scholarly journals Soil Moisture Estimation by Assimilating L-Band Microwave Brightness Temperature with Geostatistics and Observation Localization

PLoS ONE ◽  
2015 ◽  
Vol 10 (1) ◽  
pp. e0116435 ◽  
Author(s):  
Xujun Han ◽  
Xin Li ◽  
Riccardo Rigon ◽  
Rui Jin ◽  
Stefano Endrizzi
Keyword(s):  
Soil Moisture ◽  
Brightness Temperature ◽  
Microwave Brightness Temperature ◽  
L Band ◽  
Moisture Estimation

scholarly journals A New Soil Moisture Retrieval Algorithm from the L-Band Passive Microwave Brightness Temperature Based on the Change Detection Principle

2020 ◽  
Vol 12 (8) ◽  
pp. 1303
Author(s):  
Xingming Zheng ◽  
Zhuangzhuang Feng ◽  
Hongxin Xu ◽  
Yanlong Sun ◽  
Lei Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Change Detection ◽  
Brightness Temperature ◽  
Detection Algorithm ◽  
Passive Microwave ◽  
Theoretical Simulation ◽  
Microwave Brightness Temperature ◽  
Input Parameters ◽  
L Band

The launch of the SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active Passive) satellites has led to the development of a series of L-band soil moisture retrieval algorithms. In these algorithms, many input parameters (such as leaf area index and soil texture) and empirical coefficients (such as roughness coefficient (hP, NRP) and crop structure parameter (bP, ttP)) are needed to calculate surface soil moisture (SSM) from microwave brightness temperature. Many previous studies have focused on how to determine the value of these coefficients and input parameters. Nevertheless, it can be difficult to obtain their ‘real’ values with low uncertainty across large spatial scales. To avoid this problem, a passive microwave remote sensing SSM inversion algorithm based on the principle of change detection was proposed and tested using theoretical simulation and a field SSM dataset for an agricultural area in northeastern China. This algorithm was initially used to estimate SSM for radar remote sensing. First, theoretical simulation results were used to confirm the linear relationship between the change rates for SSM and surface emissivity, for both H and V polarization. This demonstrated the reliability of the change detection algorithm. Second, minimum emissivity (or the difference between maximum emissivity and minimum emissivity) was modeled with a linear relationship between vegetation water content, derived from a three-year (2016–2018) SMAP L3 SSM dataset. Third, SSM values estimated by the change detection algorithm were in good agreement with SMAP L3 SSM and field SSM, with RMSE values ranging from 0.015~0.031 cm3/cm3 and 0.038~0.051 cm3/cm3, respectively. The V polarization SSM accuracy was higher than H polarization and combined H and V polarization accuracy. The retrieved SSM error from the change detection algorithm was similar to SMAP SSM due to errors inherited from the training dataset. The SSM algorithm proposed here is simple in form, has fewer input parameters, and avoids the uncertainty of input parameters. It is very suitable for global applications and will provide a new algorithm option for SSM estimation from microwave brightness temperature.

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.

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

Joint Assimilation of Surface Temperature and L-Band Microwave Brightness Temperature in Land Data Assimilation

2013 ◽  
Vol 12 (3) ◽  
pp. vzj2012.0072 ◽  
Author(s):  
Xujun Han ◽  
Harrie-Jan Hendricks Franssen ◽  
Xin Li ◽  
Yanlin Zhang ◽  
Carsten Montzka ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Surface Temperature ◽  
Brightness Temperature ◽  
Microwave Brightness Temperature ◽  
Land Data Assimilation ◽  
L Band

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.

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