Skill and Global Trend Analysis of Soil Moisture from Reanalyses and Microwave Remote Sensing

AbstractIn situ soil moisture measurements from 2007 to 2010 for 196 stations from five networks across the world (United States, France, Spain, China, and Australia) are used to determine the reliability of three soil moisture products: (i) a revised version of the ECMWF Interim Re-Analysis (ERA-Interim; ERA-Land); (ii) a revised version of the Modern-Era Retrospective Analysis for Research and Applications (MERRA) reanalysis from NASA (MERRA-Land); and (iii) a new, microwave-based multisatellite surface soil moisture dataset (SM-MW). Evaluation of the time series and anomalies from a moving monthly mean shows a good performance of the three products in capturing the annual cycle of surface soil moisture and its short-term variability. On average, correlations (95% confidence interval) are 0.66 (±0.038), 0.69 (±0.038), and 0.60 (±0.061) for ERA-Land, MERRA-Land, and SM-MW. The two reanalysis products also capture the root-zone soil moisture well; on average, correlations are 0.68 (±0.035) and 0.73 (±0.03...

[1]  Lars Isaksen,et al.  Initialisation of Land Surface Variables for Numerical Weather Prediction , 2014, Surveys in Geophysics.

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

[3]  L. Isaksen,et al.  A simplified Extended Kalman Filter for the global operational soil moisture analysis at ECMWF , 2013 .

[4]  Yi Y. Liu,et al.  Global vegetation biomass change (1988–2008) and attribution to environmental and human drivers , 2013 .

[5]  Lionel Jarlan,et al.  Impact of a satellite-derived leaf area index monthly climatology in a global numerical weather prediction model , 2013 .

[6]  Gianpaolo Balsamo,et al.  A bare ground evaporation revision in the ECMWF land-surface scheme: evaluation of its impact using ground soil moisture and satellite microwave data , 2012 .

[7]  Lars Isaksen,et al.  Soil Moisture Analyses at ECMWF: Evaluation Using Global Ground-Based In Situ Observations , 2012 .

[8]  Yi Y. Liu,et al.  Evaluating global trends (1988–2010) in harmonized multi‐satellite surface soil moisture , 2012 .

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

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

[11]  Zhongbo Su,et al.  Maqu network for validation of satellite-derived soil moisture products , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[12]  A. B. Smith,et al.  The Murrumbidgee soil moisture monitoring network data set , 2012 .

[13]  Witold F. Krajewski,et al.  Analyses of a long‐term, high‐resolution radar rainfall data set for the Baltimore metropolitan region , 2012 .

[14]  Y. Kerr,et al.  Evaluation of remotely sensed and modelled soil moisture products using global ground-based in situ observations , 2012 .

[15]  José Martínez-Fernández,et al.  Validation of the SMOS L2 Soil Moisture Data in the REMEDHUS Network (Spain) , 2012, IEEE Transactions on Geoscience and Remote Sensing.

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

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

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

[19]  M. Bosilovich,et al.  The Effect of Satellite Observing System Changes on MERRA Water and Energy Fluxes , 2011 .

[20]  Y. Kerr,et al.  Comparing soil moisture retrievals from SMOS and ASCAT over France , 2011 .

[21]  I. Dharssi,et al.  Operational assimilation of ASCAT surface soil wetness at the Met Office , 2011 .

[22]  Yaoming Ma,et al.  The Tibetan plateau observatory of plateau scale soil moisture and soil temperature, Tibet - Obs, for quantifying uncertainties in coarse resolution satellite and model products , 2011 .

[23]  S. Schubert,et al.  MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications , 2011 .

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

[25]  Jean-Pierre Wigneron,et al.  Sensitivity of Passive Microwave Observations to Soil Moisture and Vegetation Water Content: L-Band to W-Band , 2011, IEEE Transactions on Geoscience and Remote Sensing.

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

[27]  Tao Zhang,et al.  Was there a basis for anticipating the 2010 Russian heat wave? , 2011 .

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

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

[30]  W. Wagner,et al.  Cross-evaluation of modelled and remotely sensed surface soil moisture with in situ data in southwestern France , 2010 .

[31]  S. Seneviratne,et al.  Recent decline in the global land evapotranspiration trend due to limited moisture supply , 2010, Nature.

[32]  Kelly Elder,et al.  An Improved Snow Scheme for the ECMWF Land Surface Model: Description and Offline Validation , 2010 .

[33]  Yann Kerr,et al.  The SMOS Mission: New Tool for Monitoring Key Elements ofthe Global Water Cycle , 2010, Proceedings of the IEEE.

[34]  Dick Dee,et al.  Low‐frequency variations in surface atmospheric humidity, temperature, and precipitation: Inferences from reanalyses and monthly gridded observational data sets , 2010 .

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

[36]  D. Verdon‐Kidd,et al.  Nature and causes of protracted droughts in southeast Australia: Comparison between the Federation, WWII, and Big Dry droughts , 2009 .

[37]  Paul Berrisford,et al.  The ERA-Interim Archive , 2009 .

[38]  B. Hurk,et al.  A Revised Hydrology for the ECMWF Model: Verification from Field Site to Terrestrial Water Storage and Impact in the Integrated Forecast System , 2009 .

[39]  C. Albergel,et al.  From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model simulations , 2008 .

[40]  C. Albergel,et al.  An evaluation of ASCAT surface soil moisture products with in-situ observations in Southwestern France , 2008 .

[41]  E. Wood,et al.  Global Trends and Variability in Soil Moisture and Drought Characteristics, 1950–2000, from Observation-Driven Simulations of the Terrestrial Hydrologic Cycle , 2008 .

[42]  T. McMahon,et al.  Updated world map of the Köppen-Geiger climate classification , 2007 .

[43]  W. Wagner,et al.  Initial soil moisture retrievals from the METOP‐A Advanced Scatterometer (ASCAT) , 2007 .

[44]  Jean-Christophe Calvet,et al.  In situ soil moisture observations for the CAL/VAL of SMOS: the SMOSMANIA network , 2007, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[45]  A. Simmons,et al.  The ECMWF operational implementation of four‐dimensional variational assimilation. I: Experimental results with simplified physics , 2007 .

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

[47]  B. Rudolf,et al.  World Map of the Köppen-Geiger climate classification updated , 2006 .

[48]  Klaus Scipal,et al.  Validation of ERS scatterometer‐derived soil moisture data in the central part of the Duero Basin, Spain , 2005 .

[49]  B. Hurk,et al.  The Torne-Kalix PILPS 2(e) experiment as a test bed for modifications to the ECMWF land surface scheme , 2003 .

[50]  D. Burn,et al.  Detection of hydrologic trends and variability , 2002 .

[51]  K. Taylor.  Summarizing multiple aspects of model performance in a single diagram , 2001 .

[52]  Jean-François Mahfouf,et al.  Evaluation of the Optimum Interpolation and Nudging Techniques for Soil Moisture Analysis Using FIFE Data , 2000 .

[53]  Jean-François Mahfouf,et al.  Analysis of Soil Moisture from Near-Surface Parameters: A Feasibility Study , 1991 .

[54]  T. Schmugge.  Remote Sensing of Soil Moisture: Recent Advances , 1983, IEEE Transactions on Geoscience and Remote Sensing.

[55]  R. Hirsch,et al.  Techniques of trend analysis for monthly water quality data , 1982 .

[56]  P. Sen.  Estimates of the Regression Coefficient Based on Kendall's Tau , 1968 .

[57]  G. Schneider.  Landolt-Börnstein: Zahlenwerte und Funktionen aus Physik, Chemie, Astronomie, Geophysik und Technik , 1963 .

[58]  Maurice G. Kendall,et al.  Rank Correlation Methods , 1949 .

[59]  H. Belshaw,et al.  The Food and Agriculture Organization of the United Nations , 1947, International Organization.

[60]  H. B. Mann.  Nonparametric Tests Against Trend , 1945 .

[61]  Hannah L. Cloke,et al.  Land: a global land-surface reanalysis based on ERA-interim meteorological forcing , 2012 .

[62]  Roberto Buizza,et al.  The new ECMWF seasonal forecast system (system 4) , 2011 .

[63]  Ranz,et al.  Observed and projected climate shifts 1901 – 2100 depicted by world maps of the Köppen-Geiger climate classification , 2010 .

[64]  A. K. Betts,et al.  O ine validation of the ERA 40 surface scheme , 2000 .

[65]  W. Köppen,et al.  Versuch einer Klassifikation der Klimate : vorzugsweise nach ihren Beziehungen zur Pflanzenwelt , 1900 .

[66]  J. Mahfouf,et al.  The ECMWF operational implementation of four‐dimensional variational assimilation. II: Experimental results with improved physics , 2022 .