Quantifying freeze-melt dynamics of lakes on the Tibetan Plateau using Sentinel-1 synthetic aperture radar imagery

The ice phenology of alpine lakes on the Tibetan Plateau(TP)is a rapid and direct responder to climate changes,and the variations in lake ice exhibit high temporal frequency characteristics.MODIS and passive microwave data are widely used to monitor lake ice changes with high temporal resolution.However,the low spatial resolutions make it difficult to effectively quantify the freeze-melt dynamics of lakes.This work used Sentinel-1 synthetic aperture radar(SAR)data to derive high-resolution ice maps(about 6 days),then with the aid of Sentinel-2 optical images to quantify freeze-melt processes in three typical lakes on the TP(e.g.Selin Co,Ayakekumu Lake,and Nam Co).The results showed that three lakes had an average annual ice period of 125-157 days and a complete ice cover period of 72-115 days,from 2018 to 2022.They exhibit different ice phenology patterns.Nam Co is characterized by repeated episodes of freezing,melting,and refreezing,resulting in a prolonged freeze-up period.Meanwhile,the break-up period of Nam Co lasts for a longer duration(about 19 days),and the break-up exhibits a smooth process.Similarly,Ayakekumu Lake showed more significant inter-annual fluctuations in the freeze-up period,with deviations of up to 28 days observed among different years.Compared to the other two lakes,Selin Co experienced a relatively short freeze-up and break-up period.In short,Sentinel-1 SAR data can effectively monitor the weekly and seasonal variations in lake ice on the TP.Particularly,this data facilitates quantification of the freeze-melt dynamics.

基于Sentinel-1/2改进极化指数和纹理特征的土壤含盐量反演模型

目前Sentinel-1/2协同反演植被土壤含盐量的研究大多是基于Sentinel-2光谱信息和Sentinel-1后向散射系数,没有考虑Sentinel-2光谱信息容易受土壤亮度等信息影响,Sentinel-1后向散射系数容易受土壤粗糙度和水分影响。为进一步提高Sentinel-1/2协同反演植被土壤含盐量的精度,用水云模型对雷达卫星后向散射系数进行校正,消除植被影响;然后协同Sentinel-2纹理特征,基于VIP、OOB、PCA 3种变量筛选和RF、ELM、Cubist 3种机器学习回归模型构建植被土壤含盐量反演模型。研究结果表明:经过水云模型去除植被影响后的雷达后向散射系数及其极化组合指数与土壤含盐量的相关性有一定程度的提高。不同变量选择方法与不同机器学习方法耦合模型在反演土壤含盐量中,OOB变量筛选方法与RF、ELM和Cubist 3种机器学习方法的耦合模型精度最佳,建模集和验证集的R2都在0.750以上,且验证集的RMSE和MAE均最小;其中OOB-Cubist耦合模型精度最高,且R_(v)^(2)/R_(c)^(2)为0.955,具有良好的鲁棒性。研究可为机器学习协同物理模型、光学卫星协同雷达卫星在土壤含盐量反演中的进一步应用提供思路。

双极化Sentinel-1数据在城市沉降监测中的对比研究

目的 针对地面沉降监测仅考虑VV极化Sentinel-1数据而忽略VH极化数据的问题,方法采用PS-InSAR和SBAS-InSAR技术处理延安新区2018年8月—2021年5月的84景双极化Sentinel-1数据,获取地面沉降信息,对比两种极化的沉降监测结果,探讨VH极化在不同实验方法中的意义,并提出一种双极化时序InSAR融合监测新方法。结果 结果表明,新区大部分区域为较稳定区域,形变速率为-8~8 mm/a,此外,还探测到3个较大的沉降区,分别位于桥儿沟流域、高家沟流域和新区东北部填方区域;Sentinel-1不同极化数据的监测结果相关性较高,形变趋势一致;VV极化数据整体优于VH极化,但部分区域VH极化数据得到的地表形变信息更详细;VV-VH极化PS-InSAR形变监测中,VH极化数据能够有效补充VV极化数据监测结果的不足,使结果更加密集,因此,与单一极化监测结果相比,VV-VH极化PS-InSAR形变监测更加详细地反演了地表变形情况;VV-VH极化SBAS-InSAR形变速率结果比单一极化数据相干性和稳定性更高。结论 基于双极化Sentinel-1数据的时序InSAR融合监测技术可以获得比单一极化数据更好的城市地表形变监测结果。

联合Sentinel-1与Sentinel-2的高时空分辨率玉米叶面积指数反演

叶面积指数(leaf area index,LAI)是单位地表面积上总叶面积的一半,是影响光合作用、蒸腾作用和能量平衡等地表过程的关键生物物理变量。鉴于光学遥感数据易受天气的影响,雷达遥感数据易受土壤等的影响,二者在叶面积指数反演方面各有利弊,提出了一种考虑不同数据反演结果不确定性的融合方法。研究测试了多种机器学习模型在中国张掖地区的玉米农田上估算LAI的性能。结果表明,光学和雷达两种数据分别作为模型输入进行LAI反演时,高斯过程回归(Gaussian process regression,GPR)的表现均为最优。随后,基于Sentinel-1雷达数据和Sentinel-2光学数据,使用GPR模型生成了研究区2019年的两种LAI及不确定性时空分布图。考虑不同数据反演结果的差异,使用加权滤波方法将两种LAI融合,实现了高时空分辨率玉米LAI制图。通过定性和定量分析,融合后的LAI时间序列分布图变化连贯,空间分布均匀,精度相较于融合之前有了明显改善。

基于Sentinel-1结合Sentinel-2和Landsat8影像的水体提取方法比较——以鲁班水库为例

基于sentinel-1数据结合sentinel-2数据和Landsat8数据,提取2017年—2021年鲁班水库五年间的水体年间变化,对所提取的水体面积进行分析,比较两种影像对鲁班水库水体提取的差异,经过水体指数的计算以及分析得到鲁班水库的水体面积。sentinel-2影像数据对水体提取的效果较好于Landsat8数据,sentinel-2影像总体分类精度达0.993,kappa系数为0.991,而Landsat8的总体精度为0.989,kappa系数为0.986。通过对鲁班水库这种大中型水体的水体提取选择使用sentinel-2数据较好。

基于Sentinel-1/2数据一种改进的决策树方法提取荔枝果园研究

荔枝种植在热带、亚热带地区,受限于多云多雨天气和遥感技术,获取多时相并且能够表征荔枝物候特征的有效高光谱与高空间分辨率数据是比较困难的,这阻碍了荔枝果园的提取。采用Sentinel-1时间序列微波数据构建了荔枝关键生育期特征,并结合Sentinel-2数据提出了一种改进的荔枝果园识别模型,并将该模型应用于茂名市荔枝果园的识别。结果表明:成熟期荔枝与其他植被或作物的VH极化特征差异显著;该方法在荔枝果园空间分布制图的总体精度为90.34%,Kappa系数为0.84。该方法能够有效刻画荔枝的关键生育期特征,且准确识别光谱与空间特征相似的荔枝果园。为热带和亚热带其他果园的识别提供了参考,对果园的生长监测、精确管理和产量估算具有重要意义。

基于Sentinel-1和Sentinel-2影像的河南扶沟县洪涝灾害遥感监测评估研究

洪涝灾害是我国最主要的自然灾害类型之一,发生频率高、影响范围广,对社会经济发展和人民生命财产安全构成严重威胁。选取河南郑州“7·20”特大暴雨事件中受灾较为严重的河南扶沟县为研究区,基于洪涝灾害发生前后的Sentinel-1和Sen-tinel-2影像,利用支持向量机对灾前的Sentinel-2影像进行土地利用分类,基于Sentinel-1影像利用水体指数SDWI对灾中、灾后的水体范围进行提取,并结合GIS对研究区的灾情进行评估。结果表明:①基于支持向量机提取的土地利用分类图,总体精度达95.85%;②利用SDWI水体指数法提取的水体范围结果显示灾中、灾后的水体面积分别为36.468 km^(2)、18.770 km^(2),总体精度分别为97.6%和95.4%;③由灾情评估结果可得,曹里乡的受灾情况最为严重,最大水体变化面积达到12.63 km^(2)。

Mapping soil organic matter in cultivated land based on multi-year composite images on monthly time scales

Rapid and accurate acquisition of soil organic matter(SOM)information in cultivated land is important for sustainable agricultural development and carbon balance management.This study proposed a novel approach to predict SOM with high accuracy using multiyear synthetic remote sensing variables on a monthly scale.We obtained 12 monthly synthetic Sentinel-2 images covering the study area from 2016 to 2021 through the Google Earth Engine(GEE)platform,and reflectance bands and vegetation indices were extracted from these composite images.Then the random forest(RF),support vector machine(SVM)and gradient boosting regression tree(GBRT)models were tested to investigate the difference in SOM prediction accuracy under different combinations of monthly synthetic variables.Results showed that firstly,all monthly synthetic spectral bands of Sentinel-2 showed a significant correlation with SOM(P<0.05)for the months of January,March,April,October,and November.Secondly,in terms of single-monthly composite variables,the prediction accuracy was relatively poor,with the highest R^(2)value of 0.36 being observed in January.When monthly synthetic environmental variables were grouped in accordance with the four quarters of the year,the first quarter and the fourth quarter showed good performance,and any combination of three quarters was similar in estimation accuracy.The overall best performance was observed when all monthly synthetic variables were incorporated into the models.Thirdly,among the three models compared,the RF model was consistently more accurate than the SVM and GBRT models,achieving an R^(2)value of 0.56.Except for band 12 in December,the importance of the remaining bands did not exhibit significant differences.This research offers a new attempt to map SOM with high accuracy and fine spatial resolution based on monthly synthetic Sentinel-2 images.

基于Sentinel-1和Sentinel-2的不同物候期农作物识别研究

为减少农作物提取过程中受光学数据成像质量的影响,基于Google Earth Engine平台,采用Sentinel-1和Sentinel-2数据,分别对小麦越冬期、返青期、孕穗期、成熟期四个物候期进行小麦和油菜的识别。使用随机森林方法对构建的光谱特征、植被指数特征、红边指数特征、纹理特征和极化特征共34个特征进行优选,构建特征集;并对比最小距离、决策树、支持向量机、随机森林四种分类器在四个物候期的识别结果,确定最优的分类器;同时还验证了极化特征在四个物候期对识别结果的影响。研究结果表明,在四个物候期中最优的分类器均为随机森林,其中识别精度从高到低的物候期分别为小麦的孕穗期、成熟期、返青期、越冬期,总体精度(OA)分别为92.91%、91.93%、90.24%、87.69%,Kappa系数分别为91.00%、89.92%、87.61%、84.53%。在四个物候期中加入极化特征均能提高识别的精度,其中在小麦的返青期和成熟期更为明显。

基于Sentinel-1A钦防地区地面沉降监测与分析

分析钦防地区地面沉降时空分布、演变规律和驱动因素,可为区域灾害预测防治及城市规划提供科学依据。基于小基线集时序合成孔径雷达干涉测量(small baseline subset interferometric synthetic aperture Radar,SBAS-InSAR)技术,利用45景Sentinel-1A合成孔径雷达影像提取了研究区2018—2021年的地面沉降信息,同时结合地区地质背景、降水数据、土地利用情况和道路等数据,借助于空间分析技术、数理统计和遥感图像分类及变化检测等方法对研究区地面沉降的整体特征、时空演变趋势及其影响因素进行了可视化分析和定量化分析。结果表明:①在空间维度上,研究时段内研究区地面形变速率介于-114.37~58.55 mm/a之间,研究区内地面形变分布范围广且不均匀分布明显,形成了以钦南区主城区中南部、钦州港与港口区为主的3个沉降中心区域,沉降地区的沉降面积逐年增加并呈现出向南扩张的趋势;②在时间维度上,各沉降中心区域从整体上随着时间变化呈现不均匀的下沉趋势,但出现了周期性的回升,回升值最大可达18.4 mm;③在影响因素上,城镇化扩张、道路密度、构造运动、地层作用、降水作用和海平面上升是导致研究区地面沉降的主要因子,同时也主导了地面沉降的扩张和增幅。

Sentinel-1A影像在山区管道地表形变监测中的适用性评价指标构建

Sentinel-1A卫星数据覆盖范围广、重访周期快、获取成本低,通过合成孔径雷达干涉测量(interferometric synthetic aperture radar,InSAR)技术能够高效获取管道沿线大面积地表形变信息。但是山区管道所处地形复杂、起伏大、植被茂盛,监测中易存在叠掩、阴影、失相干等现象,Sentinel-1A卫星数据在管道不同区段的适用性有差异。为了对Sentinel-1A在山区管道地表形变测量中的适用程度进行评价,以三段不同山区地形的管道为研究区,结合Sentinel-1A数据、Sentinel-2数据、ALOS DEM数据进行相关性分析,构建适用性评价指标。结果显示:管道沿线Sentinel-1A影像的叠掩阴影占总面积的比例与坡度的Pearson相关系数为-0.914,Spearman相关系数为-1,呈显著负相关;影像相干性和归一化植被指数的Pearson相关系数为-0.972,Spearman相关系数为-0.99,呈显著负相关。使用回归分析和归一化的方法建立了山区管道沿线Sentinel-1A数据的坡度适用性指标和植被适用性指标,指标可对山区管道沿线域使用Sentinel-1A数据进行形变监测的适用性进行评价。

Retrieving chlorophyll content and equivalent water thickness of Moso bamboo(Phyllostachys pubescens) forests under Pantana phyllostachysae Chao-induced stress from Sentinel-2A/B images in a multiple LUTs-based PROSAIL framework

Biochemical components of Moso bamboo(Phyllostachys pubescens)are critical to physiological and ecological processes and play an important role in the material and energy cycles of the ecosystem.The coupled PROSPECT with SAIL(PROSAIL)radiative transfer model is widely used for vegetation biochemical component content inversion.However,the presence of leaf-eating pests,such as Pantana phyllostachysae Chao(PPC),weakens the performance of the model for estimating biochemical components of Moso bamboo and thus must be considered.Therefore,this study considered pest-induced stress signals associated with Sentinel-2A/B images and field data and established multiple sets of biochemical canopy reflectance look-up tables(LUTs)based on the PROSAIL framework by setting different parameter ranges according to infestation levels.Quantitative inversions of leaf area index(LAI),leaf chlorophyll content(LCC),and leaf equivalent water thickness(LEWT)were derived.The scale conversions from LCC to canopy chlorophyll content(CCC)and LEWT to canopy equivalent water thickness(CEWT)were calculated.The results showed that LAI,CCC,and CEWT were inversely related with PPC-induced stress.When applying multiple LUTs,the p-values were<0.01;the R2 values for LAI,CCC,and CEWT were 0.71,0.68,and 0.65 with root mean square error(RMSE)(normalized RMSE,NRMSE)values of 0.38(0.16),17.56μg cm-2(0.20),and 0.02 cm(0.51),respectively.Compared to the values obtained for the traditional PROSAIL model,for October,R2 values increased by 0.05 and 0.10 and NRMSE decreased by 0.09 and 0.02 for CCC and CEWT,respectively and RMSE decreased by 0.35μg cm-2 for CCC.The feasibility of the inverse strategy for integrating pest-induced stress factors into the PROSAIL model,while establishing multiple LUTs under different pest-induced damage levels,was successfully demonstrated and can potentially enhance future vegetation parameter inversion and monitoring of bamboo forest health and ecosystems.

基于Sentinel-1的门源Ms 6.9三维同震形变提取与分析

基于Sentinel-1升、降轨合成孔径雷达(SAR)数据,利用SAR差分干涉测量(D-InSAR)、像素偏移量追踪(POT)技术,获取了2022-01-08青海门源Ms 6.9地震多角度地表形变,并采用三维形变估算模型提取了三维同震形变场。结果表明:(1)升、降轨D-InSAR最大形变分别为0.56 m和0.6 m,POT最大形变分别为0.65 m和0.72 m;(2)地震导致的地表破裂带长度超过20 km,整体呈NWW方向分布,同震形变场呈蝴蝶状,形变按幅度大小相近,方向相反的规律沿对称轴分布;(3)此次地震以东西向位移为主,水平最大位移超过1.1 m,垂直最大位移幅度约为0.5 m,符合走滑地震的特征。本文结果与实地踏勘结果较为吻合,丰富了该地震同震形变观测结果,并补充查明了地表破碎带的特征。

Monthly composites from Sentinel-1 and Sentinel-2 images for regional major crop mapping with Google Earth Engine

Rapid and accurate access to large-scale,high-resolution crop-type distribution maps is important for agricultural management and sustainable agricultural development.Due to the limitations of remote sensing image quality and data processing capabilities,large-scale crop classification is still challenging.This study aimed to map the distribution of crops in Heilongjiang Province using Google Earth Engine(GEE)and Sentinel-1 and Sentinel-2 images.We obtained Sentinel-1 and Sentinel-2 images from all the covered study areas in the critical period for crop growth in 2018(May to September),combined monthly composite images of reflectance bands,vegetation indices and polarization bands as input features,and then performed crop classification using a Random Forest(RF)classifier.The results show that the Sentinel-1 and Sentinel-2 monthly composite images combined with the RF classifier can accurately generate the crop distribution map of the study area,and the overall accuracy(OA)reached 89.75%.Through experiments,we also found that the classification performance using time-series images is significantly better than that using single-period images.Compared with the use of traditional bands only(i.e.,the visible and near-infrared bands),the addition of shortwave infrared bands can improve the accuracy of crop classification most significantly,followed by the addition of red-edge bands.Adding common vegetation indices and Sentinel-1 data to the crop classification improved the overall classification accuracy and the OA by 0.2 and 0.6%,respectively,compared to using only the Sentinel-2 reflectance bands.The analysis of timeliness revealed that when the July image is available,the increase in the accuracy of crop classification is the highest.When the Sentinel-1 and Sentinel-2 images for May,June,and July are available,an OA greater than 80%can be achieved.The results of this study are applicable to large-scale,high-resolution crop classification and provide key technologies for remote sensing-based crop classification in small-scale agricultural areas.

基于Sentinel-1A的安徽省2020年梅雨期洪水淹没监测

2020年超长梅雨期内的持续强降雨,导致安徽省发生全域性洪涝灾害,为了快速、准确地提取洪涝淹没范围,为防汛救灾提供科学支撑,选取安徽境内巢湖流域和淮河流域的灾前和灾中Sentinel-1A数据,首先,在快速预处理基础上,采用双极化水体指数(Sentinel-1A dual-polarized water index,SDWI)法,并结合地形因子对平原和山区分别提取水体信息,建立一套洪水淹没区监测流程;然后通过该流程利用灾前、灾中两期合成孔径雷达数据提取2020年7月27日巢湖流域、淮河流域行蓄洪区洪水淹没范围。结果显示:SDWI比直接用后向散射系数提取水体具有优势;7月27日巢湖流域洪水淹没区面积为524.8 km^(2),其中受洪灾较重的是白石天河子流域,西河子流域次之;淮河流域安徽境内行蓄洪区,沿淮的4个地市淹没面积从大到小依次为淮南市、阜阳市、六安市、蚌埠市。研究表明,基于Sentinel-1A数据,采用SDWI和地形因子建立的洪水淹没区监测流程对平原和山区都具有较好的准确性、适用性,且具有较高的时效性,便于及时开展洪水灾害监测。

基于Sentinel-1影像的浙江省沿海养殖池塘提取与管理

沿海养殖池塘建设能带来巨大的经济效益,对保障海产品供给和丰富居民食物多样性具有重要意义。而养殖池塘的快速扩张也会带来严重的环境危机,故有效揭示养殖池塘的时空分布特征对有序管理沿海养殖池塘至关重要。但养殖池塘大多分布于岸线曲折、靠近海洋的潮滩一侧,有效且高精度识别养殖池塘具有难度。对此,提出结合Google Earth Engine云平台和ArcGIS本地端分类后处理的养殖池塘识别方法,基于地区水体频率、对象特征和精细处理得到了浙江省沿海2016—2021年较高精度的养殖池塘空间分布数据。结果表明:养殖池塘总体精度均大于93%,Kappa系数均大于82%,表明研究方法具有较好的适用性。浙江省沿海养殖池塘面积趋于下降,2016、2019、2021年分别为30360.60、24375.35、21700.02 hm^(2)。养殖池塘地级市集中分布于宁波市、台州市、绍兴市和杭州市,县域聚集于慈溪市、宁海县、三门县、萧山区、上虞区和象山县。浙江省养殖池塘集聚性下降,聚集于海湾、河口、沿海平原与潮滩,如杭州湾、象山港、三门湾、浦坝港和乐清湾。养殖池塘空间差异性突出,其海侧大于陆侧、北部大于南部。

Rapid Extraction of Water Area in Poyang Lake Based on Sentinel-1 Satellite Images

Based on the images taken by Sentinel-1A before and after rainstorm in Poyang Lake in June 2017,the expansion range of water area in the lake area was extracted quickly and effectively using the threshold method and vector superposition method.It is proved that the method is simple and feasible,which can provide reference for the research and utilization of Sentinel-1 satellite data in the assessment of flood disaster.

基于Sentinel-1A影像的原阳县玉米和水稻分类时间窗选择

使用遥感技术可以快速、准确地识别作物类型。本研究以河南省原阳县为试验区,基于Google Earth Engine(GEE)云平台,以玉米、水稻关键生育期的Sentinel-1A影像为数据源,分析各类地物的极化特征时序曲线。对6期Sentinel-1A影像进行穷举组合,使用随机森林算法对所有影像组合分类,分析各生育期影像对作物分类的重要性,选出玉米、水稻分类最佳时间窗。结果表明,作物生长中后期影像对作物分类更重要,其中玉米的乳熟期最重要,水稻的灌浆期最重要。全生育期影像组合中玉米的用户精度和生产者精度分别为90.43%和90.53%,水稻的用户精度和生产者精度分别为88.89%和89.01%。经过优选,大喇叭口期至成熟期为玉米分类最佳时间窗,相较于全生育期影像组合,此生育期影像组合的玉米用户精度和生产者精度分别提高了3.38个百分点和5.26个百分点;拔节期至成熟期为水稻分类最佳时间窗,相较于全生育期影像组合,此生育期影像组合的水稻用户精度和生产者精度分别提高了4.73个百分点和2.66个百分点。本研究结果可以为Sentinel-1A影像在原阳县及其附近区域的玉米、水稻种植结构监测研究提供理论依据。

MRI T_2 star mapping、T_1 images与3D DESS融合图在隐匿性膝关节软骨损伤中的应用

目的探讨T_2 star mapping、T_1 images与3D DESS融合伪彩图在关节软骨损伤中的诊断价值。方法对26例关节软骨损伤患者行T_2 star mapping、T_1 images和3D DESS扫描,并将T_1 images、T_2 star mapping与3D DESS图像融合,评价患者股骨、胫骨、髌骨关节软骨损伤程度并与关节镜结果对比,计算融合伪彩图诊断软骨损伤的特异性、敏感性及与关节镜诊断结果一致性。结果 T_1 images-3D DESS融合伪彩图诊断关节软骨损伤的敏感度、特异度及Kappa值分别为92.8%、93.0%、0.769,T_2 star mapping-3D DESS融合伪彩图诊断关节软骨损伤的敏感度、特异度及Kappa值分别为91.4%、94.2%、0.787。结论 T_2 star mapping、T_1 images与3D DESS融合伪彩图在关节软骨早期损伤评价上优于关节镜。

基于植被指数的Sentinel-1失相干评估

Sentinel-1数据自全球免费开放以来已被广泛应用于合成孔径雷达干涉测量及相关应用领域,但是其短波长的特性使其容易在部分低相干区域,特别是在植被覆盖区产生失相干现象,因而目前主流的干涉后失相干评估策略可能会造成Sentinel-1数据的浪费和研究成本的提升。针对此情况,本文在顾及极化方式的前提下,分别建立了光学遥感植被指数NDVI和VV/VH极化下Sentinel-1相干系数的定量模型,并在邻近的验证区对模型精度进行了验证,证明了模型的可靠性。基于建立的模型,可以在Sentinel-1数据干涉前对失相干进行定量评估,以克服上述干涉后评估策略的缺点,提高干涉测量的效率并降低研究成本。