A coupled Land Atmosphere Radiative-Transfer Model (LA-RTM) for multi-frequency passive microwave remote sensing: development and application over Wakasa Bay and the Tibetan Plateau

Multi-frequency passive microwave sensors herald a new dawn for combined land and atmosphere observations. Past efforts to utilize microwave remote sensing of atmosphere and land surface have proceeded by treating these two areas in a parallel fashion. In this research, a unified approach is presented that can be used to improve both quantitative and qualitative understanding of land and atmosphere constituents. A coupled Land Atmosphere Radiative-Transfer Model (LA-RTM) that can be used as a forward model in retrieval algorithms, or as an observation operator in data-assimilation schemes is developed. This model is validated using data collected during the 2003 Advanced Microwave Scanning Radiometer on board the Earth Observing Satellite (AMSR/AMSR-E) validation experiment over Wakasa Bay in Japan and the Coordinated Enhanced Observing Period (CEOP) dataset for the Tibetan Plateau collected in April and August 2004. These datasets comprise satellite (AMSR-R) observations, ground-based microwave radiometers (GBMRs) and radiosonde atmosphere soundings. In both sites, good agreement between simulated and observed brightness temperatures is demonstrated. To facilitate fast retrievals, a retrieval scheme is proposed that uses LA-RTM as a forward model to generate a look-up table (LUT) for varying land-surface conditions. This LUT is used to retrieve soil-moisture and surface-roughness conditions for the target site. Using this scheme, retrieved soil moisture at in situ stations was shown to have fairly good agreement with observations.     

Spring thaw classification based on AMSR-E brightness temperature in the central Tibetan Plateau

Changes in soil freeze/thaw dynamics in the Tibetan Plateau (TP) had significant influences on regional hydrology and ecosystem processes. We developed an algorithm to detect spring thaw onset in the central TP using microwave brightness temperature (T_b) data. We assumed that the T_b at lower frequencies is more sensitive to changes in soil freeze/thaw status, while the T_b at higher frequencies is subjected more to scattering effects of snow cover and vegetation. Therefore, the standard deviation of brightness temperature at 6-GHz horizontal polarization was used to detect soil F/T status, and a scattering index based on higher frequencies was used to constrain the scattering effects of snow cover. The algorithm was calibrated and validated with surface ground temperature and daily minimum air temperature. Results showed that our algorithm had a mean bias of 3.7 ~ 17.6 days and a RMSE of 7.5 ~ 19.7 days, and this method behaved better than the previous one based on all-frequency standard deviation of microwave T_b, which had a mean bias of 8.3 ~ 41.2 days and a RMSE of 13.4 ~ 27.6 days. Further validation is needed over more extensive area with diverse surface conditions.     

An evaluation of AMSR-E derived soil moisture over Australia

This paper assesses remotely sensed near-surface soil moisture over Australia, derived from the passive microwave Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) instrument. Soil moisture fields generated by the AMSR-E soil moisture retrieval algorithm developed at the Vrije Universiteit Amsterdam (VUA) in collaboration with NASA have been used in this study, following a preliminary investigation of several other retrieval algorithms. The VUA-NASA AMSR-E near-surface soil moisture product has been compared to in-situ soil moisture data from 12 locations in the Murrumbidgee and Goulburn Monitoring Networks, both in southeast Australia. Temporally, the AMSR-E soil moisture has a strong association to ground-based soil moisture data, with typical correlations of greater than 0.8 and typical RMSD less than 0.03 vol/vol (for a normalized and filtered AMSR-E timeseries). Continental-scale spatial patterns in the VUA-NASA AMSR-E soil moisture have also been visually examined by comparison to spatial rainfall data. The AMSR-E soil moisture has a strong correspondence to precipitation data across Australia: in the short term, maps of the daily soil moisture anomaly show a clear response to precipitation events, and in the longer term, maps of the annual average soil moisture show the expected strong correspondence to annual average precipitation.     

青藏高原GPM降水数据空间降尺度研究

高分辨率的降水数据对于复杂地形区的精确水文预报和气候模拟至关重要.利用青藏高原的植被、地形和地理位置特征,建立了与降水的回归模型,将全球降水测量(GPM)IMERG的年降水量从0.1°降尺度至1 km,通过分解年降水获得月降水量数据,并用气象站点的实测数据进行校准.得出以下结论:①GPM IMERG月降水量略大于地面观...     

Sub-pixel evaluation of sea ice roughness using AMSR-E data

The rapidly changing sea ice regime in the Arctic has necessitated an evaluation of sea ice roughness at smaller scales than those provided by satellites. In this article, we evaluate sub-pixel (<5.4 km) sea ice roughness using AMSR-E brightness temperature (T_b) 89 GHz data and in situ physical roughness data acquired using a helicopter-based laser system in the southern Beaufort Sea during April–June of 2008. The analysis shows a statistically significant correlation (r² = 0.61, P-value < 0.05, regression line slope = –79.93) of T_b at horizontal polarization (H-pol) decreasing with increasing root mean square (RMS) heights. These results suggest that 89 H-pol is more sensitive (than vertical polarization (V-pol)) to the changes in physical roughness. The temporal evolution in AMSR-E T_b values at 89 H-pol and 89 V-pol shows a decrease from April to June. We conclude that solely the AMSR-E T_b at 5.4 km is insufficient to fully account for the changes occurring in the dielectric properties and surface roughness of sea ice at sub-pixel level of 1–4 km during April–June.     

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...     

Lake-area mapping in the Tibetan Plateau: an evaluation of data and methods

Lake area derived from remote-sensing data is a primary data source, because changes in lake number and area are sensitive indicators of climate change. These indicators are especially useful when the climate change is not convoluted with a signal from direct anthropogenic activities. The data used for lake-area mapping is important, to avoid introducing unnecessary uncertainty into long-term trends of lake-area estimates. The methods for identifying waterbodies from satellite data are closely linked to the quality and efficiency of surface-water differentiation. However, few studies have comprehensively considered the factors affecting the selection of data and methods for mapping lake area in the Tibetan Plateau (TP), nor of evaluating their consequences. This study tests the dominant data sets (Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) data) and the methods for automated waterbody mapping on 14 large lakes (>500 km²) distributed across different climate zones of the TP. Seasonal changes in lake area and data availability from Landsat imagery are evaluated. Data obtained in October is optimal because in this month the lake area is relatively stable. The data window can be extended to September and November if insufficient data is available in October. Grouping data into three-year bins decreases the effects of year-to-year seasonal variability and provides a long-term trend that is suitable for time series analysis. The Landsat data (Multispectral Scanner, MSS; Thematic Mapper, TM; Enhanced Thematic Mapper Plus, ETM+; and Operational Land Imager, OLI) and MODIS data (MOD09A1) showed good performance for lake-area mapping. The Otsu method is used to determine the optimal threshold for distinguishing water from non-water features. Several water extraction indices, namely NDWI_McFeeters, NDWI_Xu, and AWEI_non-shadow, yielded high overall classification accuracy (92%), kappa coefficient (0.83), and user’s accuracy (~90%) for lake-water classification using Landsat data. The MODIS data using NDWI_McFeeters and NDWI_Xu showed consistent lake area (r² = 0.99) compared with Landsat data on the corresponding date with root mean square error (RMSE) values of 86.87 and 103.33 km² and mean absolute error (MAE) values of 25.7 and 29.04 km², respectively. The MODIS data is suitable for great lake mapping, which is the case for the large lakes in the TP. Although automated water extraction indices exhibited high accuracy in separating water from non-water, visual examination and manual editing are still necessary. Combined with recent Chinese high-resolution satellites, these remotely sensed imageries will provide a wealth of data for studies of lake dynamics and long-term lake evolution in the TP.     

基于MODIS的青藏高原雪线高度遥感监测

雪线作为区分积雪覆盖区与无雪区的边界线,是冰冻圈各要素中对气候变化最为敏感的指示器。利用去云后的MODIS积雪面积比例产品并结合DEM数据,通过雪线像元及其高度的提取、雪线高度场的建立,对青藏高原近12a(2000~2011年)雪线高度的时空变化特征进行了分析。结果表明:青藏高原雪线高度的分布受地形的影响,高原内部的雪线高度明显高于周围山区;在近12a中,青藏高原的雪线高度变化虽然没有明显的年际变化趋势(升高或者降低),但是体现出较高的年内和年际波动特征,特别是青藏高原的东部和南部地区由于受西南季风的影响,夏季雪线高度的年际变化尤为强烈。提出基于MODIS的雪线高度提取方法具有较好的应用潜力,能够适用于其他地方雪线高度的遥感监测。     

Uncertainty analysis of five satellite-based precipitation products and evaluation of three optimally merged multi-algorithm products over the Tibetan Plateau

This study is the first comprehensive examination of uncertainty with respect to region, season, rain rate, topography, and snow cover of five mainstream satellite-based precipitation products over the Tibetan Plateau (TP) for the period 2005–2007. It further investigates three merging approaches in order to provide the best possible products for climate and hydrology research studies. Spatial distribution of uncertainty varies from higher uncertainty in the eastern and southern TP and relatively smaller uncertainty in the western and northern TP. The uncertainty is highly seasonal, temporally varying with a decreasing trend from January to April and then remaining relatively low and increasing after October, with an obvious winter peak and summer valley. Overall, the uncertainty also shows an exponentially decreasing trend with higher rainfall rates. The effect of topography on the uncertainty tends to rapidly increase when elevation exceeds 4000 m, while the impact slowly decreases in areas lower than that topography. The influence of the elevation on the uncertainty is significant for all seasons except for the summer. Further cross-investigation found that the uncertainty trend is highly correlated with the MODIS-derived snow cover fraction (SCF) time series over the TP (e.g. correlation coefficient ≥0.75). Finally, to reduce the still relatively large and complex uncertainty over the TP, three data merging methods are examined to provide the best possible satellite precipitation data by optimally combining the five products. The three merging methods – arithmetic mean, inverse-error-square weight, and one-outlier-removed arithmetic mean – show insignificant yet subtle differences. The Bias and RMSE of the three merging methods is dependent on the seasons, but the one-outlier-removed method is more robust and its result outperforms the five individual products in all the seasons except for the winter. The correlation coefficient of the three merging methods is consistently higher than any of five individual satellite estimates, indicating the superiority of the method. This optimally merging multi-algorithm method is a cost-effective way to provide satellite precipitation data of better quality with less uncertainty over the TP in the present era prior to the Global Precipitaton Measurement Mission.     

Snowpack and runoff generation using AMSR-E passive microwave observations in the Upper Helmand Watershed, Afghanistan

Passive microwave estimates of snow water equivalent (SWE) were examined to determine their usefulness for evaluating water resources in the remote Upper Helmand Watershed, central Afghanistan. SWE estimates from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) and the Special Sensor Microwave/Imager (SSM/I) passive microwave data were analyzed for six winter seasons, 2004-2009. A second, independent estimate of SWE was calculated for these same time periods using a hydrologic model of the watershed with a temperature index snow model driven using the Tropical Rainfall Measuring Mission (TRMM) gridded estimates of precipitation. The results demonstrate that passive microwave SWE values from SSM/I and AMSR-E are comparable. The AMSR-E sensor had improved performance in the early winter and late spring, which suggests that AMSR-E is better at detecting shallow snowpacks than SSM/I. The timing and magnitude of SWE values from the snow model and the passive microwave observations were sometimes similar with a correlation of 0.53 and accuracy between 55 and 62%. However, the modeled SWE was much lower than the AMSR-E SWE during two winter seasons in which TRMM data estimated lower than normal precipitation. Modeled runoff and reservoir storage predictions improved significantly when peak AMSR-E SWE values were used to update the snow model state during these periods. Rapid decreases in passive microwave SWE during precipitation events were also well aligned with flood flows that increased base flows by 170 and 940%. This finding supports previous northern latitude studies which indicate that the passive microwave signal's lack of scattering can be used to detect snow melt. The current study's extension to rain on snow events suggests an opportunity for added value for flood forecasting.     

Assessment of the AMSR-E soil moisture product over India

Soil moisture is a very important boundary parameter in numerical weather prediction at different spatial and temporal scales, controlling the exchange of water and energy between the atmosphere and land surface. Satellite-based microwave radiometric observations are considered to be the best for soil moisture remote sensing because of their high sensitivity, as well as their all-weather and day–night observation capabilities with high repeativity. In this study, an attempt has been made to assess the Advanced Microwave Scanning Radiometer--Earth Observing System (AMSR-EOS) soil moisture product over India. The AMSR-E soil moisture product has been assessed using in situ soil moisture observations made by the India Meteorological Department (IMD) during the monsoon period (May–August) for the years 2002–2006 over 18 meteorological stations. Apart from assessing AMSR-E soil moisture retrieval accuracy, this study also investigates the effect of vegetation, topography and coastal water contamination, and determines the regions where the AMSR-E soil moisture product could be useful for different applications.     

Analysis of 3D signatures recorded using leap motion sensor

Multimedia Tools and Applications - Signature recognition is identifying the signature’s owner, whereas verification is the process to find whether a signature is genuine or forged. Though,...     

Flood monitoring using microwave passive remote sensing (AMSR-E) in part of the Brahmaputra basin,India

In this work, Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) signatures were analysed over some critical sites in Lakhimpur District in Brahmaputra basin, India, characterized by a high frequency of flooding events. The site is mostly covered by paddy fields. Results obtained were compared with water level measurements in three stations close to the main channel of the river. Information about surface temperature, which allowed us to estimate the emissivity, was also available. Investigations were carried out at the C, X, and K_a bands of the AMSR-E channel. A multi-frequency analysis indicated that the X band would represent a good compromise between resolution and sensitivity requirements, while at the C band the resolution was too coarse and at the K_a band the signatures were affected by raindrops. Samples collected during rain were eliminated using techniques based on the 89.0 GHz channel. However, even after this correction, the K_a band showed poor sensitivity due to higher attenuation by vegetation. The correlations between different pairs of variables, viz. polarization index (PI), water level (WL), and fractional water surface area (F _WS), were also investigated. At the X band, the water level was better correlated with the PI than with emissivity and other parameters defined in the literature. The correlation was good in cases of slow variation in WL. In cases of sudden variation in the river, the PI followed the variations with some time delay related to the propagation of water within the covered AMSR-E pixel.     

Estimation of snow water equivalent over first-year sea ice using AMSR-E and surface observations

A SWE retrieval algorithm developed in-situ using passive microwave surface based radiometer data is applied to the Advanced Microwave Scanning Radiometer for Earth Observation System (AMSR-E). Snow water equivalent is predicted from two pixels located in Canadian Arctic Shelf Exchange Study (CASES) overwintering study area in Franklin Bay, N.W.T., Canada. Results show that the satellite SWE predictions are statistically valid with measured in-situ snow thickness data in both smooth and rough ice environments where predicted values range from 15 to 25 mm. Stronger correlation between measured and predicted data is found over smooth ice with R² value of 0.75 and 0.73 for both pixels respectively. Furthermore, a qualitative study of sea ice roughness using both passive and active microwave satellite data shows that the two pixels are rougher than the surrounding areas, but the SWE predictions do not seem to be affected significantly.     

Healing on the cloud: Secure cloud architecture for medical wireless sensor networks

There has been a host of research works on wireless sensor networks (WSN) for medical applications. However, the major shortcoming of these efforts is a lack of consideration of data management. Indeed, the huge amount of high sensitive data generated and collected by medical sensor networks introduces several challenges that existing architectures cannot solve. These challenges include scalability, availability and security. Furthermore, WSNs for medical applications provide useful and real information about patients’ health state. This information should be available for healthcare providers to facilitate response and to improve the rescue process of a patient during emergency. Hence, emergency management is another challenge for medical wireless sensor networks. In this paper, we propose an innovative architecture for collecting and accessing large amount of data generated by medical sensor networks. Our architecture overcomes all the aforementioned challenges and makes easy information sharing between healthcare professionals in normal and emergency situations. Furthermore, we propose an effective and flexible security mechanism that guarantees confidentiality, integrity as well as fine-grained access control to outsourced medical data. This mechanism relies on Ciphertext Policy Attribute-based Encryption (CP-ABE) to achieve high flexibility and performance. Finally, we carry out extensive simulations that allow showing that our scheme provides an efficient, fine-grained and scalable access control in normal and emergency situations.     

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...     

Evaluation of AMSR-E soil moisture results using the in-situ data over the Little River Experimental Watershed, Georgia

An operational global soil moisture data product is currently generated from the observations of the Advanced Microwave Scanning Radiometer (AMSR-E) aboard NASA's Aqua satellite using the retrieval procedure described in Njoku and Chan [Njoku, E.G. and Chan, S.K., 2006. Vegetation and surface roughness effects on AMSR-E land observations, remote sensing environment, 100(2), 190-199]. We have generated another soil moisture dataset from the same AMSR-E observed brightness temperature data using the Land Surface Microwave Emission Model (LSMEM) adopting a different estimation method. This paper focuses on a comparison study of soil moisture estimates from the above two methods. The soil moisture data from current AMSR-E product and LSMEM are compared with the in-situ measured soil moisture datasets over the Little River Experimental Watershed (LREW), Georgia, USA for the year 2003. The comparison study was carried out separately for the AMSR-E daytime and night time overpasses. The LSMEM method performed better than the current operational AMSR-E retrieval algorithm in this study. The differences between the AMSR-E and LSMEM results are mostly due to differences in various simplifications and assumptions made for variables in the radiative transfer equations and the soil and vegetation based physical models and the accuracy of the input surface temperature datasets for the LSMEM forward model approach. This study confirms that remote sensing data have the potential to provide useful hydrologic information, but the accuracy of the geophysical parameters could vary depending on the estimation methods. It cannot be concluded from this study whether the soil moisture estimation by the LSMEM approach will perform better in other geographic, climatic or topographic conditions. Nevertheless, this study sheds light on the effects of different approaches for the estimation of geophysical parameters, which may be useful for current and future satellite missions.     

CMORPH对青藏高原地区夏季降水的模拟精度研究与修正

利用青藏高原77个地面台站的2003年~2009年夏季(6月~9月)的降水资料,对月尺度和年尺度上CMORPH(Climate Prediction Center morphing)多卫星降水数据的精度进行研究,并引入Sokol模型对年尺度上的CMORPH数据进行修正,旨在为基于卫星降水数据的青藏高原地区气候、水文等方面的研究提供科学依据。研究结果表明:1CMORPH数据对青藏高原降水的时间变化趋势和空间变化趋势的模拟精度较低,且存在明显的时空不稳定性。2不同时间尺度的CMORPH数据在青藏高原东南部的模拟精度要高于其他地区,而喜马拉雅山脉北麓以及青藏高原东北部的模拟精度最低。3CMORPH年数据存在明显的高值高估、低值低估的现象,其模拟值与误差之间的相关系数均在0.53以上。4经过Sokol模型修正后,CMORPH年数据均方根误差明显降低,而相关系数均有不同程度的提高,表明该模型能够提高CMORPH数据对青藏高原地区降水的模拟精度。     

用AVHRR资料反演青藏高原东北部陆面温度和气温

利用1986年10月-1988年5月NOAA-9和NOAA-10两颗卫星的AVHRR通道1、2和4的资料和同期107个地面台站资料，建立由卫星资料反演地面温度和气温的二元、三元回归方程，并讨论了标准化植被指数NDVI在下垫面分区以及在反演陆面温度中的作用。