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Wolfgang Wagner - One of the best experts on this subject based on the ideXlab platform.
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evaluation of post retrieval de noising of active and passive microwave satellite soil moisture
Remote Sensing of Environment, 2015Co-Authors: Sugata Narsey, Alexander Gruber, Angelika Xaver, Daniel Chung, Dongryeol Ryu, Wolfgang WagnerAbstract:Abstract Active and passive microwave satellite remote sensing are enabling sub-daily global observations of surface soil moisture (SM) for hydrological, meteorological and climatological studies. Because the retrieved SM data can be quite noisy, post-retrieval processing such as de-noising can play an important role to aid interpretation of the observed dynamics or enhance their utility for data assimilation. To date, the merits of such techniques have not yet been fully evaluated. Here we consider the applications of Fourier-based de-noising filters of Su et al. (2013a) for improving SM retrieved by AMSR-E (Advanced Microwave Scanning Radiometer for Earth Observing System) and ASCAT (Advanced Scatterometer of MetOp-A) sensors. The filters are calibrated in the frequency domain based on a water-balance model, without the need for ancillary data. The evaluation of the de-noising methods was conducted globally against in situ data distributed via the International Soil Moisture Network (ISMN) at 277 AMSR-E and 385 ASCAT pixels. Systematic improvements were found for all considered metrics, namely root-mean-square deviation, linear correlation and signal-to-noise ratio, for both SM products, with improvements more striking for AMSR-E. However, the originally proposed implementation of the filters can induce undesirable over-smoothing and distortion of SM timeseries. To overcome this, based on a simple heuristic argument, we propose the use of ancillary precipitation data in the filtering process, although at some expense of overall agreements with the in situ data.
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an assessment of remotely sensed surface and root zone soil moisture through active and passive sensors in northeast asia
Remote Sensing of Environment, 2015Co-Authors: Eunsang Cho, Minha Choi, Wolfgang WagnerAbstract:Abstract Active and passive microwave remote sensing techniques provide an effective way to observe soil moisture contents. We validated Advanced Scatterometer (ASCAT) and Advanced Microwave Scanning Radiometer — Earth Observing System (AMSR-E) sensor products using estimations from nine different stations located in the Korean peninsula, in northeast Asia from May 1 to September 30, 2010. The results of the surface soil moisture (SSM) products showed a reasonable agreement with the average correlation coefficient (R) values of 0.39, 0.42, and 0.53 for the National Snow and Ice Data Centre (NSIDC), Vrije Universiteit Amsterdam — National Aeronautics and Space Administration (VUA-NASA) AMSR-E, and ASCAT SSM datasets, respectively. The root zone soil moisture (RZSM) products, derived using the NSIDC soil water index (SWI), the United States Department of Agriculture (USDA) AMSR-E, and the ASCAT SWI datasets showed relatively high R values of 0.47, 0.72, and 0.75, respectively, with in situ soil moisture at a depth of 20 cm. In particular, AMSR-E USDA RZSM data show best agreements with in-situ data at 20 cm, among the four depths (10, 20, 30, and 50 cm). In this study, the ASCAT SSM and SWI were rescaled based on the porosity and the effective saturation according to soil texture. Renormalized soil moisture products using three renormalization methods: the linear regression correction (REG), average–standard deviation (μ − σ), and cumulative distribution function (CDF) provided an improvement in biases and RMSEs, with SSM (SWI) RMSEs of 0.04 (0.02), 0.05 (0.03), and 0.05 (0.03) m3/m3 for REG, μ − σ, and CDF matching, respectively. A Taylor diagram was used to assess the accuracy of four satellite soil moisture products with in situ data on a plot. Based on these results, ASCAT soil moisture products were potentially proven to be more appropriate than AMSR-E products in northeast Asia. Remotely sensed soil moisture datasets from passive (AMSR-E) and active (ASCAT) sensors are beneficial to operational hydrological investigations and water management activities.
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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
Journal of remote sensing, 2013Co-Authors: Jianxiu Qiu, Vahid Naeimi, Suxia Liu, Zhonghui Lin, Lihu Yang, Xianfang Song, Guangying Zhang, Wolfgang WagnerAbstract: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.
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estimating root mean square errors in remotely sensed soil moisture over continental scale domains
Remote Sensing of Environment, 2013Co-Authors: Vahid Naeimi, Rolf H Reichle, Richard De Jeu, Robert Parinussa, Clara S Draper, Wolfgang WagnerAbstract:Root Mean Square Errors (RMSEs) in the soil moisture anomaly time series obtained from the Advanced Scatterometer (ASCAT) and the Advanced Microwave Scanning Radiometer (AMSR-E; using the Land Parameter Retrieval Model) are estimated over a continental scale domain centered on North America, using two methods: triple colocation (RMSETC) and error propagation through the soil moisture retrieval models (RMSEEP). In the absence of an established consensus for the climatology of soil moisture over large domains, presenting a RMSE in soil moisture units requires that it be specified relative to a selected reference data set. To avoid the complications that arise from the use of a reference, the RMSE is presented as a fraction of the local time series standard deviation (fRMSE). For both sensors, the fRMSETC and fRMSEEP show similar spatial patterns of relatively high/low errors, and the mean fRMSE for each land cover class is consistent with expectations. Triple colocation is also shown to be surprisingly robust to representativity differences between the soil moisture data sets used, and it is believed to accurately estimate the fRMSE in the remotely sensed soil moisture anomaly time series. Comparing the ASCAT and AMSR-E fRMSETC shows that in general both data sets have good skill over low to moderate vegetation cover. Additionally, they have similar accuracy even when considered by land cover class, although the AMSR-E fRMSEs show a stronger signal of the vegetation cover.
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soil moisture estimation through ascat and amsr e sensors an intercomparison and validation study across europe
Remote Sensing of Environment, 2011Co-Authors: Luca Brocca, Wolfgang Wagner, Wouter Dorigo, S Hasenauer, T Lacava, F Melone, Tommaso Moramarco, Patrick Matgen, J Martinezfernandez, Pilar LlorensAbstract:Abstract 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.
Richard De Jeu - One of the best experts on this subject based on the ideXlab platform.
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The effect of three different data fusion approaches on the quality of soil moisture retrievals from multiple passive microwave sensors
Remote Sensing, 2018Co-Authors: Robin Van Der Schalie, Yann Kerr, Jeanpierre Wigneron, Richard De Jeu, Robert Parinussa, Nemesio Rodriguez-fernandez, Amen Al-yaari, Matthias DruschAbstract:Long-term climate records of soil moisture are of increased importance to climate researchers. In this study, we aim to evaluate the quality of three different fusion approaches that combine soil moisture retrieval from multiple satellite sensors. The arrival of L-band missions has led to an increased focus on the integration of L-band-based soil moisture retrievals in climate records, emphasizing the need to improve our understanding based on its added value within a multi-sensor framework. The three evaluated approaches were developed on 10-year passive microwave data (2003-2013) from two different satellite sensors, i.e., SMOS (2010-2013) and AMSR-E (2003-2011), and are based on a neural network (NN), regressions (REG), and the Land Parameter Retrieval Model (LPRM). The ability of the different approaches to best match AMSR-E and SMOS in their overlapping period was tested using an inter-comparison exercise between the SMOS and AMSR-E datasets, while the skill of the individual soil moisture products, based on anomalies, was evaluated using two verification techniques; first, a data assimilation technique that links precipitation information to the quality of soil moisture (expressed as the R-value), and secondly the triple collocation analysis (TCA). ASCAT soil moisture was included in the skill evaluation, representing the active microwave-based counterpart of soil moisture retrievals. Besides a semi-global analysis, explicit focus was placed on two regions that have strong land-atmosphere coupling, the Sahel (SA) and the central Great Plains (CGP) of North America. The NN approach gives the highest correlation coefficient between SMOS and AMSR-E, closely followed by LPRM and REG, while the absolute error is approximately the same for all three approaches. The R-value and TCA show the strength of using different satellite sources and the impact of different merging approaches on the skill to correctly capture soil moisture anomalies. The highest performance is found for AMSR-E over sparse vegetation, for SMOS over moderate vegetation, and for ASCAT over dense vegetation cover. While the two SMOS datasets (L3 and LPRM) show a similar performance, the three AMSR-E datasets do not. The good performance for AMSR-E over spare vegetation is mainly perceived for AMSR-E LPRM, benefiting from the physically based model, while AMSR-E NN shows improved skill in densely vegetated areas, making optimal use of the SMOS L3 training dataset. AMSR-E REG has a reasonable performance over sparsely vegetated areas; however, it quickly loses skill with increasing vegetation density. The findings over the SA and CGP mainly reflect results that are found in earlier sections. This confirms that historical soil moisture datasets based on a combination of these sources are a valuable source of information for climate research.
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the merging of radiative transfer based surface soil moisture data from smos and amsr e
Remote Sensing of Environment, 2017Co-Authors: Robin Van Der Schalie, N J Rodriguezfernandez, Yann Kerr, Amen Alyaari, Jeanpierre Wigneron, Richard De Jeu, Robert Parinussa, Susanne Mecklenburg, M DruschAbstract:This paper evaluates a methodology to integrate surface soil moisture retrievals from SMOS and AMSR-E into a single, consistent dataset retrieved by the Land Parameter Retrieval Model (LPRM). In a first step, the SMOS LPRM soil moisture retrievals were used as the baseline for optimizing the internal parameterization (i.e. surface roughness and single scattering albedo) of the AMSR-E LPRM retrievals. Secondly, to overcome the uniqueness of these datasets a linear scaling approach was applied resulting in a consistent soil moisture dataset. The new parameter set from the first step is similar for the two (low) frequencies of AMSR-E (i.e. C- and X-band) further improving their inter-comparability for both soil moisture and vegetation optical depth. Soil moisture retrievals from these AMSR-E frequencies were globally merged based on the availability of brightness temperatures that are free from RFI contamination (resulting in AMSR-E LPRMN). This new product was evaluated against both the SMOS LPRM product in the overlapping period (July 2010 to October 2011), as well as the standard, publicly available AMSR-E LPRM dataset (AMSR-E LPRMV3) for an almost 9 year period (January 2003 to October 2011). For the overlapping period, the AMSR-E and SMOS LPRM products show high temporal correlation coefficients (0.60 < R < 0.90) and low root mean square errors (rmse < 0.04 m3 m− 3) for NDVI values up to 0.60. Their agreement tends to drop over the well-known challenging areas such as the arctic region and tropical rainforest. A detailed evaluation over in situ sites from 5 in situ networks worldwide showed that AMSR-E LPRMN often outperforms SMOS LPRM in sparsely vegetated areas, with generally higher correlation coefficients in areas with NDVI < 0.3, and in general a lower unbiased rmse (ubrmse). In line with theoretical expectations, SMOS LPRM outperforms the AMSR-E LPRM product over the more densely vegetated areas. The newly developed AMSR-E LPRMN product was also compared against AMSR-E LPRMV3, revealing a significant increase (from 0.48 to 0.55) in temporal correlation coefficient over 16 in situ networks. This finding was confirmed through a large scale (50°N–50°S) precipitation based verification technique, the so-called Rvalue, which shows a superior performance of the newly developed AMSR-E LPRMN product. Additionally, the linear scaling of AMSR-E LPRMN to the SMOS LPRM leads to further reducing the ubrmse from 0.09 to 0.06 m3 m− 3 and the average bias from 0.14 to 0.00 m3 m− 3 over these stations. The AMSR-E LPRMN was furthermore compared against the top layer of two re-analysis models (i.e. from the Modern-Era Retrospective analysis for Research and Applications-Land and ERA-Interim/Land models) generally demonstrating increased correlation coefficients and reduced ubrmse with the exception of the challenging areas. As a result, this study shows the significant potential of SMOS LPRM to be a successful integrator to build a long term soil moisture record based on multiple passive microwave sensors.
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long term global surface soil moisture fields using an smos trained neural network applied to amsr e data
Remote Sensing, 2016Co-Authors: N J Rodriguezfernandez, Yann Kerr, Robin Van Der Schalie, Amen Alyaari, Jeanpierre Wigneron, Richard De Jeu, Philippe Richaume, Emanuel Dutra, Arnaud Mialon, M DruschAbstract:A method to retrieve soil moisture (SM) from Advanced Scanning Microwave Radiometer—Earth Observing System Sensor (AMSR-E) observations using Soil Moisture and Ocean Salinity (SMOS) Level 3 SM as a reference is discussed. The goal is to obtain longer time series of SM with no significant bias and with a similar dynamical range to that of the SMOS SM dataset. This method consists of training a neural network (NN) to obtain a global non-linear relationship linking AMSR-E brightness temperatures ( T b ) to the SMOS L3 SM dataset on the concurrent mission period of 1.5 years. Then, the NN model is used to derive soil moisture from past AMSR-E observations. It is shown that in spite of the different frequencies and sensing depths of AMSR-E and SMOS, it is possible to find such a global relationship. The sensitivity of AMSR-E T b ’s to soil temperature ( T s o i l ) was also evaluated using European Centre for Medium-Range Weather Forecast Interim/Land re-analysis (ERA-Land) and Modern-Era Retrospective analysis for Research and Applications-Land (MERRA-Land) model data. The best combination of AMSR-E T b ’s to retrieve T s o i l is H polarization at 23 and 36 GHz plus V polarization at 36 GHz. Regarding SM, several combinations of input data show a similar performance in retrieving SM. One NN that uses C and X bands and T s o i l information was chosen to obtain SM in the 2003–2011 period. The new dataset shows a low bias (<0.02 m3/m3) and low standard deviation of the difference (<0.04 m3/m3) with respect to SMOS L3 SM over most of the globe’s surface. The new dataset was evaluated together with other AMSR-E SM datasets and the Climate Change Initiative (CCI) SM dataset against the MERRA-Land and ERA-Land models for the 2003–2011 period. All datasets show a significant bias with respect to models for boreal regions and high correlations over regions other than the tropical and boreal forest. All of the global SM datasets including AMSR-E NN were also evaluated against a large number of in situ measurements over four continents. Over Australia, all datasets show a strong level of agreement with in situ measurements. Models perform better over Europe and mountainous regions in North America. Remote sensing datasets (in particular NN and the Land Parameter Retrieval Model (LPRM)) perform as well as models for other North American sites and perform better than models over the Sahel region.
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comparison of smos and amsr e vegetation optical depth to four modis based vegetation indices
Remote Sensing of Environment, 2016Co-Authors: Jennifer Grant, Jeanpierre Wigneron, Richard De Jeu, Philippe Richaume, Arnaud Mialon, M Drusch, Heather Lawrence, Ahmad Al Bitar, M Van Marle, Y KerrAbstract:Abstract The main objectives of this study were to provide a proxy “validation” of the Soil Moisture and Ocean Salinity (SMOS) mission's vegetation optical depth product ( τ SMOS ) on a global scale, to give a first indication of the potential of τ SMOS to capture large-scale vegetation dynamics, and to contribute towards investigations into the possible use of optical vegetation indices (VI's) for the estimation of τ . The analyses were performed by comparing the spatial and temporal behaviour of τ SMOS relative to four MODIS-based VI's, with that of the vegetation optical depth from a similar sensor, AMSR-E ( τ AMSR-E ). 16-day and annual average values of the passive microwave optical depth ( τ ) for the year 2010 were obtained from SMOS (1.4 GHz) and AMSR-E (6.9 GHz) observations. The VI's chosen for this study were the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI) and Normalized Difference Water Index (NDWI). The highest global-scale, annual correlation was found between τ SMOS and τ AMSR-E from ascending orbits (Spearman's R = 0.80). On global, annual scales, τ SMOS showed higher correlations with τ AMSR-E than with the VI's, while τ AMSR-E was more highly correlated with VI's than with τ SMOS . Timeseries of both τ and the VI's were made per landcover class, for the northern hemisphere, tropics and southern hemisphere. Although the large-scale spatial and spatio-temporal behaviour of τ SMOS and τ AMSR-E is generally similar, the results highlight some notable differences in observing vegetation with optical vs . passive microwave sensors, and certain crucial differences between the two passive microwave sensors themselves. Overall, the results found in this study give a good first confidence in the SMOS L3 τ product and its potential use in vegetation studies. These results provide an essential general reference for future (global-scale) vegetation monitoring with passive microwaves, for the future inclusion of τ SMOS in long-term, multi-sensor datasets, and for passive microwave algorithm development.
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a preliminary study toward consistent soil moisture from amsr2
Journal of Hydrometeorology, 2015Co-Authors: Robert Parinussa, Thomas R H Holmes, Niko Wanders, Wouter Dorigo, Richard De JeuAbstract:AbstractA preliminary study toward consistent soil moisture products from the Advanced Microwave Scanning Radiometer 2 (AMSR2) is presented. Its predecessor, the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), has provided Earth scientists with a consistent and continuous global soil moisture dataset. A major challenge remains to achieve synergy between these soil moisture datasets, which is hampered by the lack of an overlapping observation period of the sensors. Here, observations of the multifrequency microwave radiometer on board the Tropical Rainfall Measuring Mission (TRMM) satellite were used to improve consistency between AMSR-E and AMSR2. Several scenarios to achieve synergy between the AMSR-E and AMSR2 soil moisture products were evaluated. The novel soil moisture retrievals from C-band observations, a frequency band that is lacking on board the TRMM satellite, are also presented. A global comparison of soil moisture retrievals against ERA-Interim soil moisture demons...
Robert Parinussa - One of the best experts on this subject based on the ideXlab platform.
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The effect of three different data fusion approaches on the quality of soil moisture retrievals from multiple passive microwave sensors
Remote Sensing, 2018Co-Authors: Robin Van Der Schalie, Yann Kerr, Jeanpierre Wigneron, Richard De Jeu, Robert Parinussa, Nemesio Rodriguez-fernandez, Amen Al-yaari, Matthias DruschAbstract:Long-term climate records of soil moisture are of increased importance to climate researchers. In this study, we aim to evaluate the quality of three different fusion approaches that combine soil moisture retrieval from multiple satellite sensors. The arrival of L-band missions has led to an increased focus on the integration of L-band-based soil moisture retrievals in climate records, emphasizing the need to improve our understanding based on its added value within a multi-sensor framework. The three evaluated approaches were developed on 10-year passive microwave data (2003-2013) from two different satellite sensors, i.e., SMOS (2010-2013) and AMSR-E (2003-2011), and are based on a neural network (NN), regressions (REG), and the Land Parameter Retrieval Model (LPRM). The ability of the different approaches to best match AMSR-E and SMOS in their overlapping period was tested using an inter-comparison exercise between the SMOS and AMSR-E datasets, while the skill of the individual soil moisture products, based on anomalies, was evaluated using two verification techniques; first, a data assimilation technique that links precipitation information to the quality of soil moisture (expressed as the R-value), and secondly the triple collocation analysis (TCA). ASCAT soil moisture was included in the skill evaluation, representing the active microwave-based counterpart of soil moisture retrievals. Besides a semi-global analysis, explicit focus was placed on two regions that have strong land-atmosphere coupling, the Sahel (SA) and the central Great Plains (CGP) of North America. The NN approach gives the highest correlation coefficient between SMOS and AMSR-E, closely followed by LPRM and REG, while the absolute error is approximately the same for all three approaches. The R-value and TCA show the strength of using different satellite sources and the impact of different merging approaches on the skill to correctly capture soil moisture anomalies. The highest performance is found for AMSR-E over sparse vegetation, for SMOS over moderate vegetation, and for ASCAT over dense vegetation cover. While the two SMOS datasets (L3 and LPRM) show a similar performance, the three AMSR-E datasets do not. The good performance for AMSR-E over spare vegetation is mainly perceived for AMSR-E LPRM, benefiting from the physically based model, while AMSR-E NN shows improved skill in densely vegetated areas, making optimal use of the SMOS L3 training dataset. AMSR-E REG has a reasonable performance over sparsely vegetated areas; however, it quickly loses skill with increasing vegetation density. The findings over the SA and CGP mainly reflect results that are found in earlier sections. This confirms that historical soil moisture datasets based on a combination of these sources are a valuable source of information for climate research.
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the merging of radiative transfer based surface soil moisture data from smos and amsr e
Remote Sensing of Environment, 2017Co-Authors: Robin Van Der Schalie, N J Rodriguezfernandez, Yann Kerr, Amen Alyaari, Jeanpierre Wigneron, Richard De Jeu, Robert Parinussa, Susanne Mecklenburg, M DruschAbstract:This paper evaluates a methodology to integrate surface soil moisture retrievals from SMOS and AMSR-E into a single, consistent dataset retrieved by the Land Parameter Retrieval Model (LPRM). In a first step, the SMOS LPRM soil moisture retrievals were used as the baseline for optimizing the internal parameterization (i.e. surface roughness and single scattering albedo) of the AMSR-E LPRM retrievals. Secondly, to overcome the uniqueness of these datasets a linear scaling approach was applied resulting in a consistent soil moisture dataset. The new parameter set from the first step is similar for the two (low) frequencies of AMSR-E (i.e. C- and X-band) further improving their inter-comparability for both soil moisture and vegetation optical depth. Soil moisture retrievals from these AMSR-E frequencies were globally merged based on the availability of brightness temperatures that are free from RFI contamination (resulting in AMSR-E LPRMN). This new product was evaluated against both the SMOS LPRM product in the overlapping period (July 2010 to October 2011), as well as the standard, publicly available AMSR-E LPRM dataset (AMSR-E LPRMV3) for an almost 9 year period (January 2003 to October 2011). For the overlapping period, the AMSR-E and SMOS LPRM products show high temporal correlation coefficients (0.60 < R < 0.90) and low root mean square errors (rmse < 0.04 m3 m− 3) for NDVI values up to 0.60. Their agreement tends to drop over the well-known challenging areas such as the arctic region and tropical rainforest. A detailed evaluation over in situ sites from 5 in situ networks worldwide showed that AMSR-E LPRMN often outperforms SMOS LPRM in sparsely vegetated areas, with generally higher correlation coefficients in areas with NDVI < 0.3, and in general a lower unbiased rmse (ubrmse). In line with theoretical expectations, SMOS LPRM outperforms the AMSR-E LPRM product over the more densely vegetated areas. The newly developed AMSR-E LPRMN product was also compared against AMSR-E LPRMV3, revealing a significant increase (from 0.48 to 0.55) in temporal correlation coefficient over 16 in situ networks. This finding was confirmed through a large scale (50°N–50°S) precipitation based verification technique, the so-called Rvalue, which shows a superior performance of the newly developed AMSR-E LPRMN product. Additionally, the linear scaling of AMSR-E LPRMN to the SMOS LPRM leads to further reducing the ubrmse from 0.09 to 0.06 m3 m− 3 and the average bias from 0.14 to 0.00 m3 m− 3 over these stations. The AMSR-E LPRMN was furthermore compared against the top layer of two re-analysis models (i.e. from the Modern-Era Retrospective analysis for Research and Applications-Land and ERA-Interim/Land models) generally demonstrating increased correlation coefficients and reduced ubrmse with the exception of the challenging areas. As a result, this study shows the significant potential of SMOS LPRM to be a successful integrator to build a long term soil moisture record based on multiple passive microwave sensors.
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a preliminary study toward consistent soil moisture from amsr2
Journal of Hydrometeorology, 2015Co-Authors: Robert Parinussa, Thomas R H Holmes, Niko Wanders, Wouter Dorigo, Richard De JeuAbstract:AbstractA preliminary study toward consistent soil moisture products from the Advanced Microwave Scanning Radiometer 2 (AMSR2) is presented. Its predecessor, the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), has provided Earth scientists with a consistent and continuous global soil moisture dataset. A major challenge remains to achieve synergy between these soil moisture datasets, which is hampered by the lack of an overlapping observation period of the sensors. Here, observations of the multifrequency microwave radiometer on board the Tropical Rainfall Measuring Mission (TRMM) satellite were used to improve consistency between AMSR-E and AMSR2. Several scenarios to achieve synergy between the AMSR-E and AMSR2 soil moisture products were evaluated. The novel soil moisture retrievals from C-band observations, a frequency band that is lacking on board the TRMM satellite, are also presented. A global comparison of soil moisture retrievals against ERA-Interim soil moisture demons...
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estimating root mean square errors in remotely sensed soil moisture over continental scale domains
Remote Sensing of Environment, 2013Co-Authors: Vahid Naeimi, Rolf H Reichle, Richard De Jeu, Robert Parinussa, Clara S Draper, Wolfgang WagnerAbstract:Root Mean Square Errors (RMSEs) in the soil moisture anomaly time series obtained from the Advanced Scatterometer (ASCAT) and the Advanced Microwave Scanning Radiometer (AMSR-E; using the Land Parameter Retrieval Model) are estimated over a continental scale domain centered on North America, using two methods: triple colocation (RMSETC) and error propagation through the soil moisture retrieval models (RMSEEP). In the absence of an established consensus for the climatology of soil moisture over large domains, presenting a RMSE in soil moisture units requires that it be specified relative to a selected reference data set. To avoid the complications that arise from the use of a reference, the RMSE is presented as a fraction of the local time series standard deviation (fRMSE). For both sensors, the fRMSETC and fRMSEEP show similar spatial patterns of relatively high/low errors, and the mean fRMSE for each land cover class is consistent with expectations. Triple colocation is also shown to be surprisingly robust to representativity differences between the soil moisture data sets used, and it is believed to accurately estimate the fRMSE in the remotely sensed soil moisture anomaly time series. Comparing the ASCAT and AMSR-E fRMSETC shows that in general both data sets have good skill over low to moderate vegetation cover. Additionally, they have similar accuracy even when considered by land cover class, although the AMSR-E fRMSEs show a stronger signal of the vegetation cover.
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developing an improved soil moisture dataset by blending passive and active microwave satellite based retrievals
Hydrology and Earth System Sciences, 2010Co-Authors: Robert Parinussa, Wolfgang Wagner, Wouter Dorigo, A I J M Van Dijk, Matthew F Mccabe, Jason P EvansAbstract:Combining information derived from satellite- based passive and active microwave sensors has the potential to offer improved estimates of surface soil moisture at global scale. We develop and evaluate a methodology that takes ad- vantage of the retrieval characteristics of passive (AMSR-E) and active (ASCAT) microwave satellite estimates to pro- duce an improved soil moisture product. First, volumetric soil water content (m 3 m 3 ) from AMSR-E and degree of saturation (%) from ASCAT are rescaled against a reference land surface model data set using a cumulative distribution function matching approach. While this imposes any bias of the reference on the rescaled satellite products, it adjusts them to the same range and preserves the dynamics of orig- inal satellite-based products. Comparison with in situ mea- surements demonstrates that where the correlation coefficient between rescaled AMSR-E and ASCAT is greater than 0.65 ("transitional regions"), merging the different satellite prod- ucts increases the number of observations while minimally changing the accuracy of soil moisture retrievals. These tran- sitional regions also delineate the boundary between sparsely and moderately vegetated regions where rescaled AMSR-E and ASCAT, respectively, are used for the merged product. Therefore the merged product carries the advantages of bet- ter spatial coverage overall and increased number of obser- vations, particularly for the transitional regions. The com- bination method developed has the potential to be applied to existing microwave satellites as well as to new missions. Accordingly, a long-term global soil moisture dataset can be developed and extended, enhancing basic understanding of the role of soil moisture in the water, energy and carbon cy- cles.
Minha Choi - One of the best experts on this subject based on the ideXlab platform.
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an assessment of remotely sensed surface and root zone soil moisture through active and passive sensors in northeast asia
Remote Sensing of Environment, 2015Co-Authors: Eunsang Cho, Minha Choi, Wolfgang WagnerAbstract:Abstract Active and passive microwave remote sensing techniques provide an effective way to observe soil moisture contents. We validated Advanced Scatterometer (ASCAT) and Advanced Microwave Scanning Radiometer — Earth Observing System (AMSR-E) sensor products using estimations from nine different stations located in the Korean peninsula, in northeast Asia from May 1 to September 30, 2010. The results of the surface soil moisture (SSM) products showed a reasonable agreement with the average correlation coefficient (R) values of 0.39, 0.42, and 0.53 for the National Snow and Ice Data Centre (NSIDC), Vrije Universiteit Amsterdam — National Aeronautics and Space Administration (VUA-NASA) AMSR-E, and ASCAT SSM datasets, respectively. The root zone soil moisture (RZSM) products, derived using the NSIDC soil water index (SWI), the United States Department of Agriculture (USDA) AMSR-E, and the ASCAT SWI datasets showed relatively high R values of 0.47, 0.72, and 0.75, respectively, with in situ soil moisture at a depth of 20 cm. In particular, AMSR-E USDA RZSM data show best agreements with in-situ data at 20 cm, among the four depths (10, 20, 30, and 50 cm). In this study, the ASCAT SSM and SWI were rescaled based on the porosity and the effective saturation according to soil texture. Renormalized soil moisture products using three renormalization methods: the linear regression correction (REG), average–standard deviation (μ − σ), and cumulative distribution function (CDF) provided an improvement in biases and RMSEs, with SSM (SWI) RMSEs of 0.04 (0.02), 0.05 (0.03), and 0.05 (0.03) m3/m3 for REG, μ − σ, and CDF matching, respectively. A Taylor diagram was used to assess the accuracy of four satellite soil moisture products with in situ data on a plot. Based on these results, ASCAT soil moisture products were potentially proven to be more appropriate than AMSR-E products in northeast Asia. Remotely sensed soil moisture datasets from passive (AMSR-E) and active (ASCAT) sensors are beneficial to operational hydrological investigations and water management activities.
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a microwave optical infrared disaggregation for improving spatial representation of soil moisture using amsr e and modis products
Remote Sensing of Environment, 2012Co-Authors: Minha Choi, Yoomi HurAbstract:Abstract This study validated the Advanced Microwave Scanning Radiometer E (AMSR-E) soil moisture products developed by the Vrije Universiteit Amsterdam (VUA) in collaboration with the National Aeronautics and Space Administration (NASA) using ground measurements at six Rural Development Administration network sites during the 2007 growing season (May 1 through September 30) in Korea. In order to overcome current key validation issues from the spatial scaling mismatch between the ground measurements (point scale) and the AMSR-E soil moisture products (25 km scale), a synergistic approach from 25 to 1 km spatial resolution was performed using auxiliary data from a Moderate Resolution Imaging Spectroradiometer (MODIS). The 25 km VUA-NASA AMSR-E soil moisture data had error statistics (biases = − 0.099 to − 0.170 m3 m− 3, root-mean-squared error = 0.131 to 0.179 m3 m− 3, and correlation coefficients = 0.127 to 0.725) that were worse than the suggested goal of accuracy (less than or equal to root‐mean‐squared error of 0.06 m3 m− 3) determined by the several previous validation studies. This deficiency is theoretically due to the spatial scaling mismatch and different measurement depth between the ground measurements and the VUA-NASA AMSR-E soil moisture products. The disaggregated 1 km soil moisture was reasonably similar to the 25 km VUA-NASA AMSR-E soil moisture at spatial and temporal scales with better error statistics (biases = − 0.024 to − 0.158 m3 m− 3, root-mean-squared error = 0.059 to 0.171 m3 m− 3, and correlation coefficients = 0.348 to 0.658). Although additional studies are needed under a range of field conditions, the synergistic approach used in this study appears to be a feasible method to improve the spatial distributions of VUA-NASA AMSR-E soil moisture products as well as future microwave soil moisture products.
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evaluation of multiple surface soil moisture for korean regional flux monitoring network sites advanced microwave scanning radiometer e land surface model and ground measurements
Hydrological Processes, 2012Co-Authors: Minha ChoiAbstract:In the past few decades, there have been great developments in remotely sensed soil moisture, with validation efforts using land surface models (LSMs) and ground-based measurements, because soil moisture information is essential to understanding complex land surface–atmosphere interactions. However, the validation of remotely sensed soil moisture has been very limited because of the scarcity of the ground measurements in Korea. This study validated Advanced Microwave Scanning Radiometer E (AMSR-E) soil moisture data with the Common Land Model (CLM), one of the most widely used LSMs, and ground-based measurements at two Korean regional flux monitoring network sites. There was reasonable agreement regarding the different soil moisture products for monitoring temporal trends except National Snow and Ice Data Centre (NSIDC) AMSR-E soil moisture, albeit there were essential comparison limitations by different spatial scales and soil depths. The AMSR-E soil moisture data published by the National Aeronautics and Space Administration and Vrije Universiteit Amsterdam (VUA) showed potential to replicate temporal variability patterns (root-mean-square errors = 0·10–0·14 m3 m−3 and wet BIAS = 0·09 − 0·04 m3 m−3) with the CLM and ground-based measurements. However, the NSIDC AMSR-E soil moisture was problematic because of the extremely low temporal variability and the VUA AMSR-E soil moisture was relatively inaccurate in Gwangneung site characterized by complex geophysical conditions. Additional evaluations should be required to facilitate the use of recent and forthcoming remotely sensed soil moisture data from Soil Moisture and Ocean Salinity and Soil Moisture Active and Passive missions at representative future validation sites. Copyright © 2011 John Wiley & Sons, Ltd.
Thomas R H Holmes - One of the best experts on this subject based on the ideXlab platform.
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a preliminary study toward consistent soil moisture from amsr2
Journal of Hydrometeorology, 2015Co-Authors: Robert Parinussa, Thomas R H Holmes, Niko Wanders, Wouter Dorigo, Richard De JeuAbstract:AbstractA preliminary study toward consistent soil moisture products from the Advanced Microwave Scanning Radiometer 2 (AMSR2) is presented. Its predecessor, the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), has provided Earth scientists with a consistent and continuous global soil moisture dataset. A major challenge remains to achieve synergy between these soil moisture datasets, which is hampered by the lack of an overlapping observation period of the sensors. Here, observations of the multifrequency microwave radiometer on board the Tropical Rainfall Measuring Mission (TRMM) satellite were used to improve consistency between AMSR-E and AMSR2. Several scenarios to achieve synergy between the AMSR-E and AMSR2 soil moisture products were evaluated. The novel soil moisture retrievals from C-band observations, a frequency band that is lacking on board the TRMM satellite, are also presented. A global comparison of soil moisture retrievals against ERA-Interim soil moisture demons...
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soil moisture retrievals from the windsat spaceborne polarimetric microwave radiometer
IEEE Transactions on Geoscience and Remote Sensing, 2012Co-Authors: R M Parinussa, Thomas R H Holmes, R A M De JeuAbstract:An existing methodology to derive surface soil moisture from passive microwave satellite observations is applied to the WindSat multifrequency polarimetric microwave radiometer. The methodology is a radiative-transfer-based model that has successfully been applied to a series of (historical) satellite sensors, including the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). Brightness temperature observations from the WindSat and AMSR-E radiometers were compared, and the WindSat observations were adjusted to overcome small sensor differences (e.g., frequency, bandwidth, incidence angle, and original sensor calibration procedure). The method to relate Ka-band brightness temperature observations to land surface temperature was adapted to the overpass times of WindSat. Statistical analysis with both satellite-observed and in situ soil moistures indicates that the quality of the newly derived WindSat soil moisture product is similar to that obtained with AMSR-E after the adjustment of the WindSat brightness temperature observations. The average correlation coefficients (R) between satellite soil moisture and in situ observations are similar for the two satellites with average values of R = 0.60 for WindSat and R = 0.62 for AMSR-E as calculated from 33 sites. On a global scale, the average correlation coefficient between the two satellite soil moisture products is high with a value of R = 0.83. The results of this study demonstrate that soil moisture from WindSat is consistent with existing soil moisture products derived from AMSR-E using the land parameter retrieval model. Therefore, the soil moisture retrievals from these two satellites could easily be combined to increase the temporal resolution of satellite-derived soil moisture observations.
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an evaluation of amsr e derived soil moisture over australia
Remote Sensing of Environment, 2009Co-Authors: C Draper, Jeffrey P Walker, Peter Steinle, Thomas R H HolmesAbstract:Abstract 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.