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Geoscience and Remote Sensing, IEEE Transactions on

Issue 7  Part 1 • Date July 2012

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Displaying Results 1 - 25 of 33
  • [Front cover]

    Page(s): C1
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  • IEEE Transactions on Geoscience and Remote Sensing publication information

    Page(s): C2
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  • Table of contents

    Page(s): 2421 - 2422
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  • Introduction to the Special Issue on Recent Advances in C-Band Scatterometry

    Page(s): 2423 - 2425
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  • List of reviewers

    Page(s): 2426
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  • ERS-2 Scatterometer: Mission Performances and Current Reprocessing Achievements

    Page(s): 2427 - 2448
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3934 KB) |  | HTML iconHTML  

    This paper presents an overview of the evolution of the European Remote-sensing Satellite (ERS)-2 scatterometer mission during the last 16 years, highlighting the changes in both satellite configuration and on-ground data processing algorithm. Instrument and on-ground data processor performances and evolutions are analyzed and commented; finally, future developments are emphasized. ERS-2 was launched in 1995 by the European Space Agency (ESA). Since then, the active microwave instrument, which is one of the ERS-2 payloads, is providing radar backscattering coefficient measurements by using its three nominal operational acquisition mode: synthetic aperture mode (SAR mode), scatterometer mode (wind mode), and a special combination of the two over ocean where SAR and scatterometer mode are interleaved (wind/wave mode). The main applications for data acquired in scatterometer mode are related to the estimation of the wind vector over the sea surface. In that field, the ERS-2 scatterometer measurements give a very valuable contribution to the accuracy of the numerical weather forecast models, being assimilated in several meteorological weather forecast centers since the beginning of the mission. Other applications of the ERS-2 scatterometer data are over land to retrieve information about the soil water content and over the sea-ice. A constant monitoring of the scatterometer performances is carried out since the beginning of the mission by ESA engineering teams located in ESTEC and ESRIN and the instrument manufacture (Dornier at launch time), in collaboration with several European research institutions, as the European Centre for Medium-range Weather Forecasts for product geophysical validation, the Belgian Royal Military Academy for data processing and calibration during the zero-gyro phase, and industrial partners, as Serco SpA for the routine data quality control activities since the beginning of operational phase. Results show outstanding performances even after th- failure of several hardware components that has been properly compensated on-ground with evolution of the processor, and many years of operation, which permits the creation of a homogeneous database of wind vectors for the last 16 years (20 years if the ERS-1 mission is considered), in accordance with Global Climate Observing System recommendations. View full abstract»

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  • Analysis of ASCAT Ocean Backscatter Measurement Noise

    Page(s): 2449 - 2457
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (931 KB) |  | HTML iconHTML  

    The Advanced Scatterometer (ASCAT) level 1b products provide spatially averaged calibrated backscatter measurements along with their Kp values which are estimates of the normalized standard deviation of the backscatter values. The Kp values can be regarded as a measure of the error in the mean backscatter caused by speckle noise, instrument characteristics, data processing, and spatial inhomogeneities of the target. This information assists in the retrieval of wind vectors and allows their error characteristics to be determined. This paper describes the algorithm used to calculate Kp. The algorithm considers both the correlations that occur in ASCAT data due to onboard processing and the Hamming weights used in the spatial averaging in the ground processing. ASCAT Kp values over the open ocean are investigated, and we demonstrate that their behavior is as expected and meets requirements. This indicates that the ASCAT instrument is operating as intended and is providing good quality ocean backscatter data. The behavior of Kp over Arctic sea ice is also examined, and we show that it contains geophysical information that may be useful for various applications such as ice-type classification or sea ice tracking. View full abstract»

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  • EPS-SG Windscatterometer Concept Tradeoffs and Wind Retrieval Performance Assessment

    Page(s): 2458 - 2472
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1580 KB) |  | HTML iconHTML  

    The EUMETSAT Polar System-Second Generation (EPS-SG) mission will be deployed in the 2019-2020 timeframe in order to ensure continuity of the EPS observation missions, currently realized with the MetOp satellite series, to support operational meteorology and oceanography; in particular, for numerical weather prediction (NWP), climate monitoring and to develop new environmental services. The scatterometer (SCA) is one of the high-priority payload instruments to provide vector surface wind observations over the ocean, which constitute an important input to NWP, as well as valuable information for tracking of extreme weather events. The EPS-SG SCA shall offer observations with higher spatial resolution than those provided by ASCAT on board MetOp, operating at C-band and with VV polarization. Furthermore, addition of HH or VH polarization is considered as an option. Phase 0 industrial studies, addressing the complete system design, have taken place from 2008 to 2009. Two study teams, constituted, respectively by Astrium SAS and Thales Alenia Space Italy, have performed comprehensive analyses of the system requirements, tradeoffs of various concepts, and preliminary design of the selected concepts, which included both the single and dual satellite configurations. Three distinct SCA concepts were initially considered for tradeoffs: 1) fixed fan-beam concept with six fixed antennas; 2) rotating fan-beam concept with a single rotating antenna; 3) rotating pencil-beam concept. The first two concepts were further elaborated during Phase 0, and the fixed fan-beam concept was selected as baseline after a final tradeoff. For supporting the above instrument concept elaboration by the industrial study teams during Phase 0, the Royal Dutch Meteorological Institute (KNMI) has developed retrieval algorithms tailored to those concepts, derived from the ASCAT operational algorithms, and specific metrics to characterize the associated retrieval performance. The metrics used for the pre- ent performance assessment were: 1) wind vector root-mean-square error; 2) ambiguity susceptibility; and 3) wind biases. The end-to-end performance evaluation makes use of an ensemble of wind fields as input having the mean climatology distribution, generates the output wind-fields which account for the measurement system imperfections and geophysical noise, and computes the performance metrics for comparisons. This paper describes the three SCA concepts as analysed in Phase 0 studies by the industrial study teams and summarizes the technical tradeoffs carried out. The performance metrics are described and applied to two of the concepts in order to compare their respective merits. It is shown that both concepts are able to meet the observation requirements of EPS-SG. View full abstract»

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  • Self-Consistency of Marine Surface Wind Vectors Observed by ASCAT

    Page(s): 2473 - 2480
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (407 KB) |  | HTML iconHTML  

    Marine surface wind vectors observed by the Advanced Scatterometer on the MetOp-A satellite are evaluated by assessing their self-consistency. Global statistics on wind speeds and directions were calculated from the data for a period of one year. The wind speed histograms exhibited a clear dependence on the cross-track wind vector cell (WVC) location, which corresponds to the incidence angle. This trend was reflected in the higher order statistics (standard deviation, skewness, and kurtosis) of the wind speed distribution. The histograms of the wind directions relative to the satellite flight direction clearly showed systematic errors relative to the antenna beam directions. The number density of the wind directions exhibited a systematic pattern relative to the antenna beam directions, and the pattern varied with wind speed and cross-track WVC location. These systematic errors in the wind speed and direction may affect the divergence/convergence and rotation of the wind field. The results of this study suggest the need for further refinements of the wind retrieval algorithms and the C-band geophysical model function. It was also confirmed that the evaluation technique based on the statistical distributions of scatterometer-derived vector winds is effective for identifying systematic errors in the wind speeds and directions. View full abstract»

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  • High-Resolution ASCAT Scatterometer Winds Near the Coast

    Page(s): 2481 - 2487
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (884 KB) |  | HTML iconHTML  

    The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility delivers operational wind products from the Advanced Scatterometer (ASCAT) at 25 km and 12.5 km Wind Vector Cell (WVC) spacing. In these products, based on the backscatter processing performed at EUMETSAT, data closer than ~ 70 km (25 km products) or ~ 35 km (12.5 km products) to the coast are flagged because of land contamination. An alternative wind product is presented here which uses a different way of averaging the full resolution (FR) backscatter measurements from ASCAT. The FR backscatter measurements are screened for land contamination in the coastal zone, thus allowing the construction of WVCs that follow the coast line. The implied alternative spatial averaging allows good quality winds over sea as close as 15-20 km to the shore. The alternative (coastal) and nominal products are compared, and the resulting winds are validated with buoy winds, both in coastal and open sea regions. In regions far away from the coast, the ASCAT coastal and nominal products appear to be of identical quality, but fewer WVCs pass the quality control steps for the nominal product, indicating that the coastal product better resolves sub-WVC wind variability. In the coastal region, we anticipate enhanced wind variability due to katabatic and sea breeze effects, among others. However, the quality of the coastal winds in terms of buoy wind component difference standard deviation is almost as good as for the open sea winds. View full abstract»

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  • Improved ASCAT Wind Retrieval Using NWP Ocean Calibration

    Page(s): 2488 - 2494
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (411 KB) |  | HTML iconHTML  

    The Advanced Scatterometer (ASCAT) wind data processor (AWDP) currently uses the so called CMOD5n geophysical model function (GMF), which was originally derived for the European Remote Sensing (ERS) scatterometers. In order to deliver a high-quality ASCAT wind product, the operational AWDP uses backscatter measurement corrections that are estimated visually (VOC) for each wind vector cell. We propose an alternative and previously established method for estimating correction tables based on numerical weather prediction ocean calibration residuals (NOC). It embodies a smooth incidence-angle dependent part that could serve as an appropriate ASCAT GMF correction, and a radar-beam-dependent residual. The incidence-angle-dependent part of these correction tables is due to differences in calibration procedure of the ERS and ASCAT scatterometers. For the high ASCAT incidence angles for which the GMF has not been assessed by ERS data, the modification is quite large, almost 1 dB. The incidence angle-dependent part is derived by fitting the OC residuals of all beams obtained over one year of data. It is subsequently used to adapt the GMF (yielding CMOD5na). The remaining radar-beam-dependent residual (NOCa) shows a wiggle pattern as function of incidence angle that is very persistent over time, apart from a seasonally varying offset. Both the effects of the GMF modification and the beam-dependent residual on the wind retrieval quality are investigated in this paper. Overall, the performance of NOC is better than that obtained with the previously used VOC calibration method, and the wind statistics show a much better symmetry of the left and right swath for NOC. The beam-dependent corrections improve the quality of the retrieved winds. NOC may thus be used for the intercalibration of the ERS and ASCAT scatterometers. View full abstract»

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  • Rain Effects on ASCAT-Retrieved Winds: Toward an Improved Quality Control

    Page(s): 2495 - 2506
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1264 KB) |  | HTML iconHTML  

    The quality of the Ku-band scatterometer-derived winds is known to be degraded by the presence of rain. Little work has been done in characterizing the impact of rain on C-band scatterometer winds, such as those from the Advanced Scatterometer (ASCAT) onboard Metop-A. In this paper, the rain impact on the ASCAT operational level 2 quality control (QC) and retrieved winds is investigated using the European Centre for Medium-range Weather Forecasts (ECMWF) model winds, the Tropical Rainfall Measuring Mission's (TRMM) Microwave Imager (TMI) rain data, and tropical buoy wind and precipitation data as reference. In contrast to Ku-band, it is shown that C-band is much less affected by direct rain effects, such as ocean splash, but effects of increased wind variability appear to dominate ASCAT wind retrieval. ECMWF winds do not well resolve the airflow under rainy conditions. ASCAT winds do but also show artifacts in both the wind speed and wind direction distributions for high rain rates (RRs). The operational QC proves to be effective in screening these artifacts but at the expense of many valuable winds. An image-processing method, known as singularity analysis, is proposed in this paper to complement the current QC, and its potential is illustrated. QC at higher resolution is also expected to result in improved screening of high RRs. View full abstract»

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  • Cross-Validation of Scatterometer Measurements via Sea-Level Pressure Retrieval

    Page(s): 2507 - 2517
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    A combined analysis of ocean surface wind vector measurements by the European Advanced Scatterometer (ASCAT) and the National Aeronautics and Space Administration QuikSCAT (QS) scatterometer using buoy measurements, numerical weather prediction model analyses, and spectral decomposition reveals significant statistical differences between the two data sets. While QS wind speeds agree better with buoy wind speeds than ASCAT above 15 m s-1, ASCAT wind directions agree better with buoy directions overall than QS. In contrast, it is shown that sea-level pressure (SLP) fields derived from ASCAT and QS measurements compare better with each other than the winds in a statistical sense, even though ASCAT bulk pressure gradients (BPGs) are slightly weaker than buoy pressure gradients and have slightly lower spectral energy than QS. Weaker BPGs in ASCAT are consistent with the low bias in ASCAT wind speeds. Thus, it is proposed that scatterometer-derived SLP fields can be used as a filter to improve the wind directions. This improves the QS wind directions but has less effect on the more accurate ASCAT wind directions. The unfiltered ASCAT wind vector statistics compare well with the statistics of the direction-filtered QS winds. It is suggested that this methodology might provide a basis for minimizing the discrepancies between various satellite wind measurement data sets in view of producing a long-term record of satellite-derived SLP fields and ocean surface wind vectors. View full abstract»

    Open Access
  • Using ASCAT Scatterometer Winds to Evaluate Relative Biases in the QuikSCAT-Derived Wind Vorticity

    Page(s): 2518 - 2524
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    Relative wind vorticity ω (hereafter vorticity) is a crucial parameter to understand the spatial features of the wind field. In the Mediterranean Sea, which is the area where this study is focused, these are particularly interesting because they are often the effects of the interaction between the airflow and the orography. Wind vorticity has been derived from both Quick Scatterometer (QuikSCAT) and Advanced Scatterometer (ASCAT) 12.5-km scatterometer data downloaded from the National Aeronautics and Space Administration Physical Oceanography Distributed Active Archive Center data archive, and compared in the period from March to November 2009. The monthly mean fields of the vorticity ω̅ show discrepancies which need to be understood. This paper thus aims to understand the nature of these differences, to make the two vorticity data sets compatible and usable as a common data set. Results have been provided in terms of the relative bias in vorticity 〈Δ̅ω̅〉, which is the mean difference between the ASCAT ω̅A and QuikSCAT ω̅Q monthly mean vorticities averaged over the entire Mediterranean Basin and the entire study period. This difference 〈Δ̅ω̅〉 = 0.093 ·10-5 ±0.05 ·10-5s-1) is mainly due to a relative vorticity bias in the cyclonic component of ω̅, rather than in the anticyclonic component, whose bias is four times smaller. This bias does not depend significantly on the variable accuracy of the wind speed and direction across the QuikSCAT swath. This study led us to define and analyze the so-called vorticity noise, which is present particularly in the QuikSCAT-derived vorticity, to understand if, and how, it can contribute to the relative bias in vorticity. The contribution of this kind of noise on ω̅ has bee- found relevant only for the cyclonic vorticity of ω̅Q. By applying a cyclonic denoising to each swath of QuikSCAT, 〈Δ̅ω̅〉 = -0.016 ·10-5 ±0.05 ·10-5s-1 is obtained, drastically reduced with respect to the initial value. This may be considered the typical bias over the Mediterranean Sea between ω̅A and (ω̅Q derived from the 12.5-km data, after applying the cyclonic denoising to QuikSCAT vorticity fields. View full abstract»

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  • NWP Model Error Structure Functions Obtained From Scatterometer Winds

    Page(s): 2525 - 2533
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    Wind vectors derived from scatterometer measurements are spatially detailed as compared to global numerical weather prediction (NWP) model fields. Since the Advanced Scatterometer (ASCAT)'s wind vector ambiguities are, in general, well defined, ambiguity removal results in accurate wind fields. The dense and regular spatial sampling of ASCAT winds represents a unique resource to study the NWP model field spatial error structure. The current level 2 ASCAT data processor employs 2-D variational ambiguity removal (2DVAR), in which an analysis is made from the ambiguous wind solutions and a prior NWP wind field using a variational technique, and, subsequently, the ambiguity closest to the analysis is selected as best wind. 2DVAR will yield an optimal analysis when the structure functions (background error correlations in the potential domain) are well specified. In this paper, a new method is presented to calculate structure functions from autocorrelations of observed scatterometer wind components minus NWP model predictions (O-B). It is based on direct integration of the differential equations relating structure functions and observed autocorrelations. Reprocessing ASCAT data at 12.5-km grid size with structure functions obtained this way shows a considerable increase in the spectral density of the analysis for scales from about 800 to about 100 km, with the largest effect at scales of around 250 km. In line with this finding, it is shown in a case study that a more detailed analysis leads to fewer ambiguity removal errors for ASCAT data recorded over a frontal zone with rapidly varying wind direction. View full abstract»

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  • Mapping Surface Oil Extent From the Deepwater Horizon Oil Spill Using ASCAT Backscatter

    Page(s): 2534 - 2541
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (991 KB) |  | HTML iconHTML  

    The 2010 Deepwater Horizon oil spill in the Gulf of Mexico covered a sufficiently large area to be observed by the European Space Agency Advanced Scatterometer (ASCAT) on MetOp-A. In this paper, ASCAT data and numerically computed winds from the European Centre for Medium-Range Weather Forecasts (ECMWF) are used to map the spatial extent of oil on the ocean surface over the duration of the spill event. Surface oil alters the ocean radar scattering properties, resulting in a difference between the measured backscatter and the backscatter that would be measured if oil were not present. Wind scatterometers infer the near-surface wind speed and direction using the wind geophysical model function (GMF) in conjunction with measured radar backscatter. The oil-altered backscatter error propagates through the wind retrieval process to create a difference in ASCAT-inferred winds and actual winds. Numerically computed vector winds from ECMWF are compared against ASCAT-inferred vector winds. The GMF is applied to ECMWF winds to create a predicted backscatter value to compare against ASCAT-measured backscatter. Large differences in wind or backscatter indicate areas of the ocean surface affected by oil. An objective function is developed to choose an appropriate threshold level to flag the oil-contaminated regions. Data from other sensors corroborate the ASCAT oil extent mapping. View full abstract»

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  • Assimilation of Surface- and Root-Zone ASCAT Soil Moisture Products Into Rainfall–Runoff Modeling

    Page(s): 2542 - 2555
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    Nowadays, the availability of soil moisture estimates from satellite sensors offers a great chance to improve real-time flood forecasting through data assimilation. In this paper, two real data and two synthetic experiments have been carried out to assess the effects of assimilating soil moisture estimates into a two-layer rainfall-runoff model. By using the ensemble Kalman filter, both the surface- and root-zone soil moisture (RZSM) products derived by the Advanced SCATterometer (ASCAT) have been assimilated and the model performance on flood estimation is analyzed. RZSM estimates are obtained through the application of an exponential filter. Hourly rainfall-runoff observations for the period 1994-2010 collected in the Niccone catchment (137 km2), Central Italy, are employed as case study. The ASCAT soil moisture products are found to be in good agreement with the modeled soil moisture data for both the surface layer (correlation coefficient (R) of 0.78) and the root zone (R = 0.94). In the real data experiment, the assimilation of the RZSM product has a significant impact on runoff simulation that provides a clear improvement in the discharge modeling performance. On the other hand, the assimilation of the surface soil moisture product has a small effect. The same findings are also confirmed by the synthetic twin experiments. Even though the obtained results are model dependent and site specific, the possibility to efficiently employ coarse resolution satellite soil moisture products for improving flood prediction is proven, mainly if RZSM data are assimilated into the hydrological model. View full abstract»

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  • Error Assessment of the Initial Near Real-Time METOP ASCAT Surface Soil Moisture Product

    Page(s): 2556 - 2565
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1522 KB) |  | HTML iconHTML  

    Since December 2008, the European Organisation for the Exploitation of Meteorological Satellites has been operationally distributing a global 25-km surface soil moisture product derived from the Advanced Scatterometer (ASCAT) onboard the meteorological operational platform (METOP) satellite METOP-A. Soil moisture is retrieved by using the semiempirical change detection method originally developed by the Vienna University of Technology (TU Wien) for the Active Microwave Instrument (AMI) flown on the European Remote Sensing (ERS) satellites ERS-1 and ERS-2. With the launch of the first of the three Meteorological Operational Platforms (METOP-A) in October 2006, ASCAT onboard METOP-A inherits and continues the role of his predecessor AMI. The original soil moisture retrieval algorithm (TU Wien model) was expected to be almost directly applicable for ASCAT with only minor changes, since the configuration and technical design is similar to the ERS scatterometers. Since the TU Wien model requires a robust historic long-term reference of scattering parameters, the initial near real-time METOP ASCAT soil moisture product had to rely on the model parameters derived from over 15 years of ERS-1/2. However, the combination of ASCAT backscatter measurements and ERS-1/2 historic long-term reference introduced some artifacts in the soil moisture product. The objectives of this paper were to analyze and investigate the impact of the ERS-1/2 historic long-term reference on the soil moisture retrieval. An error model has been developed to quantify the effects of the two main error sources: differences in spatial resolution and absolute calibration. The results of the study show that a simple model is able to describe the artifacts in the initial near real-time METOP ASCAT soil moisture product, which frequently occur in areas characterized by sharp backscatter contrasts. The expected overestimation of soil moisture using ERS-1/2 model parameters due to a calibration bias between AMI- and ASCAT could be modeled as well. View full abstract»

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  • ASCAT Surface State Flag (SSF): Extracting Information on Surface Freeze/Thaw Conditions From Backscatter Data Using an Empirical Threshold-Analysis Algorithm

    Page(s): 2566 - 2582
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4133 KB) |  | HTML iconHTML  

    Information on soil surface state is valuable for many applications such as climate studies and monitoring of permafrost regions. C-band scatterometer data indicate good potential to deliver information on surface freeze/thaw. Variation in state or amount of water contained in the soil causes significant alteration of dielectric properties of the soil which is markedly observable in scatterometer backscattered signal. A threshold-analysis method is developed to derive a set of parameters to be used in evaluating the normalized backscatter measurements through decision trees and anomaly detection modules for determination of freeze/thaw conditions. The model parameters are extracted from two years (2007-2008) backscatter data from ASCAT scatterometer onboard Metop satellite collocated with ECMWF ReAnalysis (ERA-Interim) soil temperature. Backscatter measurements are flagged as indicator of frozen/unfrozen surface, and snowmelt or existing water on the surface. The output product, so-called surface state flag (SSF), compares well with two modeled soil temperature data sets as well as the air temperature measurements from synoptic meteorological stations across the northern hemisphere. The SSF time series are also validated with soil temperature data available at four in situ observation sites in Siberian and Alaska regions showing the overall accuracy of about 80% to 90%. View full abstract»

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  • Probabilistic Fusion of \hbox {K}_{\rm u} - and C-band Scatterometer Data for Determining the Freeze/Thaw State

    Page(s): 2583 - 2594
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (774 KB) |  | HTML iconHTML  

    A novel sensor fusion algorithm for retrieving the freeze/thaw (f/t) state from scatterometer data is presented: It is based on a probabilistic model, which is a variant of the Hidden Markov model, and it computes the probability that the landscape is frozen, thawed, or thawing for each day. By combining Ku- and C-band scatterometer data, the distinct backscattering properties of snow, soil, and vegetation at the two radar bands are exploited. The parameters that are necessary for inferring the f/t state are estimated in an unsupervised fashion, i.e., no training data are required. Comparison with model and in situ temperature data in a test area in Siberia/northern China indicates that the approach yields promising results (typical accuracies exceeding 90%); difficulties are encountered over bare rock and areas where large fluctuations in soil moisture are common. These limitations turn out to be closely linked to the inherent assumptions of the probabilistic model. View full abstract»

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  • Diurnal Differences in Global ERS Scatterometer Backscatter Observations of the Land Surface

    Page(s): 2595 - 2602
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1253 KB) |  | HTML iconHTML  

    Soil moisture estimates from the European Remote Sensing Satellite (ERS)-1, ERS-2, and Metop scatterometer instruments are available as time series starting in 1991. To better understand the satellite signal backscatter data and the corresponding soil moisture estimates, differences between different overpass times are analyzed. An analysis of more than 15 years of ERS scatterometer data has shown distinct patterns in backscatter between different overpass times. Differences between backscatter data from descending (morning overpass) and ascending (evening overpass) tracks show spatial and temporal patterns that cannot be attributed to soil moisture. Based on regional studies, we highlight the main processes causing the diurnal differences in backscatter data. Data used for this study are based on preprocessed normalized backscatter [σ0(40)] and slope [σ'(40)] data from a modified TUWien WARP 5.0 algorithm. Results show that the diurnal differences in σ0(40) between descending and ascending data are systematic and are not artifacts from previous processing steps. Statistically significant diurnal differences [Δσ0(40)] are detected over about 30% of the land area, underscoring the potential significance for hydrologic remote sensing on a global scale. View full abstract»

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  • Detection of Large-Scale Forest Canopy Change in Pan-Tropical Humid Forests 2000–2009 With the SeaWinds Ku-Band Scatterometer

    Page(s): 2603 - 2617
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2096 KB) |  | HTML iconHTML  

    We analyzed the 10-year record (1999-2009) of SeaWinds Ku-band microwave backscatter from humid tropical forest regions in South America, Africa, and Indonesia/Malaysia. While backscatter was relatively stable across much of the region, it declined by 1-2 dB in areas of known large-scale deforestation, and increased by up to 1-2 dB in areas of secondary forest or plantation forest growth and in major metropolitan areas. The reduction in backscatter over 142 18.5 km × 18.5 km blocks of tropical forest was correlated with gross forest cover loss (as determined from Landsat data analysis) (R = -0.78); this correlation improved when restricted to humid tropical forest blocks in South America with high initial forest cover (R = -0.93, n = 22). This study shows that scatterometer-based analyses can provide an important geophysical data record leading to robust identification of the spatial patterns and timing of large-scale change in tropical forests. The coarse spatial resolution of SeaWinds ( ~ 10 km) makes it unsuitable for mapping deforestation at the scale of land-use activity. However, due to a combination of instrument stability, sensitivity to canopy change and insensitivity to atmospheric effects, and straight-forward data processing, Ku-band scatterometery can provide a fully independent assessment of large-scale tropical forest canopy dynamics which may complement the interpretation of higher resolution optical remote sensing. View full abstract»

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  • Using Diurnal Variation in Backscatter to Detect Vegetation Water Stress

    Page(s): 2618 - 2629
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    A difference has been detected between the C-band wind scatterometer measurements from the morning (descending) and evening (ascending) passes of the European Remote Sensing (ERS) 1/2 satellite. In the West African savanna, for example, these differences correspond to the onset of vegetation water stress. A literature review of the current state of knowledge regarding the diurnal variation in vegetation dielectric properties and its influence on observed backscatter is presented. A numerical sensitivity study using the Michigan microwave canopy scattering model was performed to investigate whether this difference might be explained by diurnal variation in the dielectric properties of the canopy. For vertically copolarized backscatter, as in the case of the ERS wind scatterometer, the greatest sensitivity is to leaf moisture (and, hence, dielectric constant), but the trunk moisture is significant at low values of leaf moisture content. This suggests that the ERS wind scatterometer may well detect changes in vegetation water status. The impact of leaf, branch, trunk, and soil moisture contents on L-band HH, VV, and HV backscatter was also investigated to explore the implications for the National Aeronautics and Space Administration's upcoming Soil Moisture Active Passive (SMAP) mission. Results suggest that combining the morning and evening passes of the SMAP radar observations might yield valuable insight into water stress in areas otherwise considered too densely vegetated for traditional soil moisture retrieval. View full abstract»

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  • Analysis of C-Band Scatterometer Moisture Estimations Derived Over a Semiarid Region

    Page(s): 2630 - 2638
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    Spatial and temporal variations of soil moisture strongly affect flooding, erosion, solute transport, and vegetation productivity. Their characterization offers numerous possibilities for the improvement of our understanding of complex land-surface-atmosphere interactions. In this paper, soil moisture dynamics at the soil's surface (the first centimeters) and in its root zone (at depths down to 1 m) are investigated using 25 × 25 km2 scale data (Advanced Scatterometer (ASCAT)/METorological OPerational (METOP) scatterometer), for a semiarid region in North Africa. Our study highlights the quality of the surface and root-zone soil moisture products, derived from ASCAT data recorded over a two-year period. Surface soil moisture tends to be highly variable because it is strongly influenced by atmospheric conditions (rain and evaporation). On the other hand, root-zone moisture is considerably less variable. A statistical drought-monitoring index, referred to as the “moisture anomaly index,” is derived from ASCAT and European Remote Sensing (ERS) time series. This index was tested with ERS and ASCAT products during the 1991-2010 study period. A strong correlation is found between the proposed index and the standardized precipitation index. View full abstract»

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  • Enhanced Arctic Sea Ice Drift Estimation Merging Radiometer and Scatterometer Data

    Page(s): 2639 - 2648
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    Satellites enable daily and global coverage of the polar oceans and provide a unique monitoring capability of sea ice dynamics. Sea ice drift maps can be estimated in Arctic from several satellite sensors, particularly from scatterometers and radiometers. This study presents the benefits of combining single drift fields at the same resolution into a “merged” field, built at three- and six-day lags during winters with a 62.5-km resolution. It is shown that combining these drift fields not only increases the reliability of the displacement estimation and the number of estimated vectors to almost a full ice covered area but also expands the time period over which these estimations are reliable from freeze until the melt onset. The autumn-winter-spring sea ice drift fields presented here are systematically produced at Institut Français de Recherche pour l'Exploitation de la Mer/Centre d'Exploitation et de Recherche Satellitaire, the sea ice drift 1992-2011 time series is available, and the processing is ongoing. These data are available for operational use and for the scientific community. View full abstract»

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Aims & Scope

 

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (TGRS) is a monthly publication that focuses on the theory, concepts, and techniques of science and engineering as applied to sensing the land, oceans, atmosphere, and space; and the processing, interpretation, and dissemination of this information.

 

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Meet Our Editors

Editor-in-Chief
Antonio J. Plaza
University of Extremadura