基于Triple Collocation方法的微波土壤水分产品不确定性分析与时空变化规律研究

土壤水分是连接地—气系统的重要状态变量,微波遥感为准确获取大面积土壤水分信息提供新的技术手段。准确解读微波土壤水分产品质量、深入了解其误差的时空分布特征是通过数据同化等方法将其融入陆面模型,从而成功应用于地球科学领域的重要先决条件。基于Triple Collocation(TC)方法检验了风云三号C星(FY-3C)、土壤水分主被动卫星(SMAP)及高级微波散射计(ASCAT)这3种常用微波土壤水分产品在中国陆域的质量,并通过Hovm?ller图评估了3套产品捕捉土壤水分时空变化的能力。结果显示:①TC方法得到的分析结论与地面实测资料的验证结果一致,整体上SMAP优于ASCAT和FY-3C,不同土地利用类型下SMAP信噪比均最高,三者的TC信噪比分别为1.668 dB、-0.316 dB和-2.182 dB,同时三者与实测值的相关系数分别为0.514、0.501和0.209;②FY-3C和ASCAT产品的精度在中国西北地区整体优于南部地区,3种产品均能较好地刻画土壤水分随纬度和经度变化的情况,3种产品展现的季节波动整体高于实测,其中FY-3C的季节波动在3种产品中最为剧烈;③FY-3C的质量比ASCAT和SMAP更易受到植被影响,但在裸土区FY-3C优于ASCAT。本研究基于TC分析提供了全国范围内3种主流微波土壤水分产品的误差和信噪比的空间分布,并通过Hovm?ller图评估了其描述土壤水分时空变化的能力。研究结论可为微波土壤水分产品的同化研究提供一定参考。     

Pixel-scalesoil Moisture Monitoring Network and Its Preliminary Validation of L-band Soil Moisture Products

Soil moisture is a key variable in the process of crop growth,ground-air water heat exchange and global water cycle,which plays an important role in drought monitoring,hydrological land surface processes and climate change.Passive microwave remote sensing has become the main means of monitoring soil moisture with the sensitivity to soil moisture.In this study,the authenticity test of SMAP（Soil Moisture and Active and Passive） and SMOS（Soil Moisture and Ocean Salinity）passive microwave soil moisture products using the soil moisture sensor network monitoring data carried out against the underlying surface of farmlands in Jilin Province was carried out.The following conclusions were obtained：（1）Compared with the in situ measured data,SMOS L3(ascending and descending overpasses) and SMAP L3 passive microwave soil moisture products generally underestimated the ground data,but With the occurrence of rainfall events,there will be the phenomenon which is the value of soil moisture products is higher than the in situ data; although the unbiased root mean square error (unRMSE) of the two soil moisture products was greater than 0.07 m3/m3,the unRMSE of SMAP passive microwave soil moisture product data which was 0.078 m3/m3 was slightly lower;（2）Since the depth of induction of the L-band is lighter than the depth of detection of the sensor(5cm),and the dryness of the soil surface after rainfall causes the vertical inhomogeneity of soil moisture,which is one of the reasons why SMOS and SMAP passive microwave soil moisture products underestimate soil moisture; （3）SMOS has a higher value than the range of SMAP brightness temperature,which may be caused by radio frequency interference (RFI),which makes the error of soil moisture Retrieval and affects the validation accuracy.The comparison of bright temperature distribution of SMOS and SMAP shows that the effect of RFI on SMOS is more serious due to the influence of electromagnetic radio frequency interference (RFI),which may be the reason why the RMSE of soil moisture product of SMOS is higher than that of passive microwave soil moisture product of SMAP.     

Evaluation of remotely sensed and modelled soil moisture products using global ground-based in situ observations

In situ soil moisture data from more than 200 stations located in Africa, Australia, Europe and the United States are used to determine the reliability of three soil moisture products, one analysis from the ECMWF (European Centre for Medium-Range Weather Forecasts) numerical weather prediction system (SM-DAS-2) and two remotely sensed soil moisture products, namely ASCAT (Advanced scatterometer) and SMOS (Soil Moisture Ocean Salinity). SM-DAS-2 is produced offline at ECMWF and relies on an advanced surface data assimilation system (Extended Kalman Filter) used to optimally combine conventional observations with satellite measurements. ASCAT remotely sensed surface soil moisture is provided in near real time by EUMETSAT. At ECMWF, ASCAT is used for soil moisture analyses in SM-DAS-2, also. Finally the SMOS remotely sensed soil moisture data level two product developed at CESBIO is used. Evaluation of the times series as well as of the anomaly values, shows good performances of the three products to capture surface soil moisture annual cycle and short term variability. Correlations with in situ data are very satisfactory over most of the investigated sites located in contrasted biomes and climate conditions with averaged values of 0.70 for SM-DAS-2, 0.53 for ASCAT and 0.54 for SMOS. Although radio frequency interference disturbs the natural microwave emission of the Earth observed by SMOS in several parts of the world, hence the soil moisture retrieval, performances of SMOS over Australia are very encouraging.     

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.     

Intercomparison of microwave remote-sensing soil moisture data sets based on distributed eco-hydrological model simulation and in situ measurements over the North China Plain

Intercomparisons of microwave-based soil moisture products from active ASCAT (Advanced Scatterometer) and passive AMSR-E (Advanced Microwave Scanning Radiometer for the Earth Observing System) is conducted based on surface soil moisture (SSM) simulations from the eco-hydrological model, Vegetation Interface Processes (VIP), after it is carefully validated with in situ measurements over the North China Plain. Correlations with VIP SSM simulation are generally satisfactory with average values of 0.71 for ASCAT and 0.47 for AMSR-E during 2007–2009. ASCAT and AMSR-E present unbiased errors of 0.044 and 0.053 m³ m^?3 on average, with respect to model simulation. The empirical orthogonal functions (EOF) analysis results illustrate that AMSR-E provides more consistent SSM spatial structure with VIP than ASCAT; while ASCAT is more capable of capturing SSM temporal dynamics. This is supported by the facts that ASCAT has more consistent expansion coefficients corresponding to primary EOF mode with VIP (R = 0.825, p < 0.1). However, comparison based on SSM anomaly demonstrates that AMSR-E and ASCAT have similar skill in capturing SSM short-term variability. Temporal analysis of SSM anomaly time series shows that AMSR-E provides best performance in autumn, while ASCAT provides lower anomaly bias during highly-vegetated summer with vegetation optical depth of 0.61. Moreover, ASCAT retrieval accuracy is less influenced by vegetation cover, as it is in relatively better agreement with VIP simulation in forest than in other land-use types and exhibits smaller interannual fluctuation than AMSR-E. Identification of the error characteristics of these two microwave soil moisture data sets will be helpful for correctly interpreting the data products and also facilitate optimal specification of the error matrix in data assimilation at a regional scale.     

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、卫星产品评估与土壤水分反演算法发展等方面系统回顾和总结了近年来L波段被动微波遥感及其土壤水分反演算法、产品在青藏高原的主要应用与研究进展。在此基础上,归纳了当前高原L波段被动微波辐射模拟与土壤水分反演存在的问题,主要包括缺乏高原尺度的微波辐射模拟评估和改进的卫星土壤水分产品、土壤冻结时期的水分监测产品依然缺失等问题。针对存在的问题,进一步提出了相关建议与展望,建议今后的研究应加强高原尺度的微波辐射模拟评估与土壤水分产品改进工作,并积极拓展土壤水分产品在高原水分循环和能量平衡模拟、植被生长与干旱监测的应用研究。     

A comparison study on empirical microwave soil moisture downscaling methods based on the integration of microwave-optical/IR data on the Tibetan Plateau

Soil moisture plays a vital role in land surface energy and the water cycle. Microwave remote sensing is widely used because of the physically based relationship between the land surface emission observed and soil moisture. However, the application of retrieved soil moisture data is restricted by its coarse spatial resolution. To overcome this weakness, downscaling methods should be developed to disaggregate coarse resolution microwave soil moisture data to fine resolution. The traditional method is the microwave-optical/IR synergistic approach, in which land surface temperature, vegetation index, and surface albedo are key parameters. Five purely empirical methods based on the triangle feature are selected in this study. To evaluate their performance on downscaling microwave soil moisture, these methods are applied to the Zoige Plateau in China using the Advanced Microwave Scanning Radiometer on the Earth Observing System (AMSR-E) Land Parameter Retrieval Model (LPRM) soil moisture product and Moderate Resolution Imaging Spectroradiometer (MODIS) optical/IR products. The coarse-resolution AMSR-E LPRM soil moisture data are disaggregated into the high resolution of the MODIS product, and the surface soil moisture measurements of the Maqu soil moisture observation network located in the plateau are used to validate the downscaling results. Results show that (1) the relationship models used in these methods can generally capture the variation in soil moisture, with R² around 0.6, but have a relatively high uncertainty under conditions of high soil moisture; (2) the methods can provide high-resolution soil moisture distribution, but the downscaled soil moisture presents a low level correlation with field measurements at different spatial and temporal scales. This comparative study provides insight into the performance of popular purely empirical downscaling methods on enhancing the spatial resolution of soil moisture on the Tibetan Plateau. Although synergistic methods can improve the spatial resolution of AMSR-E soil moisture data, additional studies are needed to exclude the uncertainty from AMSR-E soil moisture estimation, the low sensitivity of the relationship model under high soil moisture, and the spatial representativeness difference between coarse pixels and point measurement.     

荷兰农业水土资源利用研究与遥感技术的应用-赴荷兰访问考察团

根据中荷两国学者互访协议,中国科学院沙漠所派我们两人在1985年10月10日至11月6日对荷兰进行了为期四周的考察访问。在荷期间,我们受到荷方学者热情友好的接待,首后访问了国际农业中心(IAC—     

SMOS与SMAP过境时段表层土壤水分的稳定性研究

SMOS和SMAP都是为获取全球土壤水分信息而设计的专题卫星,均搭载了L波段辐射计。进行二者的横向对比是构建具有一致性的全球土壤水分数据集的关键基础。虽然SMAP、SMOS名义上的过境时刻是固定的,但二者的实际过境时刻随时间和空间发生变化,它们与地面实测数据三者之间难以匹配形成时序上严格统一的样本对,从而给土壤水分反演结果的精度评定带来困难。针对这一问题,以美国大陆地区为研究区,首先对2016~2017年SMOS、SMAP土壤水分数据的时间戳进行统计,判定二者过境的交叠时段;进而利用高观测频率、大空间尺度的实测数据,研究表层土壤水分在此时段内的自然变化特征。结果显示,按照全部、无降水、有降水3种条件,在样本量分别为98.14%、99.51%和88.49%的绝大多数情况下,表层土壤水分的变化量为0.007 m3/m3、0.007 m3/m3和0.012 m3/m3, 远小于SMOS、SMAP的目标精度（0.04 m3/m3）。初步证实： ①SMOS与SMAP的土壤水分反演结果（L2数据）可进行直接比对;②过境时刻差异对验证误差的影响可不计。     

结合参量统计与时空融合的土壤水分降尺度方法

土壤水分的降尺度研究为解决被动微波产品的粗分辨率问题,更好地服务于流域小尺度应用提供了技术手段。以美国俄克拉荷马州为研究区域,基于SMAP土壤水分产品和MODIS产品等多种辅助数据,在地表分类数据的支持下,结合参量统计降尺度和时空融合降尺度发展了一种土壤水分混合降尺度方法,并利用SMAP 9 km产品和站点实测数据对降尺度效果进行了评估。结果表明：混合降尺度方法可以得到细节丰富、空间覆盖完整的降尺度结果。相较于参量统计或时空融合两种单一降尺度而言,混合降尺度结果的空间分布与SMAP 9 km真实产品最为相似,并且混合降尺度结果与站点的整体时序精度最高,在不同地表分类下的时序精度也优于单一方法的降尺度结果。由此证明结合参量统计与时空融合的降尺度方法是可行的。     

Retrieval of Soil Unfrozen Water in Maqu Region of Tibetan Plateau based on SMAP Brightness Temperature Measurement

Unfrozen water and ice co-exist in frozen soil, and their mutual transformation, namely freezing-thawing change, profoundly affects the surface water circulation and energy budget in cold regions. Passive microwave remote sensing technology is the main means of soil water monitoring, but it is mostly applied to the retrieval of water in non-frozen soil, and the retrieval of unfrozen water in frozen soil under negative temperature environment is less. Based on the brightness temperature measurement data obtained from the SMAP satellite ascending and descending overpass and the improved zero-order microwave radiation model applicable to the Tibetan Plateau, using Single-Channel Algorithm (SCA) and Dual-Channel Algorithm (DCA), The content of unfrozen water in the seasonal frozen soil in Maqu region which is the source region of the Yellow River in the east of Tibetan Plateau was inverted. The results show that the in-situ measured values dynamics are better captured by the retrieval values based on the brightness temperature measurement at the different moments of SMAP satellite overpass and different algorithms of soil unfrozen water in the study area(the correlation coefficient R is greater than 0.9). Among them, the retrieval results based on the brightness temperature measurement at the SMAP descending are significantly underestimated in the transition season of freezing-thawing cycle, while the retrieval results based on the brightness temperature measurement at the SMAP ascending are more accurate. The unbiased root-mean-square error (ubRMSE) of the retrieval values which obtained based on the V-polarization Single Channel Algorithm (SCA-V) and DCA and the in-situ values is 0.035 m³m^-3 and 0.039 m³m^-3, respectively, which are both meet the established requirements of SMAP mission. Compared with SMAP standard products, the soil moisture in warm season obtained by retrieval based on SCA-V algorithm is more accurate in this study. In addition, the algorithm adopted in this study can successfully retrieval the dynamic change of soil unfrozen water during freezing period, so it is more suitable for the retrieval of soil moisture under freezing and thawing conditions in Tibetan Plateau.     

基于SMAP亮温数据反演青藏高原玛曲区域土壤未冻水

未冻水和冰共同存在于冻土中,两者的相互转化即冻融变化深刻影响寒区地表水分循环和能量收支。被动微波遥感技术是土壤水分监测的主要手段,但目前大多应用于非冻结土壤的水分反演,对负温环境下冻结土壤中未冻水的反演研究较少。基于SMAP卫星升轨和降轨时刻的亮温观测数据和经改进后适用于青藏高原地区的零阶微波辐射模型,利用单通道算法（SCA）和双通道算法（DCA）,对青藏高原东部黄河源区玛曲区域季节冻土中的未冻水含量进行反演。结果表明：基于SMAP不同过境时刻亮温观测及不同算法的土壤未冻水反演结果均较同步地反映了研究区实测值的动态变化特征（相关系数R均大于0.9）。其中,基于SMAP降轨时刻亮温观测的反演结果在冻融交替的过渡季节存在明显低估,而基于升轨时刻亮温观测得到的反演结果精度更高。基于垂直极化亮温观测的单通道（SCA-V）和DCA算法得到的升轨时刻的反演值与实测值的无偏均方根误差（ubRMSE）分别为0.035 m³m^-3和0.039 m³m^-3,均达到SMAP任务的设计要求（即ubRMSE≤0.04 m³m^-3）,其中SCA-V对该研究区土壤未冻水的反演精度最高。与SMAP标准产品相比,基于SCA-V算法反演得到的暖季土壤水分精度更高。此外,该算法能成功反演得到冻结期土壤未冻水的动态变化,因此更适用于青藏高原地区冻融土壤条件下的水分反演。     

Downscaling of passive microwave soil moisture retrievals based on spectral analysis

The retrieval of soil moisture from passive microwave remote-sensing data is presently one of the most effective methods for monitoring soil moisture. However, the spatial resolution of passive microwave soil moisture products is generally low; thus, existing soil moisture products should be downscaled in order to obtain more accurate soil moisture data. In this study, we explore the theoretical feasibility of applying the spectral downscaling method to the soil moisture in order to generate high spatial resolution soil moisture based on both Moderate Resolution Imaging Spectroradiometer and Fengyun-3B (FY3B) data. We analyse the spectral characteristics of soil moisture images covering the east-central of the Tibetan Plateau which have different spatial resolutions. The spectral analysis reveals that the spectral downscaling method is reliable in theory for downscaling soil moisture. So, we developed one spectral downscaling method for deriving the high spatial resolution (1 km) soil moister data from the FY3B data (25 km). Our results were compared with the ground truth measurements from 15 selected experimental days in 16 different sites. The average coefficient of determination (R²) of the spectral downscaling increased nearly doubled than that of the original FY3B soil moisture product. The spectral downscaled soil moister data were successfully applied to examine the water exchange between the land and atmosphere in the study regions. The spectral downscaling approach could be an efficient and effective method to improve the spatial resolution of current microwave soil moisture images.     

Soil Moisture Retrieval over Vegetated Areas based on Sentinel-1 and FY-3C Data

This study aims to develop soil moisture retrieval model over vegetated areas based on Sentinel-1 SAR and FY-3C data.In order to remove vegetation effect,the MWRI data from FY-3C was applied to establish the inversion model of vegetation water content.The model was combined with the original water-cloud model,and developing a soil moisture retrieval model by combining active and passive microwave remote sensing data.Finally,the experiment of the soil moisture retrieval was conducted in Jiangsu and Anhui province,and validating the inversion accuracy of soil moisture by measured data.The results showed that：①For the vegetation-covered surface,the Microwave Polarization Difference Index obtain from FY-3C/MWRI was suitable for removing vegetation effect.②Compared with the Sentinel-1 VH polarization data,the backscattering coefficient of VV polarization was more suitable for soil moisture retrieval and get a higher accuracy of soil moisture retrieval.③Sentinel\|1 data can obtain high precision soil moisture estimation results,and the correlation coefficient between the estimated and measured soil moisture is 0.561 2 and RMSE is 0.044 cm³/cm³.     

Impact of surface heterogeneity on surface soil moisture retrievals from passive microwave data at the regional scale: The Upper Danube case

Water and energy fluxes at the interface between the land surface and atmosphere are strongly depending on the surface soil moisture content which is highly variable in space and time. The sensitivity of active and passive microwave remote sensing data to surface soil moisture content has been investigated in numerous studies. Recent satellite borne mission concepts, as e.g. the SMOS mission, are dedicated to provide global soil moisture information with a temporal frequency of 1-3 days to capture it's high temporal dynamics. Passive satellite microwave sensors have spatial resolutions in the order of tens of kilometres. The retrieved soil moisture fields from that sensors therefore represent surface information which is integrated over large areas. It has been shown that the heterogeneity within an image pixel might have considerable impact on the accuracy of soil moisture retrievals from passive microwave data.The paper investigates the impact of land surface heterogeneity on soil moisture retrievals from L-band passive microwave data at different spatial scales between 1 km and 40 km. The impact of sensor noise and quality of ancillary information is explicitly considered. A synthetic study is conducted where brightness temperature observations are generated using simulated land surface conditions. Soil moisture information is retrieved from these simulated observations using an iterative approach based on multiangular observations of brightness temperature. The soil moisture retrieval uncertainties resulting from the heterogeneity within the image pixels as well as the uncertainties in the a priori knowledge of surface temperature data and due to sensor noise, is investigated at different spatial scales. The investigations are made for a heterogeneous hydrological catchment in Southern Germany (Upper Danube) which is dedicated to serve as a calibration and validation site for the SMOS mission.     

基于CCI土壤水分产品的干旱指数精度评价及其对东北地区粮食产量的影响

土壤水分是监测作物旱情的基本因子,以欧空局1978~2014年微波遥感土壤水分产品、中国经济与社会发展统计数据库以及气象数据为基础,结合土壤水分亏缺指数（Soil Water Deficit Index, SWDI）分析东北地区的干旱程度与玉米亩产的关系。结果表明：①东北三省干旱程度空间上呈现自东北向西南逐渐加重的空间分布模式;②基于CCI (Climate Change Initiative)土壤水分产品计算的SWDI干旱指数与降雨量和气温有良好的相关关系,可用于评估干旱发生的严重程度;③玉米生长季关键需水期——7月的SWDI与玉米产量的相关性最好,二者在黑龙江、吉林和辽宁省的R2分别为0.43、0.78和0.38,非常适合用于评估干旱对玉米单产的影响。该结论对于研究大范围土壤水分含量对农作物产量的影响以及相关农业决策具有重要指导意义。     

Precision Evaluation of Drought Index based on CCI Soil Moisture Products and Its Effect on Grain Yield in Northeast China

Soil moisture is the basic factor for monitoring crop drought. Based on the microwave remote sensing soil moisture products of ESA from 1978 to 2014, the statistical database of China's economic and social development and meteorological data, combined with the Soil Moisture Deficit Index (SWDI), the relationship between the degree of drought in Northeast China and corn yield was analyzed. The results show that: (1) the drought level of the three provinces is increasing from northeast to southwest; (2) the SWDI drought index calculated based on CCI (Climate Change Initiative) soil moisture products has a good correlation with rainfall and temperature, which can be used to evaluate the severity of drought; (3) the correlation between SWDI and maize yield is the best in the key water demand period (July), and R2 of Heilongjiang, Jilin and Liaoning provinces are 0.43, 0.78 and 0.38 respectively, which is very suitable for quantifying the effect of drought on maize yield. This conclusion has important guiding significance for the study of the influence of soil moisture content on crop yield and the relevant agricultural decision-making.     

基于Triple-Collocation方法的微波遥感土壤水分产品不确定性分析及数据融合

微波遥感可以获取大范围的地表土壤水分信息，以及由此得到全球尺度的土壤水分产品。但由于传感器观测配置和反演方法等诸多因素的影响，使得不同的土壤水分产品在精度和可靠性方面存在差异。基于Triple-Collocation（TC）方法，在青藏高原那曲地区的0.25°×0.25°和1.0°×1.0°两个空间尺度上对AMSR2、SMAP和SMOS 3种土壤水分遥感产品进行不确定性分析，开展基于随机误差的数据融合算法研究。研究结果表明:不同遥感产品间的随机误差在空间分布上存在显著的不一致性，使得应用传统的算术平均方法进行数据融合不具有普适性。基于此不确定性，对3种产品配赋相应的权重进行融合，相比于3种土壤水分原始数据集，融合产品不仅具有更丰富的数据量，也会对数据精度有所改善。当遥感产品间的随机误差接近时，等权重和优化权重的融合结果非常接近；当遥感产品间的随机误差差异较大时，基于不确定性的数据融合方法相比等权重方法可以明显的提高融合数据的精度。     

ASCAT soil wetness index validation through in situ and modeled soil moisture data in central Italy

Reliable measurements of soil moisture at global scale might greatly improve many practical issues in hydrology, meteorology, climatology or agriculture such as water management, quantitative precipitation forecasting, irrigation scheduling, etc. Remote sensing offers the unique capability to monitor soil moisture over large areas but, nowadays, the spatio-temporal resolution and accuracy required for some hydrological applications (e.g., flood forecasting in medium to large basins) have still to be met. The Advanced SCATterometer (ASCAT) onboard the Metop satellite (VV polarization, C-band at 5.255 GHz), based on a large extent on the heritage of the ERS scatterometer, provides a soil moisture product available at a coarse spatial resolution (25 km and 50 km) and at a nearly daily time step. This study evaluates the accuracy of the new 25 km ASCAT derived saturation degree product by using in situ observations and the outcomes of a soil water balance model for three sites located in an inland region of central Italy. The comparison is carried out for a 2-year period (2007-2008) and three products derived from ASCAT: the surface saturation degree, m_s, the exponentially filtered soil wetness index, SWI, and its linear transformation, SWI*, matching the range of variability of ground data. Overall, the performance of the three products is found to be quite good with correlation coefficients higher than 0.92 and 0.80 when the SWI is compared with in situ and simulated saturation degree, respectively. Considering SWI*, the root mean square error is less than 0.035 m³/m³ and 0.042 m³/m³ for in situ and simulated saturation degree, respectively. More notably, when the m_s product is compared with modeled data at 3 cm depth, this index is found able to accurately reproduce the temporal pattern of the simulated saturation degree in terms of both timing and entity of its variations also at fine temporal scale. The daily temporal resolution and the reliability obtained with the ASCAT derived saturation degree products represent the preliminary step for its effective use in operational rainfall-runoff modeling.     

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.