J. D. Bolten

University of South Carolina, Columbia, South Carolina, United States

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Publications (38)25.33 Total impact

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    J. D. Bolten, W. T. Crow
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    ABSTRACT: The added value of satellite-based surface soil moisture retrievals for agricultural drought monitoring is assessed by calculating the lagged rank correlation between remotely-sensed vegetation indices (VI) and soil moisture estimates obtained both before and after the assimilation of surface soil moisture retrievals derived from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) into a soil water balance model. Higher soil moisture/VI lag correlations imply an enhanced ability to predict future vegetation conditions using estimates of current soil moisture. Results demonstrate that the assimilation of AMSR-E surface soil moisture retrievals substantially improve the performance of a global drought monitoring system - particularly in sparsely-instrumented areas of the world where high-quality rainfall observations are unavailable.
    Geophysical Research Letters 10/2012; 39(19):19406-. · 3.98 Impact Factor
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    W. T. Crow, S. V. Kumar, J. D. Bolten
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    ABSTRACT: The lagged rank cross-correlation between model-derived root-zone soil moisture estimates and remotely sensed vegetation indices (VI) is examined between January 2000 and December 2010 to quantify the skill of various soil moisture models for agricultural drought monitoring. Examined modeling strategies range from a simple antecedent precipitation index to the application of modern land surface models (LSMs) based on complex water and energy balance formulations. A quasi-global evaluation of lagged VI/soil moisture cross-correlation suggests, when globally averaged across the entire annual cycle, soil moisture estimates obtained from complex LSMs provide little added skill (< 5% in relative terms) in anticipating variations in vegetation condition relative to a simplified water accounting procedure based solely on observed precipitation. However, larger amounts of added skill (5-15% in relative terms) can be identified when focusing exclusively on the extra-tropical growing season and/or utilizing soil moisture values acquired by averaging across a multi-model ensemble.
    Hydrology and Earth System Sciences 09/2012; 16(9):3451-3460. · 3.59 Impact Factor
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    W. T. Crow, S. V. Kumar, J. D. Bolten
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    ABSTRACT: The lagged rank cross-correlation between model-derived root-zone soil moisture estimates and remotely-sensed vegetation indices (VI) is examined between January 2000 and December 2010 to quantify the skill of various soil moisture models for agricultural drought monitoring. Examined modeling strategies range from a simple antecedent precipitation index to the application of modern land surface models (LSMs) based on complex water and energy balance formations. A quasi-global evaluation of lagged VI/soil moisture cross-correlation suggests, when averaged in bulk across the annual cycle, little or no added skill (<5% in relative terms) is associated with applying modern LSMs to off-line agricultural drought monitoring relative to simple accounting procedures based solely on observed precipitation accumulations. However, slightly larger amounts of added skill (5-15% in relative terms) are identified when focusing exclusively on the extra-tropical growing season and/or utilizing soil moisture values acquired by averaging across a multi-model ensemble.
    Hydrology and Earth System Sciences Discussions 04/2012; 9(4):5167-5193. · 3.59 Impact Factor
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    ABSTRACT: When monitoring local or regional hydrosphere dynamics for applications such as agricultural productivity or drought and flooding events, it is necessary to have accurate, high-resolution estimates of terrestrial water and energy storages. Though in-situ observations provide reliable estimates of hydrologic states and fluxes, they are only capable of accurately capturing the dynamics at relatively discrete points in space and time, which makes them inadequate for characterizing the variability of the water budget across scales. In contrast, satellite-based remote sensing is ideal for providing observations of hydrological states and fluxes because it provides spatially-distributed observations at spatial and temporal scales required for regional land surface process modeling. Due to the continued progress in algorithm development and emerging satellite technology, we now have near-real time monitoring of several components of the water cycle including precipitation, soil moisture, lake and river height, terrestrial water storage, snow cover, and evapotranspiration. As these data become more readily available, their application to hydrologic modeling is becoming more common, however there remains little consensus on the most appropriate method for optimal integration and evaluation in regard to hydrological applications. Here we present two case studies operationally applying several remotely sensed products from AMSR-E, GRACE, and MODIS and discuss assimilation strategies, ease of integration and interpretation, and methods for quantifying the success of the application methodology.
    AGU Fall Meeting Abstracts. 12/2011;
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    ABSTRACT: The northern sub-Saharan African (NSSA) region, bounded on the north and south by the Sahara and the Equator, respectively, and stretching from the West to the East African coastlines, has one of the highest biomass-burning rates per unit land area among all regions of the world. Because of the high concentration and frequency of fires in this region, with the associated abundance of heat release and gaseous and particulate smoke emissions, biomass-burning activity is believed to be one of the drivers of the regional carbon and energy cycles, with serious implications for the water cycle. A new interdisciplinary research effort sponsored by NASA is presently being focused on the NSSA region, to better understand the possible connection between the intense biomass burning observed from satellite year after year across the region and the rapid depletion of the regional water resources, as exemplified by the dramatic drying of Lake Chad. A combination of remote sensing and modeling approaches is being utilized in investigating multiple regional surface, atmospheric, and water-cycle processes, and inferring possible links between them. In this presentation, we will discuss preliminary results as well as the path toward improved understanding of the interrelationships and feedbacks between the biomass burning and the environmental change dynamics in the NSSA region.
    AGU Fall Meeting Abstracts. 12/2011;
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    ABSTRACT: The Arab region of the Middle East and North Africa (MENA) is dominated by dry, warm deserts, areas of dense population, and inefficient use of fresh water resources. Due to the scarcity, high intensity, and short duration of rainfall in the MENA, the region is prone to hydroclimatic extremes that are realized by devastating floods and times of drought. However, given its widespread water stress and the considerable demand for water, the MENA remains relatively poorly monitored. This is due in part to the shortage of MENA meteorological observations and the lack of data sharing between nations. As a result, the accurate monitoring of the dynamics of the water cycle in the MENA is difficult. This presentation will cover early results from the Land Data Assimilation System for the MENA region (MENA LDAS) designed to provide regional, gridded fields of hydrological states and fluxes relevant for water resources assessments. The MENA LDAS is envisaged to aid in the identification and evaluation of regional hydrological anomalies by synergistically combining the physically-based Catchment Land Surface Model (CLSM) with observations from several independent data products including soil-water storage variations from the Gravity Recovery and Climate Experiment (GRACE) and irrigation intensity derived from the Moderate Resolution Imaging Spectroradiometer (MODIS). In this fashion, we estimate the mean and seasonal cycle of the water budget components across the MENA to be used for flood and drought assessment.
    AGU Fall Meeting Abstracts. 12/2010;
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    ABSTRACT: The Gravity Recovery and Climate Experiment (GRACE) has provided unprecedented observations of water storage dynamics at the basin to continental scale. In order to realize the full potential of GRACE for hydrology, however, GRACE-derived regional-scale, column-integrated, monthly terrestrial water storage (TWS) anomalies must be disaggregated horizontally, vertically, and in time. The GRACE Data Assimilation System (GRACE-DAS) was designed to downscale and to disaggregate GRACE TWS observations by assimilating them into the Catchment Land Surface Model using a novel implementation of an Ensemble Kalman Smoother. As we have reported previously, this system improved model skill in the simulation of hydrological states and fluxes at sub-GRACE resolution in the Mississippi basin. Here we report on new developments in GRACE-DAS, including application of the system to North America, Europe, the Middle East, and North and East Africa, progress and conceptual challenges in improving the assimilation algorithm, and the use of newly available GRACE TWS solutions in GRACE-DAS. Emerging directions in GRACE-DAS development, including multi-sensor assimilation systems and application to coupled regional climate models, will also be discussed.
    AGU Fall Meeting Abstracts. 12/2010;
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    ABSTRACT: The Arab region of the Middle East and North Africa (MENA) suffers from arid conditions, dense population, and inefficient use of fresh water resources. In addition, the lack of data sharing between nations has made accurate monitoring of the water cycle in the MENA difficult. These factors have nearly exhausted the existing fresh water resources in the region and have led to a re-evaluation of water management plans and budgeting schemes between nations. In order to utilize the existing resources more efficiently, it is necessary that all nations within the MENA have access to optimal estimates of hydrological states and fluxes relevant to water resources. This presentation will introduce a methodology and implementation strategy designed to provide frequent regional estimates of the water budget through the development of a Land Data Assimilation System designed specifically for the Middle Eastern and North African (MENA LDAS) region. The MENA LDAS optimally merges available in situ data with satellite-based estimates of meteorological variables including data from the Gravity Recovery and Climate Experiment (GRACE), the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Tropical Rainfall Measuring Mission (TRMM) within a land surface modeling framework. As a result of this effort, a platform for data sharing among MENA nations is being developed to provide timely regional estimates of hydrological states and fluxes at 1/8th degree resolution. To be discussed will be the development and status of the system, and preliminary results from land surface model simulations over the region.
    05/2010;
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    ABSTRACT: Soil moisture is a fundamental data source used by the United States Department of Agriculture (USDA) International Production Assessment Division (IPAD) to monitor crop growth stage and condition and subsequently, globally forecast agricultural yields. Currently, the USDA IPAD estimates surface and root-zone soil moisture using a two-layer modified Palmer soil moisture model forced by global precipitation and temperature measurements. However, this approach suffers from well-known errors arising from uncertainty in model forcing data and highly simplified model physics. Here, we attempt to correct for these errors by designing and applying an Ensemble Kalman filter (EnKF) data assimilation system to integrate surface soil moisture retrievals from the NASA Advanced Microwave Scanning Radiometer (AMSR-E) into the USDA modified Palmer soil moisture model. An assessment of soil moisture analysis products produced from this assimilation has been completed for a five-year (2002 to 2007) period over the North American continent between 23° N-50° N and 128° W-65° W. In particular, a data denial experimental approach is utilized to isolate the added utility of integrating remotely sensed soil moisture by comparing EnKF soil moisture results obtained using (relatively) low-quality precipitation products obtained from real-time satellite imagery to baseline Palmer model runs forced with higher quality rainfall. An analysis of root-zone anomalies for each model simulation suggests that the assimilation of AMSR-E surface soil moisture retrievals can add significant value to USDA root-zone predictions derived from real-time satellite precipitation products.
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 04/2010; · 2.87 Impact Factor
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    ABSTRACT: Gravimetry-based terrestrial water storage time series have great potential value for hydrological research and applications, because no other observing system can provide global maps of the integrated quantity of water stored on and below the land surface. However, these data are challenging to use because their spatial and temporal resolutions are low relative to other hydrological observations and because total terrestrial water storage is a measurement unfamiliar to hydrologists. In this presentation we will review techniques for temporal, horizontal, and vertical disaggregation of GRACE terrestrial water storage anomalies, including data assimilation and integration within a land surface model. We will then discuss initial results from three efforts to use the methods for water resources applications. These include drought monitoring across North America, water cycle assessment over the Middle East - North African region, and groundwater depletion estimates for northern India.
    AGU Fall Meeting Abstracts. 12/2009;
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    ABSTRACT: The Arab region of the Middle East and Northern Africa (MENA) suffers from arid conditions, dense population, and inefficient use of fresh water resources. These factors have nearly exhausted the existing water resources in the region and have led to a re-evaluation of water management plans and budgeting schemes between nations. In order to utilize the existing resources more efficiently, it is necessary that all nations within the MENA have access to optimal estimates of hydrological states and fluxes relevant to water resources. However, the region is poorly monitored due to trans-boundary issues and sparse in situ networks. This presentation will introduce a methodology and implementation strategy envisaged to achieve these goals through the development of a Land Data Assimilation System designed specifically for the Arab region (ALDAS). The ALDAS optimally merges available in situ data with satellite-based estimates of meteorological variables including data from the Gravity Recovery and Climate Experiment (GRACE), the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Tropical Rainfall Measuring Mission (TRMM) within a land surface modeling framework. As a result of this effort, a platform for data sharing among MENA nations is being prepared to provide timely regional estimates of hydrological states and fluxes at 1/8th degree resolution. To be discussed will be development and status of the land data assimilation system, and preliminary results from land surface model simulations over the region.
    AGU Fall Meeting Abstracts. 12/2009;
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    ABSTRACT: The U.S. Department of Agriculture (USDA) International Production Assessment Division (IPAD) is responsible for providing monthly global crop estimates that heavily influence global commodity market access. These estimates are derived from a merging of many data sources including satellite and ground observations, and more than 20 years of climatology and crop behavior data over key agricultural areas. The goal of IPAD is to provide timely and accurate estimates of global crop conditions for use in up-to-date commodity intelligence reports. A crucial requirement of these global crop yield forecasts is the regional characterization of surface and sub-surface soil moisture. However, due to the spatial heterogeneity and dynamic nature of precipitation events and soil wetness, accurate estimation of regional land surface-atmosphere interactions based sparse ground measurements is difficult. Temporal resolution is particularly important for predicting adequate surface wetting and drying between precipitation events and is closely integrated with CADRE. We attempt to improve upon the existing system by applying an Ensemble Kalman filter (EnKF) data assimilation system to integrate surface soil moisture retrievals from the NASA Advanced Microwave Scanning Radiometer (AMSR-E) into the USDA soil moisture model. The improved temporal resolution and spatial coverage of the satellite-based EOS Advanced Microwave Scanning Radiometer (AMSR-E) is envisaged to provide a better characterization of root zone soil moisture at the regional scale and enable more accurate crop monitoring in key agricultural areas This work aims at evaluating the utility of merging satellite-retrieved soil moisture estimates with the IPAD two-layer soil moisture model used within the DBMS. We present a quantitative analysis of the assimilated soil moisture product over West Africa (9?N-20?N; 20?W-20?E). This region contains many key agricultural areas and has a high agro-meteorological gradient from dese- rt and semi-arid vegetation in the North, to grassland, trees and crops in the South, thus providing an ideal location for evaluating the assimilated soil moisture product over multiple land cover types and conditions. A data denial experimental approach is utilized to isolate the added utility of integrating remotely-sensed soil moisture by comparing assimilated soil moisture results obtained using (relatively) low-quality precipitation products obtained from real-time satellite imagery to baseline model runs forced with higher quality rainfall. An analysis of root-zone anomalies for each model simulation suggests that the assimilation of AMSR-E surface soil moisture retrievals can add significant value to USDA root-zone predictions derived from real-time satellite precipitation products.
    IEEE International Geoscience & Remote Sensing Symposium, IGARSS 2009, July 12-17, 2009, University of Cape Town, Cape Town, South Africa, Proceedings; 01/2009
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    ABSTRACT: A data assimilation system has been designed to integrate surface soil moisture estimates from the EOS Advanced Microwave Scanning Radiometer (AMSR-E) with an online soil moisture model used by the USDA Foreign Agriculture Service for global crop estimation. USDA's International Production Assessment Division (IPAD) of the Office of Global Analysis (OGA) ingests global soil moisture within a Crop Assessment Data Retrieval and Evaluation (CADRE) Decision Support System (DSS) to provide nowcasts of crop conditions and agricultural-drought. This information is primarily used to derive mid-season crop yield estimates for the improvement of foreign market access for U.S. agricultural products. The CADRE is forced by daily meteorological observations (precipitation and temperature) provided by the Air Force Weather Agency (AFWA) and World Meteorological Organization (WMO). The integration of AMSR-E observations into the two-layer soil moisture model employed by IPAD can potentially enhance the reliability of the CADRE soil moisture estimates due to AMSR-E's improved repeat time and greater spatial coverage. Assimilation of the AMSR-E soil moisture estimates is accomplished using a 1-D Ensemble Kalman filter (EnKF) at daily time steps. A diagnostic calibration of the filter is performed using innovation statistics by accurately weighting the filter observation and modeling errors for three ranges of vegetation biomass density estimated using historical data from the Advanced Very High Resolution Radiometer (AVHRR). Assessment of the AMSR-E assimilation has been completed for a five year duration over the conterminous United States. To evaluate the ability of the filter to compensate for incorrect precipitation forcing into the model, a data denial approach is employed by comparing soil moisture results obtained from separate model simulations forced with precipitation products of varying uncertainty. An analysis of surface and root-zone anomalies is presented for each model simulation over the conterminous United States, as well as statistical assessments for each simulation over various land cover types.
    AGU Fall Meeting Abstracts. 12/2008;
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    J. Bolten, W. Crow, X. Zhan, C. Reynolds
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    ABSTRACT: Timely and accurate monitoring of global weather anomalies and drought conditions is essential for assessing global crop conditions. Soil moisture observations are particularly important for crop yield fluctuations provided by the US Department of Agriculture (USDA) Production Estimation and Crop Assessment Division (PECAD). The current system utilized by PECAD estimates soil moisture from a 2-layer water balance model based on precipitation and temperature data from World Meteorological Organization (WMO) and US Air Force Weather Agency (AFWA). The accuracy of this system is highly dependent on the data sources used; particularly the accuracy, consistency, and spatial and temporal coverage of the land and climatic data input into the models. However, many regions of the globe lack observations at the temporal and spatial resolutions required by PECAD. This study incorporates NASA's soil moisture remote sensing product provided by the EOS Advanced Microwave Scanning Radiometer (AMSR-E) into the U.S. Department of Agriculture Crop Assessment and Data Retrieval (CADRE) decision support system. A quasi-global-scale operational data assimilation system has been designed and implemented to provide CADRE a daily product of integrated AMSR-E soil moisture observations with the PECAD two-layer soil moisture model forecasts. A methodology of the system design and a brief evaluation of the system performance over the Conterminous United States (CONUS) is presented.
    Proc SPIE 08/2008;
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    ABSTRACT: Global estimates of soil moisture are a large component of crop yield fluctuations provided by the US department of agriculture (USDA) Production estimation and crop assessment division (PECAD). The current system utilized by PECAD estimates soil moisture from a 2-layer water balance model based on daily precipitation and temperature data. However, many regions of the globe lack climate observations at the temporal and spatial resolutions required by PECAD. This study integrates NASA's soil moisture remote sensing product provided by the EOS advanced microwave scanning radiometer (AMSR-E) into the U.S. Department of agriculture crop assessment and data retrieval (CADRE) decision support system. An Ensemble Kalman Filter (EnKF) has been designed to optimally merge the AMSR-E observations with the PECAD soil moisture outputs when available. A methodology of system design and an evaluation of the system performance over the Conterminous United States (CONUS) is presented.
    IEEE International Geoscience & Remote Sensing Symposium, IGARSS 2008, July 8-11, 2008, Boston, Massachusetts, USA, Proceedings; 01/2008
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    ABSTRACT: An unresolved issue in global soil moisture retrieval using passive microwave sensors is the spatial integration of heterogeneous landscape features to the nominal 50 km footprint observed by most low frequency satellite systems. One of the objectives of the Soil Moisture Experiments 2004 (SMEX04) was to address some aspects of this problem, specifically variability introduced by vegetation, topography and convective precipitation. Other goals included supporting the development of soil moisture data sets that would contribute to understanding the role of the land surface in the concurrent North American Monsoon System. SMEX04 was conducted over two regions: Arizona — semi-arid climate with sparse vegetation and moderate topography, and Sonora (Mexico) — moderate vegetation with strong topographic gradients. The Polarimetric Scanning Radiometer (PSR/CX) was flown on a Naval Research Lab P-3B aircraft as part of SMEX04 (10 dates of coverage over Arizona and 11 over Sonora). Radio Frequency Interference (RFI) was observed in both PSR and satellite-based (AMSR-E) observations at 6.92 GHz over Arizona, but no detectable RFI was observed over the Sonora domain. The PSR estimated soil moisture was in agreement with the ground-based estimates of soil moisture over both domains. The estimated error over the Sonora domain (SEE = 0.021 cm3/cm3) was higher than over the Arizona domain (SEE = 0.014 cm3/cm3). These results show the possibility of estimating soil moisture in areas of moderate and heterogeneous vegetation and high topographic variability.
    Remote Sensing of Environment. 01/2008;
  • J. Bolten, W. Crow, X. Zhan
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    ABSTRACT: An Ensemble Kalman Filter (EnKF) system has been designed to integrate soil moisture retrievals from the EOS Advanced Microwave Scanning Radiometer (AMSR-E) into the U.S. Department of Agriculture (USDA) Crop Assessment Data Retrieval and Evaluation (CADRE) Decision Support System (DSS). The operational soil moisture model currently used by CADRE is forced by daily meteorological observations (precipitation and temperature) provided by the Air Force Weather Agency (AFWA) and World Meteorological Organization (WMO). The improved coverage and temporal resolution of AMSR-E soil moisture retrievals upon the AFWA and WMO data is envisaged to provide a better characterization of surface wetness conditions particularly where the AFWA and WMO data are sparse. This study evaluates the added value of the AMSR-E soil moisture data assimilation over the conterminous United States. The experimental methodology is based on designating a single model realization forced with reliable precipitation as truth. The EnKF is then applied to assimilate AMSR-E soil moisture estimates into the model runs forced by an error-prone precipitation dataset. Effectiveness of the soil moisture data assimilation system is evaluated by comparing the EnKF output soil moisture with the truth soil moisture. System design and model uncertainly is evaluated by comparisons with in-situ observations, and analyzing the filter divergence, and innovation statistics. Applying global AMSR-E soil moisture retrievals to the soil moisture data assimilation system for the CADRE decision support system of USDA-FAS will be discussed.
    AGU Fall Meeting Abstracts. 12/2007;
  • W. T. Crow, J. D. Bolten
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    ABSTRACT: Due to the complexity of potential error sources in land surface models, the accurate specification of model error parameters has emerged as a major challenge in the development of effective land data assimilation systems. While several on-line procedures for estimating model error parameters - based on the statistical analysis of filtering innovations - have been introduced for geophysical models, such procedures have not been widely applied to land surface models. A frequently cited concern is the computation burden of iteratively adjusting model error parameters until theoretical expectations for innovation statistics (e.g. temporally white and normalized variance of one) are met. Classical, closed-form approaches for the on-line identification of linear model error could greatly reduce this computational burden; however their applicably to nonlinear land surface models is unclear. Using a series of synthetic twin experiments and an Ensemble Kalman filter, this paper will present a framework for diagnosing the magnitude of error in hydrologic model forecasts and/or hydrologic remote sensing retrievals. Results will demonstrate the potential of applying classical adaptive filtering approaches (originally derived for purely linear systems) to land surface models. Particular attention will be paid to potential differences between highly nonlinear and chaotic atmospheric models and nonlinear, but ultimately dissipative, land surface model and the implications of these differences on the development of an on-line system for identification of model error parameters. Preliminary real data results (based on the assimilation of remotely-sensed surface soil moisture retrievals into a land surface model forced by satellite-based precipitation) will be used to underscore the potential value of the approach in an operational setting.
    AGU Spring Meeting Abstracts. 05/2007;
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    Journal of Climate 01/2007; 20(9):1792-1809. · 4.36 Impact Factor
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    01/2007;