T.J. Jackson

University of Melbourne, Melbourne, Victoria, Australia

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Publications (383)619.26 Total impact

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    ABSTRACT: Despite the continuing efforts to improve existing soil moisture retrieval algorithms, the ability to estimate soil moisture from passive microwave observations is still hampered by problems in accurately modeling the observed microwave signal. This paper focuses on the estimation of effective surface roughness parameters of the L-band Microwave Emission from the Biosphere (L-MEB) model in order to improve soil moisture retrievals from passive microwave observations. Data from the SMAP Validation Experiment 2012 conducted in Canada are used to develop and validate a simple model for the estimation of effective roughness parameters. Results show that the L-MEB roughness parameters can be empirically related to the observed brightness temperatures and the leaf area index of the vegetation. These results indicate that the roughness parameters are compensating for both roughness and vegetation effects. It is also shown, using a leave-one-out cross validation, that the model is able to accurately estimate the roughness parameters necessary for the inversion of the L-MEB model. In order to demonstrate the usefulness of the roughness parameterization, the performance of the model is compared to more traditional roughness formulations. Results indicate that the soil moisture retrieval error can be reduced to 0.054 m3/m3 if the roughness formulation proposed in this study is implemented in the soil moisture retrieval algorithm.
    IEEE Transactions on Geoscience and Remote Sensing 07/2015; 53(7):4091-4103. DOI:10.1109/TGRS.2015.2390259 · 3.51 Impact Factor
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    ABSTRACT: Aquarius satellite observations over land offer a new resource for measuring soil moisture from space. Although Aquarius was designed for ocean salinity mapping, our objective in this investigation is to exploit the large amount of land observations that Aquarius acquires and extend the mission scope to include the retrieval of surface soil moisture. The soil moisture retrieval algorithm development focused on using only the radiometer data because of the extensive heritage of passive microwave retrieval of soil moisture. The single channel algorithm (SCA) was implemented using the Aquarius observations to estimate surface soil moisture. Aquarius radiometer observations from three beams (after bias/gain modification) along with the National Centers for Environmental Prediction model forecast surface temperatures were then used to retrieve soil moisture. Ancillary data inputs required for using the SCA are vegetation water content, land surface temperature, and several soil and vegetation parameters based on land cover classes. The resulting global spatial patterns of soil moisture were consistent with the precipitation climatology. Initial assessments were performed using in situ observations from the U.S. Department of Agriculture Little Washita and Little River watershed soil moisture networks. Results showed good performance by the algorithm for these land surface conditions for the period of August 2011–June 2013 ( $hbox{rmse}=0.031 hbox{m}^{3}/hbox{m}^{3}$ , $hbox{Bias} = -0.007 hbox{m}^{3}/hbox{m}^{3}$ , and $R = 0.855$ ) . This radiometer-only soil moisture product will serve as a baseline for continuing research on both active and combined passive–active soil moisture algorithms. The products are routinely availab- e through the National Aeronautics and Space Administration data archive at the National Snow and Ice Data Center.
    IEEE Geoscience and Remote Sensing Letters 04/2015; 12(5-5):923-927. DOI:10.1109/LGRS.2014.2364151 · 2.10 Impact Factor
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    ABSTRACT: The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) satellite is scheduled for launch in January 2015. In order to develop robust soil moisture retrieval algorithms that fully exploit the unique capabilities of SMAP, algorithm developers had identified a need for long-duration combined active and passive L-band microwave observations. In response to this need, a joint Canada–U.S. field experiment (SMAPVEX12) was conducted in Manitoba (Canada) over a six-week period in 2012. Several times per week, NASA flew two aircraft carrying instruments that could simulate the observations the SMAP satellite would provide. Ground crews collected soil moisture data, crop measurements, and biomass samples in support of this campaign. The objective of SMAPVEX12 was to support the development, enhancement, and testing of SMAP soil moisture retrieval algorithms. This paper details the airborne and field data collection as well as data calibration and analysis. Early results from the SMAP active radar retrieval methods are presented and demonstrate that relative and absolute soil moisture can be delivered by this approach. Passive active L-band sensor (PALS) antenna temperatures and reflectivity, as well as backscatter, closely follow dry down and wetting events observed during SMAPVEX12. The SMAPVEX12 experiment was highly successful in achieving its objectives and provides a unique and valuable data set that will advance algorithm development.
    IEEE Transactions on Geoscience and Remote Sensing 04/2015; 53(5). DOI:10.1109/TGRS.2014.2364913 · 3.51 Impact Factor
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    ABSTRACT: A comparison between two algorithms for estimating soil moisture with microwave satellite data was carried out by using the datasets collected on the four Agricultural Research Service (ARS) watershed sites in the US from 2002 to 2009. These sites collectively represent a wide range of ground conditions and precipitation regimes (from natural to agricultural surfaces and from desert to humid regions) and provide long-term in-situ data. One of the algorithms is the artificial neural network-based algorithm developed by the Institute of Applied Physics of the National Research Council (IFAC-CNR) (HydroAlgo) and the second one is the Single Channel Algorithm (SCA) developed by USDA-ARS (US Department of Agriculture-Agricultural Research Service). Both algorithms are based on the same radiative transfer equations but are implemented very differently. Both made use of datasets provided by the Japanese Aerospace Exploration Agency (JAXA), within the framework of Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) and Global Change Observation Mission-Water GCOM/AMSR-2 programs. Results demonstrated that both algorithms perform better than the mission specified accuracy, with Root Mean Square Error (RMSE) ≤0.06 m3/m3 and Bias
    04/2015; 3. DOI:10.3389/feart.2015.00016
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    ABSTRACT: Vegetation water content (VWC) plays an important role in parameterizing the vegetation influence on microwave soil moisture retrieval. During the past decade, relationships have been developed between VWC and vegetation indices from satellite optical sensors, in order to create large-scale VWC maps based on these relationships. Among existing vegetation indices, the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI) have been most frequently used for estimating VWC. This work compiles and inter-compares a number of equations developed for VWC derivation from NDVI and NDWI using satellite data and ground samples collected from field campaigns carried out in the United States, Australia, and China. Four vegetation types are considered: corn, cereal grains, legumes, and grassland. While existing equations are reassessed against the entire compiled data sets, new equations are also developed based on the entire data sets. Comparing with existing equations, results show superiorities for the new equations based on statistical analysis against the entire data set. NDWI 1640 and NDVI are found to be the preferred indices for VWC estimation based on the availability and the error statistics of the compiled data sets. It is recommended that the new equations can be applied in the future global remote sensing application for VWC map retrieval.
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 04/2015; 8(4). DOI:10.1109/JSTARS.2015.2398034 · 2.83 Impact Factor
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    ABSTRACT: In this letter, it is shown that spaceborne observations made by the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) satellite agreed closely with the Passive Active L-band System (PALS) brightness temperature acquisitions during the Soil Moisture Active Passive (SMAP) Validation Experiment 2012. The difference between the SMOS and PALS measurements was less than 5 K and 6 K for vertical and horizontal polarizations, respectively, over the relatively homogeneous agricultural areas. These values are less than the SMOS subpixel variability determined from the PALS measurement. This result demonstrated that the measurements obtained in the experiment are scalable to spaceborne brightness temperature observations, are representative of the expected SMAP observations, and will be of value in the development of soil moisture algorithms for spaceborne missions.
    IEEE Geoscience and Remote Sensing Letters 03/2015; 12(4). DOI:10.1109/LGRS.2014.2362889 · 2.10 Impact Factor
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    ABSTRACT: Soil moisture ocean salinity (SMOS) mission has been providing L-band multiangular 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 also allow for the retrieval of additional parameters beyond soil moisture, but these are not obtained at fixed values and the resolution and accuracy change with the grid locations over SMOS snapshot images. Radio-frequency interference (RFI) issues and aliasing at lower look angles increase the uncertainty of observations and thereby affect the soil moisture retrieval that utilizes observations at specific angles. In this study, we proposed a two-step regression approach that uses a mixed objective function based on SMOS L1c data products to refine characteristics of multiangular observations. The approach was found to be robust by validation using simulations from a radiative transfer model, and valuable in improving soil moisture estimates from SMOS. In addition, refined brightness temperatures were analyzed over three external targets: Antarctic ice sheet, Amazon rainforest, and Sahara desert, by comparing with WindSat observations. These results provide insights for selecting and utilizing external targets as part of the upcoming soil moisture active passive (SMAP) mission.
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 02/2015; 8:589-603. DOI:10.1109/JSTARS.2014.2336664 · 2.83 Impact Factor
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    Dataset: IGARSS95
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    ABSTRACT: This paper investigates the feasibility of using an airborne synthetic aperture radar (SAR) to validate spaceborne SAR data. This is directed at soil moisture sensing and the recently launched soil moisture active passive (SMAP) satellite. The value of this approach is related to the fact that vicarious targets such as rain forests and oceans calibrate only the extrema of backscattering coefficients () and that the relationship between soil moisture and is nonlinear. Furthermore, corner reflectors are difficult to deploy to calibrate medium resolution (1-3 km) spaceborne sensors such as the one onboard SMAP. A challenge with the approach is the varying incidence angle () of the airborne sensor versus the constant value used by SMAP. The impact of this on the inter-comparison of airborne and SMAP data is analyzed through simulation and aircraft data analysis. In the absence of the SMAP SAR data, the airborne SAR and scatterometer from the recent field campaign provided the imaging geometry similar to the spaceborne case. The effect of on the inter-comparison using these two airborne data sets was found to be small if the landcover within the footprint is homogeneous textit{and} if (natural unit) changes very little or approximately linearly with . Over heterogeneous pixels consisting of pasture, grass, forest, and growing corn, the simulation shows that the mean and standard deviation of the difference in between the SAR and scatterometer data are smaller than 0.4 and 0.3 dB, respectively. The test results with the - irborne data are generally consistent with the simulation results: the mean and standard deviation of the difference are smaller than 0.9 dB for HH, VV, and HV. These magnitudes are comparable with those of the major sources of the difference: the relative calibration errors of the airborne instruments (), speckle noise (), effect of variation within the footprint (), and geolocation uncertainty in the airborne scatterometer data (). The findings from this study are expected to apply to the inter-comparison of the SMAP and airborne data after considering the details affecting the comparison: imaging geometry, temporal synchronization, spatial collocation, antenna gain, speckle noise, and spatial resolution. When applied, the inter-comparison will provide more confidence in the calibration of SMAP.
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 01/2015; DOI:10.1109/JSTARS.2015.2424715 · 2.83 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 11/2014; 154:89–101. DOI:10.1016/j.rse.2014.08.002 · 6.39 Impact Factor
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    ABSTRACT: In a recent paper, Leroux et al. [1] compared three satellite soil moisture data sets (SMOS, AMSR-E, and ASCAT) and ECMWF forecast soil moisture data to in situ measurements over four watersheds located in the United States. Their conclusions stated that SMOS soil moisture retrievals represent “an improvement [in RMSE] by a factor of 2- 3 compared with the other products” and that the ASCAT soil moisture data are “very noisy and unstable”. In this clarification, the analysis of Leroux et al. is repeated using a newer version of the ASCAT data and additional metrics are provided. It is shown that the ASCAT retrievals are skillful, although they show some unexpected behavior during summer for two of the watersheds. It is also noted that the improvement of SMOS by a factor of 2-3 mentioned by Leroux et al. is driven by differences in bias and only applies relative to AMSR-E and the ECWMF data in the now obsolete version investigated by Leroux et al.
    IEEE Transactions on Geoscience and Remote Sensing 05/2014; 52(3):1901-1906. DOI:10.1109/TGRS.2013.2282172 · 3.51 Impact Factor
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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. DOI:10.1109/LGRS.2013.2279255 · 2.10 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. DOI:10.1016/j.rse.2014.01.013 · 6.39 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. DOI:10.1109/TGRS.2013.2240691 · 3.51 Impact Factor
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    ABSTRACT: Soil moisture satellite estimates are available from a variety of passive microwave satellite sensors, but their spatial resolution is frequently too coarse for use by land managers and other decision makers. In this paper, a soil moisture downscaling algorithm based on a regression relationship between daily temperature changes and daily average soil moisture is developed and presented to produce an enhanced spatial resolution soil moisture product. The algorithm was developed based on the thermal inertial relationship between daily temperature changes and averaged soil moisture under different vegetation conditions, using 1/8 degrees spatial resolution North American Land Data Assimilation System (NLDAS) surface temperature and soil moisture data, as well as 5-km Advanced Very High Resolution Radiometer (AVHRR) (1981-2000) and 1-km Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) and surface temperature (2002-present) to build the look-up table at 1/8 degrees resolution. This algorithm was applied to the 1-km MODIS land surface temperature to obtain the downscaled soil moisture estimates and then used to correct the soil moisture products from Advanced Microwave Scanning Radiometer-EOS (AMSR-E). The 1-km downscaled soil moisture maps display greater details on the spatial pattern of soil moisture distribution. Two sets of ground-based measurements, the Oklahoma Mesonet and the Little Washita Micronet, were used to validate the algorithm. The overall averaged slope for 1-km downscaled results vs. Mesonet data is 0.219, which is better than AMSR-E and NLDAS, while the spatial standard deviation (0.054 m(3) m(-3)) and unbiased RMSE (0.042 m(3) m(-3)) of 1-km downscaled results are similar to the other two datasets. The overall slope and spatial standard deviation for 1-km downscaled results vs. Micronet data (0.242 and 0.021 m(3) m(-3), respectively) are significantly better than AMSR-E and NLDAS, while the unbiased RMSE (0.026 m(3) m(-3)) is better than NLDAS and further than AMSR-E. In addition, Mesonet comparisons of all three soil moisture datasets demonstrate a stronger statistical significance than Micronet comparisons, and the p value of 1-km downscaled is generally better than the other two soil moisture datasets. The results demonstrate that the AMSR-E soil moisture was successfully disaggregated to 1 km. The enhanced spatial heterogeneity and the accuracy of the soil moisture estimates are superior to the AMSR-E and NLDAS estimates, when compared with in situ observations.
    Vadose Zone Journal 08/2013; 12(4). DOI:10.2136/vzj2013.05.0089er · 2.41 Impact Factor
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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: 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

Publication Stats

9k Citations
619.26 Total Impact Points


  • 2013
    • University of Melbourne
      • Department of Infrastructure Engineering
      Melbourne, Victoria, Australia
  • 2001–2012
    • United States Department of Agriculture
      Washington, Washington, D.C., United States
    • California Institute of Technology
      • Jet Propulsion Laboratory
      Pasadena, CA, United States
  • 1999–2011
    • Science Systems and Applications, Inc.
      Maryland, United States
  • 2009
    • George Washington University
      • Department of Electrical & Computer Engineering
      Washington, D. C., DC, United States
    • Centre D'Etudes Spatiales De La Biosphere
      Tolosa de Llenguadoc, Midi-Pyrénées, France
  • 2006–2008
    • Universität Bremen
      • Institut für Umweltphysik (IUP)
      Bremen, Bremen, Germany
  • 2007
    • National Institute for Space Research, Brazil
      • Remote Sensing Division
      São José dos Campos, São Paulo, Brazil
    • University of Maryland, College Park
      CGS, Maryland, United States
    • University of Virginia
      • Department of Environmental Sciences
      Charlottesville, Virginia, United States
  • 2003–2006
    • University of South Carolina
      • Department of Biological Sciences
      Columbia, South Carolina, United States
  • 2005
    • National Oceanic and Atmospheric Administration
      Boulder, Colorado, United States
  • 1997–2000
    • Maryland Department Of Agriculture
      Annapolis, Maryland, United States
  • 1995–1999
    • Agricultural Research Service
      ERV, Texas, United States
    • University of Massachusetts Amherst
      • Department of Electrical and Computer Engineering
      Amherst Center, Massachusetts, United States
    • Hawaii Agriculture Research Center
      Honolulu, Hawaii, United States
  • 1983–1999
    • NASA
      • Goddard Space Flight Centre
      Вашингтон, West Virginia, United States
  • 1998
    • The Great Plains Laboratory
      Lenexa, Kansas, United States