Surface soil moisture estimation using multi-incidence angle ENVISAT ASAR images

2007 ◽  
Author(s):  
Yansong Bao ◽  
Shumin Tang ◽  
Li Liu
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Incidence Angle ◽  
Surface Soil Moisture ◽  
Envisat Asar ◽  
Moisture Estimation

scholarly journals Surface soil moisture estimation over the AMMA Sahelian site in Mali using ENVISAT/ASAR data

2007 ◽  
Vol 109 (4) ◽  
pp. 473-481 ◽  
Author(s):  
F. Baup ◽  
E. Mougin ◽  
P. de Rosnay ◽  
F. Timouk ◽  
I. Chênerie
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Surface Soil Moisture ◽  
Envisat Asar ◽  
Moisture Estimation

scholarly journals Assimilation of SMOS brightness temperatures or soil moisture retrievals into a land surface model

2016 ◽  
Vol 20 (12) ◽  
pp. 4895-4911 ◽  
Author(s):  
Gabriëlle J. M. De Lannoy ◽  
Rolf H. Reichle
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Surface Soil ◽  
Root Zone ◽  
Incidence Angle ◽  
Land Surface Model ◽  
Surface Model ◽  
Model Errors ◽  
Surface Soil Moisture ◽  
Brightness Temperatures

Abstract. Three different data products from the Soil Moisture Ocean Salinity (SMOS) mission are assimilated separately into the Goddard Earth Observing System Model, version 5 (GEOS-5) to improve estimates of surface and root-zone soil moisture. The first product consists of multi-angle, dual-polarization brightness temperature (Tb) observations at the bottom of the atmosphere extracted from Level 1 data. The second product is a derived SMOS Tb product that mimics the data at a 40° incidence angle from the Soil Moisture Active Passive (SMAP) mission. The third product is the operational SMOS Level 2 surface soil moisture (SM) retrieval product. The assimilation system uses a spatially distributed ensemble Kalman filter (EnKF) with seasonally varying climatological bias mitigation for Tb assimilation, whereas a time-invariant cumulative density function matching is used for SM retrieval assimilation. All assimilation experiments improve the soil moisture estimates compared to model-only simulations in terms of unbiased root-mean-square differences and anomaly correlations during the period from 1 July 2010 to 1 May 2015 and for 187 sites across the US. Especially in areas where the satellite data are most sensitive to surface soil moisture, large skill improvements (e.g., an increase in the anomaly correlation by 0.1) are found in the surface soil moisture. The domain-average surface and root-zone skill metrics are similar among the various assimilation experiments, but large differences in skill are found locally. The observation-minus-forecast residuals and analysis increments reveal large differences in how the observations add value in the Tb and SM retrieval assimilation systems. The distinct patterns of these diagnostics in the two systems reflect observation and model errors patterns that are not well captured in the assigned EnKF error parameters. Consequently, a localized optimization of the EnKF error parameters is needed to further improve Tb or SM retrieval assimilation.

Surface Soil Moisture Estimation

2013 ◽  
pp. 29-48 ◽  
Author(s):  
George Petropoulos ◽  
Hywel Griffiths ◽  
Wouter Dorigo ◽  
Angelika Xaver ◽  
Alexander Gruber
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Surface Soil Moisture ◽  
Moisture Estimation

scholarly journals Multiscale Data Fusion for Surface Soil Moisture Estimation: A Spatial Hierarchical Approach

2019 ◽  
Vol 55 (12) ◽  
pp. 10443-10465 ◽  
Author(s):  
Dhruva Kathuria ◽  
Binayak P. Mohanty ◽  
Matthias Katzfuss
Keyword(s):  
Soil Moisture ◽  
Data Fusion ◽  
Surface Soil ◽  
Hierarchical Approach ◽  
Surface Soil Moisture ◽  
Moisture Estimation
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