scholarly journals Potential of bias correction for downscaling passive microwave and soil moisture data

2015 ◽  
Vol 120 (13) ◽  
pp. 6460-6479 ◽  
Author(s):  
Kurt C. Kornelsen ◽  
Michael H. Cosh ◽  
Paulin Coulibaly
Keyword(s):  
Soil Moisture ◽  
Bias Correction ◽  
Passive Microwave ◽  
Soil Moisture Data

scholarly journals A comparison of methods for a priori bias correction in soil moisture data assimilation

2012 ◽  
Vol 48 (3) ◽  
Author(s):  
Sujay V. Kumar ◽  
Rolf H. Reichle ◽  
Kenneth W. Harrison ◽  
Christa D. Peters-Lidard ◽  
Soni Yatheendradas ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Bias Correction ◽  
A Priori ◽  
Comparison Of Methods ◽  
Soil Moisture Data

Passive Microwave Remote Sensing Soil Moisture Data in Agricultural Drought Monitoring: Application in Northeastern China

Water ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 2777
Author(s):  
Tao Cheng ◽  
Siyang Hong ◽  
Bensheng Huang ◽  
Jing Qiu ◽  
Bikui Zhao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Pearson Correlation ◽  
Microwave Remote Sensing ◽  
Northeastern China ◽  
Passive Microwave ◽  
Agricultural Drought ◽  
Passive Microwave Remote Sensing ◽  
Soil Moisture Data

Drought is the costliest disaster around the world and in China as well. Northeastern China is one of China’s most important major grain producing areas. Frequent droughts have harmed the agriculture of this region and further threatened national food security. Therefore, the timely and effective monitoring of drought is extremely important. In this study, the passive microwave remote sensing soil moisture data, i.e., the SMOS soil moisture (SMOS-SM) product, was compared to several in situ meteorological indices through Pearson correlation analysis to assess the performance of SMOS-SM in monitoring drought in northeastern China. Then, maps based on SMOS-SM and in situ indices were created for July from 2010 to 2015 to identify the spatial pattern of drought distributions. Our results showed that the SMOS-SM product had relatively high correlation with in situ indices, especially SPI and SPEI values of a nine-month scale for the growing season. The drought patterns shown on maps generated from SPI-9, SPEI-9 and sc-PDSI were also successfully captured using the SMOS-SM product. We found that the SMOS-SM product effectively monitored drought patterns in northeastern China, and this capacity would be enhanced when field capacity information became available.

scholarly journals Error characterisation of global active and passive microwave soil moisture data sets

2010 ◽  
Vol 7 (4) ◽  
pp. 5621-5645 ◽  
Author(s):  
W. A. Dorigo ◽  
K. Scipal ◽  
R. M. Parinussa ◽  
Y. Y. Liu ◽  
W. Wagner ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Prediction Models ◽  
Weather Prediction ◽  
Passive Microwave ◽  
Data Sets ◽  
Independent Data ◽  
Vegetation Density ◽  
Data Set ◽  
Soil Moisture Data

Abstract. Understanding the error structures of remotely sensed soil moisture products is essential for correctly interpreting observed variations and trends in the data or assimilating them in hydrological or numerical weather prediction models. Nevertheless, a spatially coherent assessment of the quality of the various globally available data sets is often hampered by the limited availability over space and time of reliable in-situ measurements. This study explores the triple collocation error estimation technique for assessing the relative quality of several globally available soil moisture products from active (ASCAT) and passive (AMSR-E and SSM/I) microwave sensors. The triple collocation technique is a powerful tool to estimate the root mean square error while simultaneously solving for systematic differences in the climatologies of a set of three independent data sources. In addition to the scatterometer and radiometer data sets, we used the ERA-Interim and GLDAS-NOAH reanalysis soil moisture data sets as a third, independent reference. The prime objective is to reveal trends in uncertainty related to different observation principles (passive versus active), the use of different frequencies (C-, X-, and Ku-band) for passive microwave observations, and the choice of the independent reference data set (ERA-Interim versus GLDAS-NOAH). The results suggest that the triple collocation method provides realistic error estimates. Observed spatial trends agree well with the existing theory and studies on the performance of different observation principles and frequencies with respect to land cover and vegetation density. In addition, if all theoretical prerequisites are fulfilled (e.g. a sufficiently large number of common observations is available and errors of the different data sets are uncorrelated) the errors estimated for the remote sensing products are hardly influenced by the choice of the third independent data set. The results obtained in this study can help us in developing adequate strategies for the combined use of various scatterometer and radiometer-based soil moisture data sets, e.g. for improved flood forecast modelling or the generation of superior multi-mission long-term soil moisture data sets.

scholarly journals Impact of ASCAT Soil Moisture Assimilation on Regional Precipitation Forecasts: A Case Study for Austria

2014 ◽  
Vol 142 (4) ◽  
pp. 1525-1541 ◽  
Author(s):  
Stefan Schneider ◽  
Yong Wang ◽  
Wolfgang Wagner ◽  
Jean-Francois Mahfouf
Keyword(s):  
Soil Moisture ◽  
Bias Correction ◽  
Precipitation Forecast ◽  
Convective Precipitation ◽  
Soil Moisture Data ◽  
In Situ Data ◽  
The Impact ◽  
Forecast Quality

Abstract In this study, remotely sensed soil moisture data from the Advanced Scatterometer (ASCAT) on board the Meteorological Operational (MetOp) series of satellites are assimilated in the regional forecasting model, Aire Limitée Adaptation Dynamique Développement International (ALADIN-Austria), using a simplified extended Kalman filter. A pointwise bias correction method is applied to the ASCAT data as well as quality flags prepared by the data provider. The ASCAT assimilation case study is performed over central Europe during a 1-month period in July 2009. Forecasts of those assimilation experiments are compared to the control run provided by the operational ALADIN version of the Austrian Met Service, Zentralanstalt für Meteorologie und Geodynamik (ZAMG). Forecasts are furthermore verified versus in situ data. For a single-day case study the ability of the approach to improve precipitation forecast quality in the presence of high impact weather is demonstrated. Results show that 1) based on a one station in situ data evaluation, soil moisture analysis is improved, compared to the operational analysis, when ASCAT soil moisture data is assimilated; 2) pointwise bias correction of the satellite data is beneficial for forecast quality; 3) screen level parameter forecasts can be slightly improved as a result of this approach; and 4) convective precipitation forecast is improved over flatland for the investigation period while over mountainous regions the impact is neutral.

Impact of Bias Correction Methods on Estimation of Soil Moisture When Assimilating Active and Passive Microwave Observations

2016 ◽  
Vol 54 (1) ◽  
pp. 262-278 ◽  
Author(s):  
Alejandro Monsivais-Huertero ◽  
Jasmeet Judge ◽  
Susan Steele-Dunne ◽  
Pang-Wei Liu
Keyword(s):  
Soil Moisture ◽  
Bias Correction ◽  
Passive Microwave
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