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Michael H Cosh - One of the best experts on this subject based on the ideXlab platform.
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Satellite Surface soil moisture from smap smos amsr2 and esa cci a comprehensive assessment using global ground based observations
Remote Sensing of Environment, 2019Co-Authors: Jiangyuan Zeng, Michael H Cosh, Nengcheng Chen, Xiang Zhang, Wei WangAbstract:Abstract Comprehensive assessments on the reliability of remotely sensed soil moisture products are undeniably essential for their advancement and application. With the establishment of extensive dense networks across the globe, mismatches between Satellite footprints and ground single-point observations can be feasibly relieved. In this study, five remotely sensed soil moisture products, namely, the Soil Moisture Active Passive (SMAP), two Soil Moisture and Ocean Salinity (SMOS) products, the Land Parameter Retrieval Model (LPRM) Advanced Microwave Scanning Radiometer 2 (AMSR2) and the European Space Agency (ESA) Climate Change Initiative (CCI), were systematically investigated by utilizing in-situ soil moisture observations from global dense and sparse networks. Distinguished from previous studies, several perturbing factors comprising the Surface temperature, vegetation optical depth (VOD), Surface roughness and spatial heterogeneity were taken into account in this investigation. Furthermore, products' skills under various climate regions were also evaluated. Through the results, the SMAP product captures temporal trends of ground soil moisture, exhibiting an averaged R of 0.729, whereas for overall accuracy, ESA CCI outperformed other products with a slightly smaller ubRMSE of 0.041 m3 m−3 and a bias of −0.005 m3 m−3. This complementarity between SMAP and ESA CCI was further demonstrated under different climate conditions and can afford the reference of their integration for a more reliable global soil moisture product. Though some underestimations still exist, the newly developed SMOS- INRA-CESBIO (SMOS-IC) was illustrated to gain considerable upgrades with regard to R and ubRMSE compared to SMOS-L3 product, especially in dense VOD conditions achieving the highest R compared to other products. Generally, the underestimations of the European Centre for Medium-Range-Weather Forecasts (ECMWF) Surface temperature used for SMOS under moderate or high VOD, heterogeneity, and most Surface roughness conditions were consistent with the underestimations of the soil moisture product and provide the directions of product promotions. As for LPRM Surface temperature, the worse skills can partially explain the unsatisfactory performances for LPRM soil moisture products. In spite of relatively acceptable skills of SMAP and SMOS-IC soil moisture products concerning R under moderate or dense VOD, small Surface roughness, low heterogeneity conditions and temperate and cold climate types, advances in soil moisture products under high or even slightly low VOD, high roughness or topography complexity and heterogeneity, as well as in tropical or desert regions, remain challenging. It is expected that these findings can contribute to algorithm refinements, product enhancements (e.g., fusion and disaggregation) and hydrometeorological usages.
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application of triple collocation in ground based validation of soil moisture active passive smap level 2 data products
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017Co-Authors: Fan Chen, Wade T Crow, Michael H Cosh, Rolf H Reichle, Andreas Colliander, T J Jackson, R Bindlish, S Chan, D D Bosch, Patrick J StarksAbstract:The validation of the soil moisture retrievals from the recently launched National Aeronautics and Space Administration (NASA) Soil Moisture Active/Passive (SMAP) Satellite is important prior to their full public release. Uncertainty in attempts to characterize footprint-scale Surface-layer soil moisture using point-scale ground observations has generally limited past validation of remotely sensed soil moisture products to densely instrumented sites covering an area approximating the Satellite ground footprint. However, by leveraging independent soil moisture information obtained from land Surface modeling and/or alternative remote sensing products, triple collocation (TC) techniques offer a strategy for characterizing upscaling errors in sparser ground measurements and removing the impact of such error on the evaluation of remotely sensed soil moisture products. Here, we propose and validate a TC-based strategy designed to utilize existing sparse soil moisture networks (typically with a single sampling point per Satellite footprint) to obtain an unbiased correlation validation metric for Satellite Surface soil moisture retrieval products. Application of this TC strategy at five SMAP core validation sites suggests that unbiased estimates of correlation between the Satellite product and the true footprint average can be obtained - even in cases where ground observations provide only one single reference point within the footprint. An example of preliminary validation results from the application of this TC strategy to the SMAP Level 2 Soil Moisture Passive (beta release version) product is presented.
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estimating spatial sampling errors in coarse scale soil moisture estimates derived from point scale observations
Journal of Hydrometeorology, 2010Co-Authors: Diego G Miralles, Wade T Crow, Michael H CoshAbstract:Abstract The validation of Satellite Surface soil moisture products requires comparisons between point-scale ground observations and footprint-scale (>100 km2) retrievals. In regions containing a limited number of measurement sites per footprint, some of the observed difference between the retrievals and ground observations is attributable to spatial sampling error and not the intrinsic error of the Satellite retrievals themselves. Here, a triple collocation (TC) approach is applied to footprint-scale soil moisture products acquired from passive microwave remote sensing, land Surface modeling, and a single ground-based station with the goal of the estimating (and correcting for) spatial sampling error in footprint-scale soil moisture estimates derived from the ground station. Using these three soil moisture products, the TC approach is shown to estimate point-to-footprint soil moisture sampling errors to within 0.0059 m3 m−3 and enhance the ability to validate Satellite footprint-scale soil moisture produ...
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estimating spatial sampling errors in coarse scale soil moisture estimates derived from point scale observations
Journal of Hydrometeorology, 2010Co-Authors: Diego G Miralles, Wade T Crow, Michael H CoshAbstract:The validation of Satellite Surface soil moisture products requires comparisons between point-scale ground observations and footprint-scale (.100 km 2 ) retrievals. In regions containing a limited number of measurement sites per footprint, some of the observed difference between the retrievals and ground observations is attributable to spatial sampling error and not the intrinsic error of the Satellite retrievals themselves. Here, a triple collocation (TC) approach is applied to footprint-scale soil moisture products acquired from passive microwave remote sensing, land Surface modeling, and a single ground-based station with the goal of the estimating (and correcting for) spatial sampling error in footprint-scale soil moisture estimates derived from the ground station. Using these three soil moisture products, the TC approach is shown to estimate point-tofootprint soil moisture sampling errors to within 0.0059 m 3 m 23 and enhance the ability to validate Satellite footprint-scale soil moisture products using existing low-density ground networks.
Rolf H Reichle - One of the best experts on this subject based on the ideXlab platform.
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application of triple collocation in ground based validation of soil moisture active passive smap level 2 data products
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017Co-Authors: Fan Chen, Wade T Crow, Michael H Cosh, Rolf H Reichle, Andreas Colliander, T J Jackson, R Bindlish, S Chan, D D Bosch, Patrick J StarksAbstract:The validation of the soil moisture retrievals from the recently launched National Aeronautics and Space Administration (NASA) Soil Moisture Active/Passive (SMAP) Satellite is important prior to their full public release. Uncertainty in attempts to characterize footprint-scale Surface-layer soil moisture using point-scale ground observations has generally limited past validation of remotely sensed soil moisture products to densely instrumented sites covering an area approximating the Satellite ground footprint. However, by leveraging independent soil moisture information obtained from land Surface modeling and/or alternative remote sensing products, triple collocation (TC) techniques offer a strategy for characterizing upscaling errors in sparser ground measurements and removing the impact of such error on the evaluation of remotely sensed soil moisture products. Here, we propose and validate a TC-based strategy designed to utilize existing sparse soil moisture networks (typically with a single sampling point per Satellite footprint) to obtain an unbiased correlation validation metric for Satellite Surface soil moisture retrieval products. Application of this TC strategy at five SMAP core validation sites suggests that unbiased estimates of correlation between the Satellite product and the true footprint average can be obtained - even in cases where ground observations provide only one single reference point within the footprint. An example of preliminary validation results from the application of this TC strategy to the SMAP Level 2 Soil Moisture Passive (beta release version) product is presented.
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comparison and assimilation of global soil moisture retrievals from the advanced microwave scanning radiometer for the earth observing system amsr e and the scanning multichannel microwave radiometer smmr
Journal of Geophysical Research, 2007Co-Authors: Rolf H Reichle, Randal D Koster, Sarith Mahanama, E G NjokuAbstract:[1] Two data sets of Satellite Surface soil moisture retrievals are first compared and then assimilated into the NASA Catchment land Surface model. The first Satellite data set is derived from 4 years of X-band (10.7 GHz) passive microwave brightness temperature observations by the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), and the second is from 9 years of C-band (6.6 GHz) brightness temperature observations by the Scanning Multichannel Microwave Radiometer (SMMR). Despite the similarity in the Satellite instruments, the retrieved soil moisture data exhibit very large differences in their multiyear means and temporal variability, primarily because they are computed with different retrieval algorithms. The Satellite retrievals are also compared to a soil moisture product generated by the NASA Catchment land Surface model when driven with Surface meteorological data derived from observations. The climatologies of both Satellite data sets are different from those of the model products. Prior to assimilation of the Satellite retrievals into the land model, Satellite-model biases are removed by scaling the Satellite retrievals into the land model's climatology through matching of the respective cumulative distribution functions. Validation against in situ data shows that for both data sets the soil moisture fields from the assimilation are superior to either Satellite data or model data alone. A global analysis of the innovations (defined as the difference between the observations and the corresponding model values prior to the assimilation update) reveals how changes in model and observations error parameters may enhance filter performance in future experiments.
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global assimilation of Satellite Surface soil moisture retrievals into the nasa catchment land Surface model
Geophysical Research Letters, 2005Co-Authors: Rolf H Reichle, Randal D KosterAbstract:[1] Global retrievals of Surface soil moisture from the Scanning Multichannel Microwave Radiometer for the period 1979–87 are assimilated into the NASA Catchment land Surface model as it is driven with Surface meteorological data derived from observations. Validation against ground-based measurements in Eurasia and North America from the Global Soil Moisture Data Bank demonstrates a long assumed (but rarely proven) property of soil moisture fields derived from data assimilation – that the assimilation product is superior to either Satellite data or model data alone. An analysis of the innovations reveals that the filter is only partially operating within its underlying assumptions and offers clues how spatially distributed model error parameters could further enhance filter performance.
Wolfgang Wagner - One of the best experts on this subject based on the ideXlab platform.
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monitoring multi decadal Satellite earth observation of soil moisture products through land Surface reanalyses
Remote Sensing of Environment, 2013Co-Authors: Clement Albergel, Wouter Dorigo, R A M De Jeu, Gianpaolo Balsamo, Joaquin Munozsabater, P De Rosnay, Lars Isaksen, Luca Brocca, Wolfgang WagnerAbstract:Abstract Soil moisture from ERA-Land, a revised version of the land Surface components of the European Centre for Medium-Range Weather Forecasts Interim reanalysis (ERA-Interim), is used to monitor at a global scale the consistency of a new microwave based multi-Satellite Surface soil moisture date set (SM-MW) over multi-decadal time period (1980–2010). ERA-Land results from Land Surface Model simulations forced by high quality atmospheric forcing data. It was shown to adequately capture the temporal dynamic of soil moisture. ERA-Land's large scale nature, frozen configuration, global availability and ability to accurately represent soil moisture variability make it suitable to complement typical validation approaches of soil moisture from remote sensing based on ground measurements. Considering locations that have significant correlations for each 3-year sub periods within 1980–2010, averaged soil moisture correlations of SM-MW with ERA-Land (at 95% Confidence Interval) are increasing steadily from 1986 to 2010 (from 0.52 ± 0.10, to 0.66 ± 0.04). The lower correlations mirror the periods where only passive microwave from the Special Sensor Microwave/Image (SSM/I, Ku band at 19.3 GHz) sensor was used, highlighting the importance of multi-sensor capabilities. Overall SM-MW is relatively stable over time with respect to ERA-Land. Good agreement is obtained in semi-arid areas, whilst the tropics and high latitudes (and altitudes) present lower correlations values.
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evaluating global trends 1988 2010 in harmonized multi Satellite Surface soil moisture
Geophysical Research Letters, 2012Co-Authors: Wouter Dorigo, Daniel Chung, Robert Parinussa, Wolfgang Wagner, Diego FernandezprietoAbstract:[1] Global trends in a new multi-Satellite Surface soil moisture dataset were analyzed for the period 1988–2010. 27% of the area covered by the dataset showed significant trends (p = 0.05). Of these, 73% were negative and 27% positive. Subtle drying trends were found in the Southern US, central South America, central Eurasia, northern Africa and the Middle East, Mongolia and northeast China, northern Siberia, and Western Australia. The strongest wetting trends were found in southern Africa and the subarctic region. Intra-annual analysis revealed that most trends are not uniform among seasons. The most prominent trend patterns in remotely sensed Surface soil moisture were also found in GLDAS-Noah and ERA Interim modeled Surface soil moisture and GPCP precipitation, lending confidence to the obtained results. The relationship with trends in GIMMS-NDVI appeared more complex. In areas of mutual disagreement more research is needed to identify potential deficiencies in models and/or remotely sensed products.
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error estimates for near real time Satellite soil moisture as derived from the land parameter retrieval model
IEEE Geoscience and Remote Sensing Letters, 2011Co-Authors: Robert Parinussa, Wouter Dorigo, Wolfgang Wagner, A G C A Meesters, Yi Y Liu, R A M De JeuAbstract:A time-efficient solution to estimate the error of Satellite Surface soil moisture from the land parameter retrieval model is presented. The errors are estimated using an analytical solution for soil moisture retrievals from this radiative-transfer-based model that derives soil moisture from low-frequency passive microwave observations. The error estimate is based on a basic error propagation equation which uses the partial derivatives of the radiative transfer equation and estimated errors for each individual input parameter. Results similar to those of the Monte Carlo approach show that the developed time-efficient methodology could substitute computationally intensive methods. This procedure is therefore a welcome solution for near-real-time data assimilation studies where both the soil moisture product and error estimate are needed. The developed method is applied to the C-, X-, and Ku-bands of the Aqua/Advanced Microwave Scanning Radiometer for Earth Observing System sensor to study differences in errors between frequencies.
Fred G Rose - One of the best experts on this subject based on the ideXlab platform.
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a radiation closure study of arctic stratus cloud microphysical properties using the collocated Satellite Surface data and fu liou radiative transfer model
Journal of Geophysical Research, 2016Co-Authors: Xiquan Dong, Shaoyue Qiu, Sunny Sunmack, Fred G RoseAbstract:Author(s): Dong, X; Xi, B; Qiu, S; Minnis, P; Sun-Mack, S; Rose, F | Abstract: © 2016. American Geophysical Union. Retrievals of cloud microphysical properties based on passive Satellite imagery are especially difficult over snow-covered Surfaces because of the bright and cold Surface. To help quantify their uncertainties, single-layered overcast liquid-phase Arctic stratus cloud microphysical properties retrieved by using the Clouds and the Earth’s Radiant Energy System Edition 2 and Edition 4 (CERES Ed2 and Ed4) algorithms are compared with ground-based retrievals at the Atmospheric Radiation Measurement North Slope of Alaska (ARM NSA) site at Barrow, AK, during the period from March 2000 to December 2006. A total of 206 and 140 snow-free cases (Rsfc ≤ 0.3), and 108 and 106 snow cases (Rsfcg0.3), respectively, were selected from Terra and Aqua Satellite passes over the ARM NSA site. The CERES Ed4 and Ed2 optical depth (t) and liquid water path (LWP) retrievals from both Terra and Aqua are almost identical and have excellent agreement with ARM retrievals under snow-free and snow conditions. In order to reach a radiation closure study for both the Surface and top of atmosphere (TOA) radiation budgets, the ARM precision spectral pyranometer-measured Surface albedos were adjusted (63.6% and 80% of the ARM Surface albedos for snow-free and snow cases, respectively) to account for the water and land components of the domain of 30 km × 30 km. Most of the radiative transfer model calculated SW↓sfc and SW↑TOA fluxes by using ARM and CERES cloud retrievals and the domain mean albedos as input agree with the ARM and CERES flux observations within 10Wm-2 for both snow-free and snow conditions. Sensitivity studies show that the ARM LWP and re retrievals are less dependent on solar zenith angle (SZA), but all retrieved optical depths increase with SZA.
Xiquan Dong - One of the best experts on this subject based on the ideXlab platform.
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a clear sky radiation closure study using a one dimensional radiative transfer model and collocated Satellite Surface reanalysis data sets
Journal of Geophysical Research, 2016Co-Authors: Erica K Dolinar, Xiquan Dong, Jonathan H Jiang, Norman G LoebAbstract:NASA Earth and Space Science Fellowship program; NASA CERES project [NNX14AP84G]; NOAA MAPP grant at the University of North Dakota [NA13OAR4310105]; Jet Propulsion Laboratory, California Institute of Technology; NASA
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a radiation closure study of arctic stratus cloud microphysical properties using the collocated Satellite Surface data and fu liou radiative transfer model
Journal of Geophysical Research, 2016Co-Authors: Xiquan Dong, Shaoyue Qiu, Sunny Sunmack, Fred G RoseAbstract:Author(s): Dong, X; Xi, B; Qiu, S; Minnis, P; Sun-Mack, S; Rose, F | Abstract: © 2016. American Geophysical Union. Retrievals of cloud microphysical properties based on passive Satellite imagery are especially difficult over snow-covered Surfaces because of the bright and cold Surface. To help quantify their uncertainties, single-layered overcast liquid-phase Arctic stratus cloud microphysical properties retrieved by using the Clouds and the Earth’s Radiant Energy System Edition 2 and Edition 4 (CERES Ed2 and Ed4) algorithms are compared with ground-based retrievals at the Atmospheric Radiation Measurement North Slope of Alaska (ARM NSA) site at Barrow, AK, during the period from March 2000 to December 2006. A total of 206 and 140 snow-free cases (Rsfc ≤ 0.3), and 108 and 106 snow cases (Rsfcg0.3), respectively, were selected from Terra and Aqua Satellite passes over the ARM NSA site. The CERES Ed4 and Ed2 optical depth (t) and liquid water path (LWP) retrievals from both Terra and Aqua are almost identical and have excellent agreement with ARM retrievals under snow-free and snow conditions. In order to reach a radiation closure study for both the Surface and top of atmosphere (TOA) radiation budgets, the ARM precision spectral pyranometer-measured Surface albedos were adjusted (63.6% and 80% of the ARM Surface albedos for snow-free and snow cases, respectively) to account for the water and land components of the domain of 30 km × 30 km. Most of the radiative transfer model calculated SW↓sfc and SW↑TOA fluxes by using ARM and CERES cloud retrievals and the domain mean albedos as input agree with the ARM and CERES flux observations within 10Wm-2 for both snow-free and snow conditions. Sensitivity studies show that the ARM LWP and re retrievals are less dependent on solar zenith angle (SZA), but all retrieved optical depths increase with SZA.
