首页|基于Sentinel-2与时序Sentinel-1 SAR特征的赣南柑橘种植区识别方法

基于Sentinel-2与时序Sentinel-1 SAR特征的赣南柑橘种植区识别方法

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为准确获取柑橘果园空间分布信息,实现柑橘种植结构调整、产量估算和资源管理,以赣南3个柑橘种植主产区(信丰县、安远县及寻乌县)为研究区域,针对南方地区多云多雨导致传统光学影像较为缺乏的问题,使用Sentinel系列数据和PIE-Engine平台,构建和优选了光谱特征、植被水体指数特征、红边波段特征和纹理特征,并引入时间序列Sentinel-1合成孔径雷达(SAR)数据的后向散射系数,共同探讨不同特征组合对柑橘种植园的识别提取效果,基于随机森林算法并融合Sentinel-2与时序Sentinel-1 SAR特征识别提取了赣南柑橘种植区.结果表明:5、9、11月柑橘种植园与其他地物的平均后向散射系数分离性最佳,是识别提取柑橘的关键时期;指数特征及纹理特征参与分类改善了分类效果且提高了分类精度;相较于单一 SAR特征及指数、纹理特征,加入时序SAR特征的分类结果中总体精度达90.084%,Kappa系数达0.863,错分、漏分误差较小,符合实际地物分布情况,说明了时序SAR特征的可用性和实用性.本研究可为多云多雨的南方柑橘果园的识别提取提供参考.
Identification of Gannan Citrus Planting Area Based on Sentinel-2 and Temporal Sentinel-1 SAR Features
In order to accurately obtain spatial distribution information of citrus orchards and achieve adjustments in citrus cultivation structure,yield estimation,and resource management,focusing on three main citrus-producing regions in southern Jiangxi:Xinfeng County,Anyuan County,and Xunwu County,in addressing the challenge posed by frequent cloud cover and rainfall in the southern region,resulting in a scarcity of traditional optical images,Sentinel series data and the PIE-Engine platform were employed.Spectral features,vegetation water body index features,red edge band features,and texture features were constructed and optimized.Furthermore,the backscatter coefficients of time-series Sentinel-1 synthetic aperture radar(SAR)data were incorporated to collectively explore the recognition and extraction effects of different feature combinations on citrus plantations.Based on the random forest algorithm and the fusion of Sentinel-2 and temporal Sentinel-1 SAR feature recognition,the citrus planting area in Gannan was extracted.The results indicated that the average backscatter coefficient separation between citrus plantations and other ground features was most pronounced in May,September,and November,which were the critical periods for citrus identification and extraction.The involvement of index features and texture features in classification proved advantageous for classification effectiveness and enhanced classification accuracy.In comparison with single SAR features,as well as index and texture features,the overall accuracy of the classification results with the inclusion of temporal SAR features was 90.084%,with Kappa coefficient of 0.863.misclassification and leakage errors were relatively small,aligning with the actual distribution of land objects,signifying the availability and practicality of temporal SAR features.The research result can provide reference for the identification and extraction of citrus orchards in the cloudy and rainy southern regions,and it had certain application potential.

citrusplantation area identificationPIE-Enginetemporal SARSentinel satellite

唐琪、李恒凯、周艳兵、王秀丽

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江西理工大学土木与测绘工程学院,赣州 341000

北京市农林科学院信息技术研究中心,北京 100097

江西理工大学经济管理学院,赣州 341000

柑橘 种植区识别 PIE-Engine 时序SAR Sentinel卫星

教育部产学研协同育人项目江西省高等学校人文社会科学研究项目

202102245015JC21123

2024

农业机械学报
中国农业机械学会 中国农业机械化科学研究院

农业机械学报

CSTPCD北大核心
影响因子:1.904
ISSN:1000-1298
年,卷(期):2024.55(3)
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