基于多时相Sentinel-1/2的酿酒葡萄地块识别方法  

Recognition method of wine grape plots based on multi-temporal Sentinel-1/2

作　　者：张雪纯 陈启帆 付镝 房玉林[4] 苏宝峰[1,2,3] ZHANG Xuechun;CHEN Qifan;FU Di;FANG Yulin;SU Baofeng(College of Mechanical and Electronic Engineering,Northwest A&F University,Yangling 712100,China;Key Laboratory of Agricultural Internet of Things,Ministry of Agriculture and Rural Affairs,Yangling 712100,China;Shaanxi Key Laboratory of Agriculture Information Perception and Intelligent Service,Yangling 712100,China;College of Enology,Northwest A&F University,Yangling 712100,China)

机构地区：[1]西北农林科技大学机械与电子工程学院,杨凌712100 [2]农业农村部农业物联网重点实验室,杨凌712100 [3]陕西省农业信息感知与智能服务重点实验室,杨凌712100 [4]西北农林科技大学葡萄酒学院,杨凌712100

出　　处：《农业工程学报》2025年第5期126-134,共9页Transactions of the Chinese Society of Agricultural Engineering

基　　金：宁夏回族自治区重点研发计划项目(2023BCF01001)。

摘　　要：为探究多时相Sentinel-1和Sentinel-2影像对酿酒葡萄地块识别的影响,该研究选取宁夏回族自治区3个主要的酿酒葡萄种植区域(永宁县、西夏区和青铜峡市)开展酿酒葡萄地块识别研究,其中永宁县和西夏区为测试区,青铜峡市验证区。基于Google earth engine(GEE)云平台的Sentinel-1/2影像数据和shuttle radar topography mission(SRTM)数字高程数据提取光谱特征、纹理特征、植被指数特征、极化特征和地形特征,构建7种不同的特征组合方案,选取最优特征组合方案对酿酒葡萄地块进行识别,并绘制研究区域的酿酒葡萄空间分布图。结果表明:与单独使用Sentinel-1和Sentinel-2影像特征组合相比,结合使用Sentinel-1/2影像特征可以改善酿酒葡萄的识别效果;与单时相特征组合识别结果相比,多时相特征组合也可提高酿酒葡萄的识别精度。在7种不同的特征组合方案中,优选特征组合识别酿酒葡萄结果最优,其中测试区的总体精度、Kappa系数、用户精度、生产者精度分别为95.46%、0.94、93.33%、95.06%;酿酒葡萄提取面积与统计年鉴中的面积相比,相对误差约为9.47个百分点。验证区的总体精度、Kappa系数、用户精度、生产者精度分别为91.89%、0.89、89.00%、95.93%;酿酒葡萄提取面积与统计年鉴中的面积相比,相对误差约为8.15个百分点。该研究可为基于多时相遥感影像进行酿酒葡萄分类和识别研究提供参考。Ningxia Hui Autonomous Region is a key wine grape production area in China.Obtaining timely and accurate information on the distribution of wine grape plots is crucial for adjusting planting structures and estimating yields.This study addresses the challenge of identifying fragmented and irregularly shaped wine grape cultivation areas by utilizing Sentinel-1 and Sentinel-2 remote sensing images on the Google Earth Engine(GEE)cloud platform.Firstly,the study selected three primary wine grape growing regions in Ningxia Hui Autonomous Region:Yongning County and Xixia District as test areas,and Qingtongxia City as the validation area.Sample points were randomly selected within the test area and divided into training and validation sets at a 7:3 ratio.A total of 120 random sample points were selected within the study area to analyze the temporal variation curves of the normalized difference vegetation index(NDVI)reflectance from 2020 Sentinel-2 imagery,as well as the VV and VH polarization backscatter coefficients from Sentinel-1 imagery,to determine the selection dates for multitemporal images.Based on this foundation,five categories of feature variables were extracted:spectral features(18),vegetation index features(48),texture features(18),polarization features(14),and terrain features(4).Secondly,the Gini index algorithm based on random forests is used to analyze the importance of all feature variables.Features are added in order of importance,from highest to lowest,to achieve the highest classification accuracy and determine the optimal number of features.This optimal number is then used to construct a selected feature set.Additionally,the cumulative feature values of Sentinel-1 and Sentinel-2 images on different dates are calculated.Select the image date with the highest feature score to construct a single temporal feature set.Finally,seven different feature combination schemes were constructed based on single-temporal and multitemporal feature sets to identify wine grape plots.The optimal feature combination was

关 键 词：遥感 图像识别 酿酒葡萄 随机森林 Google Earth Engine 

分 类 号：S127[农业科学—农业基础科学] S24