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Wolfgang Wagner - One of the best experts on this subject based on the ideXlab platform.
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long term soil moisture data records derived from a series of european scatterometers
Reference Module in Earth Systems and Environmental Sciences#R##N#Comprehensive Remote Sensing, 2018Co-Authors: Christoph Reimer, Thomas Melzer, Wolfgang WagnerAbstract:The article gives an in-depth overview of historical and recent advancements in the soil moisture retrieval from European C-band scatterometer missions. The TU Wien soil moisture retrieval model is outlined and complemented with an overview of available global soil moisture products derived from ERS ESCAT and MetOp ASCAT. Finally, two methodologies are presented for the creation of a consistent soil moisture data record for climate change research.
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global scale comparison of passive smos and active ascat satellite based microwave soil moisture retrievals with soil moisture simulations merra land
Remote Sensing of Environment, 2014Co-Authors: Jp. Wigneron, Agnes Ducharne, Yann Kerr, Wolfgang Wagner, Gabrielle De Lannoy, Amen Alyaari, Rolf H Reichle, Ahmad Al BitarAbstract:Global surface soil moisture (SSM) datasets are being produced based on active and passive microwave satellite observations and simulations from land surface models (LSM). This study investigates the consistency of two global satellite-based SSM datasets based on microwave remote sensing observations from the passive Soil Moisture and Ocean Salinity (SMOS;SMOSL3 version 2.5) and the active Advanced Scatterometer (ASCAT; version TUWien- WARP 5.5) with respect to LSM SSM from the MERRA-Land data product. The relationship between the global-scale SSM products was studied during the 2010-2012 period using (1) a time series statistics (considering both original SSM data and anomalies), (2) a space-time analysis using Hovmoller diagrams, and (3) a triple collocation error model. The SMOSL3 and ASCAT retrievals are consistent with the temporal dynamics of modeled SSM (correlation R (is) greater than 0.70 for original SSM) in the transition zones between wet and dry climates, including the Sahel, the Indian subcontinent, the Great Plains of North America, eastern Australia, and southeastern Brazil. Over relatively dense vegetation covers, a better consistency with MERRA-Land was obtained with ASCAT than with SMOSL3. However, it was found that ASCAT retrievals exhibit negative correlation versus MERRA-Land in some arid regions (e.g., the Sahara and the Arabian Peninsula). In terms of anomalies, SMOSL3 better captures the short term SSM variability of the reference dataset (MERRA-Land) than ASCAT over regions with limited radio frequency interference (RFI) effects (e.g., North America, South America, and Australia). The seasonal and latitudinal variations of SSM are relatively similar for the three products, although the MERRALand SSM values are generally higher and their seasonal amplitude is much lower than for SMOSL3 and ASCAT. Both SMOSL3 and ASCAT have relatively comparable triple collocation errors with similar spatial error patterns: (i) lowest errors in arid regions (e.g., Sahara and Arabian Peninsula), due to the very low natural variability of soil moisture in these areas, and Central America, and (ii) highest errors over most of the vegetated regions (e.g., northern Australia, India, central Asia, and South America). However, the ASCAT SSM product is prone to larger random errors in some regions (e.g., north-western Africa, Iran, and southern South Africa). Vegetation density was found to be a key factor to interpret the consistency with MERRA-Land between the two remotely sensed products (SMOSL3 and ASCAT) which provides complementary information on SSM. This study shows that both SMOS and ASCAT have thus a potential for data fusion into long-term data records.
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Global-scale comparison of passive (SMOS) and active (ASCAT) satellite based microwave soil moisture retrievals with soil moisture simulations (MERRA-Land)
Remote Sensing of Environment, 2014Co-Authors: Amen Al Yaari, Wolfgang Wagner, Rolf Reichle, Gabrielle De Lannoy, Wouter Dorigo, Ahmad Albitar, Philippe RichaumeAbstract:Global surface soil moisture (SSM) datasets are being produced based on active and passive microwave satellite observations and simulations from land surface models (LSM). This study investigates the consistency of two global satellite-based SSM datasets based on microwave remote sensing observations from the passive Soil Moisture and Ocean Salinity (SMOS; SMOSL3 version 2.5) and the active Advanced Scatterometer (ASCAT; version TU-Wien-WARP 5.5) with respect to LSM SSM from the MERRA-Land data product. The relationship between the global-scale SSM products was studied during the 2010-2012 period using (1) a time series statistics (considering both original SSM data and anomalies), (2) a space-time analysis using Hovmöller diagrams, and (3) a triple collocation error model. The SMOSL3 and ASCAT retrievals are consistent with the temporal dynamics of modeled SSM (correlation R > 0.70 for original SSM) in the transition zones between wet and dry climates, including the Sahel, the Indian subcontinent, the Great Plains of North America, eastern Australia, and south-eastern Brazil. Over relatively dense vegetation covers, a better consistency with MERRA-Land was obtained with ASCAT than with SMOSL3. However, it was found that ASCAT retrievals exhibit negative correlation versus MERRA-Land in some arid regions (e.g., the Sahara and the Arabian Peninsula). In terms of anomalies, SMOSL3 better captures the short term SSM variability of the reference dataset (MERRA-Land) than ASCAT over regions with limited radio frequency interference (RFI) effects (e.g., North America, South America, and Australia). The seasonal and latitudinal variations of SSM are relatively similar for the three products, although the MERRA-Land SSM values are generally higher and their seasonal amplitude is much lower than for SMOSL3 and ASCAT. Both SMOSL3 and ASCAT have relatively comparable triple collocation errors with similar spatial error patterns: (i) lowest errors in arid regions (e.g., Sahara and Arabian Peninsula), due to the very low natural variability of soil moisture in these areas, and Central America, and (ii) highest errors over most of the vegetated regions (e.g., northern Australia, India, central Asia, and South America). However, the ASCAT SSM product is prone to larger random errors in some regions (e.g., north-western Africa, Iran, and southern South Africa). Vegetation density was found to be a key factor to interpret the consistency with MERRA-Land between the two remotely sensed products (SMOSL3 and ASCAT) which provides complementary information on SSM. This study shows that both SMOS and ASCAT have thus a potential for data fusion into long-term data records.
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Compared performances of microwave passive soil moisture retrievals (SMOS) and active soil moisture retrievals (ASCAT) using land surface model estimates (MERRA-LAND)
2014Co-Authors: Amen Al Yaari, Jp. Wigneron, Agnes Ducharne, Yann Kerr, Wolfgang Wagner, Rolf Reichle, Gabrielle De Lannoy, Ahmad Al Bitar, Wouter Dorigo, Marie ParrensAbstract:Performances of two global satellite-based surface soil moisture (SSM) retrievals with respect to model-based SSM derived from the MERRA (Modern-Era Retrospective analysis for Research and Applications) rea-nalysis were explored in this paper: (i) Soil Moisture and Ocean Salinity (SMOS; passive) Level-3 SSM (SMOSL3) and (ii) the Advanced Scatterometer (ASCAT; active) SSM. Temporal correlation was used to investigate the performance of SMOSL3 and ASCAT SSM products during the period 05/2010–2012 on a global basis. Both SMOSL3 and ASCAT (slightly better) captured well (R>0.70) the long-term variability of the modelled SSM, particularly, over the Indian subcontinent, the Great Plains of North America, and the Sahel. However, ASCAT had negative correlations in arid regions, in particular across the Sahara and the Arabian Peninsula. This may be due to complex scattering mechanisms over very dry surfaces. To explore the land cover dependence of the analyzed statistical indicators, the global correlation results were averaged per biome extracted from a global map of biomes. In general, SMOSL3 and ASCAT performances behaved differently from one biome to another. For SMOSL3, the highest average correlation was observed over “tropical semi-arid” (R = ∼ 0.5) and “temperate semi-arid” biomes, whereas for ASCAT, the highest correlations were observed over “tropical semi-arid” (R = ∼ 0.7) and “tropical humid” biomes. The poorest agreement for both SMOSL3 and ASCAT was generally found over “tundra” and “desert temperate” biomes, particularly for ASCAT. This study showed that the performance of both SMOSL3 and ASCAT is highly dependent on vegetation. We also showed that both of them provide complementary information on SSM, which implies a potential for data fusion which would be pertinent for the ESA climate change initiative (CCI).
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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.
David G. Long - One of the best experts on this subject based on the ideXlab platform.
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Study of Iceberg BlOA using Scatterometer Data
2013Co-Authors: Haroon Stephen, David G. LongAbstract:Abstract- The Antarctic continent continually releases glacial ice into the ocean in the form of icebergs calving from glaciers and ice shelves. Microwave scatterometers can provide useful information about the spatial and temporal behavior of large icebergs. In this paper, results from the observation of BlOA during 1992-2000, using ERS-1/2 AMI Scatterometer (ESCAT), NASA Scatterometer (NSCAT) and SeaWinds on QuikScat (QSCAT) data are presented. Multi-sensor analysis shows a general consistency of C-band & ' measurements from ESCAT and Ku-band a " measurements from NSCAT and SeaWinds. Certain subtle differences are observed which reflect the frequency dependence of iceberg a " measurements
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Vegetation Study of Amazon using QSCAT in comparison with SASS, ESCAT and NSCAT
2013Co-Authors: Haroon Stephen, David G. Long, Perry J. HardinAbstract:Abstract- The Amazon basin presents a large geographical zone that has undergone significant land cover changes during the last few decades due to accelerated logging and other anthropogenic influences. Amazon forest consists of diverse types of vegetation, which vary with various geophysical factors, like latitude, moisture, rainfall etc. In this paper, variations in the radar backscatter measurements (a") from various scatterometers over the Amazon basin are presented for selected study regions. C-band a" from ERS scatterometer (ESCAT) are found to exhibit a general decrease over the period 1992-1999. ESCAT and Ku band NASA scatterometer (NSCAT) a " measurements are compared to study the multi-spectral signatures and are, in general, found consistent. Ku band a " measurements from Seawinds on QuikScat (QSCAT) are used in conjunction with the NSCAT and Seasat scatterometer (SASS) data to study the change since 1978
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Greenland snow accumulation estimates from satellite radar scatterometer data
2013Co-Authors: Mark R. Drinkwater, David G. Long, Andrew W. BinghamAbstract:Abstract. Data collected by the C band ERS-2 wind scatterometer (EScat), the Ku band ADEOS-1 NASA scatterometer (NSCAT), and the Ku band SeaWinds on QuikScat (QSCAT) satellite instruments are used to illustrate spatiotemporal variability in snow accumulation on the Greenland ice sheet. Microwave radar backscatter images of Greenland are derived using the scatterometer image reconstruction (SIR) method at 3-day intervals over the periods 1991–1998 and 1996–1997 for EScat and NSCAT, respectively. The backscatter coefficient � � normalized to 40 � incidence, A, and gradient in backscatter, B, in the range 20�–60 � are compared with historical snow accumulation data and recent measurements made in the Program for Arctic Regional Climate Assessment (PARCA) shallow snow pits. Empirical relationships derived from these comparisons reveal different exponential relationships between C and Ku band A values and dry snow zone mean annual accumulation, Q. Frequency difference images between overlapping scatterometer images suggest that C band data are more sensitive to snow layering and buried inhomogeneities, whereas Ku band data are more sensitive to volume scattering from recently accumulated snow. Direct comparisons between NSCAT B values and i
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AUTOMATED ANTARCTIC ICE EDGE DETECTION TJSING NSCA'I ' DATA
2013Co-Authors: Qujinn P. Remund, David G. LongAbstract:Abstract- Polar sea ice plays an important role in the global climate and other geophysical processes. Although spaccborne scatterometers such as NSC.AT have low inherent spatial resolution, resolution enhancement techniques can be utilized to make NSCAT data useful for monitoring sea ice extent in the Antarctic. Dual polarization radar measurements are A and 6: values are used in a linear discrimination analysis to identify sea ice and ocean pixels in composite images. Ice edge detection noise reduction is performed through region growing and erosion/dilation techniques. The algorithm is applied to actual NSCAT data. The resulting edge closely matches the NSIDC SSM/I derived 50 % ice concentraltion edge
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Analysis of scatterometer observations of Saharan ergs using a simple rough facet model
2013Co-Authors: Haroon Stephen, David G. LongAbstract:Abstract — The Sahara desert includes large expanses of sand dunes called ergs. These dunes are formed and constantly reshaped by prevailing winds. Previous study shows that Saharan ergs exhibit significant radar backscatter (σ ◦ ) modulation with azimuth angle (φ). We use σ ◦ measurements observed at various incidence angles (θ) andφ from the NASA scatterometer (NSCAT), the Seawinds scatterometer aboard QuikSCAT (QS-CAT), the ERS scatterometer (ESCAT) and the Tropical Rain Monitoring Mission’s Precipitation Radar (TRMM-PR) to model the σ ◦ response from sand dunes. Sand dunes are modeled as a composite of tilted rough facets and small ripples. The dune fields are modeled as composed of many simple dunes. The σ ◦ measured by the scatterometer from (θ, φ) look direction is the sum of the returns from all the rough facets in the footprint. The model is applied to linear and transverse dunes with rough facets and Gaussian tilt distributions. The model results in a σ ◦ response similar to the NSCAT and ESCAT observations over areas of known dune types in the Sahara. This analysis gives a unique insight into scattering by large scale sand bedforms. I
Amen Alyaari - One of the best experts on this subject based on the ideXlab platform.
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global scale comparison of passive smos and active ascat satellite based microwave soil moisture retrievals with soil moisture simulations merra land
Remote Sensing of Environment, 2014Co-Authors: Jp. Wigneron, Agnes Ducharne, Yann Kerr, Wolfgang Wagner, Gabrielle De Lannoy, Amen Alyaari, Rolf H Reichle, Ahmad Al BitarAbstract:Global surface soil moisture (SSM) datasets are being produced based on active and passive microwave satellite observations and simulations from land surface models (LSM). This study investigates the consistency of two global satellite-based SSM datasets based on microwave remote sensing observations from the passive Soil Moisture and Ocean Salinity (SMOS;SMOSL3 version 2.5) and the active Advanced Scatterometer (ASCAT; version TUWien- WARP 5.5) with respect to LSM SSM from the MERRA-Land data product. The relationship between the global-scale SSM products was studied during the 2010-2012 period using (1) a time series statistics (considering both original SSM data and anomalies), (2) a space-time analysis using Hovmoller diagrams, and (3) a triple collocation error model. The SMOSL3 and ASCAT retrievals are consistent with the temporal dynamics of modeled SSM (correlation R (is) greater than 0.70 for original SSM) in the transition zones between wet and dry climates, including the Sahel, the Indian subcontinent, the Great Plains of North America, eastern Australia, and southeastern Brazil. Over relatively dense vegetation covers, a better consistency with MERRA-Land was obtained with ASCAT than with SMOSL3. However, it was found that ASCAT retrievals exhibit negative correlation versus MERRA-Land in some arid regions (e.g., the Sahara and the Arabian Peninsula). In terms of anomalies, SMOSL3 better captures the short term SSM variability of the reference dataset (MERRA-Land) than ASCAT over regions with limited radio frequency interference (RFI) effects (e.g., North America, South America, and Australia). The seasonal and latitudinal variations of SSM are relatively similar for the three products, although the MERRALand SSM values are generally higher and their seasonal amplitude is much lower than for SMOSL3 and ASCAT. Both SMOSL3 and ASCAT have relatively comparable triple collocation errors with similar spatial error patterns: (i) lowest errors in arid regions (e.g., Sahara and Arabian Peninsula), due to the very low natural variability of soil moisture in these areas, and Central America, and (ii) highest errors over most of the vegetated regions (e.g., northern Australia, India, central Asia, and South America). However, the ASCAT SSM product is prone to larger random errors in some regions (e.g., north-western Africa, Iran, and southern South Africa). Vegetation density was found to be a key factor to interpret the consistency with MERRA-Land between the two remotely sensed products (SMOSL3 and ASCAT) which provides complementary information on SSM. This study shows that both SMOS and ASCAT have thus a potential for data fusion into long-term data records.
Philippe Richaume - One of the best experts on this subject based on the ideXlab platform.
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Global-scale comparison of passive (SMOS) and active (ASCAT) satellite based microwave soil moisture retrievals with soil moisture simulations (MERRA-Land)
Remote Sensing of Environment, 2014Co-Authors: Amen Al Yaari, Wolfgang Wagner, Rolf Reichle, Gabrielle De Lannoy, Wouter Dorigo, Ahmad Albitar, Philippe RichaumeAbstract:Global surface soil moisture (SSM) datasets are being produced based on active and passive microwave satellite observations and simulations from land surface models (LSM). This study investigates the consistency of two global satellite-based SSM datasets based on microwave remote sensing observations from the passive Soil Moisture and Ocean Salinity (SMOS; SMOSL3 version 2.5) and the active Advanced Scatterometer (ASCAT; version TU-Wien-WARP 5.5) with respect to LSM SSM from the MERRA-Land data product. The relationship between the global-scale SSM products was studied during the 2010-2012 period using (1) a time series statistics (considering both original SSM data and anomalies), (2) a space-time analysis using Hovmöller diagrams, and (3) a triple collocation error model. The SMOSL3 and ASCAT retrievals are consistent with the temporal dynamics of modeled SSM (correlation R > 0.70 for original SSM) in the transition zones between wet and dry climates, including the Sahel, the Indian subcontinent, the Great Plains of North America, eastern Australia, and south-eastern Brazil. Over relatively dense vegetation covers, a better consistency with MERRA-Land was obtained with ASCAT than with SMOSL3. However, it was found that ASCAT retrievals exhibit negative correlation versus MERRA-Land in some arid regions (e.g., the Sahara and the Arabian Peninsula). In terms of anomalies, SMOSL3 better captures the short term SSM variability of the reference dataset (MERRA-Land) than ASCAT over regions with limited radio frequency interference (RFI) effects (e.g., North America, South America, and Australia). The seasonal and latitudinal variations of SSM are relatively similar for the three products, although the MERRA-Land SSM values are generally higher and their seasonal amplitude is much lower than for SMOSL3 and ASCAT. Both SMOSL3 and ASCAT have relatively comparable triple collocation errors with similar spatial error patterns: (i) lowest errors in arid regions (e.g., Sahara and Arabian Peninsula), due to the very low natural variability of soil moisture in these areas, and Central America, and (ii) highest errors over most of the vegetated regions (e.g., northern Australia, India, central Asia, and South America). However, the ASCAT SSM product is prone to larger random errors in some regions (e.g., north-western Africa, Iran, and southern South Africa). Vegetation density was found to be a key factor to interpret the consistency with MERRA-Land between the two remotely sensed products (SMOSL3 and ASCAT) which provides complementary information on SSM. This study shows that both SMOS and ASCAT have thus a potential for data fusion into long-term data records.
Ahmad Al Bitar - One of the best experts on this subject based on the ideXlab platform.
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global scale comparison of passive smos and active ascat satellite based microwave soil moisture retrievals with soil moisture simulations merra land
Remote Sensing of Environment, 2014Co-Authors: Jp. Wigneron, Agnes Ducharne, Yann Kerr, Wolfgang Wagner, Gabrielle De Lannoy, Amen Alyaari, Rolf H Reichle, Ahmad Al BitarAbstract:Global surface soil moisture (SSM) datasets are being produced based on active and passive microwave satellite observations and simulations from land surface models (LSM). This study investigates the consistency of two global satellite-based SSM datasets based on microwave remote sensing observations from the passive Soil Moisture and Ocean Salinity (SMOS;SMOSL3 version 2.5) and the active Advanced Scatterometer (ASCAT; version TUWien- WARP 5.5) with respect to LSM SSM from the MERRA-Land data product. The relationship between the global-scale SSM products was studied during the 2010-2012 period using (1) a time series statistics (considering both original SSM data and anomalies), (2) a space-time analysis using Hovmoller diagrams, and (3) a triple collocation error model. The SMOSL3 and ASCAT retrievals are consistent with the temporal dynamics of modeled SSM (correlation R (is) greater than 0.70 for original SSM) in the transition zones between wet and dry climates, including the Sahel, the Indian subcontinent, the Great Plains of North America, eastern Australia, and southeastern Brazil. Over relatively dense vegetation covers, a better consistency with MERRA-Land was obtained with ASCAT than with SMOSL3. However, it was found that ASCAT retrievals exhibit negative correlation versus MERRA-Land in some arid regions (e.g., the Sahara and the Arabian Peninsula). In terms of anomalies, SMOSL3 better captures the short term SSM variability of the reference dataset (MERRA-Land) than ASCAT over regions with limited radio frequency interference (RFI) effects (e.g., North America, South America, and Australia). The seasonal and latitudinal variations of SSM are relatively similar for the three products, although the MERRALand SSM values are generally higher and their seasonal amplitude is much lower than for SMOSL3 and ASCAT. Both SMOSL3 and ASCAT have relatively comparable triple collocation errors with similar spatial error patterns: (i) lowest errors in arid regions (e.g., Sahara and Arabian Peninsula), due to the very low natural variability of soil moisture in these areas, and Central America, and (ii) highest errors over most of the vegetated regions (e.g., northern Australia, India, central Asia, and South America). However, the ASCAT SSM product is prone to larger random errors in some regions (e.g., north-western Africa, Iran, and southern South Africa). Vegetation density was found to be a key factor to interpret the consistency with MERRA-Land between the two remotely sensed products (SMOSL3 and ASCAT) which provides complementary information on SSM. This study shows that both SMOS and ASCAT have thus a potential for data fusion into long-term data records.
