FY-3/VIRR资料积雪多阈值综合判识方法研究

利用卫星遥感监测积雪分布相比地面观测具有明显优势,目前基于FY-3卫星数据在积雪监测方面的研究较少。借鉴现有积雪卫星遥感监测算法,研究出适用于FY-3/VIRR资料的积雪判识方法,利用归一化积雪指数和多波段综合阈值实现积雪判识,提取积雪信息生成区域二值化积雪分布图。通过实例分析验证算法有效可行,并与MODIS积雪产品MOD10及其L1B数据NDSI判识结果进行对比,说明算法判识结果良好。研究表明,FY-3卫星数据可作为积雪遥测的可靠资料来源,可延用于积雪监测与灾害预警业务系统中,促进国产卫星数据的应用与推广。     

华北积雪冻融循环微波辐射特征研究

积雪冻融循环监测是陆表水文过程和冰雪自然灾害研究的重要方面。被动微波遥感由于具有对水分敏感、高时间分辨率的特点,尤其适合大尺度的积雪冻融监测及相关参数的反演。该研究于2012年11月6日~27日在河北怀来遥感综合实验站使用车载多频率微波辐射计TMMR观测了积雪冻融循环微波辐射特征。研究发现,36.5GHz的观测亮温对积雪的冻融循环最敏感,18.7GHz次之,融化和冻结的时亮温差别可分别约达80K和60K;HUT单层和多层积雪微波辐射模型对18.7GHz和36.5GHz的模拟亮温能够基本反映冻融循环过程中的亮温变化;多层模型更适合模拟冻融循环的过程,18.7GHz和36.5GHz在V极化的相关系数均为0.97;冻融循环研究中,冰壳、冰层粒径的观测、雪湿度的观测和湿雪介电常数模型仍有待进一步改善。     

黑河综合遥感联合试验研究进展:概述

“黑河综合遥感联合试验”是在我国典型的内陆河流域--黑河流域开展的大型航空、卫星遥感和地面同步观测试验。28个单位的280多名科研人员、研究生和工程技术人员参加了试验。介绍了2008年加强试验期以来的研究工作进展。“黑河综合遥感联合试验”最主要的成果是一套多尺度、高质量的综合观测数据集,该数据集已正式对外发布,十分有力地支持了一系列生态、水文、定量遥感模型的发展、改进和验证,试验所期望突破“数据瓶颈”的目标已经实现。此外,已在积雪参数提取、地表冻融微波遥感、森林结构参数的遥感反演、蒸散发观测与遥感估算、土壤水分反演、生物物理参数和生物化学参数反演、水文气象观测、尺度推绎、流域水文模拟和同化应用等方面取得了丰富的研究成果。试验亮点包括蒸散发观测及其遥感模型改进、机载激光雷达的应用、多角度热红外传感器研制和应用;航空遥感在获取高质量、高分辨率数据方面依然具有不可替代的作用。     

基于文献计量的水遥感研究发展态势

利用遥感技术观测和反演水文、水循环等的重要参数的水遥感研究,是国际地球科学研究的重大研究主题。科技文献能够反映科学研究的发展动态和热点变化,以 SCIE科学引文索引数据库为数据源,利用专业数据分析工具 TDA(Thomson Data Analyzer)和Ucinet工具,对1994~2013年水遥感研究相关论文进行了定量分析。结果表明:水遥感研究论文20年来持续增速显著;美国发射多颗观测卫星而在该领域居于主导优势,高被引论文主要为卫星介绍和水体指数文章;水遥感研究主要集中于土壤水分、蒸散发、水资源、冰雪等研究,MODIS数据为主要应用的卫星数据。     

土壤光学遥感研究进展

土壤是一个时空连续的变异体,传统土壤参数测定与监测方法难以揭示土壤的时空间异质性规律;土壤光学遥感可以实现土壤主要参数的快速、宏观测定。本文综合评述了土壤不同理化参数(有机质、土壤水分、矿物组成、土壤质地、土壤结皮)在光学波段的光谱特征与遥感反演,光学遥感在土壤分类与制图方面的应用;分析了土壤线的各种影响因素(外部因素及相关土壤理化参数),以及土壤线对植被指数定量监测植被状况的重要性;归纳了土壤光学遥感存在问题与发展趋势。     

积雪遥感

遥感方法可以对区域以至全球尺度的雪盖进行有效监测,根据在光谱的光学波段和微波的各种参数可以描述积雪的反射及发射特性。可见及近红外卫星影像可用以制作积雪面积范围图,这些资料对于气候研究和业务融雪径流预报是很有价值的。航天微波辐射仪资料研究表明:用这种资料可进行积雪面积范围和水当量制图,且具有探测融雪的全天候能力。被动式微波传感器的主要缺点就是空间分辨率较差,这可以用主动式传感器来弥补。合成孔径雷达可以探测湿雪且空间分辨率高。在未来十年中,将发射许多微波传感器,挖掘这一潜力尚需要更艰苦的研究。     

土壤水分遥感研究进展

土攘的各种理化性状，地形的分异作用以及气候变化和人为的土壤管理措施对土壤水分状况有不同的影响，地表特征与土壤水分状况也存在着一定的相关性。世界土遥感研究比较先进的国家一般从70年代初就全面来统地进行了土壤水分的研究，我国系统地进行土攘水分研究是近几年的率。土壤水分遥感监测要经过复杂的中间过程，波段及变量选择，传感器性能等因素对土攘水分监测是至关重要的;研究方法及途径的选择都要根据土攘类型、传惑器性能以及工作目的等因素合理确定，该领域的研究有待进一步完善。     

被动微波遥感在地表冻融监测中的应用研究进展

地表冻融过程强烈影响着地气能量交换、地表径流、作物生长和碳循环等陆地表层过程,利用微波遥感监测地表冻融循环及其相关的地表信息对气候的响应和反馈显然极其重要。随着SMOS(Soil Moisture and Ocean Salinity mission)、SMAP(Soil Moisture Active Passive mission)计划的实施,相对于早期广泛使用的C、X、K和Ka微波波段,L波段具有更低的频率、更深的穿透深度以及对土壤介电常数变化的敏感性,不仅被用于传统的地表冻融状态监测,还被扩展应用于估算土壤冻结深度、冻结速率、相变水含量等信息,显示出更广阔的应用前景。回顾了近年来被动微波遥感在地表冻融循环监测方面的最新研究进展,包含遥感监测原理、微波传感器、遥感算法等方面,重点介绍和总结了L波段在地表冻融循环遥感监测中的前沿研究,并对其应用潜力进行了展望。     

积雪遥感动态研究的现状及展望

简要讨论了积雪遥感研究的现状,主要包括常用传感器的物理参数及其可行性和局限性,云和雪的区分技术,雪盖面积和积雪深度的提取,雪水当量换算以及积雪遥感在融雪径流模拟、雪灾监测与评价、积雪对气候变化的影响研究等方面的应用。并对积雪遥感研究的发展趋势做了简要的分析与展望。     

青藏高原L波段微波辐射观测与土壤水分反演研究进展

土壤水分是地气间水热交换的重要变量,影响着地表感热潜热划分、水分收支和植被蒸腾等过程,青藏高原土壤水分的研究对于改进高原水分循环和能量平衡的模拟研究具有重要意义。随着SMOS、SMAP等卫星的发射,L波段被动微波遥感技术成为大尺度监测土壤水分的主要手段。分别从L波段星—机—地观测与微波辐射模拟、区域尺度土壤水分观测、卫星产品评估与土壤水分反演算法发展等方面系统回顾和总结了近年来L波段被动微波遥感及其土壤水分反演算法、产品在青藏高原的主要应用与研究进展。在此基础上,归纳了当前高原L波段被动微波辐射模拟与土壤水分反演存在的问题,主要包括缺乏高原尺度的微波辐射模拟评估和改进的卫星土壤水分产品、土壤冻结时期的水分监测产品依然缺失等问题。针对存在的问题,进一步提出了相关建议与展望,建议今后的研究应加强高原尺度的微波辐射模拟评估与土壤水分产品改进工作,并积极拓展土壤水分产品在高原水分循环和能量平衡模拟、植被生长与干旱监测的应用研究。     

被动微波遥感在青藏高原积雪业务监测中的初步应用

积雪范围、积雪深度和雪水当量等参数的遥感监测与反演对气候模式的建立以及积雪灾害的评估具有重要意义。被动微波遥感在这些参数的反演方面具有明显优势,但目前尚未应用到青藏高原地区的积雪遥感业务监测上来。2001年10月至2002年4月,利用SSM/I数据对青藏高原地区的积雪范围和积雪深度进行了实时监测,为西藏、青海遥感应用部门提供逐日的雪深分布图。对这次监测的总效果进行了分析和评价,并对发生在青海省内一次较大的降雪过程进行了遥感分析,结果表明:SSM/I反演的积雪范围变化趋势与MODIS结果总体上较为一致;SSM/I的雪深监测结果为当地遥感部门对大于10 cm的雪深做出正确判断提供了重要信息,是对雪灾定位的重要信息源。     

被动微波遥感反演土壤水分进展研究

在地球系统中, 地表土壤水分是陆地和大气能量交换过程中的重要因子, 并对陆地表面蒸散、水的运移、碳循环有很强的控制作用, 大面积监测土壤水分在水文、气象和农业科学领域具有较大的应用潜力。被动微波遥感是监测土壤含水量最有效的手段之一, 相比红外与可见光, 它具有波长长, 穿透能力强的优势, 相比主动微波雷达, 被动微波辐射计具有监测面积大、周期短, 受粗糙度影响小, 对土壤水分更为敏感, 算法更为成熟的优势。然而微波辐射计观测到的亮温除了受土壤水分影响外, 还要考虑如植被覆盖、土壤温度、雪覆盖以及地形、地表粗糙度、土壤纹理和大气效应以及地表的异质性等其它因子的影响。目前, 已研究出许多使用被动微波辐射计反演土壤水分的方法,这些方法大部分是围绕着土壤湿度与亮温温度之间的关系进行, 同时也考虑其它各种不同因子对 地表微波辐射的影响。从介绍被动微波反演地表参数的原理入手, 重点介绍被动遥感反演土壤水分当前的算法进展、研究趋势等。     

The impact of simulated soil temperatures on the estimation of snow depth over Siberia from SSM/I compared to a multi-model climatology

The snowpack is a key variable of the hydrological cycle. In recent years, numerous studies have demonstrated the importance of long-term monitoring of the Siberian snowpack on large spatial scales owing to evidence of increased river discharge, changes in snow fall amount and alterations with respect to the timing of ablation. This can currently only be accomplished using remote sensing methods. The main objective of this study is to take advantage of a new land surface forcing and simulation database in order to both improve and evaluate the snow depths retrieved using a dynamic snow depth retrieval algorithm. The dynamic algorithm attempts to account for the spatial and temporal internal properties of the snow cover. The passive microwave radiances used to derive snow depth were measured by the Special Sensor Microwave/ Imager (SSM/I) data between July 1987 and July 1995.The evaluation of remotely sensed algorithms is especially difficult over regions such as Siberia which are characterized by relatively sparse surface measurement networks. In addition, existing gridded climatological snow depth databases do not necessarily correspond to the same time period as the available satellite data. In order to evaluate the retrieval algorithm over Siberia for a recent multi-year period at a relatively large spatial scale, a land surface scheme reanalysis product from the Global Soil Wetness Project-Phase 2 (GSWP-2) is used in the current study. First, the high quality GSWP-2 input forcing data were used to drive a land surface scheme (LSS) in order to derive a climatological near-surface soil temperature. Four different snow depth retrieval methods are compared, two of which use the new soil temperature climatology as input. Second, a GSWP-2 snow water equivalent (SWE) climatology is computed from 12 state-of-the-art LSS over the same time period covered by the SSM/I data. This climatology was compared to the corresponding fields from the retrievals. This study reaffirmed the results of recent studies which showed that the inclusion of ancillary data into a satellite data-based snow retrieval algorithm, such as soil temperatures, can significantly improve the results. The current study also goes a step further and reveals the importance of including the monthly soil temperature variation into the retrieval, which improves results in terms of the spatial distribution of the snowpack. Finally, it is shown that further improved predictions of SWE are obtained when spatial and temporal variations in snow density are accounted for.     

Assessing snowmelt dynamics with NASA scatterometer (NSCAT) data and a hydrologic process model

The presence of snow strongly impacts the exchange of moisture and energy between the land surface and atmosphere. In the interior of the northern hemisphere continents, snowmelt on frozen soils can cause or exacerbate major floods. Microwave remote sensing from satellite platforms has the potential to monitor the freeze-thaw status of soil and overlying snow packs over large areas. We evaluate the backscatter response of the NSCAT scatterometer to changing snow surface conditions, especially freeze and thaw status, using a macroscale hydrology model and the NSCAT backscatter data for the upper Mississippi River basin of the north central U.S. and the Boreal Ecosystem Atmosphere Study (BOREAS) region in central Canada. We compared the snowmelt conditions simulated by the Variable Infiltration Capacity (VIC) macroscale hydrology model driven with surface meteorological observations with NSCAT measurements for 1996-1997 snow season. A mid-winter thaw event (in February) and late season melt (April-May) are evaluated for both regions. Comparison of backscatter images with daily and hourly-modeled snow surface wetness and temperature showed that the model agreed with the backscatter for snow surface wetness on some days but not on others. Factors such as NSCAT overpass times, vegetation on the ground and their freeze-thaw state, and liquid moisture content appear to contribute to these discrepancies.     

利用被动微波遥感数据反演我国积雪深度及其精度评

考虑到我国西部地区使用SSM/I全球算法将高估积雪深度,故以东经105°为界将我国分为东部和西部。在西部地区采用修正后的雪深算法,东部地区沿用全球算法。对散射系数较高,容易和积雪相混淆的降雨、寒漠和冻土地表类型,通过积雪分类树进行剔除,进而发展了一套适用于全国积雪深度的业务化反演方案。最后利用MODIS积雪产品对冬季90天的结果进行了精度评价,总体精度平均达到86.4%,最高精度达到95.5%,Kappa系数均值为65.5%,最大值达到86.2%。     

Recently Research Progresses in Detecting Surface Soil Freeze-thaw Cycles with Passive Microwave Remote Sensing

Surface soil freeze-thaw processes affect the energy and water exchange between the land surface and the atmosphere,hydrological cycle process and ecological system activity.It is obviously important to study the spatial distribution and temporal dynamics of surface soil freeze-thaw cycle,frozen depth,transition water content by the remote sensing technology,and their influences on and feedback with climate change.With the implementation of the SMOS and SMAP satellite projects,the L-band,with lower frequency,deeper penetration depth and stronger dielectric sensitivity,can be used to not only monitor the surface freeze-thaw cycles,but also to estimate the soil frozen depth,frozen velocity and phas-|change water content.Compared with the widely used C,X,Ku and Ka bands,L-band has a wider application potential.As a supplement,this paper reviewed soil freeze-thaw cycle with passive microwave remote sensing,including observation principle and advantage of L band,newly improved and developed algorithms,passive radiometers and so on,particularly focusing on the development and potential of the L-band.     

Retrieving near-surface soil moisture from microwave radiometric observations: current status and future plans

Surface soil moisture is a key variable used to describe water and energy exchanges at the land surface/atmosphere interface. Passive microwave remotely sensed data have great potential for providing estimates of soil moisture with good temporal repetition on a daily basis and on a regional scale (∼10 km). However, the effects of vegetation cover, soil temperature, snow cover, topography, and soil surface roughness also play a significant role in the microwave emission from the surface. Different soil moisture retrieval approaches have been developed to account for the various parameters contributing to the surface microwave emission. Four main types of algorithms can be roughly distinguished depending on the way vegetation and temperature effects are accounted for. These algorithms are based on (i) land cover classification maps, (ii) ancillary remote sensing indexes, and (iii) two-parameter or (iv) three-parameter retrievals (in this case, soil moisture, vegetation optical depth, and effective surface temperature are retrieved simultaneously from the microwave observations). Methods (iii) and (iv) are based on multiconfiguration observations, in terms of frequency, polarization, or view angle. They appear to be very promising as very few ancillary information are required in the retrieval process. This paper reviews these various methods for retrieving surface soil moisture from microwave radiometric systems. The discussion highlights key issues that will have to be addressed in the near future to secure operational use of the proposed retrieval approaches.     

AMSR-E data inversion for soil temperature estimation under snow cover

Climate warming is the focus of several studies where the soil temperature plays an essential role as a state variable for the surface energy balance of the Earth. Many methods have been developed to determine summer surface temperature, but the determination in presence of snow is an ill-conditioned problem for microwave techniques because snow changes the emissivity of the surface. This project aims to improve the estimation of soil temperature, within the top 5 cm of the ground, under the snowpack using passive microwave remote sensing. Results show the potential of the passive microwave brightness temperature inversion at 10 GHz (derived from the Advanced Microwave Scanning Radiometer—Earth Observing System, AMSR-E) for the estimation of soil temperature using a physical multilayer snow-soil model (SNTHERM) coupled with a snow emission model (HUT). The snow model is driven with meteorological measurements from ground-based stations as well as data generated from reanalysis. The proposed iterative retrieval method minimizes the difference between the simulated and measured brightness temperature using the soil temperature as a free parameter given by SNTHERM. Results are validated against ground-based measurements at several sites across Canada through several winter seasons. The overall root mean square error and bias in the retrieved soil temperature is respectively 3.29 K and 0.56 K, lower than the error derived from the snow-soil model without the use of remote sensing. The accuracy in detection of frozen/unfrozen soil under the snowpack is 78%, which is improved up to 81% if the spring melting period is not considered. This original procedure constitutes a very promising tool to characterize the soil (frozen or not) under snow cover, as well as its evolution in northern remote locations where measurements are unavailable.     

冰沟流域积雪面积比例与雪水当量关系初探

像元尺度上积雪面积比例与雪水当量的关系是将积雪遥感面积数据引入水文模型的有效手段。以冰沟流域为例,利用合成孔径雷达ENVISAT-ASAR数据反演得到积雪面积、雪水当量信息,分析了500m像元尺度上积雪面积比例与雪水当量的关系。结果表明:1在积雪面积比例未达到全覆盖饱和状态,雪水当量和积雪面积比例呈正相关关系,积雪面积比例控制着雪水当量的最大值,但由于受到地形的影响,关系不显著;2当考虑地形因子影响,即将坡度、坡向、海拔、积雪面积比例与雪水当量进行多元线性回归,回归系数的显著性水平均小于0.05,相关系数(r)达到0.841。因此,在高分辨率地形因子已知的情况下,结合遥感积雪数据,可建立良好的积雪面积比例和雪水当量之间的关系,有利于高分辨率积雪面积比例数据在寒区分布式水文模型中的应用。     

基于积雪调查数据的东北地区被动微波积雪遥感产品精度验证与分析

积雪遥感数据产品可以提供积雪的时空分布信息,是积雪监测的重要数据源。对现有的不同遥感产品进行精度验证和对比分析,明确其适用范围,有利于积雪数据产品的进一步发展和应用。为验证积雪产品在东北地区的适用性,以中国积雪特性及分布调查项目为依托,精心设计野外实验,观测了东北地区25 km典型样方和积雪线路调查数据,验证了在阔叶林和农田两种下垫面下,FY-3B雪深产品、AMSR-2雪深产品、GlobSnow雪水当量产品在东北地区的反演精度。结果表明：GlobSnow雪水当量产品精度最高,不区分下垫面的情况下,最大偏差和均方根误差分别为10.87 cm和12.53 cm。考虑下垫面的影响,GlobSnow雪水当量产品和FY-3B雪深产品在两种下垫面下的雪深反演精度差别很小,偏差和均方根误差的差值小于2.11 cm和3.46 cm,AMSR-2积雪产品在两种下垫面下反演精度差别很大,两种下垫面下偏差和均方根误差的差值大于9.94 cm和7.19 cm。对于3种积雪产品,下垫面为农田的雪深反演精度均高于下垫面为阔叶林的反演精度。