2 consistency analysis Google Earth Engine |
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