The Experts below are selected from a list of 327 Experts worldwide ranked by ideXlab platform
Jeffrey P. Walker - One of the best experts on this subject based on the ideXlab platform.
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Root-zone Soil Moisture estimation from assimilation of downscaled Soil Moisture and Ocean Salinity data
Advances in Water Resources, 2015Co-Authors: Gift Dumedah, Jeffrey P. Walker, Olivier MerlinAbstract:The crucial role of root-zone Soil Moisture is widely recognized in land–atmosphere interaction, with direct practical use in hydrology, agriculture and meteorology. But it is difficult to estimate the root-zone Soil Moisture accurately because of its space-time variability and its nonlinear relationship with surface Soil Moisture. Typically, direct satellite observations at the surface are extended to estimate the root-zone Soil Moisture through data assimilation. But the results suffer from low spatial resolution of the satellite observation. While advances have been made recently to downscale the satellite Soil Moisture from Soil Moisture and Ocean Salinity (SMOS) mission using methods such as the Disaggregation based on Physical And Theoretical scale Change (DisPATCh), the assimilation of such data into high spatial resolution land surface models has not been examined to estimate the root-zone Soil Moisture. Consequently, this study assimilates the 1-km DisPATCh surface Soil Moisture into the Joint UK Land Environment Simulator (JULES) to better estimate the root-zone Soil Moisture. The assimilation is demonstrated using the advanced Evolutionary Data Assimilation (EDA) procedure for the Yanco area in south eastern Australia. When evaluated using in-situ OzNet Soil Moisture, the open loop was found to be 95% as accurate as the updated output, with the updated estimate improving the DisPATCh data by 14%, all based on the root mean square error (RMSE). Evaluation of the root-zone Soil Moisture with in-situ OzNet data found the updated output to improve the open loop estimate by 34% for the 0–30 cm Soil depth, 59% for the 30–60 cm Soil depth, and 63% for the 60–90 cm Soil depth, based on RMSE. The increased performance of the updated output over the open loop estimate is associated with (i) consistent estimation accuracy across the three Soil depths for the updated output, and (ii) the deterioration of the open loop output for deeper Soil depths. Thus, the findings point to a combined positive impact from the DisPATCh data and the EDA procedure, which together provide an improved Soil Moisture with consistent accuracy both at the surface and at the root-zone.
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The Soil Moisture Active Passive Experiments (SMAPEx): Toward Soil Moisture Retrieval From the SMAP Mission
IEEE Transactions on Geoscience and Remote Sensing, 2014Co-Authors: Rocco Panciera, Jeffrey P. Walker, Thomas J. Jackson, Douglas A. Gray, Mihai A. Tanase, Alessandra Monerris, Heath Yardley, Christoph Rüdiger, Xiaoling Wu, Jörg M. HackerAbstract: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 (~3 km), ii) low-resolution from radiometer (~36 km), and iii) intermediate-resolution from the fusion of radar and radiometer (~9 km). 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 mission. 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 (~1 km) and radar backscatter (~10 m) 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 (0-5 cm) Soil Moisture along with intensive ground monitoring of Soil Moisture, Soil temperature, vegetation biomass and structure, and surface roughness.
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upscaling sparse ground based Soil Moisture observations for the validation of coarse resolution satellite Soil Moisture products
Reviews of Geophysics, 2012Co-Authors: Wade T. Crow, Aaron A. Berg, Michael H Cosh, Alexander Loew, Binayak P Mohanty, Rocco Panciera, Patricia De Rosnay, Jeffrey P. WalkerAbstract:[1] The contrast between the point-scale nature of current ground-based Soil Moisture instrumentation and the ground resolution (typically >102 km2) of satellites used to retrieve Soil Moisture poses a significant challenge for the validation of data products from current and upcoming Soil Moisture satellite missions. Given typical levels of observed spatial variability in Soil Moisture fields, this mismatch confounds mission validation goals by introducing significant sampling uncertainty in footprint-scale Soil Moisture estimates obtained from sparse ground-based observations. During validation activities based on comparisons between ground observations and satellite retrievals, this sampling error can be misattributed to retrieval uncertainty and spuriously degrade the perceived accuracy of satellite Soil Moisture products. This review paper describes the magnitude of the Soil Moisture upscaling problem and measurement density requirements for ground-based Soil Moisture networks. Since many large-scale networks do not meet these requirements, it also summarizes a number of existing Soil Moisture upscaling strategies which may reduce the detrimental impact of spatial sampling errors on the reliability of satellite Soil Moisture validation using spatially sparse ground-based observations.
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Upscaling sparse ground‐based Soil Moisture observations for the validation of coarse‐resolution satellite Soil Moisture products
Reviews of Geophysics, 2012Co-Authors: Wade T. Crow, Aaron A. Berg, Michael H Cosh, Alexander Loew, Binayak P Mohanty, Rocco Panciera, Patricia De Rosnay, Jeffrey P. WalkerAbstract:[1] The contrast between the point-scale nature of current ground-based Soil Moisture instrumentation and the ground resolution (typically >102 km2) of satellites used to retrieve Soil Moisture poses a significant challenge for the validation of data products from current and upcoming Soil Moisture satellite missions. Given typical levels of observed spatial variability in Soil Moisture fields, this mismatch confounds mission validation goals by introducing significant sampling uncertainty in footprint-scale Soil Moisture estimates obtained from sparse ground-based observations. During validation activities based on comparisons between ground observations and satellite retrievals, this sampling error can be misattributed to retrieval uncertainty and spuriously degrade the perceived accuracy of satellite Soil Moisture products. This review paper describes the magnitude of the Soil Moisture upscaling problem and measurement density requirements for ground-based Soil Moisture networks. Since many large-scale networks do not meet these requirements, it also summarizes a number of existing Soil Moisture upscaling strategies which may reduce the detrimental impact of spatial sampling errors on the reliability of satellite Soil Moisture validation using spatially sparse ground-based observations.
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Root zone Soil Moisture from the assimilation of screen‐level variables and remotely sensed Soil Moisture
Journal of Geophysical Research, 2011Co-Authors: C. Draper, Jean-françois Mahfouf, Jeffrey P. WalkerAbstract:[1] In most operational NWP models, root zone Soil Moisture is constrained using observations of screen-level temperature and relative humidity. While this generally improves low-level atmospheric forecasts, it often leads to unrealistic model Soil Moisture. Consequently, several NWP centers are moving toward also assimilating remotely sensed near-surface Soil Moisture observations. Within this context, an EKF is used to compare the assimilation of screen-level observations and near-surface Soil Moisture data from AMSR-E into the ISBA land surface model over July 2006. Several issues regarding the use of each data type are exposed, and the potential to use the AMSR-E data, either in place of or together with the screen-level data, is examined. When the two data types are assimilated separately, there is little agreement between the root zone Soil Moisture updates generated by each, indicating that for this experiment the AMSR-E data could not have replaced the screen-level data to obtain similar surface turbulent fluxes. For the screen-level variables, there is a persistent diurnal cycle in the model-observations bias, which is not related to Soil Moisture. The resulting diurnal cycle in the analysis increments demonstrates how assimilating screen-level observations can lead to unrealistic Soil Moisture updates, reinforcing the need to assimilate alternative data sets. However, when the two data types are assimilated together, the near-surface Soil Moisture provides a much weaker constraint of the root zone Soil Moisture than the screen-level observations do, and the inclusion of the AMSR-E data does not substantially change the results compared to the assimilation of screen-level variables alone.
Wade T. Crow - One of the best experts on this subject based on the ideXlab platform.
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IGARSS - Evaluation of assimilated SMOS Soil Moisture data for US cropland Soil Moisture monitoring
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016Co-Authors: Zhengwei Yang, Ranjay Shrestha, John Bolten, Iva Mladenova, Genong Yu, Wade T. Crow, Liping DiAbstract:Remotely sensed Soil Moisture data can provide timely, objective and quantitative crop Soil Moisture information with broad geospatial coverage and sufficiently high resolution observations collected throughout the growing season. This paper evaluates the feasibility of using the assimilated ESA Soil Moisture Ocean Salinity (SMOS) Mission L-band passive microwave data for operational US cropland Soil surface Moisture monitoring. The assimilated SMOS Soil Moisture data are first categorized to match with the United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) survey-based weekly Soil Moisture observation data, which are ordinal. The categorized assimilated SMOS Soil Moisture data are compared with NASS's survey-based weekly Soil Moisture data for consistency and robustness using visual assessment and rank correlation. Preliminary results indicate that the assimilated SMOS Soil Moisture data highly co-vary with NASS field observations across a large geographic area. Therefore, SMOS data have great potential for US operational cropland Soil Moisture monitoring.
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upscaling sparse ground based Soil Moisture observations for the validation of coarse resolution satellite Soil Moisture products
Reviews of Geophysics, 2012Co-Authors: Wade T. Crow, Aaron A. Berg, Michael H Cosh, Alexander Loew, Binayak P Mohanty, Rocco Panciera, Patricia De Rosnay, Jeffrey P. WalkerAbstract:[1] The contrast between the point-scale nature of current ground-based Soil Moisture instrumentation and the ground resolution (typically >102 km2) of satellites used to retrieve Soil Moisture poses a significant challenge for the validation of data products from current and upcoming Soil Moisture satellite missions. Given typical levels of observed spatial variability in Soil Moisture fields, this mismatch confounds mission validation goals by introducing significant sampling uncertainty in footprint-scale Soil Moisture estimates obtained from sparse ground-based observations. During validation activities based on comparisons between ground observations and satellite retrievals, this sampling error can be misattributed to retrieval uncertainty and spuriously degrade the perceived accuracy of satellite Soil Moisture products. This review paper describes the magnitude of the Soil Moisture upscaling problem and measurement density requirements for ground-based Soil Moisture networks. Since many large-scale networks do not meet these requirements, it also summarizes a number of existing Soil Moisture upscaling strategies which may reduce the detrimental impact of spatial sampling errors on the reliability of satellite Soil Moisture validation using spatially sparse ground-based observations.
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Upscaling sparse ground‐based Soil Moisture observations for the validation of coarse‐resolution satellite Soil Moisture products
Reviews of Geophysics, 2012Co-Authors: Wade T. Crow, Aaron A. Berg, Michael H Cosh, Alexander Loew, Binayak P Mohanty, Rocco Panciera, Patricia De Rosnay, Jeffrey P. WalkerAbstract:[1] The contrast between the point-scale nature of current ground-based Soil Moisture instrumentation and the ground resolution (typically >102 km2) of satellites used to retrieve Soil Moisture poses a significant challenge for the validation of data products from current and upcoming Soil Moisture satellite missions. Given typical levels of observed spatial variability in Soil Moisture fields, this mismatch confounds mission validation goals by introducing significant sampling uncertainty in footprint-scale Soil Moisture estimates obtained from sparse ground-based observations. During validation activities based on comparisons between ground observations and satellite retrievals, this sampling error can be misattributed to retrieval uncertainty and spuriously degrade the perceived accuracy of satellite Soil Moisture products. This review paper describes the magnitude of the Soil Moisture upscaling problem and measurement density requirements for ground-based Soil Moisture networks. Since many large-scale networks do not meet these requirements, it also summarizes a number of existing Soil Moisture upscaling strategies which may reduce the detrimental impact of spatial sampling errors on the reliability of satellite Soil Moisture validation using spatially sparse ground-based observations.
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Towards the estimation root-zone Soil Moisture via the simultaneous assimilation of thermal and microwave Soil Moisture retrievals
Advances in Water Resources, 2010Co-Authors: Fuqin Li, Wade T. Crow, William P. KustasAbstract:Abstract The upcoming deployment of satellite-based microwave sensors designed specifically to retrieve surface Soil Moisture represents an important milestone in efforts to develop hydrologic applications for remote sensing observations. However, typical measurement depths of microwave-based Soil Moisture retrievals are generally considered too shallow (top 2–5 cm of the Soil column) for many important water cycle and agricultural applications. Recent work has demonstrated that thermal remote sensing estimates of surface radiometric temperature provide a complementary source of land surface information that can be used to define a robust proxy for root-zone (top 1 m of the Soil column) Soil Moisture availability. In this analysis, we examine the potential benefits of simultaneously assimilating both microwave-based surface Soil Moisture retrievals and thermal infrared-based root-zone Soil Moisture estimates into a Soil water balance model using a series of synthetic twin data assimilation experiments conducted at the USDA Optimizing Production Inputs for Economic and Environmental Enhancements (OPE 3 ) site. Results from these experiments illustrate that, relative to a baseline case of assimilating only surface Soil Moisture retrievals, the assimilation of both root- and surface-zone Soil Moisture estimates reduces the root-mean-square difference between estimated and true root-zone Soil Moisture by 50% to 35% (assuming instantaneous root-zone Soil Moisture retrievals are obtained at an accuracy of between 0.020 and 0.030 m 3 m −3 ). Most significantly, improvements in root-zone Soil Moisture accuracy are seen even for cases in which root-zone Soil Moisture retrievals are assumed to be relatively inaccurate (i.e. retrievals errors of up to 0.070 m 3 m −3 ) or limited to only very sparse sampling (i.e. one instantaneous measurement every eight days). Preliminary real data results demonstrate a clear increase in the R 2 correlation coefficient with ground-based root-zone observations (from 0.51 to 0.73) upon assimilation of actual surface Soil Moisture and tower-based thermal infrared temperature observations made at the OPE 3 study site.
Rocco Panciera - One of the best experts on this subject based on the ideXlab platform.
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The Soil Moisture Active Passive Experiments (SMAPEx): Toward Soil Moisture Retrieval From the SMAP Mission
IEEE Transactions on Geoscience and Remote Sensing, 2014Co-Authors: Rocco Panciera, Jeffrey P. Walker, Thomas J. Jackson, Douglas A. Gray, Mihai A. Tanase, Alessandra Monerris, Heath Yardley, Christoph Rüdiger, Xiaoling Wu, Jörg M. HackerAbstract: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 (~3 km), ii) low-resolution from radiometer (~36 km), and iii) intermediate-resolution from the fusion of radar and radiometer (~9 km). 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 mission. 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 (~1 km) and radar backscatter (~10 m) 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 (0-5 cm) Soil Moisture along with intensive ground monitoring of Soil Moisture, Soil temperature, vegetation biomass and structure, and surface roughness.
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upscaling sparse ground based Soil Moisture observations for the validation of coarse resolution satellite Soil Moisture products
Reviews of Geophysics, 2012Co-Authors: Wade T. Crow, Aaron A. Berg, Michael H Cosh, Alexander Loew, Binayak P Mohanty, Rocco Panciera, Patricia De Rosnay, Jeffrey P. WalkerAbstract:[1] The contrast between the point-scale nature of current ground-based Soil Moisture instrumentation and the ground resolution (typically >102 km2) of satellites used to retrieve Soil Moisture poses a significant challenge for the validation of data products from current and upcoming Soil Moisture satellite missions. Given typical levels of observed spatial variability in Soil Moisture fields, this mismatch confounds mission validation goals by introducing significant sampling uncertainty in footprint-scale Soil Moisture estimates obtained from sparse ground-based observations. During validation activities based on comparisons between ground observations and satellite retrievals, this sampling error can be misattributed to retrieval uncertainty and spuriously degrade the perceived accuracy of satellite Soil Moisture products. This review paper describes the magnitude of the Soil Moisture upscaling problem and measurement density requirements for ground-based Soil Moisture networks. Since many large-scale networks do not meet these requirements, it also summarizes a number of existing Soil Moisture upscaling strategies which may reduce the detrimental impact of spatial sampling errors on the reliability of satellite Soil Moisture validation using spatially sparse ground-based observations.
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Upscaling sparse ground‐based Soil Moisture observations for the validation of coarse‐resolution satellite Soil Moisture products
Reviews of Geophysics, 2012Co-Authors: Wade T. Crow, Aaron A. Berg, Michael H Cosh, Alexander Loew, Binayak P Mohanty, Rocco Panciera, Patricia De Rosnay, Jeffrey P. WalkerAbstract:[1] The contrast between the point-scale nature of current ground-based Soil Moisture instrumentation and the ground resolution (typically >102 km2) of satellites used to retrieve Soil Moisture poses a significant challenge for the validation of data products from current and upcoming Soil Moisture satellite missions. Given typical levels of observed spatial variability in Soil Moisture fields, this mismatch confounds mission validation goals by introducing significant sampling uncertainty in footprint-scale Soil Moisture estimates obtained from sparse ground-based observations. During validation activities based on comparisons between ground observations and satellite retrievals, this sampling error can be misattributed to retrieval uncertainty and spuriously degrade the perceived accuracy of satellite Soil Moisture products. This review paper describes the magnitude of the Soil Moisture upscaling problem and measurement density requirements for ground-based Soil Moisture networks. Since many large-scale networks do not meet these requirements, it also summarizes a number of existing Soil Moisture upscaling strategies which may reduce the detrimental impact of spatial sampling errors on the reliability of satellite Soil Moisture validation using spatially sparse ground-based observations.
Alexander Loew - One of the best experts on this subject based on the ideXlab platform.
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upscaling sparse ground based Soil Moisture observations for the validation of coarse resolution satellite Soil Moisture products
Reviews of Geophysics, 2012Co-Authors: Wade T. Crow, Aaron A. Berg, Michael H Cosh, Alexander Loew, Binayak P Mohanty, Rocco Panciera, Patricia De Rosnay, Jeffrey P. WalkerAbstract:[1] The contrast between the point-scale nature of current ground-based Soil Moisture instrumentation and the ground resolution (typically >102 km2) of satellites used to retrieve Soil Moisture poses a significant challenge for the validation of data products from current and upcoming Soil Moisture satellite missions. Given typical levels of observed spatial variability in Soil Moisture fields, this mismatch confounds mission validation goals by introducing significant sampling uncertainty in footprint-scale Soil Moisture estimates obtained from sparse ground-based observations. During validation activities based on comparisons between ground observations and satellite retrievals, this sampling error can be misattributed to retrieval uncertainty and spuriously degrade the perceived accuracy of satellite Soil Moisture products. This review paper describes the magnitude of the Soil Moisture upscaling problem and measurement density requirements for ground-based Soil Moisture networks. Since many large-scale networks do not meet these requirements, it also summarizes a number of existing Soil Moisture upscaling strategies which may reduce the detrimental impact of spatial sampling errors on the reliability of satellite Soil Moisture validation using spatially sparse ground-based observations.
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Upscaling sparse ground‐based Soil Moisture observations for the validation of coarse‐resolution satellite Soil Moisture products
Reviews of Geophysics, 2012Co-Authors: Wade T. Crow, Aaron A. Berg, Michael H Cosh, Alexander Loew, Binayak P Mohanty, Rocco Panciera, Patricia De Rosnay, Jeffrey P. WalkerAbstract:[1] The contrast between the point-scale nature of current ground-based Soil Moisture instrumentation and the ground resolution (typically >102 km2) of satellites used to retrieve Soil Moisture poses a significant challenge for the validation of data products from current and upcoming Soil Moisture satellite missions. Given typical levels of observed spatial variability in Soil Moisture fields, this mismatch confounds mission validation goals by introducing significant sampling uncertainty in footprint-scale Soil Moisture estimates obtained from sparse ground-based observations. During validation activities based on comparisons between ground observations and satellite retrievals, this sampling error can be misattributed to retrieval uncertainty and spuriously degrade the perceived accuracy of satellite Soil Moisture products. This review paper describes the magnitude of the Soil Moisture upscaling problem and measurement density requirements for ground-based Soil Moisture networks. Since many large-scale networks do not meet these requirements, it also summarizes a number of existing Soil Moisture upscaling strategies which may reduce the detrimental impact of spatial sampling errors on the reliability of satellite Soil Moisture validation using spatially sparse ground-based observations.
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On the Disaggregation of Passive Microwave Soil Moisture Data using a Priori Knowledge of Temporally Persistent Soil Moisture Fields
IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium, 2008Co-Authors: Alexander Loew, Wolfram MauserAbstract:Water and energy fluxes at the interface between the land surface and atmosphere are affected by the surface water content of the Soil, which is highly variable in space and time. Recent satellite mission concepts, as e.g. the Soil Moisture and Ocean Salinity Mission (SMOS), are dedicated to provide global Soil Moisture information with a temporal frequency of a few days to capture the high temporal dynamics of surface Soil Moisture. As passive microwave sensors have a spatial resolution in the order of tens of kilometers, the application of the data in mesoscale flood forecasting or water balance models is hampered due to the different spatial scales. The paper investigates the potential of using prior information on spatially persistent Soil Moisture fields to disaggregate SMOS scale Soil Moisture products. The approach is based on a ten-year Soil Moisture climatology, derived from a state-of-the-art land surface scheme. The developed approach shows a generally good performance for large parts of the test site, where Soil Moisture can be disaggregated with an accuracy better than the 4 vol.% benchmark of the SMOS mission.
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IGARSS (3) - On the Disaggregation of Passive Microwave Soil Moisture Data Using A Priori Knowledge of Temporally Persistent Soil Moisture Fields
IEEE Transactions on Geoscience and Remote Sensing, 2008Co-Authors: Alexander Loew, Wolfram MauserAbstract:Water and energy fluxes at the interface between the land surface and atmosphere are affected by the surface water content of the Soil, which is highly variable in space and time. The sensitivity of active and passive microwave remote sensing data to surface Soil Moisture content has been investigated in numerous studies. Recent satellite mission concepts as, for example, the Soil Moisture and ocean salinity (SMOS) mission, are dedicated to provide global Soil Moisture information with a temporal frequency of a few days to capture the high temporal dynamics of surface Soil Moisture. SMOS Soil Moisture products are expected to have geometric resolutions on the order of 40 km. Mesoscale flood forecasting or water balance models typically operate at much higher spatial resolutions on the order of 1 km. It seems therefore essential to develop appropriate disaggregation schemes to benefit from the high temporal frequency of the SMOS data for hydrological applications as well as, for example, local numerical weather prediction models that are operated at a resolution of a few kilometers. This paper investigates the potential of using prior information on spatially persistent Soil Moisture fields to disaggregate SMOS scale Soil Moisture products. The approach is based on a ten-year Soil Moisture climatology for a mesoscale hydrological catchment, situated in southern Germany, which was generated using a state-of-the-art land-surface process model. The performance of the disaggregation algorithm is verified by comparison of disaggregated Soil Moisture fields with another ten-year period. To investigate the potential of the suggested disaggregation method for SMOS Soil Moisture products, a ten-year synthetic brightness temperature data set is generated at the 1-km scale. Soil Moisture is then retrieved from the aggregated brightness temperature data at the SMOS type scale of 40 km and then disaggregated using the suggested approach. The results are compared against reference Soil Moisture at the 1-km scale. Uncertainties in the retrieval of the SMOS Soil Moisture products are explicitly considered, and the uncertainties of the disaggregated fields are quantified. The developed method shows a generally good performance for large parts of the test site, where Soil Moisture can be disaggregated with an accuracy that is better than the 4 vol.% benchmark of the SMOS mission. As the suggested method shows high sensitivity to biased Soil Moisture retrievals, uncertainties of the SMOS Soil Moisture products will directly reflect on the absolute accuracy of the disaggregated Soil Moisture fields, resulting in a much worse performance under noisy conditions. Nevertheless, the resulting Soil Moisture distributions show that it is feasible to derive relative Soil Moisture distributions in these cases.
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On the Disaggregation of Passive Microwave Soil Moisture Data Using A Priori Knowledge of Temporally Persistent Soil Moisture Fields
IEEE Transactions on Geoscience and Remote Sensing, 2008Co-Authors: Alexander Loew, Wolfram MauserAbstract:Water and energy fluxes at the interface between the land surface and atmosphere are affected by the surface water content of the Soil, which is highly variable in space and time. The sensitivity of active and passive microwave remote sensing data to surface Soil Moisture content has been investigated in numerous studies. Recent satellite mission concepts as, for example, the Soil Moisture and ocean salinity (SMOS) mission, are dedicated to provide global Soil Moisture information with a temporal frequency of a few days to capture the high temporal dynamics of surface Soil Moisture. SMOS Soil Moisture products are expected to have geometric resolutions on the order of 40 km. Mesoscale flood forecasting or water balance models typically operate at much higher spatial resolutions on the order of 1 km. It seems therefore essential to develop appropriate disaggregation schemes to benefit from the high temporal frequency of the SMOS data for hydrological applications as well as, for example, local numerical weather prediction models that are operated at a resolution of a few kilometers. This paper investigates the potential of using prior information on spatially persistent Soil Moisture fields to disaggregate SMOS scale Soil Moisture products. The approach is based on a ten-year Soil Moisture climatology for a mesoscale hydrological catchment, situated in southern Germany, which was generated using a state-of-the-art land-surface process model. The performance of the disaggregation algorithm is verified by comparison of disaggregated Soil Moisture fields with another ten-year period. To investigate the potential of the suggested disaggregation method for SMOS Soil Moisture products, a ten-year synthetic brightness temperature data set is generated at the 1-km scale. Soil Moisture is then retrieved from the aggregated brightness temperature data at the SMOS type scale of 40 km and then disaggregated using the suggested approach. The results are compared against reference Soil Moisture at the 1-km scale. Uncertainties in the retrieval of the SMOS Soil Moisture products are explicitly considered, and the uncertainties of the disaggregated fields are quantified. The developed method shows a generally good performance for large parts of the test site, where Soil Moisture can be disaggregated with an accuracy that is better than the 4 vol.% benchmark of the SMOS mission. As the suggested method shows high sensitivity to biased Soil Moisture retrievals, uncertainties of the SMOS Soil Moisture products will directly reflect on the absolute accuracy of the disaggregated Soil Moisture fields, resulting in a much worse performance under noisy conditions. Nevertheless, the resulting Soil Moisture distributions show that it is feasible to derive relative Soil Moisture distributions in these cases.
Paulin Coulibaly - One of the best experts on this subject based on the ideXlab platform.
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Design of an Optimal Soil Moisture Monitoring Network Using SMOS Retrieved Soil Moisture
IEEE Transactions on Geoscience and Remote Sensing, 2015Co-Authors: Kurt C. Kornelsen, Paulin CoulibalyAbstract:Many methods have been proposed to select sites for grid-scale Soil Moisture monitoring networks; however, calibration/validation activities also require information about where to place grid representative monitoring sites. In order to design a Soil Moisture network for this task in the Great Lakes Basin (522 000 km2), the dual-entropy multiobjective optimization algorithm was used to maximize the information content and minimize the redundancy of information in a potential Soil Moisture monitoring network. Soil Moisture retrieved from the Soil Moisture and Ocean Salinity (SMOS) mission during the frost-free periods of 2010-2013 were filtered for data quality and then used in a multiobjective search to find Pareto optimum network designs based on the joint entropy and total correlation measures of information content and information redundancy, respectively. Differences in the information content of SMOS ascending and descending overpasses resulted in distinctly different network designs. Entropy from the SMOS ascending overpass was found to be spatially consistent, whereas descending overpass entropy had many peaks that coincided with areas of high subgrid heterogeneity. A combination of both ascending and descending overpasses produced network designs that incorporated aspects of information from each overpass. Initial networks were designed to include 15 monitoring sites, but the addition of network cost as an objective demonstrated that a network with similar information content could be achieved with fewer monitoring stations.
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Data-based disaggregation of SMOS Soil Moisture
2014 IEEE Geoscience and Remote Sensing Symposium, 2014Co-Authors: Kurt C. Kornelsen, Paulin CoulibalyAbstract:The Soil Moisture and Ocean Salinity (SMOS) microwave radiometer is used to retrieve surface Soil Moisture with a grid resolution of 15 km. Due to various contributing factors SMOS Soil Moisture is known to have bias with respect to in situ Soil Moisture measurements and land surface models. For this reason it is common practice to match the cumulative distribution function (CDF) of retrieved Soil Moisture prior to analysis. Using the concept of temporal stability this study demonstrates that CDF matching is effective for correcting the bias at both grid and sub-grid scales with minimal impact on the time in-variant component of SMOS error.