scholarly journals Global soil moisture retrieval from a synthetic L-band brightness temperature data set

2003 ◽  
Vol 108 (D12) ◽  
Author(s):  
Thierry Pellarin
Keyword(s):  
Soil Moisture ◽  
Brightness Temperature ◽  
Temperature Data ◽  
Data Set ◽  
Global Soil ◽  
L Band

An Algorithm for Retrieving Soil Moisture Using L-Band H-Polarized Multiangular Brightness Temperature Data

2016 ◽  
Vol 13 (9) ◽  
pp. 1295-1299 ◽  
Author(s):  
Qian Cui ◽  
Xiaolong Dong ◽  
Jiancheng Shi ◽  
Tianjie Zhao ◽  
Chuan Xiong
Keyword(s):  
Soil Moisture ◽  
Brightness Temperature ◽  
Temperature Data ◽  
L Band

scholarly journals Analysis of SMOS brightness temperature and vegetation optical depth data with coupled land surface and radiative transfer models in Southern Germany

2012 ◽  
Vol 9 (4) ◽  
pp. 5389-5436 ◽  
Author(s):  
F. Schlenz ◽  
J. T. dall'Amico ◽  
W. Mauser ◽  
A. Loew
Keyword(s):  
Soil Moisture ◽  
Brightness Temperature ◽  
Optical Depth ◽  
Land Surface ◽  
Temperature Data ◽  
Passive Microwave ◽  
Coupled Models ◽  
Depth Data ◽  
Danube Catchment ◽  
L Band

Abstract. Soil Moisture and Ocean Salinity (SMOS) L1c brightness temperature and L2 optical depth data are analysed with a coupled land surface (PROMET) and radiative transfer model (L-MEB) that are used as tool for the analysis and validation of passive microwave satellite observations. The coupled models are validated with ground and airborne measurements under contrasting soil moisture, vegetation and temperature conditions during the SMOS Validation Campaign in May and June 2010 in the SMOS test site Upper Danube Catchment in Southern Germany with good results. The brightness temperature root-mean-squared errors are between 6 K and 9 K and can partly be attributed to a known bias in the airborne L-band measurements. The L-MEB parameterization is considered appropriate under local conditions even though it might possibly further be optimised. SMOS L1c brightness temperature data are processed and analysed in the Upper Danube Catchment using the coupled models in 2011 and during the SMOS Validation Campaign 2010 together with airborne L-band brightness temperature data. Only low to fair correlations are found for this comparison (R < 0.5). SMOS L1c brightness temperature data do not show the expected seasonal behaviour and are positively biased. It is concluded that RFI is responsible for most of the observed problems in the SMOS data products in the Upper Danube Catchment. This is consistent with the observed dry bias in the SMOS L2 soil moisture products which can also be related to RFI. It is confirmed that the brightness temperature data from the lower SMOS look angles are less reliable. This information could be used to improve the brightness temperature data filtering before the soil moisture retrieval. SMOS L2 optical depth values have been compared to modelled data and are not considered a reliable source of information about vegetation due to missing seasonal behaviour and a very high mean value. A fairly strong correlation between SMOS L2 soil moisture and optical depth was found (R = 0.65) even though the two variables are considered independent in the study area. The value of coupled models as a tool for the analysis of passive microwave remote sensing data is demonstrated by extending this SMOS data analysis from a few days during a field campaign to a long term comparison.

scholarly journals Development of a hydrometeorological forcing data set for global soil moisture estimation

2005 ◽  
Vol 25 (13) ◽  
pp. 1697-1714 ◽  
Author(s):  
A. A Berg ◽  
J. S. Famiglietti ◽  
M. Rodell ◽  
R. H. Reichle ◽  
U. Jambor ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Set ◽  
Global Soil ◽  
Moisture Estimation

scholarly journals Assessment with Controlled In-Situ Data of the Dependence of L-Band Radiometry on Sea-Ice Thickness

2020 ◽  
Vol 12 (4) ◽  
pp. 650
Author(s):  
Pablo Sánchez-Gámez ◽  
Carolina Gabarro ◽  
Antonio Turiel ◽  
Marcos Portabella
Keyword(s):  
Soil Moisture ◽  
Sea Ice ◽  
Brightness Temperature ◽  
European Space Agency ◽  
Ground Truth ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
In Situ Data ◽  
L Band

The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) and the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) missions are providing brightness temperature measurements at 1.4 GHz (L-band) for about 10 and 4 years respectively. One of the new areas of geophysical exploitation of L-band radiometry is on thin (i.e., less than 1 m) Sea Ice Thickness (SIT), for which theoretical and empirical retrieval methods have been proposed. However, a comprehensive validation of SIT products has been hindered by the lack of suitable ground truth. The in-situ SIT datasets most commonly used for validation are affected by one important limitation: They are available mainly during late winter and spring months, when sea ice is fully developed and the thickness probability density function is wider than for autumn ice and less representative at the satellite spatial resolution. Using Upward Looking Sonar (ULS) data from the Woods Hole Oceanographic Institution (WHOI), acquired all year round, permits overcoming the mentioned limitation, thus improving the characterization of the L-band brightness temperature response to changes in thin SIT. State-of-the-art satellite SIT products and the Cumulative Freezing Degree Days (CFDD) model are verified against the ULS ground truth. The results show that the L-band SIT can be meaningfully retrieved up to 0.6 m, although the signal starts to saturate at 0.3 m. In contrast, despite the simplicity of the CFDD model, its predicted SIT values correlate very well with the ULS in-situ data during the sea ice growth season. The comparison between the CFDD SIT and the current L-band SIT products shows that both the sea ice concentration and the season are fundamental factors influencing the quality of the thickness retrieval from L-band satellites.

Soil Moisture–Evapotranspiration Overcoupling and L-Band Brightness Temperature Assimilation: Sources and Forecast Implications

2020 ◽  
Vol 21 (10) ◽  
pp. 2359-2374 ◽  
Author(s):  
Wade T. Crow ◽  
Concepcion Arroyo Gomez ◽  
Joaquín Muñoz Sabater ◽  
Thomas Holmes ◽  
Christopher R. Hain ◽  
...  
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Brightness Temperature ◽  
Weather Prediction ◽  
Soil Water Storage ◽  
Surface Model ◽  
Forecast Errors ◽  
Central United States ◽  
L Band

AbstractThe assimilation of L-band surface brightness temperature (Tb) into the land surface model (LSM) component of a numerical weather prediction (NWP) system is generally expected to improve the quality of summertime 2-m air temperature (T2m) forecasts during water-limited surface conditions. However, recent retrospective results from the European Centre for Medium-Range Weather Forecasts (ECMWF) suggest that the assimilation of L-band Tb from the European Space Agency’s (ESA) Soil Moisture Ocean Salinity (SMOS) mission may, under certain circumstances, degrade the accuracy of growing-season 24-h T2m forecasts within the central United States. To diagnose the source of this degradation, we evaluate ECMWF soil moisture (SM) and evapotranspiration (ET) forecasts using both in situ and remote sensing resources. Results demonstrate that the assimilation of SMOS Tb broadly improves the ECMWF SM analysis in the central United States while simultaneously degrading the quality of 24-h ET forecasts. Based on a recently derived map of true global SM–ET coupling and a synthetic fraternal twin data assimilation experiment, we argue that the spatial and temporal characteristics of ECMWF SM analyses and ET forecast errors are consistent with the hypothesis that the ECMWF LSM overcouples SM and ET and, as a result, is unable to effectively convert an improved SM analysis into enhanced ET and T2m forecasts. We demonstrate that this overcoupling is likely linked to the systematic underestimation of root-zone soil water storage capacity by LSMs within the U.S. Corn Belt region.

A Global Root-Zone Soil Moisture Analysis Using Simulated L-band Brightness Temperature in Preparation for the Hydros Satellite Mission

2006 ◽  
Vol 7 (5) ◽  
pp. 1126-1146 ◽  
Author(s):  
G. Balsamo ◽  
J-F. Mahfouf ◽  
S. Bélair ◽  
G. Deblonde
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Brightness Temperature ◽  
Land Surface ◽  
Root Zone ◽  
Satellite Observations ◽  
Reference State ◽  
Observation Error ◽  
Moisture Analysis ◽  
L Band

Abstract The aim of this study is to test a land data assimilation prototype for the production of a global daily root-zone soil moisture analysis. This system can assimilate microwave L-band satellite observations such as those from the future Hydros NASA mission. The experiments are considered in the framework of the Interaction Soil Biosphere Atmosphere (ISBA) land surface scheme used operationally at the Meteorological Service of Canada for regional and global weather forecasting. A land surface reference state is obtained after a 1-yr global land surface simulation, forced by near-surface atmospheric fields provided by the Global Soil Wetness Project, second initiative (GSWP-2). A radiative transfer model is applied to simulate the microwave L-band passive emission from the surface. The generated brightness temperature observations are distributed in space and time according to the satellite trajectory specified by the Hydros mission. The impact of uncertainties related to the satellite observations, the land surface, and microwave emission models is investigated. A global daily root-zone soil moisture analysis is produced with a simplified variational scheme. The applicability and performance of the system are evaluated in a data assimilation cycle in which the L-band simulated observations, generated from a land surface reference state, are assimilated to correct a prescribed initial root-zone soil moisture error. The analysis convergence is satisfactory in both summer and winter cases. In summer, when considering a 3-K observation error, 90% of land surface converges toward the reference state with a soil moisture accuracy better than 0.04 m3 m−3 after a 4-week assimilation cycle. A 5-K observation error introduces 1-week delay in the convergence. A study of the analysis error statistics is performed for understanding the properties of the system. Special features associated with the interactions between soil water and soil ice, and the presence of soil moisture vertical gradients, are examined.

scholarly journals Brief communication: Mapping Greenland's perennial firn aquifers using enhanced-resolution L-band brightness temperature image time series

2020 ◽  
Vol 14 (9) ◽  
pp. 2809-2817
Author(s):  
Julie Z. Miller ◽  
David G. Long ◽  
Kenneth C. Jezek ◽  
Joel T. Johnson ◽  
Mary J. Brodzik ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Soil Moisture ◽  
Brightness Temperature ◽  
Ice Sheets ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Enhanced Resolution ◽  
L Band ◽  
Coherent Radar

Abstract. Enhanced-resolution L-band brightness temperature (TB) image time series generated from observations collected over the Greenland Ice Sheet by NASA's Soil Moisture Active Passive (SMAP) satellite are used to map Greenland's perennial firn aquifers from space. Exponentially decreasing L-band TB signatures are correlated with perennial firn aquifer areas identified via the Center for Remote Sensing of Ice Sheets (CReSIS) Multi-Channel Coherent Radar Depth Sounder (MCoRDS) that was flown by NASA's Operation IceBridge (OIB) campaign. An empirical algorithm to map extent is developed by fitting these signatures to a set of sigmoidal curves. During the spring of 2016, perennial firn aquifer areas are found to extend over ∼66 000 km2.

scholarly journals Application of satellite microwave remote sensed brightness temperature in the regional soil moisture simulation

2009 ◽  
Vol 6 (1) ◽  
pp. 1233-1260 ◽  
Author(s):  
X. K. Shi ◽  
J. Wen ◽  
L. Wang ◽  
T. T. Zhang ◽  
H. Tian ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Brightness Temperature ◽  
Land Surface ◽  
Surface Soil ◽  
Microwave Remote Sensing ◽  
Temperature Data ◽  
Fine Tuning ◽  
Tibet Plateau ◽  
Surface Model ◽  
Satellite Microwave

Abstract. As the satellite microwave remote sensed brightness temperature is sensitive to land surface soil moisture (SM) and SM is a basic output variable in model simulation, it is of great significance to use the brightness temperature data to improve SM numerical simulation. In this paper, the theory developed by Yan et al. (2004) about the relationship between satellite microwave remote sensing polarization index and SM was used to estimate the land surface SM from AMSR-E (Advanced Microwave Scanning Radiometer – Earth Observing System) brightness temperature data. With consideration of land surface soil texture, surface roughness, vegetation optical thickness, and the AMSR-E monthly SM products, the regional daily land surface SM was estimated over the eastern part of the Qinghai-Tibet Plateau. The results show that the estimated SM is lower than the ground measurements and the NCEP (American National Centers for Environmental Prediction) reanalysis data at the Maqu Station (33.85° N, 102.57° E) and the Tanglha Station (33.07° N, 91.94° E), but its regional distribution is reasonable and somewhat better than that from the daily AMSR-E SM product, and its temporal variation shows a quick response to the ground daily precipitations. Furthermore, in order to improve the simulating ability of the WRF (Weather Research and Forecasting) model to land surface SM, the estimated SM was assimilated into the Noah land surface model by the Newtonian relaxation (NR) method. The results indicate that, by fine tuning of the quality factor in NR method, the simulated SM values are improved most in desert area, followed by grassland, shrub and grass mixed zone. At temporal scale, Root Mean Square Error (RMSE) values between simulated and observed SM are decreased 0.03 and 0.07 m3/m3 by using the NR method in the Maqu Station and the Tanglha Station, respectively.

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