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Multi-polarized radarsat-2 satellite sensor in assessing forest vigor from above ground biomass

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Journal of Forestry Research 2018年第4期29卷 1127-1133页

作者： Suman Sinha Abhisek Santra Laxmikant Sharma C.Jeganathan Mahendra Singh Nathawat Anup Kumar Das Shiv Mohan Department of Civil Engineering Haldia Institute of Technology Department of Environmental Science Central University of Rajasthan NH 8 Department of Remote Sensing Birla Institute of Technology School of Sciences Indira Gandhi National Open University(IGNOU) Department of Space Space Application Centre(ISRO)Government of India PLANEX Physical Research LaboratoryThaltej Campus

Accurate estimates of forest biomass are increasingly important in relation to sequestration of carbon by forest trees. Satellite remote sensing is a useful tool for biomass estimation and monitoring of forest ecological processes. Microwave synthetic aperture radar （SAR） can increase the accuracy of estimations of forest biomass in comparison to optical remote sensing, due to the unique capacities of SAR, including high penetrability, volumetric scattering, interaction with surface roughness, and dielectric property. We studied the potential of multi-polarized C-band radarsat-2, a SAR technology, with HH, HV and VV polarization for estimating biomass of moist tropical Indian forest. Backscatter values are correlated with fieldbased biomass values and axe regressed to generate models for estimating biomass. HH polarization provided maximum information regarding tree biomass. A coefficient of determination of 0.49 was calculated for HH polarized C-band image with in situ measurements. An exponential model was proved to be best suited for estimating forest biomass. Correlation of 0.62 and RMSE of 24.6 t ha-1 were calculated for the relationship between estimated and predicted biomass values for the best fit model. The aver- age absolute accuracy of the model was 61%, while Willmott＇s index of agreement was 0.87. Results suggest that most of the biomass of the area ranged within 70 t ha-1 a probably due to the saturation of C-band around 60-70 t ha-1 for tropical forests.

关键词： SAR radarsat-2 Polarization BiomassREDD

基于升降轨InSAR技术的砀山县地面沉降监测与分析

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测绘技术装备 2024年第2期26卷 88-93页

作者：殷幼松李晓芳王娜浙江环球星云遥感科技有限公司浙江湖州313200

针对皖北平原区地面沉降监测网络尚不完善的现状,本文以砀山县为研究区,采用升降轨异源时序合成孔径雷达干涉测量(InSAR)技术监测,获取该区域2017年5月—2020年12月间的地面沉降信息,并对其时空发育特征及研究区内铁路工程的基础结构稳... 详细信息

针对皖北平原区地面沉降监测网络尚不完善的现状,本文以砀山县为研究区,采用升降轨异源时序合成孔径雷达干涉测量(InSAR)技术监测,获取该区域2017年5月—2020年12月间的地面沉降信息,并对其时空发育特征及研究区内铁路工程的基础结构稳定性进行了详细分析。结果表明,研究区监测时段内存在严重的沉降漏斗,中心处沉降速率超过60 mm/a,且主要集中在城区;此外,受地面沉降影响,陇海铁路和徐兰高铁部分路段出现不同程度的形变病害,最大沉降速率值分别超过25 mm/a和12 mm/a,需引起关注。为了验证所获取沉降信息的可靠性,对升降轨异源监测成果进行了相关性分析,通过对比发现二者的相关系数达到0.95,具有较高的一致性。

关键词：地面沉降升降轨InSAR Sentinel-1A radarsat-2

Evaluation of radarsat-2 quad-pol SAR time-series images for monitoring groundwater irrigation

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International Journal of Digital Earth 2019年第10期12卷 1177-1197页

作者： Amit Kumar Sharma Laurence Hubert-Moy Buvaneshwari Sriramulu M.Sekhar Laurent Ruiz S.Bandyopadhyay Shiv Mohan Samuel Corgne Univ Rennes UMR LETG CNRSRennesFrance Indo-French Cell for Water Science Indian Institute of ScienceBangaloreIndia Department of Civil Engineering Indian Institute of ScienceBangaloreIndia UMR 1069 SAS INRAAgro-campus OuestRennesFrance Géosciences Environnement Toulouse Universitéde ToulouseCNRSIRDToulouseFrance EOS Indian Space Research OrganizationBangaloreIndia PLANEX Physical Research LaboratoryAhmedabadIndia

Groundwater assists farmers to irrigate crops for fulfilling the crop-water requirement.Indian agriculture system is characterized by three cropping seasons known as Kharif(monsoon),Rabi(post-monsoon)and summer(pre-monsoon).In tropical countries like India,monitoring cropping practices using optical remote sensing during Kharif and Rabi seasons is constraint due to the cloud cover,which can be well addressed by microwave remote sensing.In the proposed research,the strength of C-band polarimetric Synthetic Aperture Radar(SAR)time series images were evaluated to classify groundwater irrigated croplands for the Kharif and Rabi cropping seasons of the year 2013.The present study was performed in the Berambadi experimental watershed of Kabini river basin,southern peninsular India.A total of fifteen polarimetric variables were estimated includes four backscattering coefficients(HH,HV,VH,VV)and eleven polarimetric indices for all radarsat-2 SAR images.The cumulative temporal sum(seasonal and dual-season)of these parameters was supervised classified using Support Vector Machine(SVM)classifier with intensive ground observation samples.Classification results using the best equation(highest accuracy and kappa)shows that the Kharif,Rabi and irrigated double croplands are respectively 9.58 km2(20.6%),16.14 km2(34.7%)and 6.22 km2(13.4%)with a kappa coefficient respectively 0.84,0.74 and 0.94.

关键词： radarsat-2 Synthetic Aperture Radar Polarimetric Indices Irrigated Cropland Support Vector Machine Classifier Kabini Critical Zone Observatory

Water resource applications with radarsat-2-a preview

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International Journal of Digital Earth 2008年第1期1卷 130-147,I0009页

作者： B.Brisco R.Touzi J.J.van der Sanden F.Charbonneau T.J.Pultz M.D’Iorio Canada Centre for Remote Sensing 588 Booth St.OttawaCanada Geological Survey of Canada 615 Booth St.OttawaCanada

Fresh water is arguably the most vital resource for many aspects of a healthy and stable environment.Monitoring the extent of surface water enables resource managers to detect perturbations and long term trends in water availability,and set consumption guidelines accordingly.Potential end-users of water-related observations are numerous and reflect society as a whole.They encompass scientists and managers at all levels of government,aboriginal groups,water/power utility managers,farmers,planners,engineers,hydrologists,medical researchers,climate scientists,recreation enthusiasts,public school to postgraduate students,many special interest groups and the general public.Water data and analyses generate information products that benefit water resources planning and management,engineering design,plant operations,navigation activities,health research,water quality assessments and ecosystem management.As well,they serve as inputs for flood and drought warnings and weather and climate prediction models.Radar data in general,and radarsat in particular,are very good for detecting open surface water and have been used operationally for flood monitoring in many countries.Significant radar data archives now exist to analyse seasonal,annual and decadal trends,in order to attain a better understanding of the freshwater cycle.Radar data are also useful for wetland classification and soil moisture estimation.With the increasing pressure on water resources,both from a quality as well as a quantity perspective,the need will continue to increase for reliable information.radarsat-2 has several innovations that will enhance the ability to provide useful information about water resources.This paper provides an overview of the use of radar in general,and radarsat-2 in particular,for the generation of information products useful to water resource managers.

关键词： flooding water resources wetlands SAR applications radarsat-2

基于radarsat-2的天津市地面沉降监测与精度验证

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测绘通报 2022年第S2期 135-139页

作者：周洪月周大山易长荣常明天津市测绘院有限公司天津300381 天津市地质事务中心天津300042

利用16期高分辨率radarsat-2(R2)SAR影像数据,以天津市为主要研究区,获取该城市2020-09至2021-10期间地面沉降速率信息。经初步分析天津市沉降速率超过30 mm/a的区域分布较广泛,超过50 mm/a的辖区包括武清、静海及西青。其中,静海地面... 详细信息

利用16期高分辨率radarsat-2(R2)SAR影像数据,以天津市为主要研究区,获取该城市2020-09至2021-10期间地面沉降速率信息。经初步分析天津市沉降速率超过30 mm/a的区域分布较广泛,超过50 mm/a的辖区包括武清、静海及西青。其中,静海地面沉降整体趋势最为明显。根据收集GNSS监测与水准同期观测数据对获取的InSAR结果利用临近点法匹配同名点进行精度验证分析,由结果可知,23个GNSS监测站统计结果显示,整体偏差的平均误差为6.1 mm,中误差为7.3 mm,正态分布趋势不显著;GNSS与1400余个水准点进行动态平差结果统计分析显示,平均误差为4.73 mm,中误差为6.27 mm,偏差趋近正态分布规范,验证精度优于单GNSS监测结果。本文综合利用InSAR与常规地面沉降观测技术,较好地提取了城市地面沉降相关资料,可为天津精细化控沉管理提供基础数据。

关键词： radarsat-2 时序InSAR 地面沉降水准和GNSS 精度分析

基于radarsat-2 SAR数据的冬小麦覆盖地表土壤水分反演

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基于RADARSAT-2 SAR数据的冬小麦覆盖地表土壤水分反演

作者：陈林电子科技大学

学位级别：硕士

土壤含水量是陆面生态水循环的关键参数,控制着地表径流和地表水蒸发。在农业生产中,土壤水分是农作物赖以生长和发育的重要来源,也是影响土壤肥力的关键因素。因此,农田土壤水分可以作为反映农田健康状况的有效指标。大范围农田土壤水... 详细信息

土壤含水量是陆面生态水循环的关键参数,控制着地表径流和地表水蒸发。在农业生产中,土壤水分是农作物赖以生长和发育的重要来源,也是影响土壤肥力的关键因素。因此,农田土壤水分可以作为反映农田健康状况的有效指标。大范围农田土壤水分的获取可以有效指导农作物的生产。遥感技术为大范围土壤水分的精准监测提供了新的技术支撑。微波遥感穿透性强,具备全天时全天候监测的能力,并且微波信号对土壤水分的敏感性高,使得微波遥感成为了遥感监测土壤水分的主要手段。目前基于裸土的散射模型已经广泛应用于土壤水分反演,但是在植被覆盖区域,土壤水分的反演较为困难,主要有以下两方面原因:首先是微波散射模型的使用受限于缺失的难以实测的地表粗糙度、植被覆盖度等参数;其次植被的存在会衰减地表微波信号且产生复杂的体散射,使得微波信号对于土壤水分的敏感性降低。为了解决冬小麦覆盖地表土壤水分反演中可能存在的上述问题,本研究以加拿大安大略省冬小麦种植区为研究区域,以全极化radarsat-2 SAR数据为主要遥感数据源,结合地面调查数据与光学遥感数据,构建了多种基于radarsat-2SAR数据的冬小麦覆盖地表的土壤水分估算模型。完成的工作主要包括以下部分:(1)利用两种常用的半经验模型(比值方法、水云模型(Water Cloud Model,WCM))表征植被的散射特征,分离植被散射贡献,然后基于有效粗糙度参数耦合校正的积分方程模型(Calibrated Integrated Equation Model,CIEM),构建了基于半经验模型与有效粗糙度的土壤水分反演方法。CIEM用于模拟裸土后向散射系数,初始参考入射角设为30°。叶面积指数(Leaf Area Index,LAI)、雷达植被指数(Radar Vegetation Index,RVI)和归一化植被指数(Normalized Difference Vegetation Index,NDVI)分别用于参数化半经验模型。在半经验模型估算的裸土后向散射系数与有效粗糙度参数基础上,土壤水分可以由CIEM构建的查找表反演得到。结果表明:有效粗糙度和合适的参考入射角可以有效改善模型的估算精度。基于NDVI参数化比值方法和WCM时能获得最高的估算精度,土壤水分反演结果的决定系数(Coefficient of Determination,R)分别为0.68、0.66,均方根误差(Root Mean Square Error,RMSE)分别为4.15 vol.%、4.27 vol.%。(2)基于Freeman-Durden分解方法从原始SAR信号中分离植被散射贡献,得到表面散射分量,构建了基于极化SAR分解的土壤水分反演方法。首先利用H-A-Alpha分解和Freeman-Durden分解分析研究区散射机制,忽略掉占比较小的二次散射分量,然后使用不同的体散射矩阵分离体散射贡献,求得表面散射分量对应的后向散射系数,最后土壤水分可以由结合了有效粗糙度参数的CIEM和Dubois模型通过进一步建立查找表反演获得。结果表明:基于Dubois模型的估算精度总体优于CIEM,R和RMSE分别为0.63和5.16 vol.%,基于CIEM估算结果的R和RMSE分别为0.53和5.62 vol.%。(3)利用多种极化分解方法从原始SAR影像中提取多种极化特征参数扩充SAR数据特征空间,结合支持向量机(Support Vector Machine,SVM)、梯度提升树(Gradient Boosting Regression Tree,GBRT)、随机森林(Random Forest,RF)与三种特征选择方法,分别基于相关系数、SVM递归特征消除(SVM Recursive Feature Elimination,SVM-RFE)、RF因子重要性,构建基于机器学习的土壤水分估算模型。研究结果表明:基于RF因子重要性和SVM-RFE的特征选择方法是有效的,模型使用少量特征能达到最佳估算精度。对三种模型的特征采用RF特征重要性进行筛选后均实现了最佳的土壤水分估算,并且RF模型的估算精度最高,R和RMSE分别为0.79和4.03 vol.%。

关键词： radarsat-2 土壤水分反演微波散射模型极化分解机器学习

A method to obtain soil-moisture estimates over bare agricultural fields in arid areas by using multi-angle radarsat-2 data

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Research in Cold and Arid Regions 2018年第2期10卷 145-150页

作者： JunZhan Wang JianJun Qu LiHai Tan KeCun Zhang Dunhuang Gobi and Desert Ecology and Environment Research Station Northwest Institute of Eco-Environment and Resources Chinese Academy of Sciences

Soil moisture is an important parameter for agriculture, meteorological, and hydrological studies. This paper focuses on soil-moisture estimation methodology based on the multi-angle high-and low-incidence-angle mode radarsat-2 data obtained over bare agricultural fields in an arid area. Backscattering of the high-and low-incidence angles is simulated by using AIEM(advanced integral equation model), with the surface-roughness estimation model built based on the simulated data. Combining the surface-roughness estimation model with the backscattering model of the low-incidence-angle mode, a soil-moisture estimation method is put forward. First, the natural logarithm(ln) of soil moisture was obtained and then the soil moisture calculated. Soil moisture of the study area in Dunhuang, Gansu Province, was obtained based on this method; a good agreement was observed between the estimated and measured soil moisture. The coefficient of determination was 0.85, and the estimation precision reached 4.02% in root mean square error(RMSE). The results illustrate the high potential of the approach developed and radarsat-2 data to monitor soil moisture.

关键词： radarsat-2 bare soil soil moisture

基于改进水云模型和radarsat-2数据的农田土壤含水量估算

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农业工程学报 2016年第22期32卷 146-153页

作者：杨贵军岳继博李长春冯海宽杨浩兰玉彬北京农业信息技术研究中心北京100097 国家农业信息化工程技术研究中心北京100097 农业部农业信息技术重点实验室北京100097 河南理工大学测绘与国土信息工程学院焦作454000 北京市农业物联网工程技术研究中心北京100097

为了直接将雷达遥感中"水云模型"进行反演应用,该研究将"水云模型"中植被参数改为雷达植被指数,利用全极化数据直接支持遥感反演土壤含水量,无需遥感反演植被参数输入。改进模型为利用雷达遥感结合"水云模型"进行土壤含水量监测提供了... 详细信息

为了直接将雷达遥感中"水云模型"进行反演应用,该研究将"水云模型"中植被参数改为雷达植被指数,利用全极化数据直接支持遥感反演土壤含水量,无需遥感反演植被参数输入。改进模型为利用雷达遥感结合"水云模型"进行土壤含水量监测提供了一种高效便捷方法。基于radarsat-2全极化数据对冬小麦覆盖的农田土壤含水量进行估算,利用2014年在陕西杨凌区获取的4个生育期内radarsat-2卫星数据及同步田间测量108组冬小麦农田土壤含水量地面测量数据进行模型参数校正和精度验证。验证结果精度为:改进的雷达植被指数模型>原叶面积指数模型(实测叶面积指数验证)>原叶面积指数模型(光学遥感反演叶面积指数验证),且改进的雷达植被指数模型可以在多个生育期内对农田土壤含水量进行监测。

关键词：土壤水分植被水云模型 radarsat-2 冬小麦叶面积指数

基于radarsat-2全极化数据反演周期性地表土壤湿度

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农业工程学报 2013年第17期29卷 72-79页

作者：尹楠姜琦刚孟治国李远华吉林大学地球探测科学与技术学院长春130026 吉林建筑大学测绘与勘查工程学院长春130118

土壤湿度是农作物估产和旱情监测的关键参量。目前常用的土壤湿度反演模型都建立在随机粗糙地表条件下,对周期性垄行结构的土壤并不适用。基于radarsat-2全极化数据和野外实测数据,分析了交叉极化(vh)后向散射系数对垄行方位角无明显响... 详细信息

土壤湿度是农作物估产和旱情监测的关键参量。目前常用的土壤湿度反演模型都建立在随机粗糙地表条件下,对周期性垄行结构的土壤并不适用。基于radarsat-2全极化数据和野外实测数据,分析了交叉极化(vh)后向散射系数对垄行方位角无明显响应;同极化(hh,vv)对方位角的响应为余弦函数,但在方位角为90o±2o位置易出现偏离曲线的异常高亮度值。通过雷达影像上采样点的实测值和Oh模型推算的理论值,拟合得到周期性地表和随机地表之间的误差函数,进而对同极化影像进行纠正。纠正后的同极化比(p)去除了方位角和异常值的影响,交叉极化比(q)受到异常值的影响。通过Oh模型中的p和vh对研究区的地表参数进行反演,17个检验点的验证结果表明,预测的土壤湿度平均相对误差为11.13%,标准差为0.0256cm3/cm3;预测的均方根高度平均相对误差为13%,标准差为0.1315cm。结果与随机粗糙地表土壤湿度和均方根高度的反演精度相当,证明了该模型的有效性。

关键词：土壤湿度模型雷达图像周期性地表 radarsat-2