Comparing ERA-40-Based L-Band Brightness Temperatures with Skylab Observations: A Calibration/Validation Study Using the Community Microwave Emission Model

2009 ◽  
Vol 10 (1) ◽  
pp. 213-226 ◽  
Author(s):  
Matthias Drusch ◽  
Thomas Holmes ◽  
Patricia de Rosnay ◽  
Gianpaolo Balsamo
Keyword(s):  
Soil Moisture ◽  
Dynamic Range ◽  
Microwave Radiometer ◽  
Space Station ◽  
Microwave Emission ◽  
Emission Model ◽  
Soil Database ◽  
Brightness Temperatures ◽  
L Band ◽  
Rms Errors

Abstract The Community Microwave Emission Model (CMEM) has been used to compute global L-band brightness temperatures at the top of the atmosphere. The input data comprise surface fields from the 40-yr ECMWF Re-Analysis (ERA-40), vegetation data from the ECOCLIMAP dataset, and the Food and Agriculture Organization’s (FAO) soil database. Modeled brightness temperatures have been compared against (historic) observations from the S-194 passive microwave radiometer onboard the Skylab space station. Different parameterizations for surface roughness and the vegetation optical depth have been used to calibrate the model. The best results have been obtained for rather simple approaches proposed by Wigneron et al. and Kirdyashev et al. The rms errors after calibration are 10.7 and 9.8 K for North and South America, respectively. Comparing the ERA-40 soil moisture product against the corresponding in situ observations suggests that the uncertainty in the modeled soil moisture is the predominant contributor to these rms errors. Although the bias between model and observed brightness temperatures are reduced after the calibration, systematic differences in the dynamic range remain. For NWP analysis applications, bias correction schemes should be applied prior to data assimilation. The calibrated model has been used to compute a 10-yr brightness temperature climatology based on ERA-40 data.

scholarly journals Hourly soil moisture mapping over West Africa using AMSR-E observations and a satellite-based rainfall product

2009 ◽  
Vol 6 (3) ◽  
pp. 4035-4064
Author(s):  
T. Pellarin ◽  
T. Tran ◽  
J.-M. Cohard ◽  
S. Galle ◽  
J.-P. Laurent ◽  
...  
Keyword(s):  
Soil Moisture ◽  
West Africa ◽  
Temporal Frequency ◽  
Microwave Emission ◽  
Emission Model ◽  
Brightness Temperatures ◽  
Antecedent Precipitation ◽  
The Difference ◽  
Satellite Microwave Measurements ◽  
Antecedent Precipitation Index

Abstract. This paper provides an original and simple methodology to map surface soil moisture with a fine temporal and spatial resolution over large areas based on a satellite rainfall accumulation product and soil microwave emission measurements at C-band. The first motivation of this study was to obtain high temporal frequency (~1 h) in order to study the possible feedback mechanisms between soil moisture and convection in West Africa. The use of soil moisture maps derived from satellite microwave measurements was not possible due to the low (at best daily) temporal resolution. Thus, a rainfall accumulation product based on Meteosat geostationary satellite measurements was used together with a simple Antecedent Precipitation Index (API) model to produce soil moisture map at the 10×10 km2 and 30 min resolution. Due to uncertainties on the satellite-based rainfall accumulation product, derived soil moisture maps were found to be erroneous. An assimilation technique based on AMSR-E C-band measurements into a microwave emission model was developed. The assimilation technique described in this study consists of modulating the rainfall accumulation estimate between two successive AMSR-E brightness temperatures (TB) measurements in order to match simulated and observed TB. When a rainfall event happens, the initial rainfall accumulation estimate is modulated using a multiplicative factor ranging from 0 to 7. The best solution is given by the rainfall rate which minimizes the difference between observed and simulated TB. Ground-based soil moisture measurements obtained at three sites in Niger, Mali and Benin were used to assess the methodology which was found to improve the soil moisture estimates over the three sites.

The implementation of Community Microwave Emission Model (CMEM) as the observation operator in TerrsysMP-PDAF

2020 ◽  
Author(s):  
Shaoning Lv ◽  
Stefan Poll ◽  
Bernd Schalge ◽  
Pablo Garfias ◽  
Clemens Simmer
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Passive Microwave ◽  
Microwave Emission ◽  
Emission Model ◽  
Ideal Case ◽  
Observation Operator ◽  
Community Land Model ◽  
L Band

<p>Studies with satellite-based passive microwave L-band observations have been fostered strongly by the launch of NASA's Soil Moisture Active Passive (SMAP) satellite on January 31, 2015 (Entekhabi et al. 2010), which complements and extends the observations at L-band by the ESA's Soil Moisture Ocean Salinity (SMOS) mission in orbit since 2009 (Kerr et al. 2001, Mecklenburg et al. 2012, Lievens et al. 2014). SMOS and SMAP data assimilation studies started during their pre- and post-launch period. Flores et al. (2012) used an Ensemble Kalman Filter to constrain the uncertainties of the simulated soil moisture fields from physical-based hydrological models. Our work intends to explore the use and value of passive L-band satellite observations for ensemble-based data assimilation with fully-coupled terrestrial system models for mesoscale catchments. An observation operator for satellite-based passive microwave (PMW) observations based on the community microwave emission model (CMEM) (de Rosnay et al. 2009, Drusch et al. 2009) has been modified, applied and tested in an ideal case developed within the FOR2131 (Schalge et al. 2016) with the coupled subsurface-land surface-atmosphere simulation platform TerrSysMP (Shrestha et al. 2014), which couples ParFlow (subsurface), Community Land Model (CLM, surface), and COSMO (atmosphere). We achieve the development of a satellite simulator for passive L-band observations of the satellite missions SMAP and SMOS and its adaptation to the ideal case, and the lower-resolution TerrSysMP model applied for data assimilation (TerrSysMP-PDAF).</p>

scholarly journals Spatio-Temporal Mapping of L-Band Microwave Emission on a Heterogeneous Area with ELBARA III Passive Radiometer

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3447
Author(s):  
Łukasz Gluba ◽  
Mateusz Łukowski ◽  
Radosław Szlązak ◽  
Joanna Sagan ◽  
Kamil Szewczak ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Water Resources ◽  
Microwave Radiometer ◽  
European Space Agency ◽  
Test Site ◽  
Microwave Emission ◽  
Satellite Mission ◽  
Space Agency ◽  
L Band ◽  
Spatio Temporal

Water resources on Earth become one of the main concerns for society. Therefore, remote sensing methods are still under development in order to improve the picture of the global water cycle. In this context, the microwave bands are the most suitable to study land–water resources. The Soil Moisture and Ocean Salinity (SMOS), satellite mission of the European Space Agency (ESA), is dedicated for studies of the water in soil over land and salinity of oceans. The part of calibration/validation activities in order to improve soil moisture retrieval algorithms over land is done with ground-based passive radiometers. The European Space Agency L-band Microwave Radiometer (ELBARA III) located near the Bubnów wetland in Poland is capable of mapping microwave emissivity at the local scale, due to the azimuthal and vertical movement of the horn antenna. In this paper, we present results of the spatio-temporal mapping of the brightness temperatures on the heterogeneous area of the Bubnów test-site consisting of an area with variable organic matter (OM) content and different type of vegetation. The soil moisture (SM) was retrieved with the L-band microwave emission of the biosphere (L-MEB) model with simplified roughness parametrization (SRP) coupling roughness and optical depth parameters. Estimated soil moisture values were compared with in-situ data from the automatic agrometeorological station. The results show that on the areas with a relatively low OM content (4–6%—cultivated field) there was good agreement between measured and estimated SM values. Further increase in OM content, starting from approximately 6% (meadow wetland), caused an increase in bias, root mean square error (RMSE), and unbiased RMSE (ubRMSE) values and a general drop in correlation coefficient (R). Despite a span of obtained R values, we found that time-averaged estimated SM using the L-MEB SRP approach strongly correlated with OM contents.

scholarly journals Using TRMM/TMI to Retrieve Surface Soil Moisture over the Southern United States from 1998 to 2002

2006 ◽  
Vol 7 (1) ◽  
pp. 23-38 ◽  
Author(s):  
H. Gao ◽  
E. F. Wood ◽  
T. J. Jackson ◽  
M. Drusch ◽  
R. Bindlish
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Hydrological Modeling ◽  
Dynamic Range ◽  
Microwave Remote Sensing ◽  
Microwave Emission ◽  
Southern United States ◽  
Retrieval Algorithm ◽  
Brightness Temperatures

Abstract Passive microwave remote sensing has been recognized as a potential method for measuring soil moisture. Combined with field observations and hydrological modeling brightness temperatures can be used to infer soil moisture states and fluxes in real time at large scales. However, operationally acquiring reliable soil moisture products from satellite observations has been hindered by three limitations: suitable low-frequency passive radiometric sensors that are sensitive to soil moisture and its changes; a retrieval model (parameterization) that provides operational estimates of soil moisture from top-of-atmosphere (TOA) microwave brightness temperature measurements at continental scales; and suitable, large-scale validation datasets. In this paper, soil moisture is retrieved across the southern United States using measurements from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) X-band (10.65 GHz) radiometer with a land surface microwave emission model (LSMEM) developed by the authors. Surface temperatures required for the retrieval algorithm were obtained from the Variable Infiltration Capacity (VIC) hydrological model using North American Land Data Assimilation System (NLDAS) forcing data. Because of the limited information content on soil moisture in the observed brightness temperatures over regions characterized by heavy vegetation, active precipitation, snow, and frozen ground, quality control flags for the retrieved soil moisture are provided. The resulting retrieved soil moisture database will be available through the NASA Goddard Space Flight Center (GSFC) Distributed Active Archive Center (DAAC) at a 1/8° spatial resolution across the southern United States for the 5-yr period of January 1998 through December 2002. Initial comparisons with in situ observations obtained from the Oklahoma Mesonet resulted in seasonal correlation coefficients exceeding 0.7 for half of the time covered by the dataset. The dynamic range of the satellite-derived soil moisture dataset is considerably higher compared to the in situ data. The spatial pattern of the TMI soil moisture product is consistent with the corresponding precipitation fields.

scholarly journals Snow wetness retrieved from close-range L-band radiometry in the western Greenland ablation zone

2020 ◽  
pp. 1-12
Author(s):  
Reza Naderpour ◽  
Derek Houtz ◽  
Mike Schwank
Keyword(s):  
Greenland Ice Sheet ◽  
Microwave Emission ◽  
Microwave Antenna ◽  
Observation Angle ◽  
Emission Model ◽  
Ablation Zone ◽  
Single Observation ◽  
Brightness Temperatures ◽  
Close Range ◽  
L Band

Abstract Close-range (CR) L-band radiometry and quasi-simultaneous in situ snow characterizations were conducted in May 2019 at the Swiss Camp research site in the ablation zone of the western Greenland ice sheet. Snow liquid-water and its melt/refreeze are retrieved from microwave antenna temperatures measured with the ground-based L-band radiometer ELBARA-III. The emission model (EM) used in the retrieval is a two-layer configuration of the ‘L-Band Specific Microwave Emission Model of Layered Snowpack’. Consistent snow wetness retrievals were achieved from both single- and multi-angle CR observations of L-band antenna temperatures. This suggests that multi-angle observation is not a pre-requisite for snow wetness retrieval. Therefore, in addition to soil moisture and ocean salinity (SMOS) multi-angle measurements, snow wetness can be estimated from spaceborne L-band brightness temperatures measured at a single observation angle, such as from NASA's SMAP satellite. Our results provide partial validation of a recently presented snow wetness retrieval approach based on the same EM and applied over Greenland using multi-angle SMOS brightness temperatures. Agreement between measured CR antenna temperatures and SMOS brightness temperatures is found to be within the 95% confidence intervals of ELBARA-III and SMOS measurement uncertainties. Our measurements confirm the modeled response of antenna temperatures to diurnal variations of snow wetness.

scholarly journals Snow thickness retrieval from L-band brightness temperatures: a model comparison

2015 ◽  
Vol 56 (69) ◽  
pp. 9-17 ◽  
Author(s):  
Nina Maass ◽  
Lars Kaleschke ◽  
Xiangshan Tian-Kunze ◽  
Rasmus T. Tonboe
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Brightness Temperature ◽  
Model Comparison ◽  
Complex Model ◽  
Microwave Emission ◽  
Emission Model ◽  
Brightness Temperatures ◽  
L Band ◽  
Snow Thickness

AbstractThe Soil Moisture and Ocean Salinity (SMOS) satellite’s L-band (1.4 GHz) measurements have been used to retrieve Snow thickness over thick sea Ice in a previous study. Here we consider brightness temperature simulations for 2.5–4.5m thick Arctic multi-year Ice and compare the results of the relatively simple emission model (M2013) used previously for the retrieval with simulations from a more complex model (T2011) that combines a sea-Ice version of the Microwave Emission Model for Layered Snowpacks (MEMLS) with a thermodynamic model. We find that L-band brightness temperature is mainly determined by Ice temperature. In the M2013 model, Ice temperature in turn is mainly determined by surface temperature and Snow thickness, and this dependence has been used previously to explain the potential for a Snow thickness retrieval. Our comparisons suggest that the M2013 retrieval model may benefit from a more sophisticated thermodynamic calculation of the Ice temperature or from using independent temperature data (e.g. from 6 GHz channels). In both models, horizontally polarized brightness temperatures increase with Snow thickness while holding surface temperature, Ice thickness and Snow density near constant. The increase in the T2011 model is steeper than in M2013, suggesting a higher sensitivity to Snow thickness than found earlier.

scholarly journals A simple parameterisation for retrieving soil moisture from passive microwave data

2001 ◽  
Vol 5 (1) ◽  
pp. 39-48 ◽  
Author(s):  
E. J. Burke ◽  
L. P. Simmonds
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Passive Microwave ◽  
Microwave Emission ◽  
Balance Model ◽  
Retrieval Algorithm ◽  
Emission Model ◽  
Near Surface ◽  
Wide Range ◽  
L Band

Abstract. MICRO-SWEAT, a physically based soil water and energy balance model coupled with a microwave emission model, was used to investigate the relationship between near surface soil moisture (θ0-5) and L-band microwave brightness temperature (TB) under a wide range of conditions. The effects of soil texture, look angle and vegetation on this relationship were parameterised and combined into a simple summary model relating θ0-5 to TB. This model retains much of the physical basis of MICRO-SWEAT but can be used in more data limiting circumstances. It was tested using a variety of truck-based L-band data sets collected between 1980 and 1982. This paper emphasises the need to have an accurate estimate of the vegetation optical depth (a parameter that describes the degree of influence of the vegetation on the microwave emission from the soil surface) in order to retrieve correctly the soil water content. Keywords: passive microwave, soil moisture, remote sensing, vegetation, retrieval algorithm

scholarly journals Modelling the L-Band Snow-Covered Surface Emission in a Winter Canadian Prairie Environment

2018 ◽  
Vol 10 (9) ◽  
pp. 1451 ◽  
Author(s):  
Alexandre Roy ◽  
Marion Leduc-Leballeur ◽  
Ghislain Picard ◽  
Alain Royer ◽  
Peter Toose ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Brightness Temperature ◽  
Frozen Soil ◽  
Microwave Emission ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Emission Model ◽  
Canadian Prairie ◽  
Seasonal Snow ◽  
L Band

Detailed angular ground-based L-band brightness temperature (TB) measurements over snow covered frozen soil in a prairie environment were used to parameterize and evaluate an electromagnetic model, the Wave Approach for LOw-frequency MIcrowave emission in Snow (WALOMIS), for seasonal snow. WALOMIS, initially developed for Antarctic applications, was extended with a soil interface model. A Gaussian noise on snow layer thickness was implemented to account for natural variability and thus improve the TB simulations compared to observations. The model performance was compared with two radiative transfer models, the Dense Media Radiative Transfer-Multi Layer incoherent model (DMRT-ML) and a version of the Microwave Emission Model for Layered Snowpacks (MEMLS) adapted specifically for use at L-band in the original one-layer configuration (LS-MEMLS-1L). Angular radiometer measurements (30°, 40°, 50°, and 60°) were acquired at six snow pits. The root-mean-square error (RMSE) between simulated and measured TB at vertical and horizontal polarizations were similar for the three models, with overall RMSE between 7.2 and 10.5 K. However, WALOMIS and DMRT-ML were able to better reproduce the observed TB at higher incidence angles (50° and 60°) and at horizontal polarization. The similar results obtained between WALOMIS and DMRT-ML suggests that the interference phenomena are weak in the case of shallow seasonal snow despite the presence of visible layers with thicknesses smaller than the wavelength, and the radiative transfer model can thus be used to compute L-band brightness temperature.

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.

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