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.     

The contribution of AMSR-E 18.7 and 10.7 GHz measurements to improved boreal forest snow water equivalent retrievals

Four seasons (2004–2007) of snow surveys across the boreal forest of northern Manitoba were utilized to determine relationships between vertically polarized Advanced Microwave Scanning Radiometer (AMSR-E) brightness temperatures (T_B) and ground measurements of snow water equivalent (SWE). Regression analysis identified moderate strength, yet statistically significant relationships between SWE and T_B differences (36.5–18.7; 36.5–10.7; 18.7–10.7) for individual seasons. When multiple seasons were considered collectively, however, the 36.5–18.7 and 36.5–10.7 differences were insignificant because the seasonal linear relationships shifted from year to year over the same T_B range regardless of SWE. This inter-seasonal consistency in T_B was explained through significant correlations with vegetation density as characterized by a MODIS-derived forest transmissivity dataset. More encouraging results were found for the 18.7–10.7 difference: the relationship with SWE remained statistically significant when multiple years were considered together, and the 18.7–10.7 difference was not significantly associated with vegetation density. Additional snow survey data from the Northwest Territories (2005–2007) were used to verify the 18.7–10.7 relationship with SWE across the northern boreal forest. These results suggest use of the 18.7–10.7 T_B difference, rather than the traditional 36.5–18.7 T_B difference, is necessary to capture inter-seasonal SWE variability across forested regions.     

Validation of AMSR-E soil moisture using L-band airborne radiometer data from National Airborne Field Experiment 2006

AMSR-E has been extensively evaluated under a wide range of ground and climate conditions using in situ and aircraft data, where the latter were primarily used for assessing the T_B calibration accuracy. However, none of the previous work evaluates AMSR-E performance under the conditions of flood irrigation or other forms of standing water. Also, it should be mentioned that global soil moisture retrievals from AMSR-E typically utilize X-band data. Here, C-band based AMSR-E soil moisture estimates are evaluated using 1 km resolution retrievals derived from L-band aircraft data collected during the National Airborne Field Experiment (NAFE'06) field campaign in November 2006. NAFE'06 was conducted in the Murrumbidgee catchment area in southeastern Australia, which offers diverse ground conditions, including extensive areas with dryland, irrigation, and rice fields. The data allowed us to examine the impact of irrigation and standing water on the accuracy of satellite-derived soil moisture estimates from AMSR-E using passive microwave remote sensing. It was expected that in fields with standing water, the satellite estimates would have a lower accuracy as compared to soil moisture values over the rest of the domain. Results showed sensitivity of the AMSR-E to changes in soil moisture caused by both precipitation and irrigation, as well as good spatial (average R = 0.92 and RMSD = 0.049 m³/m³) and temporal (R = 0.94 and RMSD = 0.04 m³/m³) agreement between the satellite and aircraft soil moisture retrievals; however, under the NAFE'06 ground conditions, the satellite retrievals consistently overestimated the soil moisture conditions compared to the aircraft.     

青藏高原地表微波比辐射率的反演与分析

利用Aqua卫星上同时搭载的AMSR-E和MODIS提供同步观测的微波和红外资料,反演青藏高原地区陆面微波比辐射率。结合MODIS反演的地表类型资料,分析该地区陆地微波比辐射率随地表类型、微波频率、不同时间尺度的变化特征。结果表明：该地区主要的3种地表类型中,草地比辐射率普遍高于裸地和灌木丛,并且后两者比辐射率的量值和...     

区域尺度雪深验证数据的加密研究

在区域尺度的微波遥感雪深反演中,由于气象站点的稀少和分布不均匀,导致雪深数据真实性检验难以进行,本文提出使用克里金插值法来解决这一问题,并证明了插值结果作为AMSR-E雪深反演验证数据的可靠性和可行性。     

Hydrological consistency using multi-sensor remote sensing data for water and energy cycle studies

A multi-sensor/multi-platform approach to water and energy cycle prediction is demonstrated in an effort to understand the variability and feedback of land surface and atmospheric processes over large space and time scales. Remote sensing-based variables including soil moisture (from AMSR-E), surface heat fluxes (from MODIS) and precipitation rates (from TRMM) are combined with North American Regional Reanalysis derived atmospheric components to examine the degree of hydrological consistency throughout these diverse and independent hydrologic data sets. The study focuses on the influence of the North American Monsoon System (NAMS) over the southwestern United States, and is timed to coincide with the SMEX04 North American Monsoon Experiment (NAME). The study is focused over the Arizona portion of the NAME domain to assist in better characterizing the hydrometeorological processes occurring across Arizona during the summer monsoon period. Results demonstrate that this multi-sensor approach, in combination with available atmospheric observations, can be used to obtain a comprehensive and hydrometeorologically consistent characterization of the land surface water cycle, leading to an improved understanding of water and energy cycles within the NAME region and providing a novel framework for future remote observation and analysis of the coupled land surface-atmosphere system.     

AMSR-E亮温数据与MODIS陆表分类产品结合反演全球陆表温度

AMSR-E被动微波传感器获取的亮温数据与MODIS陆表分类产品(MOD12)相结合,将全球陆表分为16类,并假设每种类型的地表在各个被动微波通道具有较一致的发射率,在此基础上针对每种陆表类型分别建立了陆表温度反演算法。在算法的建立过程中,为了避免混合像元以及冻土、积雪发射率不确定性带来的影响,仅对单一地表类型占90%以上以及MODIS陆表温度产品高于273K的被动微波像元进行回归。同时,考虑到降雨对回归结果的影响,在数据选择中加入了降雨判识,在被动微波亮温数据中除去了降雨像元。利用上述算法,用2004年1～10月的全球部分地区AMSR-E数据在MODIS陆表分类产品的基础上对每种地表类型分别进行了陆表温度反演,并与MODIS陆表温度产品进行对比,结果显示相关性较好,均方根误差为2～4 K。     

星载微波辐射计AMSR-E在台风分析中的应用

基于洋面上的微波辐射传输模型,采用多元线性回归算法,建立了海洋-大气参数的反演算式,并对其进行了验证。在此基础上,根据AMSR-E的反演结果,结合微波图像,对2005年“麦莎”和“泰利”两次台风的发生发展过程进行了初步分析和比较。结果表明,反演算式的精度接近于美国雪冰数据中心公布的业务产品,反演结果比较符合实际情况,微波图像可以较好地反映台风发生发展过程中的变化特征。     

MWRI与AMSR-亮温数据在极地冰盖区的对比分析

作为我国第一个星载微波遥感仪器,搭载在FY\|3B上的微波成像仪(MWRI)的数据质量和应用前景引起了广大科研工作者的普遍关注。为充分了解该传感器的数据质量,采用星星交叉对比的方法,以极地为研究区域,AMSR\|E亮温数据作为对比值,开展针对MWRI的亮温数据处理和评价等工作。研究结果表明:MWRI与AMSR\|E对极地区域的观测数据基本一致,整体趋势略偏小1.64 K;亮温差异随目标亮温的变化而变化,偏差值与亮温值呈正相关;不同通道比较,10 v和36 v对比结果最差,偏差绝对值和均方根误差均超过3 K;10、18和23 h通道对比结果最好,平均偏差绝对值小于0.8 K,均方根误差小于1.4 K,小于3 K的误差比例在98%以上;从线性回归分析所得斜率、截距、可决系数和均方根误差值显示,两者点对点数据值存在一定的偏差,但在数值整体趋势上具有明显的一致性,H极化比V极化对比结果更好;不同间隔的观测时次结果表明,间隔时间越长,V极化的对比差异越大。