A simple retrieval method of land surface temperature from AMSR-E passive microwave data—A case study over Southern China during the strong snow disaster of 2008

The analysis of the passive microwave radiance transfer equation certifies that there is a linear relationship between satellite-generated brightness temperatures (BT) and in situ observation temperature and that land surface temperature (LST) is largely influenced by vegetation cover conditions. Microwave polarization difference index (MPDI) is an effective indicator for characterizing the land surface vegetation cover density. Based on the analysis of LST models from AMSR-E BT with 6.9 GHz MPDI intervals at 0.04, 0.02 and 0.01, respectively, this paper developed a simplified LST regression model with MPDI-based five land cover types, combining observation temperatures from 86 meteorological observation stations. The study shows that smaller MPDI intervals can obtain higher accuracy of AMSR-E LST simulation, and that the combination of HDF Explorer and ArcGIS software was useful for automatically processing the pixel latitude, longitude and BT information from the AMSR-E HDF imagery files. The RMSE of the five LST simulation algorithms is between 1.47 and 1.92 °C, with an average LST retrieval error of 0.91–1.30 °C. Besides, only 7 polarization bands and 5 land surface types are required by the proposed simplified model. The new LST simulation models appears to be more effective for producing LST compared to past most studies, of which the accuracy used to be more than 2 °C. This study is one of the rare applications that combine the meteorological observation temperature with MPDI to produce the LST regression analysis algorithms with less RMSE from AMSR-E data. The results can be referred to similar areas of the world for LST retrieval or land surface process research, in particular under extreme bad weather conditions.     

2002—2011年南极海冰变化的遥感分析

基于2002—2011年南极地区AMSR-E逐日海冰密集度数据, 计算相应时间段内的海冰外缘线和海冰面积, 分析了南极地区这10年来各时间尺度上的海冰变化, 揭示了海冰变化的时空特征。结果表明: 2002— 2011年南极海冰外缘线、海冰面积分别增加了3.64%、3.8%, 总体上呈现增加的趋势, 其中2008年海冰面积最大。罗斯海、西太平洋和威德尔海的海冰面积呈现增加趋势, 而印度洋和别林斯高晋海/阿蒙森海的海冰面积则趋于减小。南极海冰面积一般夏季最小、冬季最大, 相同季节海冰面积变化波动较小, 不同海区只是变化范围不同。南极一年冰增长速度较低, 平均每年增加约0.1×106 km2, 且大范围地分布在南极大陆(除威德尔海外)周围。多年冰平均每年减少0.05×106 km2, 且多处于威德尔海。海冰面积变化与气温有负相关关系。     

基于MEMLS模型的积雪深度反演方法

利用被动微波遥感反演积雪深度一直是积雪遥感领域中的研究热点。在现有的积雪深度反演算法中,NASA算法因其简洁、易于扩展的特点,成为应用最为广泛的算法。但NASA算法存在着一定不足:首先,由于NASA算法基于线性拟合得出,在应用到其他研究区域时需要对反演公式进行重新拟合,适用范围受到一定限制;其次,由于算法中引入的19GHz与37GHz的亮温差在雪深达到一定范围时会达到饱和,因此算法会低估积雪深度。本文针对现有反演算法的不足之处,结合蚁群智能算法的特点,发展了基于蚁群算法的积雪深度反演算法;此外,针对NASA算法中存在的雪深低估问题,引入了AMSR-E10.7GHz亮温数据,对算法进行了改进。利用MEMLS模型的模拟数据与AMSR-E辐射亮温数据对算法进行实验,并采用实测数据与AMSR-E雪水当量产品对算法的反演精度进行评价。结果表明,两种积雪深度反演算法均是可行的,反演精度与现有产品相比有较为明显的改进。     

Digitizing the thermal and hydrological parameters of land surface in subtropical China using AMSR-E brightness temperatures

ABSTRACT

Digitizing the land surface temperature (T_s) and surface soil moisture (m_v) is essential for developing the intelligent Digital Earth. Here, we developed a two parameter physical-based passive microwave remote sensing model for jointly retrieving T_s and m_v using the dual-polarized T_b of Aqua satellite advanced microwave scanning radiometer (AMSR-E) C-band (6.9 GHz) based on the simplified radiative transfer equation. Validation using in situ T_s and m_v in southern China showed the average root mean square errors (RMSE) of T_s and m_v retrievals reach 2.42 K (R² = 0.61, n = 351) and 0.025 g cm^?3 (R² = 0.68, n = 663), respectively. The results were also validated using global in situ T_s (n = 2362) and m_v (n = 1657) of International Soil Moisture Network. The corresponding RMSE are 3.44 k (R² = 0.86) and 0.039 g cm^?3 (R² = 0.83), respectively. The monthly variations of model-derived Ts and mv are highly consistent with those of the Moderate Resolution Imaging Spectroradiometer T_s (R² = 0.57; RMSE = 2.91 k) and ECV_SM m_v (R² = 0.51; RMSE = 0.045 g cm^?3), respectively. Overall, this paper indicates an effective way to jointly modeling T_s and m_v using passive microwave remote sensing.     

基于AMSR-E数据的多年冰密集度反演算法研究

In recent years, the rapid decline of Arctic sea ice area(SIA) and sea ice extent(SIE), especially for the multiyear(MY) ice, has led to significant effect on climate change. The accurate retrieval of MY ice concentration retrieval is very important and challenging to understand the ongoing changes. Three MY ice concentration retrieval algorithms were systematically evaluated. A similar total ice concentration was yielded by these algorithms, while the retrieved MY sea ice concentrations differs from each other. The MY SIA derived from NASA TEAM algorithm is relatively stable. Other two algorithms created seasonal fluctuations of MY SIA, particularly in autumn and winter. In this paper, we proposed an ice concentration retrieval algorithm, which developed the NASA TEAM algorithm by adding to use AMSR-E 6.9 GHz brightness temperature data and sea ice concentration using 89.0GHz data. Comparison with the reference MY SIA from reference MY ice, indicates that the mean difference and root mean square(rms) difference of MY SIA derived from the algorithm of this study are 0.65×106 km2 and0.69×106 km2 during January to March, –0.06×106 km2 and 0.14×106 km2 during September to December respectively. Comparison with MY SIE obtained from weekly ice age data provided by University of Colorado show that, the mean difference and rms difference are 0.69×106 km2 and 0.84×106 km2, respectively. The developed algorithm proposed in this study has smaller difference compared with the reference MY ice and MY SIE from ice age data than the Wang's, Lomax' and NASA TEAM algorithms.     

一种基于AMSR-E的地表温度半经验反演模型

基于被动微波辐射传输方程,结合De Jeu建立的透过率与微波极化差异指数的通用关系式,以及Smiths建立的地表温度与大气总可降水量的经验关系,构建了一套基于AMSR-E影像的地表温度半经验反演模型,该模型无需借助其他辅助数据,便可从AMSR-E 6.9GHz和10.7GHz两个波段的亮度温度模拟得到地表温度变量。对2009年我国地表温度进行实例模拟和验证,结果显示,该地表温度模型的平均反演精度达到2.54℃（R2=0.79）,是一种简单有效的被动微波遥感地表温度模拟方法。     

应用AMSR-E 89GHz遥感数据反演北极多年冰密集度

被动微波辐射计AMSR-E在89GHz频段的空间分辨率几乎是其它低频段的2倍。到目前为止,已经有成熟的算法采用AMSR-E 89GHz频段的数据来反演整体海冰的密集度,而本文根据89GHz频段亮温数据针对一年冰、多年冰以及无冰水面的不同特性,提出了一种反演多年冰密集度的方法(AC)。计算了2007年北极的整体海冰和多年冰密集度,并与NASA TEAM算法(NT)的结果作了比较。对于整体海冰密集度,两种算法反演的结果基本一致。从日平均上来看,AC算法得到的整体海冰覆盖面积只比NT算法多25万km2。但是对于多年冰的反演结果,AC算法得到的结果比NT的结果要多出146万km2,冬季差别尤其大。造成这种差别的原因可能是AC算法对于北极表面气温的变化比较敏感。另外,AC算法计算得到2007年最大海冰覆盖面积为1400万km2,最小海冰覆盖面积为400万km2,这与其它研究结果一致。     

Relationships among remotely sensed soil moisture,precipitation and landslide events

Landslides are triggered by earthquakes, volcanoes, floods, and heavy continuous rainfall. For most types of slope failure, soil moisture plays a critical role because increased pore water pressure reduces the soil strength and increases stress. However, in-situ soil moisture profiles are rarely measured. To establish the soil moisture and landslide relationship, a qualitative comparison among soil moisture derived from AMSR-E, precipitation from TRMM and major landslide events was conducted. This study shows that it is possible to estimate antecedent soil moisture conditions using AMSR-E and TRMM satellite data in landslide prone areas. AMSR-E data show distinct annual patterns of soil moisture that reflect observed rainfall patterns from TRMM. Results also show enhanced AMSR-E soil moisture and TRMM rainfall prior to major landslide events in landslide prone regions of California, U.S.; Leyte, Philippines; and Dhading, Nepal.     

基于MODIS和AMSR-E反演京津冀地区大气水汽含量及比较

为了长时间、大范围获取水汽数值,利用2005~2008年光学遥感的MODIS近红外、红外水汽产品,以及微波遥感AMSR-E数据,2种方法反演水汽。微波AMSR-E亮温数据采用Merritt N.Deeter(2007)亮温极化差方法,选取18.7GHz和23.8GHz 2个波段,得到AMSR-E升轨、降轨大气水汽数值。以京津冀地区为研究区域,通过地统计相关性分析、时间序列分析、年际间变化分析,可知2种方法4种资料反演的大气水汽数值的R2都达到0.95,时间分布符合中国雨带移动规律,空间分布不均。MODIS数据反演值比AMSR-E值要低,得到2种方法反演水汽的各自优缺点。     

两种星载微波辐射计被动亮温数据的交叉定标

针对两种不同星载微波辐射计在长时间序列的变化探测研究中存在时空间差异问题,应用时空匹配交叉定标方法,选取亚马逊雨林作为高亮温地面观测目标和格陵兰地区为低亮温地面观测目标,采用原始轨道扫描数据对微波成像仪和先进微波扫描辐射计的时空上相匹配的5通道水平和垂直极化亮度温度数据进行交叉定标。研究结果表明,校正决定系数都在0.99以上,均方根误差均小于1.3K,定标系数精确度高,匹配数据拟合程度非常好。精确的交叉定标系数,对于我国FY3号以及后续卫星等微波定标参数确定、同国外卫星的长时间序列衔接以及数据质量的评估等具有重要意义。