Experiences in building a Grid-based platform to serve Earth observation training activities

Earth observation data processing and storing can be done nowadays only using distributed systems. Experiments dealing with a large amount of data are possible within the timeframe of a lesson and can give trainees the freedom to innovate. Following these trends and ideas, we have built a proof-of-the-concept platform, named GiSHEO, for Earth observation educational tasks. It uses Grid computing technologies to analyze and store remote sensing data, and combines them with eLearning facilities. This paper provides an overview of the GiSHEO's platform architecture and of its technical and innovative solutions.     

Comparative study on the observation duration of the two-polar regions of the Earth from four specific sites on the Moon

ABSTRACT

There are increasing concerns about the Arctic region and the Antarctic region, which are known as indicators of the global change. To conduct research on the global change issue, especially for the two-polar regions of the Earth, the long-term continuous Earth observations are required. However, the existing Earth observation systems lack the ability to meet this need. In this paper, we propose to use the Moon as an Earth observation platform. This Moon-based platform could have large spatial coverage and long temporal duration. In addition, the site selection is a complex issue for the Moon-based platform because of the vast space on the Moon surface. We research this problem by placing the sensor at four potential sites on the Moon, respectively – the North Pole, the South Pole, the Sinus Iridum area and the Mare Nectaris area. Furthermore, the latitude and longitude of nadir point, the time length, the observation duration and the time length during a single observation duration of the four sites are calculated. Analyses are conducted to compare some observation performances of the four sites. Finally, we found that the latitude variation of the nadir point has an impact on the time window and the observation duration. If the sensor is placed at the polar regions of the Moon, there will be an 18.6-year period between the two adjacent longest observation duration. Thus, we give the next best time window to observe the two-polar regions of the Earth, when the sensor is placed at the polar region of the Moon. If the sensor is placed at the lower latitude regions of the Moon, there will be a 9.3-year period between the two adjacent longest observation duration. Moreover, on account of the observation duration and the time length during a single observation duration, the sites with relative low latitudes on the Moon are good choices.     

The effects of speckle reduction on classification of ERS SAR data

Optical Earth observation data have been used frequently in agricultural applications. The problem of cloud cover found with optical data can be overcome by using radar Earth observation data, but in turn these data have a speckle problem. This paper describes six types of speckle reduction filter and assesses the capability of each to improve classification accuracy in agricultural applications. The best filters are Lee-Sigma, Gamma MAP (Maximum a Posteriori ) and simulated annealing. The filters are assessed in the context of identifying fields growing potatoes in the UK using Earth observation data, and results for other agricultural crops are also included.     

Temporal sampling error analysis of the Earth’s outgoing radiation from a Moon-based platform

This study investigates the temporal sampling error on the Earth’s outgoing radiation, with a potential Earth observation platform, the Moon-based platform. To simulate the Earth’s outgoing radiation viewed from a Moon-based platform, we used the datasets of the NASA’s Goddard Earth Observing System Version 5 (GEOS-5) systems as the truth. The analysis is proposed by sampling the simulated time series. The sampling uncertainty associated with a given sampling interval is measured by computing the Root-Mean-Square-Error (RMSE) of the original and subsampled time series. The effects of different sampling intervals are evaluated by maximum bias. The effects of different sampling time are estimated by comparing correlations between the subsampled time series of different start time at specific temporal interval and the origin. The results show that the temporal sampling errors have the characteristics of the periodical uncertainties and bias, and 4-h sampling interval is a turning point. The sampling interval larger than 4 h will result in large uncertainties and bias.     

Neural network modelling for environmental prediction

We describe the choice and assessment of neural network and statistical methods for data modelling, feature selection and forecasting. We deal in particular with how empirical environmental and Earth observation data can be used in conjunction with physical simulation models.     

基于OSGEARTH的国产航天三维仿真软件设计

针对目前航天领域缺乏经过验证、能够可靠应用于全国产化平台的三维仿真软件,本文基于OSGEARTH设计和实现了一款国产航天三维仿真软件.通过地球影像和高程数据的差异化加载、场景树的优化构建、弹道数据筛选等设计策略,解决了在全国产化平台上进行三维仿真的可靠性和渲染效率问题,并得到了仿真实验的有效验证.     

Review article Multisensor image fusion in remote sensing: Concepts,methods and applications

With the availability of multisensor, multitemporal, multiresolution and multifrequency image data from operational Earth observation satellites the fusion of digital image data has become a valuable tool in remote sensing image evaluation. Digital image fusion is a relatively new research field at the leading edge of available technology. It forms a rapidly developing area of research in remote sensing. This review paper describes and explains mainly pixel based image fusion of Earth observation satellite data as a contribution to multisensor integration oriented data processing.     

基于GEE和Sentinel-2时序数据的呼伦贝尔沙地及其周边植被类型识别研究

沙地及其周边植被对固定沙丘、防止水土流失和环境治理等方面具有重要作用,开展沙地及其周边植被类型识别研究对于客观地反映沙地及其周边的生态环境,进而为沙地恢复治理政策制定具有重要意义。GEE云平台丰富的长时间序列遥感数据和强大的云计算能力,为开展大区域植被类型识别提供了便捷。本研究基于GEE云平台存储的2019年Sentinel-2时序数据,采用RF算法开展呼伦贝尔沙地及其周边主要植被类型的空间判识研究,探索了GEE平台下顾及植被物候信息的植被类型识别效果。研究发现：①Sentinel-2影像的光谱信息和近红外波段的纹理信息对研究区的主要植被类型识别能力有限,而物候特征有效地弥补了原始光谱特征等对研究区不同植被类型区分能力的不足;②基于RF算法顾及物候特征的植被类型识别精度达到84.37%,Kappa系数为0.8,比单一时相数据的识别精度提高了10.01%;③呼伦贝尔沙地及其周边主要植被类型的物候特征差异明显,有助于不同类型植被的空间识别,特别是提高了灌草丛和草原的识别精度。研究表明利用Sentinel-2数据和GEE云平台对沙地等大区域植被类型的识别具有较大的潜力和广阔的应用前景。     

基于对地观测需求分析的多星协同任务规划研究

由于对地观测的应用目标不同,各业务部门对遥感数据的需求存在差异。为了在多星协同任务规划中最大限度地满足用户提出的任务需求,构建了对地观测任务需求模型,整合了观测要素对于传感器的观测时限、空间分辨率和光谱需求,并基于改进层次分析法(AHP)评估了卫星资源对于任务需求的适宜度;进而,提出了以任务需求适宜度及任务优先级为优化子目标的约束满足模型,通过遗传禁忌混合算法实现了时空谱一体化的多星对地协同观测。以“一路”重点区域南海及周边地区为试验区域,结果表明,本文提出的方法取得的目标函数平均值及任务需求适宜度平均值较大,能够对成像点目标分配适宜观测的卫星资源。通过对比三种算法,本算法的运行时间较短,能够满足实际应用需求。     

MERIS potential for land applications

The Medium Resolution Imaging Spectrometer (MERIS), to be flown on the Envisat platform, contributes to the effort made by space agencies to generate Earth observation data that are responsive to the needs of the users. Although optimized for oceanic applications, this instrument should also be useful for a range of terrestrial investigations. This paper reviews the relevance of a suite of features specific to MERIS (e.g. fine radiometric and spectral resolution, programmability, the availability of an onboard calibration system) for terrestrial applications. Scientific challenges related to scale issues, the definition of appropriate algorithms for the optimal exploitation of these data, the opportunity for synergistic studies and the comparison of MERIS data with other data collected by the Advanced Very High Resolution Radiometer (AVHRR) and other precursor or future instruments are reviewed. The urgent need for an intensive and sustained research and development programme to define and validate a panoply of highlevel products optimized for terrestrial applications is stressed.     

Optimization of earth observation satellite system based on parallel systems and computational experiments

Earth observation satellite system (EOSS) is the main space platform to collect ground information. Optimization of EOSS is still a difficult problem, as it is a complex system concerning a great deal of design variables and uncertain factors. To solve the problem, an optimization framework based on parallel system and computational experiments is proposed. An artificial system for EOSS is firstly constructed, which is the integration of resource data, task data, environment data and related operation rules. Real EOSS together with artificial EOSS constitute the parallel systems for EOSS. Based on the parallel systems, concept of computational experiments is detailed. Moreover, surrogate models are built to approximate real EOSS. Genetic algorithm and improved general pattern search method are adopted to optimize the model. According to the framework, a case study is carried out. Through the results, we illustrated the proposed framework to be useful and effective for EOSS optimization problem.     

Earth observation satellite sensors for biodiversity monitoring: potentials and bottlenecks

Many biologists, ecologists, and conservationists are interested in the possibilities that remote sensing offers for their daily work and study site analyses as well as for the assessment of biodiversity. However, due to differing technical backgrounds and languages, cross-sectorial communication between this group and remote-sensing scientists is often hampered. Hardly any really comprehensive studies exist that are directed towards the conservation community and provide a solid overview of available Earth observation sensors and their different characteristics. This article presents, categorizes, and discusses what spaceborne remote sensing has contributed to the study of animal and vegetation biodiversity, which different types of variables of value for the biodiversity community can be derived from remote-sensing data, and which types of spaceborne sensor data are available for which time spans, and at which spatial and temporal resolution. We categorize all current and important past sensors with respect to application fields relevant for biologists, ecologists, and conservationists. Furthermore, sensor gaps and current challenges for Earth observation with respect to data access and provision are presented.     

Inversion of the Baseline Incline Angle for Tiangong-2 Interferometric Imaging Radar Altimeter

Tiangong-2 Interferometric Imaging Radar Altimeter(InIRA) is the first wide-swath radar altimeter adopting small incidences angles with short baseline.The knowledge of the roll angle of the baseline is crucial for measuring the wide-swath Sea Surface Height(SSH) with centimeter-level accuracy.In this work,we aim to validate the technique of baseline angle determination from spaceborne nadir interferometric echoes which has been tested by airborne experiment.According to the observation geometry,the interferometric phases of nadir echoes acquired by Tiangong-2 Imaging Radar Altimeter(InIRA) are related to the roll angle of the baseline,so it should be possible for us to retrieve the incline angle from the interferometric phases under some ocean conditions not so high.In order to do so,the Tiangong-2 was tilted about 5° so as to realize a 0° baseline and in this way,a lot of data was collected.In this paper,we present the retrieved roll angles and compare them with the measured angles by the platform.Due to the Earth is an ellipsoid,and the control of the Tiangong-2 is referring to the Earth center,while the retrieved roll angle is referring to the nadir point on the Earth surface,there is a systematic error related to the orbit,and after calibration of which,the expected results are obtained：two measurements agree with each other very well not only for the measured trend but also for the standard bias between them.     

基于Landsat 8的全球精选遥感数据集汇聚方法

空间对地观测技术为地球系统科学研究提供了多时相、宽覆盖、立体化的遥感数据资源，使对地球系统行为的观测、理解、模拟及预测变为可能。然而，如何从海量数据资源中及时有效地获取信息也成为日渐凸显的问题。现有遥感数据服务系统的检索结果冗余度高，既无法直接满足用户多样化需求，也增加了高质量数据清洗成本。为此，基于2017年全球Landsat 8卫星元数据检索信息，建立由空间完整性、时间邻近性及云覆盖量3个专有指标组成的质量评价体系，设计满足最简化和定制化需求的精选遥感数据集汇聚方法。实验结果表明:该方法在不改变现有遥感数据服务系统的同时提高数据的检索效率和精度。     

Integrating Method of Global Selected Remote Sensing Dataset based on Landsat 8

Earth observation technology provides massive multi-temporal, wide-coverage and three-dimensional remote sensing data resources for Earth System science, making it possible to observe, understand, simulate and predict the complex behavior of earth systems. However, large redundant information from the existing remote sensing data retrieval systems fails to directly meet the diverse research needs and also increases the high-quality data cleaning cost. In this study, a newly retrieval results optimization method was established based on Landsat 8 satellite metadata database in 2017. The method is to select three indicators as a data quality evaluation system, including spatial integrity, temporal proximity and cloud cover, to filter out the high-quality datasets. The simplest and customized experimental results show that this method can improve the efficiency and accuracy of data retrieval and no changes for the existing data service system.     

遥感数据融合方法分析与评价综述

论述了几种常用的基于像元级遥感影像融合方法的原理、特点、作用及限制条件，对各种相应的算法进行了分析和评价，归纳并阐明了遥感数据融合效果定量评价指标及其意义，展望了遥感数据融合方法的发展与应用前景。     

Earth observations for terrestrial biodiversity and ecosystems

Earth observations, comprising satellite, aerial, and in situ systems, are increasingly recognized as critical observations for monitoring the Earth system and systems. Earth observation data are especially needed to fulfil the requirements of a host of international treaties and conventions, and to provide data and information to support conservation and resource management. The Group on Earth Observations, GEO was established to implement the Global Earth Observing Systems of Systems, GEOSS, which includes in its mandate the protection of ecosystems — Improving the management and protection of terrestrial, coastal, and marine ecosystems, and understanding, monitoring, and conserving biodiversity. This Special Issue focuses on Earth observations for terrestrial ecosystems and biodiversity. As such, it is a sampler of remote sensing assessments of the status and trends of biodiversity (species), and ecosystems.     

面向应急系统的城市虚拟环境构建方法研究

提出面向应急系统的一种路途全景图像与GE相结合的虚拟环境构建方法。以汽车为移动平台,利用摄像机和GPS设备采集数据,通过视频拼接生成路途全景图像,根据GPS位置信息生成三维景物立面,再以路途全景图像为纹理对景物立面进行贴图,最后把生成的虚拟模型导入Google Earth。实验证明该方法简单实用,对硬件以及网络环境要求低,且具有较强的扩展能力,能够在一定程度上提高指挥员应急指挥和处置能力。     

The PROBA-V mission: the space segment

PROBA-V (Project for On-Board Autonomy – Vegetation) is an ESA (European Space Agency) mission developed within the framework of the Agency’s General Support Technology Programme (GSTP) devoted to the observation of the Earth’s vegetation, providing data continuity with the SPOT (Satellite pour l’Observation de la Terre) 4 and 5 VEGETATION payloads as a gap-filler to the ESA Sentinel-3 mission. The PROBA-V space segment is based on a three-axis stabilized PROBA small-satellite platform of about 140 kg equipped with a state-of-the-art compact 4-band multi-spectral imager with a large field of view. The instrument’s optomechanics is based on three very compact TMA (three mirror anastigmat) telescopes placed on an optical bench. At an altitude of 820 km, the instrument is able to provide daily coverage of the Earth in three VNIR (visible and near-infrared) bands and one SWIR (short-wave infrared) spectral band, with a spatial resolution of up to 100 m × 100 m at nadir for the VNIR. The instrument raw data will be downlinked with an X-band transmitter to the ground reception station in Kiruna, Sweden. The mission control centre is located in Redu, Belgium. The image processing centre, the so-called ‘user segment’, automatically accesses the raw data and is responsible for the processing and the dissemination of the data products towards the user community. The PROBA-V spacecraft was launched on board the new European launcher Vega on 7 May 2013. It is designed for a nominal mission lifetime of 2.5 years with a possible extension to 5 years.     

The potential for estimating chlorophyll content from a vegetation canopy using the Medium Resolution Imaging Spectrometer (MERIS)

In 2000, the European Space Agency aims to launch the Envisat-1 satellite platform which will carry the Medium Resolution Imaging Spectrometer (MERIS), an advanced optical sensor designed to acquire Earth observation data at regional to global scales. Of particular relevance to terrestrial ecosystems functioning, the MERIS offers the potential to estimate the spectral location of the maximum slope, termed the red edge position (REP), which marks the boundary between chlorophyll absorption in the red wavelengths and the high infrared reflectance due to leaf internal scattering. However, although a first derivative transformation of the reflectance spectra will highlight the maximum slope position, the accurate location of the REP is limited by the spectral sampling resolution of the sensor. A theoretical analysis, using a combined leaf-canopy radiative transfer model, demonstrates that the MERIS, having five coarsely spaced wavebands in the region of the REP, can be utilized for monitoring spectral shifts of the REP, resulting from variation in leaf chlorophyll content or leaf area index.