Effects of Near Surface Soil Moisture Profiles During Evaporation on Far-Field Ground-Penetrating Radar Data: A Numerical Study

2013 ◽  
Vol 12 (2) ◽  
pp. vzj2012.0138 ◽  
Author(s):  
Davood Moghadas ◽  
Khan Zaib Jadoon ◽  
Jan Vanderborght ◽  
Sébastien Lambot ◽  
Harry Vereecken
Keyword(s):  
Soil Moisture ◽  
Ground Penetrating Radar ◽  
Surface Soil ◽  
Numerical Study ◽  
Radar Data ◽  
Far Field ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
Ground Penetrating ◽  
Moisture Profiles

scholarly journals Mapping shallow soil moisture profiles at the field scale using full-waveform inversion of ground penetrating radar data

Geoderma ◽  
2011 ◽  
Vol 161 (3-4) ◽  
pp. 225-237 ◽  
Author(s):  
Julien Minet ◽  
Agung Wahyudi ◽  
Patrick Bogaert ◽  
Marnik Vanclooster ◽  
Sébastien Lambot
Keyword(s):  
Soil Moisture ◽  
Ground Penetrating Radar ◽  
Waveform Inversion ◽  
Radar Data ◽  
Full Waveform Inversion ◽  
Field Scale ◽  
Full Waveform ◽  
Shallow Soil ◽  
Ground Penetrating ◽  
Moisture Profiles

scholarly journals Simultaneously estimating surface soil moisture and roughness of bare soils by combining optical and radar data

2021 ◽  
Vol 100 ◽  
pp. 102345
Author(s):  
Xingming Zheng ◽  
Zhuangzhuang Feng ◽  
Lei Li ◽  
Bingzhe Li ◽  
Tao Jiang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Radar Data ◽  
Surface Soil Moisture ◽  
Bare Soils

Field test of the surface soil moisture mapping using Sentinel-1 radar data

2021 ◽  
pp. 151121
Author(s):  
A.M. Zeyliger ◽  
K.V. Muzalevsky ◽  
E.V. Zinchenko ◽  
O.S. Ermolaeva
Keyword(s):  
Soil Moisture ◽  
Field Test ◽  
Surface Soil ◽  
Radar Data ◽  
Surface Soil Moisture

Processing Ground Penetrating Radar Data To Improve Resolution Of Near-Surface Targets

1993 ◽  
Author(s):  
Kevin Gerlitz ◽  
Michael D. Knoll ◽  
Guy M. Cross ◽  
Robert D. Luzitano ◽  
Rosemary Knight
Keyword(s):  
Ground Penetrating Radar ◽  
Radar Data ◽  
Near Surface ◽  
Ground Penetrating

scholarly journals The impact of near-surface soil moisture assimilation at subseasonal, seasonal, and inter-annual timescales

2015 ◽  
Vol 19 (12) ◽  
pp. 4831-4844 ◽  
Author(s):  
C. Draper ◽  
R. Reichle
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Surface Soil ◽  
Root Zone ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
In Situ Observations ◽  
The Impact

Abstract. A 9 year record of Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) soil moisture retrievals are assimilated into the Catchment land surface model at four locations in the US. The assimilation is evaluated using the unbiased mean square error (ubMSE) relative to watershed-scale in situ observations, with the ubMSE separated into contributions from the subseasonal (SMshort), mean seasonal (SMseas), and inter-annual (SMlong) soil moisture dynamics. For near-surface soil moisture, the average ubMSE for Catchment without assimilation was (1.8 × 10−3 m3 m−3)2, of which 19 % was in SMlong, 26 % in SMseas, and 55 % in SMshort. The AMSR-E assimilation significantly reduced the total ubMSE at every site, with an average reduction of 33 %. Of this ubMSE reduction, 37 % occurred in SMlong, 24 % in SMseas, and 38 % in SMshort. For root-zone soil moisture, in situ observations were available at one site only, and the near-surface and root-zone results were very similar at this site. These results suggest that, in addition to the well-reported improvements in SMshort, assimilating a sufficiently long soil moisture data record can also improve the model representation of important long-term events, such as droughts. The improved agreement between the modeled and in situ SMseas is harder to interpret, given that mean seasonal cycle errors are systematic, and systematic errors are not typically targeted by (bias-blind) data assimilation. Finally, the use of 1-year subsets of the AMSR-E and Catchment soil moisture for estimating the observation-bias correction (rescaling) parameters is investigated. It is concluded that when only 1 year of data are available, the associated uncertainty in the rescaling parameters should not greatly reduce the average benefit gained from data assimilation, although locally and in extreme years there is a risk of increased errors.

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