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1.
基于集合Kalman滤波及SCE-UA(shuffled complex evolution)算法发展了能够直接同化微波亮温的土壤湿度同化方案. 该方案以陆面过程模式CLM 3.0中的土壤水模型作为预报算子, 以辐射传输模型作为观测算子. 整个同化过程分为参数优化和土壤湿度同化两个阶段, 利用SCE-UA算法优化辐射传输模型中难以确定的植被光学厚度参数和地表粗糙度参数, 并利用优化参数作为观测算子的模型参数进行同化. 通过人工理想试验表明该同化方案可以明显改善表层土壤湿度的模拟精度, 并且对深层土壤湿度的模拟也有一定程度的改善; 利用AMSR-E亮温(10.65 GHz垂直极化)所进行的实际同化试验表明顶层(0~10 cm)土壤湿度同化结果与观测的均方根误差(RMSE)由模拟的0.05052减小到0.03355, 相对减小了33.6%, 而较深层(10~50 cm)平均减小了20.9%. 这些同化试验显示该同化方案的合理性. 相似文献
2.
《地球物理学进展》2020,(4)
地表发射率是地表的固有属性,也是反演地表信息和大气温湿度廓线的重要参数.为了获取准确且具有具体物理含义的沙漠地区微波地表发射率,首先选取塔克拉玛干沙漠部分地区为反演区域,根据二元函数泰勒定理,推导了该地区的微波地表发射率与地表温度、地表湿度的线性、非线性函数关系.其次,利用最优控制原理,结合FY-3C微波成像仪的观测亮温资料与辐射传输模式(CRTM)模拟亮温数据,构建了沙漠地区微波地表发射率的线性与非线性反演模型.通过对比发现,利用线性和非线性反演模型得到的地表发射率不仅提高了反演区域亮温的模拟精度,而且模拟亮温的变化趋势也与观测更吻合.最后,对地表发射率的线性和非线性反演模型进行了不同时间与空间上的独立性检验,结果表明:除了反演区域外,在整个塔克拉玛干沙漠地区,两种模型反演的地表发射率仍比原地表发射率模拟亮温更接近观测.总的来说,线性和非线性反演模型对沙漠地区的微波地表发射率反演均具有一定的有效性和普适性,且非线性反演模型优于线性反演模型. 相似文献
3.
土壤湿度在陆气相互作用过程中扮演着重要的角色,是气候、水文、农业、林业等研究中重要的地球物理参数之一.土壤湿度影响地面蒸散,径流、地表反射率、地表发射率以及地表感热和潜热通量,从而对气候有重要影响,它对大气的影响在全球尺度上仅次于海面温度,在陆地尺度其影响甚至超过海面温度.本文介绍了基于EnKF及陆面过程模型的中国区域陆面土壤湿度同化系统（CLSMDAS,China Land Soil Moisture Data Assimilation System）,以及该系统应用于中国区域陆面土壤湿度同化试验的结果.CLSMDAS包括以下几个部分：1）陆面模式采用美国国家大气研究中心NCAR的陆面过程模型Community Land Model Version3.0（简写为CLM3.0）;2）大气驱动场数据中的降水和地面入射太阳辐射数据来自FY2静止气象卫星每小时产品;3）陆面数据同化方法采用EnKF（Ensemble Kalman Filter）同化方法;4）观测数据包括AMSR-E卫星反演土壤湿度产品以及地面土壤湿度观测资料.利用CLSMDAS对2006年6~9月的土壤湿度同化试验结果的分析表明：陆面模式模拟和同化结果都能比较合理地反映出土壤湿度时空分布,同化的土壤湿度分布与2006年8月重庆、四川发生建国以来最严重的夏伏旱有非常好的对应关系,与发生在9月的湖北东部、广西南部等地的干旱区也有非常好的对应关系. 相似文献
4.
湖冰物候影响着区域及全球气候,是全球变化的敏感因子,青藏高原湖泊众多,冻融现场监测数据缺乏,而微波具有对冰水相变敏感、时间分辨率高、历史存档数据长等特点,这对于长时间序列湖冰物候研究具有重要意义.然而,被动微波遥感空间分辨率低、湖泊亮温的精准定位难.论文通过获取AMSR-E/Aqua和AMSR-2/Gcom-W1的亮温数据,构建了基于轨道亮温数据的阈值判别法,通过对青藏高原不同区域和不同大小的青海湖、色林错、哈拉湖以及阿其克库勒湖进行测试研究:与青海湖现场观测对比,湖泊完全冻结日期与开始融化日期最大误差小于3天;与无云光学遥感判别结果相比,4个湖泊的冻融参数误差为2~4天.结果表明,被动微波轨道亮温数据可实现青藏高原地区亚像元级中大型湖泊冻融信息的获取,历史卫星资料可为湖冰物候的监测提供重要的支撑. 相似文献
5.
由辐射传输方程推导星载被动微波遥感SSM/I辐射观测的极化指数PI,凸现地表土壤湿度的变化与距平异常ΔnPI,用特征性指数PI、ΔnPI和散射指数SI来监测大尺度陆地地面的降雨和地区土壤湿度、洪涝汛情的变化.用中国淮河流域7年同月SSM/I观测数据的平均<PI>,洪涝期间的PI,ΔnPI和SI监测该流域2003年7月的洪涝汛情.提出距平异常值指数K分布,定量评估了洪涝汛情的范围和程度. 相似文献
6.
作为一种新兴的被动遥感技术,全极化微波辐射计不仅可以提供海面风速产品,还可以提供海面风向产品.以往利用全极化微波辐射计观测亮温进行海面风场反演仅在晴空条件下进行,本文通过对观测亮温结合台风区域海面风场的分布特征进行分析,验证了全极化微波辐射计具有在台风等恶劣天气条件下进行海面风场观测的能力.基于敏感性分析实验,确定使用6.8 GHz和10.7 GHz等低频通道组合可进行台风区域内海面风场反演.其中,海面风速反演使用基于统计的多元线性回归算法,同时对海面温度、大气水汽含量、云中液态水含量及降水强度等物理量进行反演计算,为海面风向反演做准备.海面风向反演使用物理统计法进行,借鉴散射计风向反演使用的最大似然估计法.通过在全极化辐射传输前向模型中加入降水对大气透过率的影响、设计第三和第四Stokes通道亮温环境影响修正函数,在实现台风区域内海面风向反演的同时减小了反演误差.通过对“云娜”台风境内海面风场进行数值计算,验证了本文反演算法的可行性,并对反演误差的空间分布特征进行了分析.将2004年各台风过程的海面风场反演结果与散射计风场产品进行对比,海面风速和海面风向反演的均方根误差分别为1.64 m·s-1和18.02°. 相似文献
7.
FY3B-MWRI中国区域雪深反演算法改进 总被引：1，自引：0，他引：1
基于2002～2009年全国753个国家基本气象站观测的地面雪深和温度资料,以及同期的高级微波扫描辐射计(Advanced Microwave Scanning Radiometer for EOS,AMSR-E)亮温数据,利用不同频率亮温对雪深的敏感性差异,建立了中国区域雪深半经验统计反演算法.经2006年地面台站观测雪深验证,其反演均方根误差为5.6 cm.具体反演思路如下:根据全国1 km网格土地利用覆盖度数据,结合中国区域的下垫面微波辐射特征,划分成森林、农田、草地和裸地四种主要地物类型;首先建立这四种主要地物类型相对较纯像元下的雪深反演算法,然后利用线性混合像元分解技术,建立微波像元下高精度的雪深反演算法.将本算法分别应用于风云三号B星搭载的微波成像仪(Fengyun-3B/Mcirwoave Radiation Imagery,FY3BMWRI)和AMSR-E数据,进行了2010～2011年冬季雪盖制图,与相应时段的MODIS日积雪产品(MYD10C1)相比,尽管两者数据源有所不同,本算法估算雪盖的精度均达到84%以上.此外,利用本算法和FY3B-MWRI数据在北半球进行了雪当量估算测试,与AMSR-E标准雪当量产品进行了比较,发现二者结果较为一致.但在中国地区,AMSR-E雪当量值明显高于FY3B-MWRI估算值,这与目前已有AMSR-E雪当量产品的验证结果较为一致,FY3B-MWRI雪深估算值与站点观测值更为吻合.该算法已被作为国家卫星气象中心FY3B-MWRI雪深产品的业务化算法. 相似文献
8.
基于AMSR-E的微波波段地表发射率反演——以青藏高原为例 总被引：1，自引：0，他引：1
青藏高原以其独特的特征在气候变化中起着重要的作用,而地表发射率对地表参数和大气参数的准确反演也非常重要,因此本文发展了青藏高原微波波段地表发射率的反演算法.首先通过辐射传输方程对地表发射率的反演算法进行了推导,并利用被动微波一维大气辐射模拟器的模拟数据对算法进行了验证,结果显示了较高的精度.接着结合微波辐射计AMSR-E的亮温数据和MODIS提供的大气廓线数据,利用本文发展的算法反演了青藏高原微波波段的地表发射率.最后,分析了青藏高原地表发射率的时空分布特征:从空间特征上分析,反演结果的空间分布符合青藏高原地表覆盖类型的变化,植被湖泊等可以在反演结果中很明显的显现;从时间特征上分析,在一个月的时间尺度上,发射率随时间变化并不明显,每天的变化值在0.01之内.另外通过对青藏高原裸露地表发射率的时间序列研究发现,地表发射率对降雨有非常敏感的响应.反演结果的合理性表明本文的算法具有可行性,可以利用该算法反演青藏高原的地表发射率并建立长时间序列的地表发射率数据库,为青藏高原其他地球系统参数的遥感反演提供基础,为青藏高原的相关地学研究提供数据支持. 相似文献
9.
本文利用搭载于我国风云三号B星上的微波成像仪(MWRI)观测亮温数据,结合戈达德廓线反演算法,对1102号"桑达"台风地面雨强和降雨云结构进行反演试验.利用AMSR-E业务降水产品对地面雨强反演结果进行了检验,结果表明,MWRI和AMSR-E反演的地面雨强在空间分布上非常吻合,相关性达76%,均方根误差约2.8 mm/h,二者的观测亮温及地面雨强反演结果具有较好的一致性.提取洋面台风雨区的平均水凝物廓线,其垂直结构显示,雨水和可降冰含量丰富,随高度变化明显,且具有明显峰值高度,云水和云冰含量则较少,且随高度变化不明显;当降水增强时,雨水和可降冰各层含量稳定增加,且峰值高度基本保持不变,云水和云冰含量则增幅不稳,且峰值高度有所改变.地面雨强随距台风中心距离的变化阐释了台风的螺旋结构及降水特点,距台风中心距离0.3°和0.6°附近分别出现了地面雨强峰值和次峰值,且66%的降水集中在距台风中心距离1°的空间范围内.MWRI提供的台风地面雨强和降雨云垂直信息具有较高的可信度,对于我们监测台风降水、分析台风降水结构的时空演变特征以及数值预报模式应用等具有重要的参考价值. 相似文献
10.
欧洲遥感卫星（ERS）和美国防卫气象卫星计划（DMSP）空对地微波遥感是当今研究全球大气地表微波散射辐射和反演地球物理与水文参数的主要数据来源．本文研究了ERS－1散射计和SSM／I多通道辐射计在中国海域观测到的后向散射和热辐射数据,论证了同一地区同一时间段内ERS主动散射计和SSM／I被动辐射计观测数据的相关性．用海域主、被动遥感数据的比较,阐述了主、被动联合多通道分析方法有利于监视和分析复杂地表和海面在时间和空间尺度上的变化．用带泡沫散射层的双尺度随机粗糙面的复合模型计算后向散射和热发射,用以数值模拟ERS和SSM／I数据．并讨论了后向散射与热辐射数值模拟结果的相关性,以及与星载微波遥感器实际观测结果的比较． 相似文献
11.
A soil moisture assimilation scheme based on the ensemble Kalman filter using microwave brightness temperature 总被引：1，自引：0，他引：1
This study presents a soil moisture assimilation scheme, which could assimilate microwave brightness temperature directly,
based on the ensemble Kalman filter and the shuffled complex evolution method (SCE-UA). It uses the soil water model of the
land surface model CLM3.0 as the forecast operator, and a radiative transfer model (RTM) as the observation operator in the
assimilation system. The assimilation scheme is implemented in two phases: the parameter calibration phase and the pure soil
moisture assimilation phase. The vegetation optical thickness and surface roughness parameters in the RTM are calibrated by
SCE-UA method and the optimal parameters are used as the final model parameters of the observation operator in the assimilation
phase. The ideal experiments with synthetic data indicate that this scheme could significantly improve the simulation of soil
moisture at the surface layer. Furthermore, the estimation of soil moisture in the deeper layers could also be improved to
a certain extent. The real assimilation experiments with AMSR-E brightness temperature at 10.65 GHz (vertical polarization)
show that the root mean square error (RMSE) of soil moisture in the top layer (0–10 cm) by assimilation is 0.03355 m3 · m−3, which is reduced by 33.6% compared with that by simulation (0.05052 m3 · m−3). The mean RMSE by assimilation for the deeper layers (10–50 cm) is also reduced by 20.9%. All these experiments demonstrate
the reasonability of the assimilation scheme developed in this study. 相似文献
12.
Global Soil Moisture Patterns Observed by Space Borne Microwave Radiometers and Scatterometers 总被引：3，自引：0，他引：3
R. A. M. de Jeu W. Wagner T. R. H. Holmes A. J. Dolman N. C. van de Giesen J. Friesen 《Surveys in Geophysics》2008,29(4-5):399-420
Within the scope of the upcoming launch of a new water related satellite mission (SMOS) a global evaluation study was performed on two available global soil moisture products. ERS scatterometer surface wetness data was compared to AMSR-E soil moisture data. This study pointed out a strong similarity between both products in sparse to moderate vegetated regions with an average correlation coefficient of 0.83. Low correlations were found in densely vegetated areas and deserts. The low values in the vegetated regions can be explained by the limited soil moisture retrieval capabilities over dense vegetation covers. Soil emission is attenuated by the canopy and tends to saturate the microwave signal with increasing vegetation density, resulting in a decreased sensor sensitivity to soil moisture variations. It is expected that the new low frequency satellite mission (SMOS) will obtain soil moisture products with a higher quality in these regions. The low correlations in the desert regions are likely due to volume scattering or to the dielectric dynamics within the soil. The volume scattering in dry soils causes a higher backscatter under very dry conditions than under conditions when the sub-surface soil layers are somewhat wet. In addition, at low moisture levels the dielectric constant has a reduced sensitivity in response to changes in the soil moisture content. At a global scale the spatial correspondence of both products is high and both products clearly distinguish similar regions with high seasonal and inter annual variations. Based on the global analyses we concluded that the quality of both products was comparable and in the sparse to moderate vegetated regions both products may be beneficial for large scale validation of SMOS soil moisture. Some limitations of the studied products are different, pointing to significant potential for combining both products into one superior soil moisture data set. 相似文献
13.
Jasmeet Judge Anthony W. England John R. Metcalfe David McNichol Barry E. Goodison 《Advances in water resources》2008
In this study, a soil vegetation and atmosphere transfer (SVAT) model was linked with a microwave emission model to simulate microwave signatures for different terrain during summertime, when the energy and moisture fluxes at the land surface are strong. The integrated model, land surface process/radiobrightness (LSP/R), was forced with weather and initial conditions observed during a field experiment. It simulated the fluxes and brightness temperatures for bare soil and brome grass in the Northern Great Plains. The model estimates of soil temperature and moisture profiles and terrain brightness temperatures were compared with the observed values. Overall, the LSP model provides realistic estimates of soil moisture and temperature profiles to be used with a microwave model. The maximum mean differences and standard deviations between the modeled and the observed temperatures (canopy and soil) were 2.6 K and 6.8 K, respectively; those for the volumetric soil moisture were 0.9% and 1.5%, respectively. Brightness temperatures at 19 GHz matched well with the observations for bare soil, when a rough surface model was incorporated indicating reduced dielectric sensitivity to soil moisture by surface roughness. The brightness temperatures of the brome grass matched well with the observations indicating that a simple emission model was sufficient to simulate accurate brightness temperatures for grass typical of that region and surface roughness was not a significant issue for grass-covered soil at 19 GHz. Such integrated SVAT-microwave models allow for direct assimilation of microwave observations and can also be used to understand sensitivity of microwave signatures to changes in weather forcings and soil conditions for different terrain types. 相似文献
14.
Using remotely-sensed data, various soil moisture estimation models have been developed for bare soil areas. Previous studies have shown that the brightness temperature (BT) measured by passive microwave sensors were affected by characteristics of the land surface parameters including soil moisture, vegetation cover and soil roughness. Therefore knowledge of vegetation cover and soil roughness is important for obtaining frequent and global estimations of land surface parameters especially soil moisture.In this study, a model called Simultaneous Land Parameters Retrieval Model (SLPRM) that is an iterative least-squares minimization method is proposed. The algorithm estimates surface soil moisture, land surface temperature and canopy temperature simultaneously in vegetated areas using AMSR-E (Advance Microwave Scanning Radiometer-EOS) brightness temperature data. The simultaneous estimations of the three parameters are based on a multi-parameter inversion algorithm which includes model construction, calibration and validation using observations carried out for the SMEX03 (Soil Moisture Experiment, 2003) region in the South and North of Oklahoma.Roughness parameter has also been included in the algorithm to increase the soil parameters retrieval accuracy. Unlike other methods, the SLPRM method works efficiently in all land covers types.The study focuses on soil parameters estimation by comparing three different scenarios with the inclusion of roughness data and selects the most appropriate one. The difference between the resulted accuracies of scenarios is due to the roughness calculation approach.The analysis on the retrieval model shows a meaningful and acceptable accuracy on soil moisture estimation according to the three scenarios.The SLPRM method has shown better performance when the SAR (Synthetic Aperture RADAR) data are used for roughness calculation. 相似文献
15.
Assessment of the suitability of satellite soil moisture products at large scales is urgently needed for numerous climatic and hydrological researches, particularly in arid mountainous watersheds where soil moisture plays a key role in landatmosphere exchanges. This study presents evaluation of the SMOS(L2) and SMAP(L2_P_E and L2_P) products against ground-based observations from the Upstream of the Heihe River Watershed in situ Soil Moisture Network(UHRWSMN) and the Ecological and Hydrological Wireless Sensor Network(EHWSN) over arid high mountainous watersheds, Northwest China.Results show that all the three products are reliable in catching the temporal trend of the in situ observations at both point and watershed scales in the study area. Due to the uncertainty in brightness temperature and the underestimation of effective temperature, the SMOS L2 product and both the SMAP L2 products show "dry bias" in the high, cold mountainous area. Because of the more accurate brightness temperature observations viewing at a constant angle and more suitable estimations of single scattering albedo and optical depth, both the SMAP L2 products performed significantly better than the SMOS product.Moreover, comparing with station density of in situ network, station representation is much more important in the evaluation of the satellite soil moisture products. Based on our analysis, we propose the following suggestions for improvement of the SMOS and SMAP product suitability in the mountainous areas: further optimization of effective temperature; revision of the retrieval algorithm of the SMOS mission to reduce the topographic impacts; and, careful selection of in situ observation stations for better representation of in situ network in future evaluations. All these improvements would lead to better applicability of the SMOS and SMAP products for soil moisture estimation to the high elevation and topographically complex mountainous areas in arid regions. 相似文献
16.
XiaoHang Wen XiaoHan Liao WenPing Yuan XiaoDong Yan ZhiGang Wei HuiZhi Liu JinMing Feng ShiHua Lu WenJie Dong 《中国科学:地球科学(英文版)》2014,57(10):2340-2356
The default fractional vegetation cover and terrain height were replaced by the estimated fractional vegetation cover, which was calculated by the Normalized Difference Vegetation Index(NDVI) of Earth Observing System Moderate-Resolution Imaging Spectroradiometer(EOS-MODIS) and the Digital Elevation Model of the Shuttle Radar Topography Mission(SRTM) system. The near-surface meteorological elements over northeastern China were assimilated into the three-dimensional variational data assimilation system(3DVar) module in the Weather Research and Forecasting(WRF) model. The structure and daily variations of air temperature, humidity, wind and energy fields over northeastern China were simulated using the WRF model. Four groups of numerical experiments were performed, and the simulation results were analyzed of latent heat flux, sensible heat flux, and their relationships with changes in the surface energy flux due to soil moisture and precipitation over different surfaces. The simulations were compared with observations of the stations Tongyu, Naiman, Jinzhou, and Miyun from June to August, 2009. The results showed that the WRF model achieves high-quality simulations of the diurnal characteristics of the surface layer temperature, wind direction, net radiation, sensible heat flux, and latent heat flux over semiarid northeastern China in the summer. The simulated near-surface temperature, relative humidity, and wind speed were improved in the data assimilation case(Case 2) compared with control case(Case 1). The simulated sensible heat fluxes and surface heat fluxes were improved by the land surface parameterization case(Case 3) and the combined case(Case 4). The simulated temporal variations in soil moisture over the northeastern arid areas agree well with observations in Case 4, but the simulated precipitation should be improved in the WRF model. 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. The assimilation datasets generated by this work can be applied to research on climate change and environmental monitoring of arid lands, as well as research on the formation and stability of climate over semiarid areas. 相似文献
17.
Connecting Satellite Observations with Water Cycle Variables Through Land Data Assimilation: Examples Using the NASA GEOS-5 LDAS 总被引：1，自引：1，他引：0
Rolf H. Reichle Gabriëlle J. M. De Lannoy Barton A. Forman Clara S. Draper Qing Liu 《Surveys in Geophysics》2014,35(3):577-606
A land data assimilation system (LDAS) can merge satellite observations (or retrievals) of land surface hydrological conditions, including soil moisture, snow, and terrestrial water storage (TWS), into a numerical model of land surface processes. In theory, the output from such a system is superior to estimates based on the observations or the model alone, thereby enhancing our ability to understand, monitor, and predict key elements of the terrestrial water cycle. In practice, however, satellite observations do not correspond directly to the water cycle variables of interest. The present paper addresses various aspects of this seeming mismatch using examples drawn from recent research with the ensemble-based NASA GEOS-5 LDAS. These aspects include (1) the assimilation of coarse-scale observations into higher-resolution land surface models, (2) the partitioning of satellite observations (such as TWS retrievals) into their constituent water cycle components, (3) the forward modeling of microwave brightness temperatures over land for radiance-based soil moisture and snow assimilation, and (4) the selection of the most relevant types of observations for the analysis of a specific water cycle variable that is not observed (such as root zone soil moisture). The solution to these challenges involves the careful construction of an observation operator that maps from the land surface model variables of interest to the space of the assimilated observations. 相似文献
18.
Improvement of snow depth retrieval for FY3B-MWRI in China 总被引：3，自引：0，他引：3
The primary objective of this work is to develop an operational snow depth retrieval algorithm for the FengYun3B Microwave Radiation Imager(FY3B-MWRI)in China.Based on 7-year(2002–2009)observations of brightness temperature by the Advanced Microwave Scanning Radiometer-EOS(AMSR-E)and snow depth from Chinese meteorological stations,we develop a semi-empirical snow depth retrieval algorithm.When its land cover fraction is larger than 85%,we regard a pixel as pure at the satellite passive microwave remote-sensing scale.A 1-km resolution land use/land cover(LULC)map from the Data Center for Resources and Environmental Sciences,Chinese Academy of Sciences,is used to determine fractions of four main land cover types(grass,farmland,bare soil,and forest).Land cover sensitivity snow depth retrieval algorithms are initially developed using AMSR-E brightness temperature data.Each grid-cell snow depth was estimated as the sum of snow depths from each land cover algorithm weighted by percentages of land cover types within each grid cell.Through evaluation of this algorithm using station measurements from 2006,the root mean square error(RMSE)of snow depth retrieval is about 5.6 cm.In forest regions,snow depth is underestimated relative to ground observation,because stem volume and canopy closure are ignored in current algorithms.In addition,comparison between snow cover derived from AMSR-E and FY3B-MWRI with Moderate-resolution Imaging Spectroradiometer(MODIS)snow cover products(MYD10C1)in January 2010 showed that algorithm accuracy in snow cover monitoring can reach 84%.Finally,we compared snow water equivalence(SWE)derived using FY3B-MWRI with AMSR-E SWE products in the Northern Hemisphere.The results show that AMSR-E overestimated SWE in China,which agrees with other validations. 相似文献
19.
Long-term highly accurate surface soil moisture data of TP(Tibetan Plateau)are important to the research of Asian monsoon and global atmospheric circulation.However,due to the sparse in-situ networks,the lack of soil moisture observations has seriously hindered the progress of climate change researches of TP.Based on the Dual-Channel soil moisture retrieval algorithm and the satellite observation data of AMSR-E(Advanced Microwave Scanning Radiometer for EOS),we have produced the surface soil moisture data of TP from 2003 to 2010 and analyzed the seasonal characteristic of the soil moisture spatial distribution and its multi-year changing trend in area of TP.Compared to the in-situ observations,the accuracy of the soil moisture retrieved by the proposed algorithm is evaluated.The evaluation result shows that the new soil moisture product has a better accuracy in the TP region than the official product of AMSR-E.The spatial distribution of the annual mean values of soil moisture and the seasonal variations of the monthly-averaged soil moisture are analyzed.The results show that the soil moisture variations in space and time are consistent with the precipitation distribution and the water vapor transmission path in TP.Based on the new soil moisture product,we also analyzed the spatial distribution of the changing trend of multi-year soil moisture in TP.From the comparisons with the precipitation changing trend obtained from the meteorological observation sites in TP,we found that the spatial pattern of the changing trend of soil moisture coincides with the precipitation as a whole. 相似文献
20.
A physics-based statistical algorithm for retrieving land surface temperature from AMSR-E passive microwave data 总被引：5，自引：0，他引：5
Mao KeBiao Shi JianCheng Li ZhaoLiang Qin ZhiHao Li ManChun Xu Bin 《中国科学D辑(英文版)》2007,50(7):1115-1120
AMSR-E and MODIS are two EOS (Earth Observing System) instruments on board the Aqua satellite. A regression analysis between the brightness of all AMSR-E bands and the MODIS land surface tem-perature product indicated that the 89 GHz vertical polarization is the best single band to retrieve land surface temperature. According to simulation analysis with AIEM,the difference of different frequen-cies can eliminate the influence of water in soil and atmosphere,and also the surface roughness partly. The analysis results indicate that the radiation mechanism of surface covered snow is different from others. In order to retrieve land surface temperature more accurately,the land surface should be at least classified into three types:water covered surface,snow covered surface,and non-water and non-snow covered land surface. In order to improve the practicality and accuracy of the algorithm,we built different equations for different ranges of temperature. The average land surface temperature er-ror is about 2―3℃ relative to the MODIS LST product. 相似文献