A framework for combining multiple soil moisture retrievals based on maximizing temporal correlation

A method for combining two microwave satellite soil moisture products by maximizing the temporal correlation with a reference data set has been developed. The method was applied to two global soil moisture data sets, Japan Aerospace Exploration Agency (JAXA) and Land Parameter Retrieval Model (LPRM), retrieved from the Advanced Microwave Scanning Radiometer 2 observations for the period 2012–2014. A global comparison revealed superior results of the combined product compared to the individual products against the reference data set of ERA-Interim volumetric water content. The global mean temporal correlation coefficient of the combined product with this reference was 0.52 which outperforms the individual JAXA (0.35) as well as the LPRM (0.45) product. Additionally, the performance was evaluated against in situ observations from the International Soil Moisture Network. The combined data set showed a significant improvement in temporal correlation coefficients in the validation compared to JAXA and minor improvements for the LPRM product.

[1]  Wouter Dorigo,et al.  A Preliminary Study toward Consistent Soil Moisture from AMSR2 , 2015 .

[2]  Yi Y. Liu,et al.  Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals , 2011 .

[3]  A. Robock,et al.  The International Soil Moisture Network: a data hosting facility for global in situ soil moisture measurements , 2011 .

[4]  Wouter Dorigo,et al.  Characterizing Coarse‐Scale Representativeness of in situ Soil Moisture Measurements from the International Soil Moisture Network , 2013 .

[5]  W. Wagner,et al.  Soil moisture from operational meteorological satellites , 2007 .

[6]  Wade T. Crow,et al.  Correcting rainfall using satellite‐based surface soil moisture retrievals: The Soil Moisture Analysis Rainfall Tool (SMART) , 2011 .

[7]  Wolfgang Wagner,et al.  Inter-comparison of microwave satellite soil moisture retrievals over the Murrumbidgee Basin, southeast Australia , 2013 .

[8]  Diego G. Miralles,et al.  Reconciling spatial and temporal soil moisture effects on afternoon rainfall , 2015, Nature Communications.

[9]  Rolf H. Reichle,et al.  Assimilation of passive and active microwave soil moisture retrievals , 2012 .

[10]  R. Koster,et al.  Assessment and Enhancement of MERRA Land Surface Hydrology Estimates , 2011 .

[11]  A. Timmermann.  Chapter 4 Forecast Combinations , 2006 .

[12]  W. Wagner,et al.  Soil moisture estimation through ASCAT and AMSR-E sensors: An intercomparison and validation study across Europe , 2011 .

[13]  R. Clemen.  Combining forecasts: A review and annotated bibliography , 1989 .

[14]  R. Jeu,et al.  Multisensor historical climatology of satellite‐derived global land surface moisture , 2008 .

[15]  Matthias Drusch,et al.  Global Automated Quality Control of In Situ Soil Moisture Data from the International Soil Moisture Network , 2013 .

[16]  W. Wagner,et al.  Soil as a natural rain gauge: Estimating global rainfall from satellite soil moisture data , 2014 .

[17]  Wade T. Crow,et al.  The Optimality of Potential Rescaling Approaches in Land Data Assimilation , 2013 .

[18]  Randal D. Koster,et al.  Bias reduction in short records of satellite soil moisture , 2004 .

[19]  C. Taylor,et al.  Afternoon rain more likely over drier soils , 2012, Nature.

[20]  Ashish Sharma,et al.  Improved spatial prediction: A combinatorial approach , 2013 .

[21]  Thomas R. H. Holmes,et al.  An evaluation of AMSR–E derived soil moisture over Australia , 2009 .

[22]  J. M. Bates,et al.  The Combination of Forecasts , 1969 .

[23]  W. Wagner,et al.  Global Soil Moisture Patterns Observed by Space Borne Microwave Radiometers and Scatterometers , 2008 .

[24]  Keiji Imaoka,et al.  Improvement of the AMSR-E Algorithm for Soil Moisture Estimation by Introducing a Fractional Vegetation Coverage Dataset Derived from MODIS Data , 2009 .

[25]  Ashish Sharma,et al.  Global Sea Surface Temperature Forecasts Using an Improved Multimodel Approach , 2014 .

[26]  Yi Y. Liu,et al.  Trend-preserving blending of passive and active microwave soil moisture retrievals , 2012 .

[27]  Seokhyeon Kim,et al.  A global comparison of alternate AMSR2 soil moisture products: Why do they differ? , 2015 .

[28]  Wade T. Crow,et al.  A Quasi-Global Evaluation System for Satellite-Based Surface Soil Moisture Retrievals , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[29]  R. Jeu,et al.  Land surface temperature from Ka band (37 GHz) passive microwave observations , 2009 .

[30]  Robert M. Parinussa,et al.  Error Estimates for Near-Real-Time Satellite Soil Moisture as Derived From the Land Parameter Retrieval Model , 2011, IEEE Geoscience and Remote Sensing Letters.

[31]  W. Wagner,et al.  Evaluation of the ESA CCI soil moisture product using ground-based observations , 2015 .

[32]  Wade T. Crow,et al.  The impact of land surface temperature on soil moisture anomaly detection from passive microwave observations , 2011 .

[33]  W. Wagner,et al.  Fusion of active and passive microwave observations to create an Essential Climate Variable data record on soil moisture , 2012 .

[34]  J. Thepaut,et al.  The ERA‐Interim reanalysis: configuration and performance of the data assimilation system , 2011 .

[35]  Luca Brocca,et al.  ASCAT soil wetness index validation through in situ and modeled soil moisture data in central Italy , 2010 .

[36]  T. Mo,et al.  A model for microwave emission from vegetation‐covered fields , 1982 .

[37]  Derek Karssenberg,et al.  The suitability of remotely sensed soil moisture for improving operational flood forecasting , 2013 .

[38]  Jeffrey P. Walker,et al.  Upscaling sparse ground‐based soil moisture observations for the validation of coarse‐resolution satellite soil moisture products , 2012 .

[39]  Wade T. Crow,et al.  Performance Metrics for Soil Moisture Retrievals and Application Requirements , 2009 .

[40]  Yi Y. Liu,et al.  Error characterisation of global active and passive microwave soil moisture datasets. , 2010 .

[41]  C. Granger,et al.  Improved methods of combining forecasts , 1984 .

[42]  Y. Kerr,et al.  Soil moisture active and passive microwave products : intercomparison and evaluation over a Sahelian site , 2009 .