A semi-empirical inversion model for assessing surface soil moisture using AMSR-E brightness temperatures

2012 ◽  
Vol 456-457 ◽  
pp. 1-11 ◽  
Author(s):  
Xiu-zhi Chen ◽  
Shui-sen Chen ◽  
Ruo-fei Zhong ◽  
Yong-xian Su ◽  
Ji-shan Liao ◽  
...  
2016 ◽  
Vol 20 (12) ◽  
pp. 4895-4911 ◽  
Author(s):  
Gabriëlle J. M. De Lannoy ◽  
Rolf H. Reichle

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.


2014 ◽  
Vol 13 (1) ◽  
pp. vzj2013.04.0075 ◽  
Author(s):  
M. Dimitrov ◽  
J. Vanderborght ◽  
K. G. Kostov ◽  
K. Z. Jadoon ◽  
L. Weihermüller ◽  
...  

2006 ◽  
Vol 101 (3) ◽  
pp. 415-426 ◽  
Author(s):  
Kauzar Saleh ◽  
Jean-Pierre Wigneron ◽  
Patricia de Rosnay ◽  
Jean-Christophe Calvet ◽  
Yann Kerr

2019 ◽  
Vol 11 (14) ◽  
pp. 1647 ◽  
Author(s):  
Hala K. AlJassar ◽  
Marouane Temimi ◽  
Dara Entekhabi ◽  
Peter Petrov ◽  
Hussain AlSarraf ◽  
...  

In this study, we address the variations of bare soil surface microwave brightness temperatures and evaluate the performance of a dielectric mixing model over the desert of Kuwait. We use data collected in a field survey and data obtained from NASA Soil Moisture Active Passive (SMAP), European Space Agency Soil Moisture and Ocean Salinity (SMOS), Advanced Microwave Scanning Radiometer 2 (AMSR2), and Special Sensor Microwave/Imager (SSM/I). In situ measurements are collected during two intensive field campaigns over bare, flat, and homogeneous soil terrains in the desert of Kuwait. Despite the prevailing dry desert environment, a large range of soil moisture values was monitored, due to precedent rain events and subsequent dry down. The mean relative difference (MRD) is within the range of ±0.005 m3·m−3 during the two sampling days. This reflects consistency of soil moisture in space and time. As predicted by the model, the higher frequency channels (18 to 19 GHz) demonstrate reduced sensitivity to surface soil moisture even in the absence of vegetation, topography and heterogeneity. In the 6.9 to 10.7 GHz range, only the horizontal polarization is sensitive to surface soil moisture. Instead, at the frequency of 1.4 GHz, both polarizations are sensitive to soil moisture and span a large dynamic range as predicted by the model. The error statistics of the difference between observed satellite brightness temperature (Tb) (excluding SMOS data due to radio frequency interference, RFI) and simulated brightness temperatures (Tbs) show values of Root Mean Square Deviation (RMSD) of 5.05 K at vertical polarization and 4.88 K at horizontal polarization. Such error could be due to the performance of the dielectric mixing model, soil moisture sampling depth and the impact of parametrization of effective temperature and roughness.


2014 ◽  
Vol 5 (7) ◽  
pp. 662-671 ◽  
Author(s):  
Xiuzhi Chen ◽  
Yongxian Su ◽  
Yong Li ◽  
Liusheng Han ◽  
Jishan Liao ◽  
...  

Author(s):  
Xingming Zheng ◽  
Zhuangzhuang Feng ◽  
Lei Li ◽  
Bingzhe Li ◽  
Tao Jiang ◽  
...  

2010 ◽  
Vol 24 (18) ◽  
pp. 2507-2519 ◽  
Author(s):  
Y. Zhao ◽  
S. Peth ◽  
X. Y. Wang ◽  
H. Lin ◽  
R. Horn

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