卫星遥感解译湖泊蓝藻水华的几个关键问题探讨

蓝藻水华是全球性的水环境健康问题,对水华暴发过程信息的快速准确获取是制定有效防治措施的关键.卫星遥感因具有大范围、周期性观测的特点,被广泛地用于湖泊蓝藻水华的时空动态监测.本文指出在利用遥感对湖泊蓝藻水华进行研究时,需要注意的4个问题:(1)湖泊水体中泥沙等信号对藻华存在干扰;(2)大气程辐射及水陆边界影响藻华特征提取结果;(3)卫星数据的有效观测频次影响获取的藻华时空变化趋势;(4)卫星遥感难以实现藻华暴发区的叶绿素浓度准确反演.本文分析了形成上述问题的主要原因,并建议相关的研究工作者在选用合适的遥感数据及方法时,对它们的潜在影响进行评估.     

Coupling the WRF model with a temperature index model based on remote sensing for snowmelt simulations in a river basin in the Altay Mountains,north‐west China

Snowmelt water is an important freshwater resource in the Altay Mountains in north‐west China; however, warming climate and rapid spring snowmelt can cause floods that endanger both public and personal property and safety. This study simulates snowmelt in the Kayiertesi River catchment using a temperature index model based on remote sensing coupled with high‐resolution meteorological data obtained from National Centers for Environmental Prediction (NCEP) reanalysis fields that were downscaled using the Weather Research Forecasting model and then bias corrected using a statistical downscaled model. Validation of the forcing data revealed that the high‐resolution meteorological fields derived from the downscaled NCEP reanalysis were reliable for driving the snowmelt model. Parameters of the temperature index model based on remote sensing were calibrated for spring 2014, and model performance was validated using Moderate Resolution Imaging Spectroradiometer snow cover and snow observations from spring 2012. The results show that the temperature index model based on remote sensing performed well, with a simulation mean relative error of 6.7% and a Nash–Sutcliffe efficiency of 0.98 in spring 2012 in the river of Altay Mountains. Based on the reliable distributed snow water equivalent simulation, daily snowmelt run‐off was calculated for spring 2012 in the basin. In the study catchment, spring snowmelt run‐off accounts for 72% of spring run‐off and 21% of annual run‐off. Snowmelt is the main source of run‐off for the catchment and should be managed and utilized effectively. The results provide a basis for snowmelt run‐off predictions, so as to prevent snowmelt‐induced floods, and also provide a generalizable approach that can be applied to other remote locations where high‐density, long‐term observational data are lacking. Copyright © 2016 John Wiley & Sons, Ltd.     

Data assimilation methods in the Earth sciences

Although remote sensing data are often plentiful, they do not usually satisfy the users’ needs directly. Data assimilation is required to extract information about geophysical fields of interest from the remote sensing observations and to make the data more accessible to users. Remote sensing may provide, for example, measurements of surface soil moisture, snow water equivalent, snow cover, or land surface (skin) temperature. Data assimilation can then be used to estimate variables that are not directly observed from space but are needed for applications, for instance root zone soil moisture or land surface fluxes. The paper provides a brief introduction to modern data assimilation methods in the Earth sciences, their applications, and pertinent research questions. Our general overview is readily accessible to hydrologic remote sensing scientists. Within the general context of Earth science data assimilation, we point to examples of the assimilation of remotely sensed observations in land surface hydrology.     

On utilization ofa priori knowledge in inversion of remote sensing models

Satellite remote sensing deals with a complex system coupling atmosphere and surface. Any physical model with reasonable precision needs several to tens of parameters. Without a priori knowledge of these parameters, Proposition 3 of Verstraete et al. requires the number of independent observations to be greater than the number of unknown parameters. This requirement can hardly be satisfied even in the coming EOS era. As Tarantola pointed out, the inversion problems in geoscience are always underdetermined in some sense. In order to make good use of every kind of a priori knowledge for effectively extracting information from remote sensing observations, the right question to set is as follows:Given an imperfect model and a certain amount ofa priori information on model parameters, in which sense should one modify thea priori information, given the actual observation with noise?A priori knowledge of physical parameters can be presented in different ways such as physical limits, global statistical means and variance fora certain landcover type, or previous statistics and temporal variation of a specific target. When sucha priori knowledge can be expressed as joint probability density. Bayessian theorem can be used in the inversion to obtain posterior probability densities of parameters using newly acquired observations. There is no prerequirement on how many independent observations must be made, and the knowledge gained merely depends on the information content of the new observations. Some specific problems about knowledge accumulation and renewal are also discussed.     

Remote sensing of snow and ice / La télédétection de neige et de glace

Abstract

Monitoring of snow and ice on the Earth's surface will require increasing use of satellite remote sensing techniques. These techniques are evolving rapidly. Active and passive sensors operating in the visible, near infrared, thermal infrared, and microwave wavelengths are described in regard to general applications and in regard to specific USA or USSR satellites. Meteorological satellites (frequent images of relatively crude resolution) and Earth resources satellites such as Landsat (less frequent images of higher resolution) have been used to monitor the areal extent of seasonal snow, but problems exist with cloud cover or dense forest canopies. Snow mass (water equivalent) can be measured from a low-flying aircraft using natural radioactivity, but cannot yet be measured from satellite altitudes. A combination of active and passive microwave sensors may permit this kind of measurement, but not until more is known about radiation scattering in snow. Satellite observations are very useful in glacier inventories, correcting maps of glacier extent, estimating certain mass balance parameters, and monitoring calving or surging glaciers. Ground ice is virtually impossible to monitor from satellites; ice on rivers and lakes can be monitored only with very high-resolution sensors. Microwave sensors, due to their all-weather capability (the ability to see through clouds) provide exciting data on sea ice distribution. Analysis of digital tapes of satellite data requires the archiving and scanning of huge amounts of data. Simple methods for extracting quantitative data from satellite images are described.     

Surveying earth resources by remote sensing from satellites

This paper reviews the techniques and recent results of orbital remote sensing, with emphasis on Landsat and Skylab imagery. Landsat (formerly ERTS) uses electronic sensors (scanners and television) for repetitive observations with moderate ground resolution. The Skylab flights used a wider range of electro-optical sensors and returned film cameras with moderate and high ground resolution. Data from these programs have been used successfully in many fields. For mineral resources, satellite observations have proven valuable in geologic mapping and in exploration for metal, oil, and gas deposits, generally as a guide for other (conventional) techniques. Water resource monitoring with satellite data has included hydrologic mapping, soil moisture studies, and snow surveys. Marine resources have been studied, with applications in the fishing industry and in ocean transportation. Agricultural applications, benefiting from the repetitive coverage possible with satellites, have been especially promising. Crop inventories are being conducted, as well as inventories of timber and rangeland. Overgrazing has been monitored in several areas. Finally, environmental quality has also proven susceptible to orbital remote sensing; several types of water pollution have been successfully monitored. The effects of mining and other activities on the land can also be studied. The future of orbital remote sensing in global monitoring of the Earth's resources seems assured. However, efforts to extend spectral range, increase resolution, and solve cloud-cover problems must be continued. Broad applications of computer analysis techniques are vital to handle the immense amount of information produced by satellite sensors.Abbreviations SSRERTS Symposium on Significant Results Obtained from the Earth Resources Technology Satellite - TERTSS Third Earth Resources Technology Satellite-1 Symposium National Academy of Sciences-National Research Council Research Associate at NASA Goddard Space Flight Center, 1974/75.Goddard Space Flight Center     

Understanding the Moon’s internal structure through moonquake observations and remote sensing technologies

Explorations for the interior structure of the Moon mainly involve three technologies: the early gravitational observations via circumlunar satellites, the moonquake observations during the Apollo period, and the recent high-resolution remote sensing observations. Based on these technologies, we divided the development of the moon’s interior structure into three stages. The first stage is the discovery of high-density anomalous masses (mascons) on the lunar surface with the low-order gravitational field models, which were obtained by observing perturbations of the early lunar orbital satellites. The second stage is the preliminary understanding of the layer structure with the help of moonquake observations during the Apollo period. The third stage is the deep understanding of the structure of the lunar crust, mantle, and core, with the use of high-resolution remote sensing data and the reassessment of moonquake data from the Apollo’s mission. This paper gave detailed introduction and comments on different observation technologies, gathered data, and data processing techniques used at the three stages. In addition, this paper analyzed the current issues in the researches on the Moon’s internal structure and discussed the prospects for future explorations.     

Quality Assessment of Atmospheric Motion Vectors Over the Indian Ocean

Because conventional observations over the oceans are not available, especially during tropical cyclones, multi-spectral atmospheric motion vectors (AMVs) estimated from geostationary satellites are routinely assimilated in the numerical weather prediction models at different operational centres across the globe. The derived AMVs are generally validated with radiosonde observations available over land at synoptic hours; however, over the ocean there is a limited scope to assess the quality of AMVs. Over ocean, AMVs can be validated with radiosonde data available from opportunistic ships or using dropsonde data available from aircrafts. In this study, the accuracy of the AMVs derived from the geostationary satellites Kalpana-1 and Meteosat-7 is evaluated over the oceanic region. Radiosonde data available from a ship cruise held in the Bay of Bengal during the period 09 July–08 August 2012 and from the Cal/Val site situated at Kavaratti Island (72.62°E, 10.57°N) in the southern Indian Ocean are used to assess the AMV accuracy. In this study, 83 radiosonde profiles are used to validate the Kalpana-1 AMVs, to allow a better understanding of AMV errors over the Indian Ocean. The RMSVD of Kalpana-1 AMVs for the high-, mid- and low-levels are found to be 7.9, 9.4 and 5.3 m s^?1, respectively, while the corresponding RMSVD for Meteosat-7 AMVs are 9.1, 5.5 and 3.7 m s^?1. A similar accuracy is observed when the AMVs are validated against the NCEP analyses collocated with the nearest radiosonde locations. The high RMSVD and bias for Kalpana-1 AMVs at the mid-level and Meteosat-7 AMVs at the high-level are associated with the limitation of satellite winds to resolve the upper-level easterly jet in conjunction with errors in the height assignment. This study could help the numerical modellers to assign appropriate observation error over this region during the assimilation of AMVs into the NWP models.     

Calibration of hydrological models in ungauged basins based on satellite radar altimetry observations of river water level

This paper proposes a new orientation to address the problem of hydrological model calibration in ungauged basin. Satellite radar altimetric observations of river water level at basin outlet are used to calibrate the model, as a surrogate of streamflow data. To shift the calibration objective, the hydrological model is coupled with a hydraulic model describing the relation between streamflow and water stage. The methodology is illustrated by a case study in the Upper Mississippi Basin using TOPEX/Poseidon (T/P) satellite data. The generalized likelihood uncertainty estimation (GLUE) is employed for model calibration and uncertainty analysis. We found that even without any streamflow information for regulating model behavior, the calibrated hydrological model can make fairly reasonable streamflow estimation. In order to illustrate the degree of additional uncertainty associated with shifting calibration objective and identifying its sources, the posterior distributions of hydrological parameters derived from calibration based on T/P data, streamflow data and T/P data with fixed hydraulic parameters are compared. The results show that the main source is the model parameter uncertainty. And the contribution of remote sensing data uncertainty is minor. Furthermore, the influence of removing high error satellite observations on streamflow estimation is also examined. Under the precondition of sufficient temporal coverage of calibration data, such data screening can eliminate some unrealistic parameter sets from the behavioral group. The study contributes to improve streamflow estimation in ungauged basin and evaluate the value of remote sensing in hydrological modeling. Copyright © 2011 John Wiley & Sons, Ltd.     

2013年甘肃岷县漳县6.6级地震前热红外异常分析

应用中国静止气象卫星FY-2E红外遥感资料对2013年甘肃岷县漳县6.6级地震前的热红外异常信息进行研究,结果表明：由地震黑体辐射热异常时空演化图分析知,中强地震前,往往会有较大范围的热异常演变,热异常大面积沿断层分布,震中位置往往位于边界或热异常变化最大处.对比实时的长波辐射涡度场变化,能更好的对应未来地震的发震区域,涡度异常经常分布在震中边界.     

Publications received by the Editor

Abstract

Remote sensing is the use of electromagnetic energy to measure the physical properties of distant objects. It includes photography and geophysical surveying as well as newer techniques that use other parts of the electromagnetic spectrum. The history of remote sensing begins with photography. The origin of other types of remote sensing can be traced to World War II, with the development of radar, sonar, and thermal infrared detection systems. Since the 1960s, sensors have been designed to operate in virtually all of the electromagnetic spectrum. Today a wide variety of remote sensing instruments are available for use in hydrological studies; satellite data, such as Skylab photographs and Landsat images are particularly suitable for regional problems and studies. Planned future satellites will provide a ground resolution of 10–80 m.

Remote sensing is currently used for hydrological applications in most countries of the world. The range of applications includes groundwater exploration determination of physical water quality, snowfield mapping, flood-inundation delineation, and making inventories of irrigated land. The use of remote sensing commonly results in considerable hydrological information at minimal cost. This information can be used to speed-up the development of water resources, to improve management practices, and to monitor environmental problems.     

Terrestrial carbon cycle model-data fusion: Progress and challenges

The terrestrial carbon cycle is an important component of global biogeochemical cycling and is closely related to human well-being and sustainable development. However, large uncertainties exist in carbon cycle simulations and observations.Model-data fusion is a powerful technique that combines models and observational data to minimize the uncertainties in terrestrial carbon cycle estimation. In this paper, we comprehensively overview the sources and characteristics of the uncertainties in terrestrial carbon cycle models and observations. We present the mathematical principles of two model-data fusion methods, i.e., data assimilation and parameter estimation, both of which essentially achieve the optimal fusion of a model with observational data while considering the respective errors in the model and in the observations. Based upon reviewing the progress in carbon cycle models and observation techniques in recent years, we have highlighted the major challenges in terrestrial carbon cycle model-data fusion research, such as the "equifinality" of models, the identifiability of model parameters,the estimation of representativeness errors in surface fluxes and remote sensing observations, the potential role of the posterior probability distribution of parameters obtained from sensitivity analysis in determining the error covariance matrixes of the models, and opportunities that emerge by assimilating new remote sensing observations, such as solar-induced chlorophyll fluorescence. It is also noted that the synthesis of multisource observations into a coherent carbon data assimilation system is by no means an easy task, yet a breakthrough in this bottleneck is a prerequisite for the development of a new generation of global carbon data assimilation systems. This article also highlights the importance of carbon cycle data assimilation systems to generate reliable and physically consistent terrestrial carbon cycle reanalysis data products with high spatial resolution and longterm time series. These products are critical to the accurate estimation of carbon cycles at the global and regional scales and will help future carbon management strategies meet the goals of carbon neutrality.     

遥感卫星的防震减灾应用研究

整理了遥感卫星的发展历程和发展趋势,总结了目前国内外遥感卫星的特点、卫星遥感数据的现状及其应用于防震减灾领域的前景。在此基础上,研究卫星遥感应用于防震减灾的优势、面临的困难和目前的现状,并深入分析了卫星遥感数据应用于汶川地震救灾的成就和不足。最后对进一步推动卫星遥感的防震减灾应用展开了分析和讨论。     

Application of ensemble kalman filter to geophysical parameters retrieval in remote sensing:A case study of kernel-driven BRDF model inversion

Remote sensing can provide multi-spatial resolution, multi-temporal resolution multi-spectral band and multi-angular data for the observation of land surface. At present, one of research focuses is how to make the best of these data to retrieve geophysical parameters in conjunction with their a priori knowledge and simul-taneously consider the influence of data uncertainties on inversion results[1-5]. The essence of remote sensing lies in inversion. It is difficult to precisely retrieve parame…     

基于可视化遥感的地震大区域灾情收集系统

地震现场与一般灾害现场不同,其受灾区域较大,常规的基于遥感信息进行灾情收集时,采集信息零散混乱无法形成可视化直观分析结果。为解决该问题,设计基于可视化遥感技术的地震大区域灾情收集系统,利用遥感信息采集大区域的优势,配合设计网络通信层、数据收集层、数据库层、GIS遥感平台层和应用层组成的系统硬件,采用GIS三维重构技术,对灾情进行可视化呈现。实验结果说明,系统最长响应时间为1.857 s,最大CPU占用率和消耗分别是80.8%和1 656 MB,信息可视化重现程度高。     

Review of Methodologies for Offshore Wind Resource Assessment in European Seas

The wind resource offshore is generally larger than at geographically nearby onshore sites, which can offset the higher installation, operation and maintenance costs associated with offshore wind parks. Successful offshore wind energy development relies to some extent on accurate prediction of wind resources, but since installing and operating a meteorological mast in situ is expensive, prospective sites must be carefully evaluated. Accordingly, one can conceptualize the wind resource assessment process as a two-phase activity: (i) an evaluation of wind resources at the regional scale to locate promising wind farm sites and (ii) a site specific evaluation of wind climatology and vertical profiles of wind and atmospheric turbulence, in addition to an assessment of historical and possibly future changes due to climate non-stationarity. Phase (i) of the process can involve use of in situ observations of opportunity derived from ships, lighthouses and buoys in conjunction with model tools and remote sensing products. The reliability of such data sources has been extensively investigated in different national and European projects especially in Northern Europe, and the results are summarized herein. Phase (ii) of the project often still requires in situ observations (which may or may not be supplemented with ground-based remote sensing technologies) and application of tools to provide a climatological context for the resulting measurements. Current methodologies for undertaking these aspects of the resource assessment are reviewed.     

Response of remotely sensed Normalized Difference Water Deviation Index to the 2006 Drought of eastern Sichuan Basin

One of the most serious droughts in last century occurred in eastern Sichuan Basin in the summer of 2006 (hereinafter called the Drought). The response of Moderate Resolution Imaging Spectroradiometer (MODIS, boarding on NASA satellites of Terra and Aqua) to the Drought was analyzed in order to reach one practicable monitoring solution for regional soil moisture. Temporal process and spatial extension of the Drought were firstly estimated with ground meteorological and hydrological observations. Then, for the whole region of Sichuan and Chongqing, the remotely sensed Normalized Difference Water In- dex (NDWI) for the summers of 2001―2006 were calculated based on 8-day composite MODIS products, which were further used to construct a new water index (Normalized Difference Water Deviation Index, NDWDI) to examine the sensitivity of remote sensing in the Drought. The study showed that the NDWDI is more sensitive to regional drought than other absolute-soil-moisture-based indices. With the new index, the study extracted the spatial-temporal characteristics of the 2006 Drought, and explored its developing and withdrawing processes, which agreed with related statistics. Compared with ground method of drought observation, the NDWDI-based remote sensing solution of this paper is more pref- erable and practicable in that the local soil properties of water consumption and supply are implicitly taken into account, and the spatial representativity limit of ground observation is circumvented to a degree as satellite remotely senses the earth surface in a way of two-dimensional pixel matrix. So, the NDWDI-based method can be used to monitor regional soil water stress situation more practically and efficiently.     

多参量联合分析的地震空间探测异常研究进展

卫星对地观测技术是全球范围内实时监测孕震区地震活动和震后应急救援的有力工具。过去的几十年里,不同种类的卫星观测数据被应用于地震遥感异常探测领域中,并逐渐开展了从单个震例分析,到普遍规律探索,再到内部机理研究的多方面探讨。但由于地球系统的复杂性和孕震过程的不确定性,地震遥感异常研究仍充满困难和挑战。本文利用文献计量分析方法,探究了现今国内外地震遥感异常研究的关注热点和趋势方向,认为目前对于地震遥感异常的研究已由单一参量发展为多参量联合分析阶段;通过3个前沿研究案例分析了常用的综合分析参量与异常分析方法,以及多参量之间的时空同步性关系,表明多参量遥感异常的结合研究和交叉验证是地震遥感异常研究中的重要手段,可以在未来地震前兆研究中发挥重要作用。      

Establishment and analysis of a High-Resolution Assimilation Dataset of the water-energy cycle in China

For better prediction and understanding of land-atmospheric interaction, in-situ observed meteorological data acquired from the China Meteorological Administration (CMA) were assimilated in the Weather Research and Forecasting (WRF) model and the monthly Green Vegetation Coverage (GVF) data, which was calculated using the Normalized Difference Vegetation Index (NDVI) of the Earth Observing System Moderate-Resolution Imaging Spectroradiometer (EOS-MODIS) and Digital Elevation Model (DEM) data of the Shuttle Radar Topography Mission (SRTM) system. Furthermore, the WRF model produced a High-Resolution Assimilation Dataset of the water-energy cycle in China (HRADC). This dataset has a horizontal resolution of 25 km for near surface meteorological data, such as air temperature, humidity, wind vectors and pressure (19 levels); soil temperature and moisture (four levels); surface temperature; downward/upward short/long radiation; 3-h latent heat flux; sensible heat flux; and ground heat flux. In this study, we 1) briefly introduce the cycling 3D-Var assimilation method and 2) compare results of meteorological elements, such as 2 m temperature and precipitation generated by the HRADC with the gridded observation data from CMA, and surface temperature and specific humidity with Global Land Data Assimilation System (GLDAS) output data from the National Aeronautics and Space Administration (NASA). We find that the simulated results of monthly 2 m temperature from HRADC is improved compared with the control simulation and has effectively reproduced the observed patterns. The simulated special distribution of ground surface temperature and specific humidity from HRADC are much closer to GLDAS outputs. The spatial distribution of root mean square errors (RMSE) and bias of 2 m temperature between observations and HRADC is reduced compared with the bias between observations and the control run. The monthly spatial distribution of surface temperature and specific humidity from HRADC is consistent with the GLDAS outputs over China. This study could improve the land surface parameters by utilizing remote sensing data and could further improve atmospheric elements with a data assimilation system. This work provides an effective attempt at combining multi-source data with different spatial and temporal scales into numerical simulations, and the simulated results could be used in further research on the long-term climatic effects and characteristics of the water-energy cycle over China.     

Quantitative comparison of spatial fields for hydrological model assessment––some promising approaches

The current practice for assessing spatial predictions from distributed hydrological models is simplistic, with visual inspection and occasional point observations generally used for model assessment. With the increasing availability of spatial observations from remote sensing and intensive field studies, the current methods for assessing the spatial component of model predictions need to advance. This paper emphasises the role that spatial field comparisons can play in model assessment. A review of the current methods used in hydrology, and other disciplines where spatial field comparisons are widely used, reveals some promising methods for quantitatively comparing spatial fields. These promising approaches––segmentation, importance maps, fuzzy comparison and multiscale comparison––are for local comparison of spatial fields. They address some of the weaknesses with the current approaches to spatial field comparison used in hydrological modelling and, in doing so, emulate some aspects of human visual comparison. The potential of these approaches for assessing spatial predictions and understanding model performance is illustrated with a simple example.