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.     

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.     

Seasonality and autocorrelation of satellite-derived soil moisture products

This study presents an analysis of temporal behaviour of in situ and satellite-derived soil moisture data. The main objective is to evaluate the temporal reliability of the satellite products, comparing them with in situ data, for applications that would benefit from the use of consistent time series of soil moisture, such as studies on climate and hydrological cycle. The time series, seasonalities, and anomalies of Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) soil moisture and European Remote Sensing (ERS) satellite soil wetness index data sets were analysed over five test sites. The agreement of temporal behaviours and autocorrelation functions and the correlation with in situ data were investigated. A good agreement between the seasonalities of both satellite data sets and in situ data with high correlations (i.e. 0.9) was found over the sites with a large soil moisture variability range and short vegetation cover. Noisier seasonalities were found over sites with small soil moisture variability ranges, affected by radiofrequency interference (RFI) and characterized by croplands. In spite of ERS soil moisture being characterized by a longer time series, the seasonality is much noisier than the AMSR-E products due to the numerous gaps in the data set. The correlation among the anomalies is lower than 0.6, mainly due to the noise in the satellite products. However, the autocorrelation functions show that the anomalies are not random, although noisy. Although the stability of the anomaly correlograms is affected by the relatively short time series available for this study, the analysis shows that there are statistical similarities between the satellite soil moisture anomalies and the in situ data anomalies. The results show that AMSR-E and ERS products are consistent over long time periods and do contain useful information about soil moisture seasonality and anomaly behaviour, although they are affected by noise.     

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.     

Evaluation of soil moisture from satellite observations over South America

A study was performed to evaluate the surface soil moisture derived from Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) sensor observations over South America. Other soil moisture and rainfall datasets were also used for the analysis. The information for the soil data came from the Eta regional climate model, and for the rainfall data from the Tropical Rainfall Microwave Mission (TRMM) satellite. Statistical analysis was used to evaluate the quality of the soil moisture and rainfall products, with estimates of the correlation coefficient (R), χ² and Cramer's phi (?_c). The results show high correlations (R > 0.8) of the AMSR-E soil moisture products with the Eta model for different regions of South America. Comparison of soil moisture products with rainfall datasets showed that the AMSR-E C-band soil moisture product was highly correlated with the TRMM satellite rainfall datasets, with the highest values of χ² and ?. The results show that the AMSR-E C-band soil moisture products contain important information that can be used for various purposes, such as monitoring floods or droughts in arid areas or as input within the framework of an assimilation scheme of numerical weather prediction models.     

Global surface soil moisture from the Microwave Radiation Imager onboard the Fengyun-3B satellite

Soil moisture retrievals from China’s recently launched meteorological Fengyun-3B satellite are presented. An established retrieval algorithm – the Land Parameter Retrieval Model (LPRM) – was applied to observations of the Microwave Radiation Imager (MWRI) onboard this satellite. The newly developed soil moisture retrievals from this satellite mission may be incorporated in an existing global microwave-based soil moisture database. To reach consistency with an existing data set of multi-satellite soil moisture retrievals, an intercalibration step was applied to correct brightness temperatures for sensor differences between MWRI and the radiometer of the Tropical Rainfall Measuring Mission’s (TRMM’s) Microwave Imager (TMI), resulting from their individual calibration procedures. The newly derived soil moisture and vegetation optical depth product showed a high degree of consistency with parallel retrievals from both TMI and WindSat, the two satellites that are observing during the same time period and are already part of the LPRM database. High correlation (R > 0.60 at night-time) between the LPRM and official MWRI soil moisture products was shown over the validation networks experiencing semiarid climate conditions. The skills drop below 0.50 over forested regions, with the performance of the LPRM product slightly better than the official MWRI product. To demonstrate the promising use of the MWRI soil moisture in drought monitoring, a case study for a recent and unusually dry East Asian summer Monsoon was conducted. The MWRI soil moisture products are able to effectively delineate the regions that are experiencing a considerable drought, highly in agreement with spatial patterns of precipitation and temperature anomalies. The results in this study give confidence in the soil moisture retrievals from the MWRI onboard Fengyun-3B. The integration of the newly derived products into the existing database will allow a better understanding the diurnal, seasonal and interannual variations, and long-term (35 year) changes of soil moisture at the global scale, consequently enhancing hydrological, meteorological, and climate studies.     

AMSR-E卫星反演土壤湿度与ECWMF,NECP再分析土壤湿度比较分析

土壤湿度是气象学、气候学研究领域的重要环境因子和过程参数。AMSR-E可提供全球范围的较长时序的卫星反演土壤湿度产品,将ECWMF和NECP再分析资料与AMSR-E土壤湿度产品进行时空比较,在评价三者一致性的同时对AMSR-E土壤湿度进行检验,并进一步使用站点观测资料(土壤湿度、降水量)对中国区域的AMSR-E、ECWMF以及NECP土壤湿度进行检验。结果表明：全球及中国区域AMSR-E、ECWMF与NECP土壤湿度空间分布特征一致性较好,但与ECWMF、NCEP相比AMSR-E土壤湿度在数值上明显偏小,尤其当AMSR-E土壤湿度数值较小时,与另两者的差距较大;三者土壤湿度均与降水量有较好的对应关系,比较而言,ECWMF和NECP土壤湿度与降水量的对应关系更好;与站点土壤湿度相比,ECWMF和NECP土壤湿度偏大,AMSR-E土壤湿度偏小,全国范围内2009年159个站点统计结果显示:ECWMF、NECP与站点的均方根误差(0.107、0.124)小于AMSR-E的均方根误差(0.127)。     

Soil moisture mapping and AMSR-E validation using the PSR in SMEX02

Field experiments (SMEX02) were conducted to evaluate the effects of dense agricultural crop conditions on soil moisture retrieval using passive microwave remote sensing. Aircraft observations were collected using a new version of the Polarimetric Scanning Radiometer (PSR) that provided four C band and four X band frequencies. Observations were also available from the Aqua satellite Advanced Microwave Scanning Radiometer (AMSR-E) at these same frequencies. SMEX02 was conducted over a three-week period during the summer near Ames, Iowa. Corn and soybeans dominate the region. During the study period the corn was approaching its peak water content state and the soybeans were at the mid point of the growth cycle. Aircraft observations are compared to ground observations. Subsequently models are developed to describe the effects of corn and soybeans on soil moisture retrieval. Multiple altitude aircraft brightness temperatures were compared to AMSR-E observations to understand brightness temperature scaling and provide validation. The X-band observations from the two sensors were in reasonable agreement. The AMSR-E C-band observations were contaminated with anthropogenic RFI, which made comparison to the PSR invalid. Aircraft data along with ancillary data were used in a retrieval algorithm to map soil moisture. The PSR estimated soil moisture retrievals on a field-by-field comparison had a standard error of estimate (SEE) of 5.5%. The error reduced when high altitude soil moisture estimates were aggregated to 25 km resolution (same as AMSR-E EASE grid product resolution) (SEE ∼ 2.85%). These soil moisture products provide a validation of the AMSR retrievals. PSR/CX soil moisture images show spatial and temporal patterns consistent with meteorological and soil conditions. The dynamic range of the PSR/CX observations indicates that reasonable soil moisture estimates can be obtained from AMSR, even in areas of high vegetation biomass content (∼ 4-8 kg/m2).     

Error Characteristics of Microwave Soil Moisture Products based on Triple Collocation and Its Spatial-temporal Pattern

Soil moisture is an important state variable connecting the land surface-atmosphere system, and its information can be efficiently acquired by the new technique of microwave remote sensing. Accurate interpretation of the microwave soil moisture products qualities and in-depth understanding of their temporal and spatial distributions are important prerequisites for their successful application in earth science through data assimilation. In this study, three microwave soil moisture products, FengYun-3C(FY-3C), Soil Moisture Active Passive (SMAP) and Advanced Scatterometer(ASCAT), were evaluated over China based on the triple collocation (TC) method. The abilities of three products to obtain temporal and spatial variations of soil moisture were illustrated by Hovm?ller diagram. The results show that: (1) SMAP generally outperforms ASCAT and FY-3C, with highest TC-based signal-to-noise ratio(SNR) under different land use types. The TC-based SNRs are 1.668dB, -0.316dB and -2.182dB for SMAP, ASCAT and FY-3C respectively; and their correlation coefficients with ground observations are 0.514, 0.501 and 0.209, respectively. (2) The accuracies of FY-3C and ASCAT in Northwest China are overall higher than those in the southern China. All three products can capture the latitudinal and longitudinal gradients of soil moisture, whereas their seasonal fluctuations are higher than those of in-situ measurements. Among three products, FY-3C shows highest spatial gradient and strongest seasonal fluctuations. (3) FY-3C product performance is more susceptible to vegetation coverage than ASCAT and SMAP, but it outperforms ASCAT in barren areas. The results of our study could provide useful insights for assimilating microwave soil moisture products into land surface models to improve hydrological prediction.     

A soil moisture assimilation scheme based on the microwave Land Emissivity Model and the Community Land Model

Applications of microwave remote-sensing data in land data assimilation are a topic of current interest and importance due to their high temporal and spatial resolution and availability. However, there have been few studies on land surface sub-grid scale heterogeneity and calculating microwave wetland surface emissivity when directly assimilating gridded Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) satellite brightness temperature (BT) data to estimate soil moisture. How to assimilate gridded AMSR-E BT data for land surface model (LSM) grid cells including various land cover types, especially wetland, is worthy of careful study. The ensemble Kalman filter (EnKF) method is able to resolve the non-linearity and discontinuity in forecast and observation operators, and is widely used in land data assimilation. In this study, considering the influences of land surface sub-grid scale heterogeneity, a satellite data simulation scheme based on the National Center for Atmosphere Research (NCAR) Community Land Model version 2.0 (CLM2.0), microwave Land Emissivity Model (LandEM), Shuffled Complex Evolution (SCE-UA) algorithm and AMSR-E BT observation data is presented to simulate AMSR-E BT data and calibrate microwave wetland surface emissivity; then, a soil moisture data assimilation scheme is developed to directly assimilate the gridded AMSR-E BT data, which consists of the CLM2.0, LandEM and EnKF. The experimental results indicate that the calibrated microwave wetland surface emissivities possess excellent transportability, and that the assimilation scheme is practical and can significantly improve soil moisture estimation accuracy. This study provides a promising solution to improve soil moisture estimation accuracy through directly assimilating gridded AMSR-E BT data for various land cover types such as bare soil, vegetation, snow, lake and wetland.     

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.     

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。     

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.     

Monitoring of soil moisture over the Kuwait desert using remote sensing techniques

Field experiments were conducted in synchronous with Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) passes over the Kuwait desert covering one pixel of 25 km circular diameter. Forty-five soil samples were collected within a pixel resolution to estimate the effective soil moisture, and nine such campaigns were conducted during the period December 2005 to March 2006. Field-estimated soil moisture values up to 5 cm depth were compared with AMSR-E soil moisture values and our model results. It was observed that the field soil moisture values are consistently lower than AMSR-E and our model values. However, the difference is within the errors. AMSR-E soil moisture and our model values agree with each other. Monthly average soil moisture maps of Kuwait were generated from AMSR-E data to study the temporal and spatial variability of soil moisture. It is observed that the maximum soil moisture during January is about 10%, and most of the year the values are about 5% soil moisture.     

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.     

亚洲区域AMSR2与SMOS土壤水分产品对比研究

遥感反演土壤水分(SM)产品越来越多地应用于农业、气象、水文等研究,而微波土壤水分数据产品的区域适用性分析是其合理使用的必要前提。使用MERRA-2(Modern Era Retrospective-analysis for Research and Applications,Version 2)模拟土壤水分为参考数据,运用传统统计方法(原始数据相关性、距平相关性、偏差以及无偏均方根差)和TC(Triple-Collocation)不确定性误差模型分析的方法,对亚洲区域2012年7月～2016年7月两种被动微波土壤水分SMOS-L3-SM(Soil Moisture and Ocean Salinity,L3)和AMSR2-LPRM-SM(The Advanced Microwave Scanning Radiometer 2,Land Parameter Retrieval Model Product)进行对比评估。结果表明:①空间上SMOS-L3较AMSR2-LPRM数据与参考数据MERRA-2土壤水分的相关性较好,表现为SMOS-L3-SM具有较好的空间连续性,且在亚洲大多数地区有较小的无偏均方根差;②湿季条件下遥感土壤水分与参考值的相关性比干季条件下的相关性更好,且干季出现高纬地区(约55°)缺失值较多的情况;③两遥感土壤水分的TC误差呈现相似的分布,区域TC平均误差两者均为0.076 m~3/m~3。总之,SMOS-L3-SM和AMSR2-LPRM-SM在空间相关性及TC误差评价方面都具有合理性,为遥感土壤水分在农业、气象、水文等方面的应用提供参考。     

An evaluation of satellite-based drought indices on a regional scale

We compared conventional and satellite-based drought indices from drought vulnerable sites in South Korea during 2004–2013. Satellite-based drought indices, the energy-based water deficit index (EWDI), and the standalone Moderate Resolution Imaging Spectroradiometer (MODIS)-based evaporative stress index (stMOD_ESI) were evaluated using MODIS imagery to assess its capability to analyse the complex topography of the Korean peninsula. Of the drought indices examined, the EWDI and stMOD_ESI were accurate when capturing moderate drought conditions, compared to the observed precipitation-based conventional drought indices (standardized precipitation index (SPI-3) and Palmer drought severity index (PDSI)). In addition, the satellite-basedsoil moisture index (SSMI) developed from the Advanced Microwave Scanning Radiometer (AMSR-E) and Advanced Scatterometer (ASCAT) soil moisture products were reasonably correlated with the EWDI and stMOD_ESI. These results suggest that the satellite-based drought indices (EWDI and stMOD_ESI) may be applicable on a regional scale.     

Improving soil wetness variations monitoring from passive microwave satellite data: The case of April 2000 Hungary flood

Precipitation-runoff processes are correlated with catchment's hydrological conditions before the precipitation. Thus, an estimation of these conditions, particularly regarding soil wetness variations, is of considerable importance to improve the reliability of flood warning. In this paper, a new methodology is presented which, on the basis of microwave satellite observations, could permit us to monitor soil wetness variations at a global scale. The proposed method seems able to overcome the problems connected to surface roughness and vegetation cover that mainly limit the soil moisture estimations from satellite in the microwave region.Preliminary results achieved for the flooding event which occurred in the Carpathian basin (Hungary) in April 2000 will be described in detail. They seem to confirm the reliability of the proposed technique in the identification of different amounts of soil wetness, not only during and after the considered event, but, in order to possibly use it for warning system purposes, in the phase preceding the event as well.Such an approach is automatic and, for construction, globally exportable. Moreover, because of the complete independence from the specific satellite platform, such a technique could be easily exported to the new generation of satellite sensors with improved performances like AMSR-E aboard EOS-Aqua and MIRAS aboard SMOS.     

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.