T.J. Jackson

University of Melbourne, Melbourne, Victoria, Australia

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Publications (356)383.75 Total impact

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    ABSTRACT: The radar vegetation index (RVI) has low sensitivity to changes in environmental conditions and has the potential as a tool to monitor vegetation growth. In this letter, we expand on previous research by investigating the radar response over a wheat canopy. RVI was computed using observations made with a ground-based multifrequency polarimetric scatterometer system over an entire wheat growth cycle. We analyzed the temporal variations of backscattering coefficients for L-, C-, and X-bands; RVI; vegetation water content (VWC); and fresh weight. We found that the L-band RVI was highly correlated with both VWC (r = 0.98) and fresh weight (r = 0.98). Based upon these analyses, linear equations were developed for estimation of VWC (root-mean-square error (RMSE = 0.126 kg m-2)) and fresh weight (RMSE = 0.12 kg m-2). In addition, the results of the wheat study were combined with previous investigations with other crops (e.g., rice and soybean). We found that a single linear relationship between L-band RVI and VWC can be used for all crop types (RMSE = 0.47 kg m-2). These results clearly demonstrate the potential of RVI as a robust method for characterizing vegetation canopies. VWC is a key input requirement for retrieving soil moisture from microwave remote sensing observations. The results of this investigation will be useful for the Soil Moisture Active and Passive mission (2014), which is designed to measure global soil moisture.
    IEEE Geoscience and Remote Sensing Letters 04/2014; 11(4):808-812. · 1.82 Impact Factor
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    ABSTRACT: Satellite-based passive microwave remote sensing has been shown to be a valuable tool in mapping and monitoring global soil moisture. The Advanced Microwave Scanning Radiometer on the Aqua platform (AMSR-E) has made significant contributions to this application. As the result of agency and individual initiatives, several approaches for the retrieval of soil moisture from AMSR-E have been proposed and implemented. Although the majority of these are based on the same Radiative Transfer Equation, studies have shown that the resulting soil moisture estimates can differ significantly. A primary goal of this investigation is to understand these differences and develop a suitable approach to potentially improve the algorithm currently used by NASA in producing its operational soil moisture product. In order to achieve this goal, the theoretical basis of several alternative soil moisture retrieval algorithms are examined. Analysis has focused on five established approaches: the operational algorithm adopted by NASA, which is referred to as the Normalized Polarization Difference algorithm, the Single Channel Algorithm, the Land Parameter Retrieval Model, the University of Montana soil moisture algorithm, and the HydroAlgo Artificial Neural Network algorithm. Previous comparisons of these algorithms in the literature have typically focused on the retrieved soil moisture products, and employed different metrics and data sets, and have resulted in differing conclusions. In this investigation we attempt to provide a more thorough understanding of the fundamental differences between the algorithms and how these differences affect their performance in terms of range of soil moisture provided. The comparative overview presented in the paper is based on the operating versions of the source codes of the individual algorithms. Analysis has indicated that the differences between algorithms lie in the specific parameterizations and assumptions of each algorithm. The comparative overview of the theoretical basis of the approaches is linked to differences found in the soil moisture retrievals, leading to suggestions for improvements and increased reliability in these algorithms.
    Remote Sensing of Environment 03/2014; 144:197–213. · 5.10 Impact Factor
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    ABSTRACT: Overlapping soil moisture time series derived from two satellite microwave radiometers (the Soil Moisture and Ocean Salinity (SMOS) and the Advanced Microwave Scanning Radiometer-Earth Observing System) are used to generate a soil moisture time series from 2003 to 2010. Two statistical methodologies for generating long homogeneous time series of soil moisture are considered. Generated soil moisture time series using only morning satellite overpasses are compared to ground measurements from four watersheds in the U.S. with different climatologies. The two methods, cumulative density function (CDF) matching and copulas, are based on the same statistical theory, but the first makes the assumption that the two data sets are ordered the same way, which is not needed by the second. Both methods are calibrated in 2010, and the calibrated parameters are applied to the soil moisture data from 2003 to 2009. Results from these two methods compare well with ground measurements. However, CDF matching improves the correlation, whereas copulas improve the root-mean-square error.
    IEEE Transactions on Geoscience and Remote Sensing 01/2014; 52(1):393-405. · 3.47 Impact Factor
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    ABSTRACT: The objective of this study was to compare several approaches to soil moisture (SM) retrieval using l-band microwave radiometry. The comparison was based on a brightness temperature (TB) data set acquired since 2010 by the L-band radiometer ELBARA-II over a vineyard field at the Valencia Anchor Station (VAS) site. ELBARA-II, provided by the European Space Agency (ESA) within the scientific program of the SMOS (Soil Moisture and Ocean Salinity) mission, measures multiangular TB data at horizontal and vertical polarization for a range of incidence angles (30°–60°). Based on a three year data set (2010–2012), several SM retrieval approaches developed for spaceborne missions including AMSR-E (Advanced Microwave Scanning Radiometer for EOS), SMAP (Soil Moisture Active Passive) and SMOS were compared. The approaches include: the Single Channel Algorithm (SCA) for horizontal (SCA-H) and vertical (SCA-V) polarizations, the Dual Channel Algorithm (DCA), the Land Parameter Retrieval Model (LPRM) and two simplified approaches based on statistical regressions (referred to as ‘Mattar’ and ‘Saleh’). Time series of vegetation indices required for three of the algorithms (SCA-H, SCA-V and ‘Mattar’) were obtained from MODIS observations. The SM retrievals were evaluated against reference SM values estimated from a multiangular 2-Parameter inversion approach. As no in situ SM data was used, the evaluation made here is relative to the use of this specific reference data set. The results obtained with the current base line algorithms developed for SMAP (SCA-H and -V) are in very good agreement with the ‘reference’ SM data set derived from the multi-angular observations (R2 ≈ 0.90, RMSE varying between 0.035 and 0.056 m3/m3 for several retrieval configurations). This result showed that, provided the relationship between vegetation optical depth and a remotely-sensed vegetation index can be calibrated, the SCA algorithms can provide results very close to those obtained from multi-angular observations in this study area. The approaches based on statistical regressions provided similar results and the best accuracy was obtained with the ‘Saleh’ methods based on either bi-angular or bipolarization observations (R2 ≈ 0.93, RMSE ≈ 0.035 m3/m3). The LPRM and DCA algorithms were found to be slightly less successful in retrieving the ‘reference’ SM time series (R2 ≈ 0.75, RMSE ≈ 0.055 m3/m3). However, the two above approaches have the great advantage of not requiring any model calibrations previous to the SM retrievals.
    Remote Sensing of Environment 01/2014; 154:89–101. · 5.10 Impact Factor
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    ABSTRACT: Aquarius is a combined passive/active L-band microwave instrument developed to map the ocean surface salinity field from space [1]. The primary science objective of this mission is to monitor the seasonal and interannual variation of the large scale features of the surface salinity field in the open ocean with a spatial resolution of 150 km and a retrieval accuracy of 0.2 psu globally on a monthly basis. The measurement principle is based on the response of the L-band (1.413 GHz) sea surface brightness temperatures to sea surface salinity.
    IGARSS 2013 - 2013 IEEE International Geoscience and Remote Sensing Symposium; 07/2013
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    ABSTRACT: The Soil Moisture Ocean Salinity (SMOS) mission has been providing L-band multi-angular brightness temperature observations at a global scale since its launch in November 2009 and has performed well in the retrieval of soil moisture. The multiple incidence angle observations are not obtained at fixed values and the resolution and accuracy change with the grid locations over SMOS snapshot images. Radio frequency interference issues and aliasing at lower look angles increases the uncertainty of observations and thereby affects the soil moisture retrieval that utilizes observations at specific angles. In this study, we propose a processing chain that uses a mixed objective function based on SMOS L1c data products to refine the characteristics of multi-angular observations. The approach was validated using simulations from a radiative transfer model and analyzed over three external targets: Amazon rainforest, Sahara desert, and Antarctic ice. These results could provide insights for selecting and utilizing external targets as part of the upcoming Soil Moisture Active Passive (SMAP) mission.
    IGARSS 2013 - 2013 IEEE International Geoscience and Remote Sensing Symposium; 07/2013
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    ABSTRACT: A ground-based fully polarimetric scatterometer operating at multiple frequencies was used to continuously monitor soybean growth over the course of a growing season. Polarimetric backscatter data at L-, C-, and X-bands were acquired every 10 min. We analysed the relationships between L-, C-, and X-band signatures, and biophysical measurements over the entire soybean growth period. Temporal changes in backscattering coefficients for all bands followed the patterns observed in the soybean growth measurements leaf area index LAI and vegetation water content VWC. The difference between the backscattering coefficients for horizontally transmitted horizontally received HH and vertically transmitted vertically received VV polarizations at the L-band was apparent after the R2 stage DOY 224 due to the double-bounce scattering effect. Results indicated that L-, C-, and X-band radar backscatter data can be used to detect different soybean growth stages. The results of correlation analyses between the backscattering coefficient for specific bands/polarizations and soybean growth data showed that L-band HH-polarization had the highest correlation with the vegetation parameters LAI r = 0.98 and VWC r = 0.97. Prediction equations for estimation of soybean growth parameters from the L-HH were developed. The results indicated that L-HH could be used for estimating the vegetation biophysical parameters considered here with high accuracy. These results provide a basis for developing a method to retrieve crop biophysical properties and guidance on the optimum microwave frequency and polarization necessary to monitor crop conditions. The results are directly applicable to systems such as the proposed NASA Soil Moisture Active Passive SMAP satellite.
    International Journal of Remote Sensing 06/2013; 34(11):4069-4082. · 1.36 Impact Factor
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    ABSTRACT: Land surface temperature plays an important role in surface processes and is a key input for physically based retrieval algorithms of soil moisture and evaporation. This study presents a framework for using independent estimates of land surface temperature from five microwave satellite sensors to improve the accuracy of land surface temperature output from a numerical weather prediction system in an off-line (postprocessing) analysis. First, structural differences in timing and amplitude of the temperature signal were addressed. Then, satellite observations were assimilated into an auto-regressive error model, formulated to estimate errors in the numerical weather prediction output. Errors in daily minimum and amplitude were treated separately. Results of this study provide new insights about potential added benefits of preprocessing and off-line assimilation of microwave remote sensing-based and model-based temperature retrievals. It is shown that the satellite observations may be used to reduce errors in surface temperature, particularly for day-time hours. Preprocessing is responsible for the bulk of this reduction in temperature error; data assimilation is shown to further reduce the random temperature error by a few tenths of a Kelvin, accounting for a 10% reduction in RMSE.
    Journal of Geophysical Research Atmospheres 01/2013; 118(2):577-591. · 3.44 Impact Factor
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    ABSTRACT: The Canadian Experiment for Soil Moisture in 2010 (CanEx-SM10) was carried out in Saskatchewan, Canada, from 31 May to 16 June, 2010. Its main objective was to contribute to Soil Moisture and Ocean Salinity (SMOS) mission validation and the prelaunch assessment of the proposed Soil Moisture Active and Passive (SMAP) mission. During CanEx-SM10, SMOS data as well as other passive and active microwave measurements were collected by both airborne and satellite platforms. Ground-based measurements of soil (moisture, temperature, roughness, bulk density) and vegetation characteristics (leaf area index, biomass, vegetation height) were conducted close in time to the airborne and satellite acquisitions. Moreover, two ground-based in situ networks provided continuous measurements of meteorological conditions and soil moisture and soil temperature profiles. Two sites, each covering 33 km × 71 km (about two SMOS pixels) were selected in agricultural and boreal forested areas in order to provide contrasting soil and vegetation conditions. This paper describes the measurement strategy, provides an overview of the data sets, and presents preliminary results. Over the agricultural area, the airborne L-band brightness temperatures matched up well with the SMOS data (prototype 346). The radio frequency interference observed in both SMOS and the airborne L-band radiometer data exhibited spatial and temporal variability and polarization dependency. The temporal evolution of the SMOS soil moisture product (prototype 307) matched that observed with the ground data, but the absolute soil moisture estimates did not meet the accuracy requirements (0.04 m3/m3) of the SMOS mission. AMSR-E soil moisture estimates from the National Snow and Ice Data Center more closely reflected soil moisture measurements.
    IEEE Transactions on Geoscience and Remote Sensing 01/2013; 51(1):347-363. · 3.47 Impact Factor
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    ABSTRACT: The National Aeronautics and Space Administration's (NASA) proposed Soil Moisture Active Passive (SMAP) satellite mission ( ~ 2014) will include a radar system that will provide L-band multi-polarization backscatter at a constant incidence angle of 40 °. During the pre-launch phase of the project, there is a need for observations that will support the radar-based soil moisture algorithm development and validation. A valuable resource for providing these observations is the NASA Jet Propulsion Laboratory Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). However, SMAP will observe at a constant incidence angle of 40 °, and UAVSAR collects data over a wide range of incidence angles (25 °-60°). In this investigation, a technique was developed and tested for normalizing UAVSAR data to a constant incidence angle. The approach is based on a histogram matching procedure. The data used to develop and demonstrate this approach were collected as part of the Canadian Soil Moisture Experiment 2010 (CanEx-SM10). Land cover in the region included agriculture and forest. Evaluation was made possible by the acquisition of numerous overlapping UAVSAR flight lines that provided multiple incidence angle observations of the same locations. Actual observations at a 40° incidence angle were compared to the normalized data to assess performance of the normalization technique. An optimum technique should be able to reduce the systematic error (Bias) to 0 dB and to lower the total root mean square error (RMSE) computed after correction to the level of the initial residual error (RMSEres) present in the data set. The normalization approach developed here achieved both of these. Bias caused by the incidence angle variability was minimized to ~ 0 dB, whereas the residual error caused by instrument related random errors and amplitude fluctuations due to ground variability was r- duced to approximately 3 dB for agricultural areas and 2.6 dB for forests; these values were consistent with the initial RMSEres estimated using the un-corrected data. The residual error can be reduced further by aggregating the radar observations to a coarser grid spacing. The technique adequately adjusted the backscatter over the full swath width irrespective of the original incidence angle, polarization, and ground conditions (vegetation cover and soil moisture). In addition to providing a basis for fully exploiting UAVSAR (or similar aircraft systems) for SMAP algorithm development and validation, the technique could also be adapted to satellite radar systems. This normalization approach will also be beneficial in terms of reducing the number of flight lines required to cover a study area, which would eventually result in more cost-effective soil moisture field campaigns.
    IEEE Transactions on Geoscience and Remote Sensing 01/2013; 51(3):1791-1804. · 3.47 Impact Factor
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    ABSTRACT: NASA’s Soil Moisture Active Passive (SMAP) mission will carry the first combined spaceborne L-band radiometer and Synthetic Aperture Radar (SAR) system with the objective of mapping near-surface soil moisture and freeze/thaw state globally every 2-3 days. SMAP will provide three soil moisture products; (i) high-resolution from radar (~3km), (ii) low-resolution from radiometer (~36km), and (iii) intermediate-resolution from the fusion of radar and radiometer (~9km). The Soil Moisture Active Passive Experiments (SMAPEx) are a series of three airborne field experiments designed to provide prototype SMAP data for the development and validation of soil moisture retrieval algorithms applicable to the SMAP system. This paper describes the SMAPEx sampling strategy and presents an overview of the data collected during the three experiments: SMAPEx-1 (July 5-10, 2010), SMAPEx-2 (December 4-8, 2010) and SMAPEx-3 (September 5-23, 2011). The SMAPEx experiments were conducted in a semi-arid agricultural and grazing area located in southeastern Australia, timed so as to acquire data over a seasonal cycle at various stages of the crop growth. Airborne L-band brightness temperature (~1km) and radar backscatter (~10m) observations were collected over an area the size of a single SMAP footprint (38 km × 36 km at 35° latitude) with a 2-3 days revisit time, providing SMAP-like data for testing of radiometer-only, radar-only and combined radiometer-radar soil moisture retrieval and downscaling algorithms. Airborne observations were supported by continuous monitoring of near-surface (0-5cm) soil moisture along with intensive ground monitoring of soil moisture, soil temperature, vegetation biomass and structure, and surface roughness.
    IEEE Transactions on Geoscience and Remote Sensing 01/2013; 52(1):490-507. · 3.47 Impact Factor
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    ABSTRACT: As part of the Soil Moisture and Ocean Salinity (SMOS) validation process, a comparison of the skills of three satellites [SMOS, Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) or Advanced Microwave Scanning Radiometer, and Advanced Scatterometer (ASCAT)], and one-model European Centre for Medium Range Weather Forecasting (ECMWF) soil moisture products is conducted over four watersheds located in the U.S. The four products compared in for 2010 over four soil moisture networks were used for the calibration of AMSR-E. The results indicate that SMOS retrievals are closest to the ground measurements with a low average root mean square error of $0.061~{rm m}^{3}cdot{rm m}^{-3}$ for the morning overpass and $0.067~{rm m}^{3}cdot{rm m}^{-3}$ for the afternoon overpass, which represents an improvement by a factor of 2–3 compared with the other products. The ECMWF product has good correlation coefficients (around 0.78) but has a constant bias of $0.1hbox{--}0.2~{rm m}^{3}cdot{rm m}^{-3}$ over the four networks. The land parameter retrieval model AMSR-E product gives reasonable results in terms of correlation (around 0.73) but has a variable seasonal bias over the year. The ASCAT soil moisture index is found to be very noisy and unstable.
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    ABSTRACT: NASA's (National Aeronautics and Space Administration) Soil Moisture Active Passive (SMAP) Mission is scheduled for launch in late 2014. The objective of the mission is global mapping of soil moisture and freeze/thaw state. Merging of active and passive L-band observations of the mission will enable unprecedented combination of accuracy, resolution, coverage and revisit-time for soil moisture and freeze/thaw state retrieval. For pre-launch algorithm development and validation the SMAP project and NASA coordinated a field campaign named as SMAPVEX12 (Soil Moisture Active Passive Validation Experiment 2012) together with Agriculture and Agri-Food Canada, and other Canadian and US institutions in the vicinity of Winnipeg, Canada in June-July, 2012. The main objective of SMAPVEX12 was acquisition of a data record that features long time-series with varying soil moisture and vegetation conditions over an aerial domain of multiple parallel flight lines. The coincident active and passive L-band data was acquired with the PALS (Passive Active L-band System) instrument. The measurements were conducted over the experiment domain every 2-3 days on average, over a period of 43 days. The preliminary calibration of the brightness temperatures obtained in the campaign has been performed. Daily lake calibrations were used to adjust the radiometer calibration parameters, and the obtained measurements were compared against the raw in situ soil moisture measurements. The evaluation shows that this preliminary calibration of the data produces already a consistent brightness temperature record over the campaign duration, and only secondary adjustments and cleaning of the data is need before the data can be applied to the development and validation of SMAP algorithms.
    Proc SPIE 10/2012;
  • 39th COSPAR Scientific Assembly; 07/2012
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    ABSTRACT: Once launched in late 2014, NASA's Soil Moisture Active Passive (SMAP) mission will use a combination of a four-channel L-band radiometer and a three-channel L-band radar to provide high resolution global mapping of soil moisture and landscape freeze/thaw state every 2-3 days. These measurements are valuable to improved understanding of the Earth's water, energy, and carbon cycles, and to many applications of societal benefit. In order for soil moisture and freeze/thaw to be retrieved accurately from SMAP microwave data, prelaunch activities are concentrating on developing improved geophysical retrieval algorithms for each of the SMAP baseline products using data from simulations, from existing satellite missions such as SMOS, and from field campaign data, such as the SMAPEx airborne study in Australia discussed in this paper.
    IEEE International Geoscience and Remote Sensing Symposium; 07/2012
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    ABSTRACT: Vegetation water content (VWC) is an important biophysical parameter and has a significant role in the retrieval of soil moisture using microwave remote sensing. Here, the radar vegetation index (RVI) was evaluated for estimating VWC. Analysis utilized a data set obtained by a ground-based multifrequency polarimetric scatterometer system, with a single incidence angle of 40$^{\circ}$, during an entire growth period of rice and soybean. Temporal variations of the backscattering coefficients for the L-, C-, and X-bands, RVI, VWC, leaf area index, and normalized difference vegetation index were analyzed. The L-band RVI was found to be correlated to the different vegetation indices. Prediction equations for the estimation of VWC from the RVI were developed. The results indicated that it was possible to estimate VWC with an accuracy of 0.21 $\hbox{kg}\cdot\hbox{m}^{-2}$ using L-band RVI observations. These results demonstrate that valuable new information can be extracted from current and future radar satellite systems on the vegetation condition of two globally important crop types. The results are directly applicable to systems such as the proposed NASA Soil Moisture Active Passive satellite.
    IEEE Geoscience and Remote Sensing Letters 07/2012; 9(4):564-568. · 1.82 Impact Factor
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    ABSTRACT: This study reports on the utilization of microwave modeling, together with ground truth, and L-band (1.4-GHz) brightness temperatures to investigate the passive microwave characteristics of a conifer forest floor. The microwave data were acquired over a natural Virginia Pine forest in Maryland by a ground-based microwave active/passive instrument system in 2008/2009. Ground measurements of the tree biophysical parameters and forest floor characteristics were obtained during the field campaign. The test site consisted of medium-sized evergreen conifers with an average height of 12 m and average diameters at breast height of 12.6 cm. The site is a typical pine forest site in that there is a surface layer of loose debris/needles and an organic transition layer above the mineral soil. In an effort to characterize and model the impact of the surface litter layer, an experiment was conducted on a day with wet soil conditions, which involved removal of the surface litter layer from one half of the test site while keeping the other half undisturbed. The observations showed detectable decrease in emissivity for both polarizations after the surface litter layer was removed. A first-order radiative transfer model of the forest stands including the multilayer nature of the forest floor in conjunction with the ground truth data are used to compute forest emission. The model calculations reproduced the major features of the experimental data over the entire duration, which included the effects of surface litter and ground moisture content on overall emission. Both theory and experimental results confirm that the litter layer increases the observed canopy brightness temperature and obscure the soil emission.
    IEEE Transactions on Geoscience and Remote Sensing 04/2012; · 3.47 Impact Factor
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    ABSTRACT: [1] Surface soil temperature estimates at approximately 0.05 m depth are needed to retrieve soil moisture from the planned Soil Moisture Active Passive (SMAP) L-band (1.4 GHz) satellite. Numerical weather prediction (NWP) systems as operated by various weather centers produce global estimates of soil temperature. In this study in situ data collected over the state of Oklahoma are used to assess surface (soil) temperature from three NWP systems: (1) the integrated forecast system from the European Center for Medium range Weather Forecasts (ECMWF), (2) the modern-era retrospective analysis for research and applications (MERRA) from the NASA Global Modeling and Assimilation Office, and (3) the global data assimilation system used by the National Center for Environmental Prediction (NCEP). The results are presented by hour of day with specific attention directed to the SMAP early morning overpass time at around 6 A.M. local time, and the period of 1 April to 1 October 2009. It was found that the NWP systems estimate the 0.05 m soil temperature at this time of day with an overall root mean square error of 1.9 to 2.0 K. It is shown that this error can be reduced to 1.6 to 1.8 K when differences between the modeling and measurement depth are accounted for by synchronizing each NWP set to match the mean phase of the in situ data and adjusting the amplitude in accordance with heat flow principles. These results indicate that with little calibration all products meet the SMAP error budget criteria over Oklahoma.
    Water Resources Research. 02/2012; 48(2).
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    ABSTRACT: Microwave remote sensing can provide reliable measurements of surface soil moisture. However, some land surface conditions can have a perturbing influence on soil moisture retrievals. In the soil moisture experiments in 2005 (SMEX05), we attempted to contribute to the understanding of the effect of dew using concurrent ground and aircraft observations. Early morning flights were conducted with an airborne microwave radiometer from June 19 to July 2, 2005, in Iowa, USA over an agricultural domain. Results of the experiment indicated that dew had a small but measurable effect on the observed 10.7-GHz brightness temperatures. The results indicate that the H-pol emissivity increased 0.015 to 0.04 for the corn sites, 0.014 to 0.02 for soybean, and 0.01 for forest sites as dew evaporated. These results suggest that the presence of dew decreases X-band land surface emissivity slightly and the effect of dew varies with vegetation types. Our findings are consistent with other works in the literature that has found that the effect of dew depends on both the type of vegetation and the wavelength of observation, but further studies should be conducted to verify this hypothesis.
    Journal of Applied Remote Sensing. 01/2012;
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    ABSTRACT: Soil moisture is an intrinsic state variable that varies considerably in space and time. From a hydrologic viewpoint, soil moisture controls runoff, infiltration, storage and drainage. Soil moisture determines the partitioning of the incoming radiation between latent and sensible heat fluxes. Although soil moisture may be highly variable in space and time, if measurements of soil moisture at the field or small watershed scale are repeatedly observed, certain locations can often be identified as being temporally stable and representative of the an area average. This study is aimed at determining the adequacy of long term point-scale surface soil moisture measurements in representing local field scale averages which may ultimately serve as in situ locations for the calibration and validation of remotely sensed soil moisture. Experimental data were obtained by frequency-domain reflectometry (FDR) sensors permanently installed in two agricultural fields, AS1 and AS2 (2.23 and 2.71 ha, respectively) at a depth of 5 cm. Twenty additional FDR sensors, spaced 35 m apart, were installed horizontally at a depth of 5 cm in each field with automated data collection being transmitted every 30 min from July 15 through September 20, 2009. Additionally, meteorological data were obtained from existing weather stations in each field. The FDR sensors revealed persistent patterns in surface soil moisture within each field and identified sites that were temporally stable. The locations that were optimal for estimating the area-average field water contents were different from the permanent sensor locations in both fields. Permanent sensor data showed approximately 4 and 10% mean relative differences for fields AS1 and AS2, respectively, with relatively large standard deviations. Thus, minimum offset values could be applied to the temporally stable field sites to obtain representative field average values of surface soil moisture. However, use of permanent sensor data for offset estimates gave poor results. These findings are of relevance for applications of geospatial surface soil moisture data assimilation in hydrologic modeling when only point-scale observations are available, as well as, remotely sensed surface soil moisture calibration and validation studies.
    Geoderma 01/2012; 170:195–205. · 2.35 Impact Factor

Publication Stats

6k Citations
383.75 Total Impact Points


  • 2013
    • University of Melbourne
      • Department of Infrastructure Engineering
      Melbourne, Victoria, Australia
  • 2009
    • George Washington University
      • Department of Electrical & Computer Engineering
      Washington, D. C., DC, United States
    • Iowa State University
      • Department of Agronomy
      Ames, Iowa, United States
    • Centre D'Etudes Spatiales De La Biosphere
      Tolosa de Llenguadoc, Midi-Pyrénées, France
  • 2001–2009
    • United States Department of Agriculture
      • Agricultural Research Service (ARS)
      Washington, D. C., DC, United States
    • California Institute of Technology
      • Jet Propulsion Laboratory
      Pasadena, CA, United States
  • 2008
    • The University of Arizona
      Tucson, Arizona, United States
    • George Mason University
      Fairfax, Virginia, United States
    • Texas A&M University
      College Station, Texas, United States
  • 2007–2008
    • Universität Bremen
      • Institut für Umweltphysik (IUP)
      Bremen, Bremen, Germany
    • University of Virginia
      Charlottesville, Virginia, United States
    • National Institute for Space Research, Brazil
      São José dos Campos, São Paulo, Brazil
  • 2006
    • University of California, Santa Barbara
      Santa Barbara, California, United States
  • 2003–2006
    • University of South Carolina
      • Department of Biological Sciences
      Columbia, South Carolina, United States
  • 2005
    • University of California, Irvine
      • Department of Earth System Science
      Irvine, CA, United States
    • University of Liverpool
      • School of Engineering
      Liverpool, ENG, United Kingdom
  • 2004
    • The University of Manchester
      Manchester, England, United Kingdom
  • 1994–2000
    • Princeton University
      • • Department of Civil and Environmental Engineering
      • • Department of Operations Research and Financial Engineering
      Princeton, NJ, United States
  • 1995
    • Agricultural Research Service
      Kerrville, Texas, United States
    • Hawaii Agriculture Research Center
      Honolulu, Hawaii, United States
  • 1993
    • NASA
      Washington, West Virginia, United States
  • 1987–1988
    • Wageningen University
      Wageningen, Gelderland, Netherlands