An analysis of spatiotemporal variations of soil and vegetation moisture from a 29‐year satellite‐derived data set over mainland Australia

The spatiotemporal behavior of soil and vegetation moisture over mainland Australia was analyzed using passive microwave observations by four satellites going back to late 1978. Differences in measurement specifications prevented merging the data directly. A continuous product was developed for Australia by scaling percentiles of the cumulative moisture distribution within each grid cell to the percentiles of a reference sensor. The coefficient of correlation and root‐mean‐square error between rescaled values and the reference generally suggest good agreement. Using the merged data product, a strong El Niño–Southern Oscillation signal in near‐surface hydrology across Australia was confirmed. Spatial patterns of trends in annual averages show that western and northwestern Australia have experienced an increase in vegetation moisture content, while the east and southeast experienced a decrease. Soil moisture showed a similar spatial pattern but with larger regions experiencing a decrease. This could be explained by decreasing rainfall and increasing potential evapotranspiration during the extended winter period (May–September). The results give us reasonable confidence in the time series of soil and vegetation moisture derived by the scaling method developed in this study. Development of a global data set along these lines should enable better estimation of hydrological variables and should increase understanding of the impacts of ocean circulations on terrestrial hydrology and vegetation dynamics.

[1]  N. Nicholls,et al.  Seasonal Relationships between Australian Rainfall and the Southern Oscillation , 1983 .

[2]  Victor S. Frost,et al.  Probability of Error And Radiometric Resolution for Target Discrimination in Radar Images , 1984, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Ann Henderson-Sellers,et al.  Biosphere-atmosphere Transfer Scheme (BATS) for the NCAR Community Climate Model , 1986 .

[4]  C. Ropelewski,et al.  Global and Regional Scale Precipitation Patterns Associated with the El Niño/Southern Oscillation , 1987 .

[5]  L. Ogallo.  Relationships between seasonal rainfall in East Africa and the Southern Oscillation , 1988 .

[6]  Thomas J. Jackson,et al.  Attenuation of soil microwave emission by corn and soybeans at 1.4 and 5 GHz , 1990 .

[7]  D. Rosenfeld,et al.  Climatologically tuned reflectivity-rain rate relations and links to area-time integrals , 1990 .

[8]  T. Schmugge,et al.  Vegetation effects on the microwave emission of soils , 1991 .

[9]  Eric F. Wood,et al.  Global Scale Hydrology: Advances in Land Surface Modeling , 1991 .

[10]  D. Lettenmaier,et al.  A simple hydrologically based model of land surface water and energy fluxes for general circulation models , 1994 .

[11]  M. Wigmosta,et al.  A distributed hydrology-vegetation model for complex terrain , 1994 .

[12]  T. J. Schmugge,et al.  Surface soil moisture measurement with microwave radiometry , 1995 .

[13]  Y. Kerr,et al.  Soil moisture and temperature profile effects on microwave emission at low frequencies , 1995 .

[14]  E. Njoku,et al.  Passive microwave remote sensing of soil moisture , 1996 .

[15]  A. Dai,et al.  Surface Observed Global Land Precipitation Variations during 1900-88 , 1997 .

[16]  Statistical Adjustment of Satellite Microwave Monthly Rainfall Estimates over Amazonia , 1999 .

[17]  Paolo Ferrazzoli,et al.  A parametric study on passive and active microwave observations over a soybean crop , 1999, IEEE Trans. Geosci. Remote. Sens..

[18]  Frank J. Wentz,et al.  Precise climate monitoring using complementary satellite data sets , 2000, Nature.

[19]  Chris Derksen,et al.  Influence of Sensor Overpass Time on Passive Microwave-Derived Snow Cover Parameters , 2000 .

[20]  Niko E. C. Verhoest,et al.  The importance of the spatial patterns of remotely sensed soil moisture in the improvement of discharge predictions for small-scale basins through data assimilation , 2001 .

[21]  Jeffrey P. Walker,et al.  A methodology for surface soil moisture and vegetation optical depth retrieval using the microwave polarization difference index , 2001, IEEE Trans. Geosci. Remote. Sens..

[22]  Paul R. Houser,et al.  A methodology for initializing soil moisture in a global climate model: Assimilation of near‐surface soil moisture observations , 2001 .

[23]  Dennis P. Lettenmaier,et al.  Variable infiltration capacity cold land process model updates , 2003 .

[24]  M. Owe,et al.  Further validation of a new methodology for surface moisture and vegetation optical depth retrieval , 2003 .

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

[26]  Jeffrey P. Walker,et al.  THE GLOBAL LAND DATA ASSIMILATION SYSTEM , 2004 .

[27]  Richard de Jeu,et al.  Analytical derivation of the vegetation optical depth from the microwave polarization difference index , 2005, IEEE Geoscience and Remote Sensing Letters.

[28]  Thomas J. Jackson,et al.  Microwave remote sensing of soil moisture: evaluation of the TRMM microwave imager (TMI) satellite for the Little River Watershed Tifton, Georgia , 2005 .

[29]  Michael H. Glantz,et al.  ENSO as an Integrating Concept in Earth Science , 2006, Science.

[30]  Albert I. J. M. van Dijk,et al.  TRMM‐TMI satellite observed soil moisture and vegetation density (1998–2005) show strong connection with El Niño in eastern Australia , 2007 .

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

[32]  R. Koster,et al.  Comparison and assimilation of global soil moisture retrievals from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR‐E) and the Scanning Multichannel Microwave Radiometer (SMMR) , 2007 .

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