Development of a water clear of sea ice detection algorithm from enhanced SeaWinds/QuikSCAT and AMSR-E measurements

We develop and evaluate water clear of sea ice (open water following ice cover) detection algorithms that make use of Scatterometer Image Reconstruction (SIR) SeaWinds/QuikSCAT (QuikSCAT) backscatter (σ°) and Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) brightness temperature (T_B) measurements. Algorithm validation was performed within Canadian Arctic waters using the Canadian Ice Service Digital Archive (CISDA) ice charts, NASATeam ice concentration estimates, extended AVHRR Polar Pathfinder (APP-x) albedo data, RADARSAT-1 imagery, and MODIS imagery. Results indicate that the temporal evolution of QuikSCAT σ°, AMSR-E polarization ratio (PR18), and AMSR-E vertical spectral gradient ratio (GR3618) can detect water clear of sea ice events, however mean differences due to frequency dependent characteristics of the data (spatial resolution; sensitivity to open water) were apparent. All water clear of sea ice algorithms are in good agreement with the timing and clearing patterns given by the CISDA. The QuikSCAT algorithm provided a more representative ice edge and more details on the ice clearing process due to higher spatial resolution, however, transient clearing events were better represented by the AMSR-E PR(18) or (GR3618) algorithm. By exploiting the strengths of each sensor, we found that a QuikSCAT and AMSR-E fused algorithm provide improved open water area estimates by as much as 11%. The fusion of QuikSCAT and AMSR-E PR(18) yielded in the most spatially representative open water detection. The residual surface of the water clear of sea ice algorithms was found to provide another measure of the average September minimum pan-Arctic sea ice extent within 6% of the NASATeam algorithm estimates.     

地形坡面对被动微波遥感影响的试验研究

地表起伏所形成的倾斜表面,特别是在山区,受地形坡度和坡向变化的影响,地表的微波辐射特征较之平坦地表发生明显变化。基于地基微波辐射地形试验,模拟星载被动微波辐射计AMSR\|E的观测参数,通过建立地形坡面的地貌微缩景观进行观测,探索地表斜坡对被动微波辐射特征的影响,用AIEM模型 和 Fresnel 方程分别模拟裸土地形坡面的微波辐射特征。结果表明,倾斜坡面对被动微波辐射的亮度温度产生了10~15 K的偏差,由坡度形成的本地入射角改变了地表的有效发射率,并随坡向的变化发生微波极化旋转。经试验数据和模型模拟结果对比,认为AIEM 在考虑了表面粗糙度影响时可以较好地模拟地形坡面的被动微波辐射特征。     

Study of Monitoring Snow Cover Using GOESGeostationary Satellite and AMSR-E

Snow cover is an important variable in global climate change research and meteorological applications.In this paper,we use visible and infrared bands of GOES-12(Geostationary Operational Environmental Satellite) geostationary satellite and AMSR-E(Advanced Microwave Scanning Radiometer for the Earth Observing System) to monitor snow cover in east of USA and discuss the application method.Snow index,multi-temporal data combination and decision-tree classification are used in monitoring the snow cover.Temporal stability test and climatic surface temperature test are used to further discriminate snow and cloud.Snow cover result also combined with AMSR-E daily snow products to remove the cloud effect.This method performs well in monitoring snow cover which removes more cloud and get more information of the surface.Using NOAA/NESDIS IMS daily snow cover products and station observations to compare and validate the snow cover images,the accuracy is about 86% and 90%,respectively.The operational geostationary satellites of China are FY-2D/2E,which have the same band information of GOES.This method can be used in monitoring snow cover of China in further study.     

基于SPOT-VGT可见光/短波红外波段数据对AMSR-E土壤湿度的降尺度研究

基于SPOT-VGT数据,由短波红外、红和蓝波段反射率计算了表征地表土壤湿度的可见光—短波红外干旱指数(VSDI),通过对1km空间分辨率的VSDI影像进行空间升尺度处理,采用多种函数建立了25km空间分辨率AMSR-E土壤湿度数据与VSDI指数的关系,发现二者关系最符合S型曲线模型,拟合残差在空间上呈现随机分布的特征。基于S曲线函数关系下的1km预测土壤湿度和残差值,对AMSR-E土壤湿度进行降尺度模拟,得到1km空间分辨率的土壤湿度。将原始AMSR-E土壤湿度和实测数据对降尺度结果分别比较验证后,表明基于该方法获得的土壤湿度模拟精度较高。     

SZ-4飞船辐射模态沙漠地区数据模拟验证

对SZ-4飞船辐射模态获取的亮温数据,进行了模拟研究。辐射模态的工作频率范围为6．6～37GHz,对应的地面像元尺寸为6～32km。以新疆塔中地区为研究对象,通过分析被动微波遥感中裸露地表的辐射特性,基于裸露地表的辐射模型（AIEM模型）和地表的实测属性数据来模拟该地区的辐射亮温值,并将其与SZ-4辐射模态和AMSR-E实际的亮温观测值进行了比较,通过对正演结果的比较分析检验了SZ-4亮温数据的质量及其在沙漠地区反演地表参数的潜力。     

基于可见光红外与被动微波遥感的土壤水分协同反演

利用MODIS传感器的可见光、红外波段数据反演土壤水分在一定时段内的基准值,用被动微波传感器AMSR-E数据反演其变化量,提出将被动微波遥感数据与热红外遥感数据在模型级别协同反演大范围地表土壤水分的方法,这样每天可输出1 km×1 km的升、降轨土壤水分反演结果.以新疆为研究区,对上述方法进行了土壤水分协同反演实验,以地面实测数据为参考的验证结果表明,所提模型得到的土壤水分值与地面实测值之间相关性较高,均方根误差较小,优于单一传感器数据的反演结果,可更好地满足新疆土壤水分监测的需求.     

利用AMSR-E资料反演实时海面气象参数的个例

利用2005年1月的AMSR-E卫星资料作为一个例子,探讨了AMSR-E的12个通道的亮温与海表温度、海面气温、湿度及风速4个气象参数的关系,把亮温通道分3大类组合分别进行参数模拟,确定模拟这4个气象参数最合理的通道组合,并利用多参数回归方法建立海表温度、海面气温、湿度及风速与亮温之间的经验关系.反演结果与TAO浮标实测资料进行了比较,实时海表温度、海面气温、湿度及风速的均方根差分别为0.53℃、0.74℃、3.2%和1.1m/s.是一个利用卫星资料同时反演四个参数(海表温度、海面气温、湿度及风速)的成功例子,为计算海气热通量提供了数据,并为实时观测和研究气候变化提供了一种简单而有效的方法.     

基于被动微波遥感的2008年广东省春季低温与典型作物寒害研究

以2008年作物寒害为例,基于AMSR\|E亮温和地面气象温度数据构建了广东省气温反演模型,平均误差为2.5 ℃。寒害期间,全省最低气温均小于9 ℃,大部分地区降温幅度在5~15 ℃之间,降温持续天数均在10 d以上。综合最低气温、降温幅度和降温持续天数3个重要因子,构建了广东省寒害指数。结果显示,寒害程度从北部山区向南部沿海平原逐步递减:严重寒害主要发生在北部山区,较严重寒害发生在中部地区,以平行于海岸线的狭长带状形式分布,一般寒害和无寒害区域主要分布在沿海地区。其中,严重和较严重寒害约占省域总面积的40.1%,遭受严重寒害和较严重寒害的典型亚热带作物园地面积占总园地面积的49.61%。     

Soil moisture estimation through ASCAT and AMSR-E sensors: An intercomparison and validation study across Europe

Global soil moisture products retrieved from various remote sensing sensors are becoming readily available with a nearly daily temporal resolution. Active and passive microwave sensors are generally considered as the best technologies for retrieving soil moisture from space. The Advanced Microwave Scanning Radiometer for the Earth observing system (AMSR-E) on-board the Aqua satellite and the Advanced SCATterometer (ASCAT) on-board the MetOp (Meteorological Operational) satellite are among the sensors most widely used for soil moisture retrieval in the last years. However, due to differences in the spatial resolution, observation depths and measurement uncertainties, validation of satellite data with in situ observations and/or modelled data is not straightforward. In this study, a comprehensive assessment of the reliability of soil moisture estimations from the ASCAT and AMSR-E sensors is carried out by using observed and modelled soil moisture data over 17 sites located in 4 countries across Europe (Italy, Spain, France and Luxembourg). As regards satellite data, products generated by implementing three different algorithms with AMSR-E data are considered: (i) the Land Parameter Retrieval Model, LPRM, (ii) the standard NASA (National Aeronautics and Space Administration) algorithm, and (iii) the Polarization Ratio Index, PRI. For ASCAT the Vienna University of Technology, TUWIEN, change detection algorithm is employed. An exponential filter is applied to approach root-zone soil moisture. Moreover, two different scaling strategies, based respectively on linear regression correction and Cumulative Density Function (CDF) matching, are employed to remove systematic differences between satellite and site-specific soil moisture data. Results are shown in terms of both relative soil moisture values (i.e., between 0 and 1) and anomalies from the climatological expectation.Among the three soil moisture products derived from AMSR-E sensor data, for most sites the highest correlation with observed and modelled data is found using the LPRM algorithm. Considering relative soil moisture values for an ~ 5 cm soil layer, the TUWIEN ASCAT product outperforms AMSR-E over all sites in France and central Italy while similar results are obtained in all other regions. Specifically, the average correlation coefficient with observed (modelled) data equals to 0.71 (0.74) and 0.62 (0.72) for ASCAT and AMSR-E-LPRM, respectively. Correlation values increase up to 0.81 (0.81) and 0.69 (0.77) for the two satellite products when exponential filtering and CDF matching approaches are applied. On the other hand, considering the anomalies, correlation values decrease but, more significantly, in this case ASCAT outperforms all the other products for all sites except the Spanish ones. Overall, the reliability of all the satellite soil moisture products was found to decrease with increasing vegetation density and to be in good accordance with previous studies. The results provide an overview of the ASCAT and AMSR-E reliability and robustness over different regions in Europe, thereby highlighting advantages and shortcomings for the effective use of these data sets for operational applications such as flood forecasting and numerical weather prediction.     

Landslide susceptibility mapping using downscaled AMSR-E soil moisture: A case study from Cleveland Corral, California, US

As soil moisture increases, slope stability decreases. Remotely sensed soil moisture data can provide routine updates of slope conditions necessary for landslide predictions. For regional scale landslide investigations, only remote-sensing methods have the spatial and temporal resolution required to map hazard increases. Here, a dynamic physically-based slope stability model that requires soil moisture is applied using remote-sensing products from multiple Earth observing platforms. The resulting landslide susceptibility maps using the advanced microwave scanning radiometer (AMSR-E) surface soil moisture are compared to those created using variable infiltration capacity (VIC-3L) modeled soil moisture at Cleveland Corral landslide area in California, US. Despite snow cover influences on AMSR-E surface soil moisture estimates, a good relationship between the downscaled AMSR-E's surface soil moisture and the VIC-3L modeled soil moisture is evident. The AMSR-E soil moisture mean (0.17 cm³/cm³) and standard deviation (0.02 cm³/cm³) are very close to the mean (0.21 cm³/cm³) and standard deviation (0.09 cm³/cm³) estimated by VIC-3L model. Qualitative results show that the location and extent of landslide prone regions are quite similar. Under the maximum saturation scenario, 0.42% and 0.49% of the study area were highly susceptible using AMSR-E and VIC-3L model soil moisture, respectively.