Comparisons of passive microwave remote sensing sea ice concentrations with ship-based visual observations during the CHINARE Arctic summer cruises of 2010–2018

In order to apply satellite data to guiding navigation in the Arctic more effectively,the sea ice concentrations(SIC)derived from passive microwave(PM)products were compared with ship-based visual observations(OBS)collected during the Chinese National Arctic Research Expeditions(CHINARE).A total of 3667 observations were collected in the Arctic summers of 2010,2012,2014,2016,and 2018.PM SIC were derived from the NASA-Team(NT),Bootstrap(BT)and Climate Data Record(CDR)algorithms based on the SSMIS sensor,as well as the BT,enhanced NASA-Team(NT2)and ARTIST Sea Ice(ASI)algorithms based on AMSR-E/AMSR-2 sensors.The daily arithmetic average of PM SIC values and the daily weighted average of OBS SIC values were used for the comparisons.The correlation coefficients(CC),biases and root mean square deviations(RMSD)between PM SIC and OBS SIC were compared in terms of the overall trend,and under mild/normal/severe ice conditions.Using the OBS data,the influences of floe size and ice thickness on the SIC retrieval of different PM products were evaluated by calculating the daily weighted average of floe size code and ice thickness.Our results show that CC values range from 0.89(AMSR-E/AMSR-2 NT2)to 0.95(SSMIS NT),biases range from−3.96%(SSMIS NT)to 12.05%(AMSR-E/AMSR-2 NT2),and RMSD values range from 10.81%(SSMIS NT)to 20.15%(AMSR-E/AMSR-2 NT2).Floe size has a significant influence on the SIC retrievals of the PM products,and most of the PM products tend to underestimate SIC under smaller floe size conditions and overestimate SIC under larger floe size conditions.Ice thickness thicker than 30 cm does not have a significant influence on the SIC retrieval of PM products.Overall,the best(worst)agreement occurs between OBS SIC and SSMIS NT(AMSR-E/AMSR-2 NT2)SIC in the Arctic summer.

A new strategy for ionospheric remote sensing using the 130.4/135.6 nm airglow intensity ratios

We demonstrate here that global-scale determination of a key ionospheric parameter,the peak height of the F_(2)region(h_(m)F_(2)),can be obtained by making a simple ratio measurement of the atomic oxygen 130.4 and 135.6 nm emissions in the far-ultraviolet nightglow with a nadir-viewing system such as a pair of photometers suitable for flight on a CubeSat.We further demonstrate that measurements from an altitude that is within the typical range of nighttime h_(m)F_(2)250−450 km can provide the ratios that are needed for retrieval of the h_(m)F_(2).Our study is conducted mostly through numerical simulations by using radiative transfer models of the two emissions coupled with empirical models of the atmosphere and ionosphere.Modeling results show that the relationship between the h_(m)F_(2)and the intensity ratio is sensitive to the altitude from which the emissions are observed,primarily because of the distinctly different degrees of resonant scattering of the two emissions in the atmosphere.A roughly quadratic relationship can be established for observations from an orbit of~400 km,which enables h_(m)F_(2)retrieval.Parametric analysis indicates that the relationship can be affected by the ambient atmospheric conditions through resonant scattering and O2 absorption.For typical nighttime conditions with h_(m)F_(2)250−450 km,retrieval of the h_(m)F_(2)from synthetic observations shows that the typical errors are only a few kilometers(up to~20 km),depending on the accuracy of the ambient conditions predicted by the empirical models.Our findings pave the way for use of the 130.4/135.6 nm intensity ratios for global-scale monitoring of the nighttime ionosphere at mid to low latitudes.

Improved strategy for estimating stem volume and forest biomass using moderate resolution remote sensing data and GIS

This study presents the utility of remote sensing （RS）, GIS and field observation data to estimate above ground biomass （AGB） and stem volume over tropical forest environment. Application of those data for the modeling of forest properties is site specific and highly uncertain, thus further study is encouraged. In this study we used 1460 sampling plots collected in 16 transects measuring tree diameter （DBH） and other forest properties which were useful for the biomass assessment. The study was carded out in tropical forest region in East Kalimantan, Indo- nesia. The AGB density was estimated applying an existing DBH - biomass equation. The estimate was superimposed over the modified GIS map of the study area, and the biomass density of each land cover was calculated. The RS approach was performed using a subset of sample data to develop the AGB and stem volume linear equation models. Pearson correlation statistics test was conducted using ETM bands reflectance, vegetation indices, image transform layers, Principal Component Analysis （PCA） bands, Tasseled Cap （TC）, Grey Level Co-Occurrence Matrix （GLCM） texture features and DEM data as the predictors. Two linear models were generated from the significant RS data. To analyze total biomass and stem volume of each land cover, Landsat ETM images from 2000 and 2003 were preprocessed, classified using maximum likelihood method, and filtered with the majority analysis. We found 158±16 m^3.ha^-1 of stem volume and 168±15 t.ha^-1 of AGB estimated from RS approach, whereas the field measurement and GIS estimated 157±92 m^3.ha^-1 and 167±94 t.ha^-1 of stem volume and AGB, respectively. The dynamics of biomass abundance from 2000 to 2003 were assessed from multi temporal ETM data and we found a slightly declining trend of total biomass over these periods. Remote sensing approach estimated lower biomass abundance than did the GIS and field measurement data. The earlier approach predicted 10.5 Gt and 10.3 Gt of total biomasses in 2000 and 2003, while the later estimated 11.9 Gt and 11.6 Gt of total biomasses, respectively. We found that GLCM mean texture features showed markedly strong correlations with stem volume and biomass.

Remote Sensing Applications: Beyond Land-Use and Land-Cover Change

Remotely sensed (RS) imagery is increasingly being adopted in investigations and applications outside of traditional land-use land-cover change (LUCC) studies. This is due to the increased awareness by governments, NGOs and Industry that earth observation data provide important and useful spatial and temporal information that can be used to make better decisions, design policies and address problems that range in scale from local to global. Additionally, citizens are increasingly adopting spatial analysis into their work as they utilize a suite of readily available geospatial tools. This paper examines some of the ways remotely sensed images and derived maps are being extended beyond LUCC to areas such as fire modeling, coastal and marine applications, infrastructure and urbanization, archeology, and to ecological, or infrastructure footprint analysis. Given the interdisciplinary approach of such work, this paper organizes selected studies into broad categories identified above. Findings demonstrate that RS data and technologies are being widely used in many fields, ranging from fishing to war fighting. As technology improves, costs go down, quality increases and data become increasingly available, greater numbers of organizations and local citizens will be using RS in important everyday applications.

A NEW PROGRESS IN OCEAN SURFACE WIND VECTOR DETECTION USING MICROWAVE REMOTE SENSING

In this paper, a new progress in scattering measurement of ocean surface wind vector using a space borne scanning scatterometer (CNSCAT) has been described. The CNSCAT developed during the past five years in the laboratory for Microwave Remote Sensing and Information Technology (MIRIT), CSSAR,The Chinese Academy of Sciences, will be launched in early next decade. This paper also discussed CNSCAT system design, system calibration and some theoretical analysis.

The First Global Map of Atmospheric Ammonia(NH_(3)) as Observed by the HIRAS/FY-3D Satellite

Atmospheric ammonia(NH_(3)) is a chemically active trace gas that plays an important role in the atmospheric environment and climate change. Satellite remote sensing is a powerful technique to monitor NH_(3) concentration based on the absorption lines of NH_(3) in the thermal infrared region. In this study, we establish a retrieval algorithm to derive the NH_(3)column from the Hyperspectral Infrared Atmospheric Sounder(HIRAS) onboard the Chinese Feng Yun(FY)-3D satellite and present the first atmospheric NH_(3) column global map observed by the HIRAS instrument. The HIRAS observations can well capture NH_(3) hotspots around the world, e.g., India, West Africa, and East China, where large NH_(3) emissions exist. The HIRAS NH_(3) columns are also compared to the space-based Infrared Atmospheric Sounding Interferometer(IASI)measurements, and we find that the two instruments observe a consistent NH_(3) global distribution, with correlation coefficient(R) values of 0.28–0.73. Finally, some remaining issues about the HIRAS NH_(3) retrieval are discussed.

Remote sensing of soil degradation:Pro gress andperspective

Soils constitute one of the most critical natural resources and maintaining their health is vital for agricultural development and ecological sustainability,providing many essential ecosystem services.Driven by climatic variations and anthropogenic activities,soil degradation has become a global issue that seriously threatens the ecological environment and food security.Remote sensing(RS)technologies have been widely used to investigate soil degradation as it is highly efficient,time-saving,and broad-scope.This review encompasses recent advances and the state-of-the-art of ground,proximal,and novel Rs techniques in soil degradation-related studies.We reviewed the RS-related indicators that could be used for monitoring soil degradation-related properties.The direct indicators(mineral composition,organic matter,surface roughness,and moisture content of soil)and indirect proxies(vegetation condition and land use/land cover change)for evaluating soil degradation were comprehensively summarized.The results suggest that these above indicators are effective for monitoring soil degradation,however,no indicators system has been established for soil degradation monitoring to date.We also discussed the RS's mechanisms,data,and methods for identifying specific soil degradation-related phenomena(e.g.,soil erosion,salinization,desertification,and contamination).We investigated the potential relations between soil degradation and Sustainable Development Goals(SDGs)and also discussed the challenges and prospective use of RS for assessing soil degradation.To further advance and optimize technology,analysis and retrieval methods,we identify critical future research needs and directions:(1)multi-scale analysis of soil degradation;(2)availability of RS data;(3)soil degradation process modelling and prediction;(4)shared soil degradation dataset;(5)decision support systems;and(6)rehabilitation of degraded soil resource and the contribution of RS technology.Because it is difficult to monitor or measure all soil properties in the large scale,remotely sensed characterization of soil properties related to soil degradation is particularly important.Although it is not a silver bullet,RS provides unique benefits for soil degradation-related studies from regional to global scales.

Assessing factors impacting inter-satellite variability of grassland curing estimates for fire monitoring in Victoria, Australia using remote sensing

Grassland fires in Victoria,Australia pose significant environmental issues.Monitoring these fres relies on the Grassland Curing Degree(GCD)indicator and the corresponding Grassland Curing Map(GCM)for spatial distribution.Inter-satellite variability(IsV)assesses the variations in Grassland Curing Maps(GCMs)produced from remote sensing data with varying spatial resolutions(SR).Higher SR data improves GCM accuracy but increases processing time.IsV helps identify priority areas that need higher SR imageries.This study analyzes sample sites in Victoria and finds correlations between Isv,seasonality,temperature,precipitation,and distance to residential areas.Results reveal lower Isv during summers and autumns compared to winters and springs.Temperature shows a strong negative linear relationship with ISV,indicating that higher temperatures result in lower ISVv.Precipitation exhibits a weak positive correlation with IsV,suggesting heavier precipitation leads to increased ISV.Distance to grasslands negatively correlates with IsV,indicating that greater distances from residential lands result in lower Isv.Based on these findings,it is recommended to use higher SR satellite data for GCM creation during winters and springs when temperatures are low,precipitation is heavy,and areas are closer to residential lands.Implementation suggestions are provided for fire management based on these results.

基于遥感和通量观测的实际蒸散发时空变化特征——以黄河流域水源涵养区为例

蒸散发是地表水热平衡的关键要素,分析蒸散发的时空分布特征及其影响因素,对于深入理解区域水文循环与生态系统过程至关重要。基于通量观测站数据,评估GLEAM、MTE、GLDAS和AVHRR共4种蒸散发产品在黄河流域水源涵养区的适用性,利用水量平衡方法验证其在流域尺度上的精度,并探讨实际蒸散发的影响因素。结果表明:在站点尺度上,GLEAM蒸散发产品精度最高;在流域尺度上,校正后的GLEAM蒸散发产品与实际蒸散发的相对误差在渭河南山支流区最小,其次是兰州以上地区;1982—2015年黄河流域水源涵养区实际蒸散发整体呈增加趋势,空间分布上自西到东逐渐增加;不同植被类型年均蒸散发差异较大,阔叶林的蒸散发最大(575.2 mm),其次是农田(504.3 mm),高山草甸的蒸散发最小(358.2 mm);实际蒸散发变化主要受到太阳辐射与气温的影响。

地基遥感垂直观测系统数据质量评估与产品融合分析

针对地基遥感垂直观测系统风向与风速、温度与相对湿度、云底高度与云顶高度、大气瑞利信号拟合误差与有效探测距离、大气可降水量与对流层总延迟等10种探测要素的数据准确性评估及融合产品有效性验证问题。以2024年1月1日至7月1日宜昌地基遥感垂直观测系统的实际探测资料为例,采用同址的L波段探空雷达观测数据作为参考标准,选取时空匹配的垂直廓线产品进行对比,评估地基遥感垂直观测系统探测数据的准确性,并综合运用系统高时空分辨率的融合产品,从大气动力结构、云雨结构等方面剖析宜昌地区2024年6月27—28日梅雨期降雨过程。研究结果表明:宜昌地基遥感垂直观测系统探测设备性能优良,运行较为稳定,能够在垂直方向上综合解析大气结构;运行评估期内,宜昌地基遥感垂直观测系统探测数据的准确性均满足评估指标,与探空观测资料的相关性总体较好;多条垂直廓线融合产品可以清晰展示降雨过程发生、发展、消散的垂直动态变化细节,证实了融合产品在天气分析中的有效性和实用性。

基于无线传感器网络的卫星遥感观测非均匀地表重点区域监测系统设计

卫星遥感观测非均匀地表重点区域监测数据通常具有复杂性和不确定性,难以精准确定重点监测区域和获取有用信息,导致监测范围受限,监测结果存在较大误差;因此,为了给非均匀地表的治理提供数据支持,利用无线传感器网络优化设计卫星遥感观测非均匀地表重点区域监测系统;加设卫星遥感观测图像传感器,调整无线传感器网络模块,改装监测数据处理器,利用调整的系统电路连接硬件元件,完成硬件系统的优化;在硬件和数据库的支持下,从拓扑结构和传输协议等方面,构建无线传感器网络;实现高空遥感与无线传感器网络的深度融合,利用无线传感器网络采集地表卫星遥感观测图像,获取地表信息,扩宽监测范围;并通过滤波、几何纠正等步骤,实现初始图像的预处理,并提取图像特征,根据特征图像的匹配结果,确定非均匀地表重点区域,结合地表倾斜角度、地表非均匀程度等指标完成卫星遥感观测非均匀地表重点区域监测;经实验测试发现改进系统与传统监测系统相比,优化设计系统输出地表倾斜角度和地表变形量的监测误差分别减小0.28°和0.73 m^(3),同时系统监测范围明显扩大。

从单星观测到体系协同:风云卫星智慧协同观测体系的技术特征与发展展望

风云卫星是我国民用遥感卫星中应用范围最广泛、成效最为显著的卫星系列之一,在推动国民经济发展和加强装备自主建设等方面发挥了重要作用。面向气象高质量发展、防灾减灾、应对气候变化、生态文明建设和国家安全等多重挑战,风云卫星将坚持创新驱动发展战略,以体系化、智能化为主要发展方向,突破平台和载荷关键技术,构建智慧协同观测体系。本文探讨了风云卫星智慧协同观测体系在气象综合观测效能提升、体系化智能化网络赋能及全方位环境保障等方面的必要性,在分析国内外气象卫星发展历程的基础上,提出了新一代风云气象卫星构建智慧协同观测体系的建设思路和初步方案,并对体系构建中的关键技术进行了分析。最后,展望了风云卫星智慧协同观测体系的应用前景。

中法海洋卫星的系统设计与技术特点

中法海洋卫星(China-France Oceanography SATellite,CFOSAT)是中国和法国联合研制的海洋动力探测遥感卫星。它是世界上第一颗具备全球海面波浪谱和海面风场联合同步观测能力的卫星,同时也是中国首次在系统层面与世界先进宇航机构开展全方位、全流程合作的航天工程项目。文中介绍了中法海洋卫星的系统设计和研制历程等内容,展示了中法海洋卫星高度的技术先进性以及鲜明的技术特点,同时回顾了项目国际合作中的主要工作与成就。

Spatio-temporal-spectral observation model for urban remote sensing

Taking cities as objects being observed,urban remote sensing is an important branch of remote sensing.Given the complexity of the urban scenes,urban remote sensing observation requires data with a high temporal resolution,high spatial resolution,and high spectral resolution.To the best of our knowledge,however,no satellite owns all the above character-istics.Thus,it is necessary to coordinate data from existing remote sensing satellites to meet the needs of urban observation.In this study,we abstracted the urban remote sensing observation process and proposed an urban spatio-temporal-spectral observation model,filling the gap of no existing urban remote sensing framework.In this study,we present four applications to elaborate on the specific applications of the proposed model:1)a spatiotemporal fusion model for synthesizing ideal data,2)a spatio-spectral observation model for urban vegetation biomass estimation,3)a temporal-spectral observation model for urban flood mapping,and 4)a spatio-temporal-spectral model for impervious surface extraction.We believe that the proposed model,although in a conceptual stage,can largely benefit urban observation by providing a new data fusion paradigm.

国外民用光学卫星对地遥感行业发展综述

光学对地遥感是国家战略性行业。介绍了国外若干典型的由政府主导的民用光学对地遥感项目的发起背景、发展过程、发展现状及后续发展方向,还介绍了国外光学商业遥感公司建设运营的光学遥感星座的发展历程、现状和下一步发展计划,梳理了项目中光学遥感仪器的主要参数,简述了若干正在建设的商业光学遥感星座项目。汇总了外国空间卫星遥感相关的政策法规,总结归纳了美国商业遥感发展的4个阶段。最后展望了民用光学对地遥感行业的发展趋势。

论智能遥感卫星的“快、准、灵”应用服务

过去几十年里,中国遥感卫星实现了从有到好的技术跨越,当前中国遥感卫星数量已达300余颗.然而,遥感卫星与通信和导航卫星相比还存在明显不足.遥感卫星走向智能化和大众化,必须解决“快、准、灵”的技术问题.结合现有研究成果,论述了实现智能遥感卫星“快、准、灵”实时智能服务的关键技术.通过无地面控制高精度定位、在轨智能处理和星群组网协同观测技术,快速生成用户所需产品;通过成像模型构建、高精度姿态测量和在轨自主几何定标技术,为用户提供精准的定位信息;通过遥感卫星软件定义、时空谱综合优化和通导遥一体化集成技术,提高单星服务能力,实现从卫星端到用户终端(Business-to-Consumer,B2C)的实时智能大众服务.

植物物候研究进展

植物物候是指植物受周围环境影响而呈现周期性变化的自然现象。为总结植物物候研究进展,展望植物物候研究的未来发展,本文总结了主要环境因素(如气候、土壤等)及其变化对植物物候的影响,分析比较了研究植物物候不同方法的优缺点,揭示了植物物候研究中存在的问题及潜在的解决方案。综述植物物候研究进展能够为理解环境变化条件下植物物候动态、物种共存以及生态系统结构稳定提供参考。

面向海浪遥感卫星标定的漂流波浪浮标关键技术研究

在精细化海洋观测领域,遥感卫星海浪方向谱的获取存在精度低、累积误差大等问题,设计了一种基于Transformer神经网络的海浪方向谱估计方法,通过引入稀疏自注意力机制以及物理引导模块等,实现海浪方向谱的高精度表达;海上试验测试结果表明,新提出的海浪方向谱估计方法与传统海浪方向谱估计方法相比,文章提出的模型估计方向谱更符合理论谱形,伪峰较少,平滑性和一致性方面表现较好,并且对数据噪声的容忍度更高。同时,设计了一种基于人工智能芯片的漂流波浪浮标系统,通过AI软硬件系统对不同任务进行动态调度分配,实现浮标低功耗运行,有效提升了波浪测量性能及海上工作时间,是遥感卫星高精度获取大范围、长时序全球性海洋观测数据的一种有效补充。

草甸草原植物beta多样性高光谱遥感估算方法

目前,全球生物多样性的状态非常堪忧,因此,利用光谱技术估算生物多样性成为了生态学家和遥感学家共同关注的热点。目前关于植物alpha多样性的研究很多,而关于beta多样性的研究较少,仍存在一些问题值得去探索。为了探究利用遥感技术估算植物beta多样性的最佳光谱指数以及最佳影像空间分辨率,以草甸草原为研究区,基于无人机高光谱遥感影像,从光谱距离、光谱角度、生物多样性概念三个方面计算了6种beta多样性估算指数(4种为我们构建的新指数,2种为已有的指数),并采用Mantel tests和相关系数筛选最佳的光谱指数。然后将筛选出来的指数应用于不同空间分辨率的影像,以期得到最佳观测尺度。另外,为了提高指数的估算能力,对比了一阶导数变换和Savitzky-Golay滤波两种光谱变换方法,以及相关系数法、连续投影法、竞争性自适应重加权法三种特征波段选择方法。结果表明,不论是采用亚尺度观测(像元大小<样方大小)还是等尺度观测(像元大小=样方大小),最佳的光谱指数均为光谱距离指数,且光谱距离指数在不同影像空间分辨率下均表现良好。在草原地区,当影像空间分辨率约为0.25 m时,该指数可以取得最佳的估算结果。经一阶导数变换并用相关系数法提取特征波段后构建的光谱距离指数与beta多样性拥有最强的相关性,今后可利用该指数构建估算模型或者直接表征beta多样性。该研究对于科学的选取光谱指数和影像空间分辨率去估算植物beta多样性具有一定的指导意义。

不同延时条件下基于激光诱导击穿光谱的烧结混合料定量检测

烧结混合料是烧结工艺中极为重要的一环,其成分检测的准确性直接影响烧结过程,激光诱导击穿光谱技术通过激光光谱分析烧结混合料中的元素成分和含量,实时性好,但定量检测效果相比于其他实验室方法仍有差距。研发了一套遥测激光诱导击穿光谱装置,并应用于烧结混合料成分检测,采用PLSR与PCA相结合的方法对烧结混合料中TFe,CaO,SiO_(2),MgO进行定量检测。通过对1.28μs和5μs不同延时的光谱数据计算发现,不同物质在不同探测延时条件下R_(2)有着明显差异,对于SiO_(2)和MgO这两种浓度相对较低的物质,采用较小的1.28μs探测延时,R_(2)分别达到了0.937和0.985,而浓度相对较高的TFe,CaO则采用更大的5μs探测延时效果更好,较采用1.28μs延时,TFe定量结果的R^(2)从0.903提高到0.987,CaO的R^(2)也从0.816提高到0.980。针对不同物质采用不同探测延时的方法显著提高了检测准确性,简化了检测流程,对烧结工艺起到指导作用。