scholarly journals Effect of Structural Uncertainty in Passive Microwave Soil Moisture Retrieval Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1225
Author(s):  
Lanka Karthikeyan ◽  
Ming Pan ◽  
Dasika Nagesh Kumar ◽  
Eric F. Wood
Keyword(s):  
Soil Moisture ◽  
Global Scale ◽  
Passive Microwave ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Structural Uncertainty ◽  
Three Solutions ◽  
Brightness Temperatures ◽  
Retrieval Algorithms

Passive microwave sensors use a radiative transfer model (RTM) to retrieve soil moisture (SM) using brightness temperatures (TB) at low microwave frequencies. Vegetation optical depth (VOD) is a key input to the RTM. Retrieval algorithms can analytically invert the RTM using dual-polarized TB measurements to retrieve the VOD and SM concurrently. Algorithms in this regard typically use the τ-ω types of models, which consist of two third-order polynomial equations and, thus, can have multiple solutions. Through this work, we find that uncertainty occurs due to the structural indeterminacy that is inherent in all τ-ω types of models in passive microwave SM retrieval algorithms. In the process, a new analytical solution for concurrent VOD and SM retrieval is presented, along with two widely used existing analytical solutions. All three solutions are applied to a fixed framework of RTM to retrieve VOD and SM on a global scale, using X-band Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) TB data. Results indicate that, with structural uncertainty, there ensues a noticeable impact on the VOD and SM retrievals. In an era where the sensitivity of retrieval algorithms is still being researched, we believe the structural indeterminacy of RTM identified here would contribute to uncertainty in the soil moisture retrievals.

scholarly journals Comparison of measured brightness temperatures from SMOS with modelled ones from ORCHIDEE and H-TESSEL over the Iberian Peninsula

2015 ◽  
Vol 12 (12) ◽  
pp. 13019-13067
Author(s):  
A. Barella-Ortiz ◽  
J. Polcher ◽  
P. de Rosnay ◽  
M. Piles ◽  
E. Gelati
Keyword(s):  
Soil Moisture ◽  
Radiative Transfer ◽  
Iberian Peninsula ◽  
Land Surface ◽  
Function Analysis ◽  
Microwave Emission ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Brightness Temperatures

Abstract. L-Band radiometry is considered to be one of the most suitable techniques to estimate surface soil moisture by means of remote sensing. Brightness temperatures are key in this process, as they are the main input in the retrieval algorithm. The work exposed compares brightness temperatures measured by the Soil Moisture and Ocean Salinity (SMOS) mission to two different sets of modelled ones, over the Iberian Peninsula from 2010 to 2012. The latter were estimated using a radiative transfer model and state variables from two land surface models: (i) ORganising Carbon and Hydrology In Dynamic EcosystEms (ORCHIDEE) and (ii) Hydrology – Tiled ECMWF Scheme for Surface Exchanges over Land (H-TESSEL). The radiative transfer model used is the Community Microwave Emission Model (CMEM). A good agreement in the temporal evolution of measured and modelled brightness temperatures is observed. However, their spatial structures are not consistent between them. An Empirical Orthogonal Function analysis of the brightness temperature's error identifies a dominant structure over the South-West of the Iberian Peninsula which evolves during the year and is maximum in Fall and Winter. Hypotheses concerning forcing induced biases and assumptions made in the radiative transfer model are analysed to explain this inconsistency, but no candidate is found to be responsible for it at the moment. Further hypotheses are proposed at the end of the paper.

scholarly journals The Sensitivity of Rainfall Estimation to Error Assumptions in a Bayesian Passive Microwave Retrieval Algorithm

2015 ◽  
Vol 54 (2) ◽  
pp. 408-422 ◽  
Author(s):  
Gregory S. Elsaesser ◽  
Christian D. Kummerow
Keyword(s):  
General Circulation ◽  
Circulation Model ◽  
Mathematical Expression ◽  
Passive Microwave ◽  
Bayesian Probability ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Model Errors ◽  
Frequency Distributions

AbstractThe Goddard profiling algorithm (GPROF) uses Bayesian probability theory to retrieve rainfall over the global oceans. A critical component of GPROF and most Bayes theorem–based retrieval frameworks is the specification of uncertainty in the observations being utilized to retrieve the parameter of interest. In the case of GPROF, for any sensor, uncertainties in microwave brightness temperatures (Tbs) arise from radiative transfer model errors, satellite sensor noise and/or degradation, and nonlinear, scene-dependent Tb offsets added during sensor intercalibration procedures. All mentioned sources impact sensors in a varying fashion, in part because of sensor-dependent fields of view. It is found that small errors in assumed Tb uncertainty (ranging from 0.57 K at 10 GHz to 2.29 K at 85 GHz) lead to a 3.6% change in the retrieved global-average oceanic rainfall rate, and 10%–20% (20%–40%) shifts in the pixel-level (monthly) frequency distributions for given rainfall bins. A mathematical expression describing the sensitivity of retrieved rainfall to uncertainty is developed here. The strong global sensitivity is linked to rainfall variance scaling systematically as Tb varies. For ocean scenes, the same emission-dominated rainfall–Tb physics used in passive microwave rainfall retrieval is also responsible for the substantial underestimation (overestimation) of global rainfall if uncertainty is overestimated (underestimated). Proper uncertainties are required to quantify variability in surface rainfall, assess long-term trends, and provide robust rainfall benchmarks for general circulation model evaluations. The implications for assessing global and regional biases in active versus passive microwave rainfall products, and for achieving rainfall product agreement among a constellation of orbiting microwave radiometers [employed in the Global Precipitation Measurement (GPM) mission], are also discussed.

scholarly journals Space-borne measurements of mesospheric magnesium species – a retrieval algorithm and preliminary profiles

2008 ◽  
Vol 8 (7) ◽  
pp. 1963-1983 ◽  
Author(s):  
M. Scharringhausen ◽  
A. C. Aikin ◽  
J. P. Burrows ◽  
M. Sinnhuber
Keyword(s):  
Wavelength Range ◽  
Optimal Estimation ◽  
Global Scale ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Resonance Fluorescence ◽  
Altitude Range ◽  
Air Density ◽  
Metallic Species

Abstract. We present a joint retrieval as well as first results for mesospheric air density and mesospheric Magnesium species (Mg and Mg+) using limb data from the SCIAMACHY instrument on board the European ENVISAT satellite.considered. These species feature Metallic species like neutral Mg, ionized Mg+ and others (Fe, Si, Li, etc.) ablate from meteoric dust, enter the gas phase and occur at high altitudes (≥70 km). Emissions from these species are clearly observed in the SCIAMACHY limb measurements. These emissions are used to retrieve total and thermospheric column densities as well as preliminary profiles of metallic species in the altitude range of 70–92 km. In this paper, neutral Magnesium as well as its ionized counterpart Mg+ is considered. These species feature resonance fluorescence in the wavelength range 279 and 285 nm and thus have a rather simple excitation process. A radiative transfer model (RTM) for the mesosphere has been developed and validated. Based on a ray tracing kernel, radiances in a large wavelength range from 240–300 nm covering limb as well as nadir geometry can be calculated. The forward model has been validated and shows good agreement with established models in the given wavelength range and a large altitude range. The RTM has been coupled to a retrieval based on Optimal Estimation. Air density is retrieved from Rayleigh backscattered light. Mesospheric Mg and Mg+ number densities are retrieved from their emission signals observed in the limb scans of SCIAMACHY. Other species like iron, silicon, OH and NO can be investigated in principle with the same algorithm. Based on the retrieval presented here, SCIAMACHY offers the opportunity to investigate mesospheric species on a global scale and with good vertical resolution for the first time.

scholarly journals Microwave signatures of ice hydrometeors from ground-based observations above Summit, Greenland

2015 ◽  
Vol 15 (23) ◽  
pp. 34497-34532
Author(s):  
C. Pettersen ◽  
R. Bennartz ◽  
M. S. Kulie ◽  
A. J. Merrelli ◽  
M. D. Shupe ◽  
...  
Keyword(s):  
Brightness Temperature ◽  
Temperature Difference ◽  
Liquid Water ◽  
High Frequency ◽  
Passive Microwave ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Cloud Radar ◽  
Brightness Temperatures ◽  
High Frequency Microwave

Abstract. Multi-instrument, ground-based measurements provide unique and comprehensive datasets of the atmosphere for a specific location over long periods of time and resulting data compliments past and existing global satellite observations. This paper explores the effect of ice hydrometeors on ground-based, high frequency passive microwave measurements and attempts to isolate an ice signature for summer seasons at Summit, Greenland from 2010–2013. Data from a combination of passive microwave, cloud radar, radiosonde, and ceilometer were examined to isolate the ice signature at microwave wavelengths. By limiting the study to a cloud liquid water path of 40 g m−2 or less, the cloud radar can identify cases where the precipitation was dominated by ice. These cases were examined using liquid water and gas microwave absorption models, and brightness temperatures were calculated for the high frequency microwave channels: 90, 150, and 225 GHz. By comparing the measured brightness temperatures from the microwave radiometers and the calculated brightness temperature using only gas and liquid contributions, any residual brightness temperature difference is due to emission and scattering of microwave radiation from the ice hydrometeors in the column. The ice signature in the 90, 150, and 225 GHz channels for the Summit Station summer months was isolated. This measured ice signature was then compared to an equivalent brightness temperature difference calculated with a radiative transfer model including microwave single scattering properties for several ice habits. Initial model results compare well against the four years of summer season isolated ice signature in the high-frequency microwave channels.

scholarly journals Estimating bias in the OCO-2 retrieval algorithm caused by 3-D radiation scattering from unresolved boundary layer clouds

2015 ◽  
Vol 8 (4) ◽  
pp. 1641-1656 ◽  
Author(s):  
A. Merrelli ◽  
R. Bennartz ◽  
C. W. O'Dell ◽  
T. E. Taylor
Keyword(s):  
Radiative Transfer ◽  
Scattering Problem ◽  
Three Dimensional ◽  
Co2 Concentration ◽  
Bare Soil ◽  
Short Wave ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Retrieval Algorithms

Abstract. Due to the complexity of the multiple scattering problem for shortwave radiative transfer in Earth's atmosphere, operational physical retrieval algorithms commonly use a plane parallel radiative transfer model (RTM). This so-called one-dimensional (1-D) assumption allows practical retrieval algorithms to be implemented. In order to understand the impacts of this assumption for low altitude, unresolved clouds observed by OCO-2, the three-dimensional (3-D) radiative transfer model SHDOM is used to generate synthetic observations which are then processed by the operational retrieval algorithm based on a 1-D RTM. Simulations are performed over three realistic surface spectral albedos, corresponding to snow, vegetation, and bare soil. The results show that the existing cloud screening algorithm has difficulty identifying sub-field of view (FOV), unresolved clouds that fill less than half of the FOV. The unresolved clouds introduce a bias in the retrieved CO2 concentration, as quantified by the dry air mole fraction (XCO2). The biases are relatively small (less than 1 ppm) when the albedo at 2.1 μm is high, which is common over bare land surfaces. For cases with low 2.1 μm albedo, such as snow, the bias becomes much larger, up to 5 ppm. These results indicate that the XCO2 retrieval appears robust to 3-D scattering effects from unresolved low level clouds when the short wave infrared surface albedo is large, but for darker surfaces these clouds can introduce significant biases.

scholarly journals Comparison of measured brightness temperatures from SMOS with modelled ones from ORCHIDEE and H-TESSEL over the Iberian Peninsula

2017 ◽  
Vol 21 (1) ◽  
pp. 357-375 ◽  
Author(s):  
Anaïs Barella-Ortiz ◽  
Jan Polcher ◽  
Patricia de Rosnay ◽  
Maria Piles ◽  
Emiliano Gelati
Keyword(s):  
Radiative Transfer ◽  
Iberian Peninsula ◽  
Land Surface ◽  
Function Analysis ◽  
Forthcoming Paper ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
State Variables ◽  
Brightness Temperatures

Abstract. L-band radiometry is considered to be one of the most suitable techniques to estimate surface soil moisture (SSM) by means of remote sensing. Brightness temperatures are key in this process, as they are the main input in the retrieval algorithm which yields SSM estimates. The work exposed compares brightness temperatures measured by the SMOS mission to two different sets of modelled ones, over the Iberian Peninsula from 2010 to 2012. The two modelled sets were estimated using a radiative transfer model and state variables from two land-surface models: (i) ORCHIDEE and (ii) H-TESSEL. The radiative transfer model used is the CMEM. Measured and modelled brightness temperatures show a good agreement in their temporal evolution, but their spatial structures are not consistent. An empirical orthogonal function analysis of the brightness temperature's error identifies a dominant structure over the south-west of the Iberian Peninsula which evolves during the year and is maximum in autumn and winter. Hypotheses concerning forcing-induced biases and assumptions made in the radiative transfer model are analysed to explain this inconsistency, but no candidate is found to be responsible for the weak spatial correlations at the moment. Further hypotheses are proposed and will be explored in a forthcoming paper. The analysis of spatial inconsistencies between modelled and measured TBs is important, as these can affect the estimation of geophysical variables and TB assimilation in operational models, as well as result in misleading validation studies.

scholarly journals Long term soil moisture mapping over the Tibetan plateau using Special Sensor Microwave/Imager

2014 ◽  
Vol 18 (4) ◽  
pp. 1323-1337 ◽  
Author(s):  
R. van der Velde ◽  
M. S. Salama ◽  
T. Pellarin ◽  
M. Ofwono ◽  
Y. Ma ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Tibetan Plateau ◽  
Mean Squared Error ◽  
Potential Evapotranspiration ◽  
Warm Season ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
The Tibetan Plateau ◽  
Near Surface

Abstract. This paper discusses soil moisture retrievals over the Tibetan Plateau from brightness temperature (TB's) observed by the Special Sensor Microwave Imagers (SSM/I's) during the warm seasons of the period from July 1987 to December 2008. The Fundamental Climate Data Record (FCDR) of F08, F11 and F13 SSM/I satellites by the Precipitation Research Group of Colorado State University is used for this study. A soil moisture retrieval algorithm is developed based on a radiative transfer model that simulates top-of-atmosphere TB's whereby effects of atmosphere are calculated from near-surface forcings obtained from a bias-corrected dataset. Validation of SSM/I retrievals against in situ measurements for a two-and-half year period (225 matchups) gives a Root Mean Squared Error of 0.046 m3 m−3. The agreement between retrievals and Noah simulations from the Global Land Data Assimilation System is investigated to further provide confidence in the reliability of SSM/I retrievals at the Plateau-scale. Normalised soil moisture anomalies (N) are computed on a warm seasonal (May–October) and on a monthly basis to analyse the trends present within the products available from July 1987 to December 2008. The slope of linear regression functions between N and time is used to quantify the trends. Both the warm season and monthly N indicate severe wettings of 0.8 to almost 1.6 decade−1 in the centre of the Plateau. Correlations are found by the trend with elevation for the warm season as a whole and the individual months May, September and October. The observed wetting of the Tibetan Plateau agrees with recent findings on permafrost retreat, precipitation increase and potential evapotranspiration decline.

scholarly journals Estimating bias in the OCO-2 retrieval algorithm caused by 3-D radiation scattering from unresolved boundary layer clouds

2014 ◽  
Vol 7 (11) ◽  
pp. 11547-11591
Author(s):  
A. Merrelli ◽  
R. Bennartz ◽  
C. W. O'Dell ◽  
T. E. Taylor
Keyword(s):  
Radiative Transfer ◽  
Scattering Problem ◽  
Three Dimensional ◽  
Co2 Concentration ◽  
Bare Soil ◽  
Short Wave ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Retrieval Algorithms

Abstract. Due to the complexity of the multiple scattering problem for shortwave radiative transfer in Earth's atmosphere, operational physical retrieval algorithms commonly use a plane parallel Radiative Transfer Model (RTM). This so-called One-Dimensional (1-D) assumption allows practical retrieval algorithms to be implemented. In order to understand the impacts of this assumption for low altitude, unresolved clouds observed by OCO-2, the Three-Dimensional (3-D) radiative transfer model SHDOM is used to generate synthetic observations which are then processed by the operational retrieval algorithm based on a 1-D RTM. Simulations are performed over three realistic surface spectral albedos, corresponding to snow, vegetation, and bare soil. The results show that the existing cloud screening algorithm has difficulty identifying sub-Field of View (FOV), unresolved clouds that fill less than half of the FOV. The unresolved clouds introduce a bias in the retrieved CO2 concentration, as quantified by the dry air mole fraction (XCO2). The biases are relatively small (less than 1 ppm) when the albedo at 2.1 μm is high, which is common over bare land surfaces. For cases with low 2.1 μm albedo, such as snow, the bias becomes much larger, up to 5 ppm. These results indicate that the XCO2 retrieval appears robust to 3-D scattering effects from unresolved low level clouds when the short wave infrared surface albedo is large, but for darker surfaces these clouds can introduce significant biases.

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