Impact of Moisture Distribution Within the Sensing Depth on L- and C-Band Emission in Sandy Soils

The performances of the soil moisture retrieval and assimilation algorithms using microwave observations rely on realistic estimates of brightness temperatures (TB) from microwave emission models. This study identifies circumstances when current models fail to reliably relate near-surface soil moisture to an observed TB at L-band; offers a plausible explanation of the physical cause of these failures; and recommends improvements needed so that L-band observations can provide reliable estimates of soil moisture, more universally. Physically consistent soil parameters and moisture at the surface were estimated by using dual-polarized C-band observations during an intensive field experiment, for an irrigation event and subsequent drydown. These derived parameters were used in conjunction with the in situ moisture in deeper layers and different moisture profiles within the moisture sensing depth to obtain estimates of H-pol TB at L-band, that provided best matches with the observed TB. The general assumptions of linear moisture distribution, with uniform or exponentially decaying weighting functions provided realistic TB during the later stages of the drydown. However, the RMSDs of the TBs were up to 10.37 K during the wet period. In addition, the use of one value of moisture representing the entire moisture sensing depth during this highly dynamic stage of the drydown provides unrealistic estimates of emissivity, and hence, TB at L-band. This study recommends use of a hydrological model to provide dynamic, realistic soil moisture profiles within the sensing depth and also an improved emissivity model that utilizes these detailed profiles for estimating TB.

[1]  Y. Kerr,et al.  Effective soil moisture sampling depth of L-band radiometry: A case study , 2010 .

[2]  Yann Kerr,et al.  A simple parameterization of the L-band microwave emission from rough agricultural soils , 2001, IEEE Trans. Geosci. Remote. Sens..

[3]  Y. Kerr,et al.  L-band Microwave Emission of the Biosphere (L-MEB) Model: Description and calibration against experimental data sets over crop fields , 2007 .

[4]  T. Mo,et al.  Calculations of the Microwave Brightness Temperature of Rough Soil Surfaces: Bare Field , 1987, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Valery L. Mironov,et al.  Physically and Mineralogically Based Spectroscopic Dielectric Model for Moist Soils , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Jeffrey P. Walker,et al.  Improved Understanding of Soil Surface Roughness Parameterization for L-Band Passive Microwave Soil Moisture Retrieval , 2009, IEEE Geoscience and Remote Sensing Letters.

[7]  Malcolm Davidson,et al.  Dense Temporal Series of C- and L-band SAR Data for Soil Moisture Retrieval Over Agricultural Crops , 2011, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[8]  W. Marsden.  I and J , 2012 .

[9]  T. Mo,et al.  A Parameterization of the Effect of Surface Roughness on Microwave Emission , 1987, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Mike Schwank,et al.  Topsoil Structure Influencing Soil Water Retrieval by Microwave Radiometry , 2004 .

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

[12]  J. D. Tarpley,et al.  Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model , 2003 .

[13]  Yann Kerr,et al.  Global Simulation of Brightness Temperatures at 6.6 and 10.7 GHz Over Land Based on SMMR Data Set Analysis , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Arnaud Mialon,et al.  Evaluating an Improved Parameterization of the Soil Emission in L-MEB , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[15]  Urs Wegmüller,et al.  Rough bare soil reflectivity model , 1999, IEEE Trans. Geosci. Remote. Sens..

[16]  F. Ulaby,et al.  Microwave Dielectric Behavior of Wet Soil-Part II: Dielectric Mixing Models , 1985, IEEE Transactions on Geoscience and Remote Sensing.

[17]  W. J. Burke,et al.  Comparison of 2.8‐ and 21‐cm microwave radiometer observations over soils with emission model calculations , 1979 .

[18]  Thomas J. Jackson,et al.  Soil moisture retrieval from AMSR-E , 2003, IEEE Trans. Geosci. Remote. Sens..

[19]  Qin Li,et al.  Emission of rough surfaces calculated by the integral equation method with comparison to three-dimensional moment method simulations , 2003, IEEE Trans. Geosci. Remote. Sens..

[20]  J. Judge,et al.  Estimation of energy and moisture fluxes for dynamic vegetation using coupled SVAT and crop‐growth models , 2008 .

[21]  Jean-Pierre Wigneron,et al.  Frequency and angular variations of land surface microwave emissivities: can we estimate SSM/T and AMSU emissivities from SSM/I emissivities? , 2000, IEEE Trans. Geosci. Remote. Sens..

[22]  Richard K. Moore,et al.  Microwave Remote Sensing, Active and Passive , 1982 .

[23]  Thomas J. Schmugge,et al.  A comparison of radiative transfer models for predicting the microwave emission from soils , 1981 .

[24]  Arnaud Mialon,et al.  Comparison of Two Bare-Soil Reflectivity Models and Validation With L-Band Radiometer Measurements , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[25]  J. Judge.  Microwave Remote Sensing of Soil Water: Recent Advances and Issues , 2007 .

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

[27]  Jiancheng Shi,et al.  The Soil Moisture Active Passive (SMAP) Mission , 2010, Proceedings of the IEEE.

[28]  A. Fung.  Microwave Scattering and Emission Models and their Applications , 1994 .

[29]  Wade T. Crow,et al.  An adaptive ensemble Kalman filter for soil moisture data assimilation , 2007 .

[30]  Yang Du,et al.  Sensitivity to soil moisture by active and passive microwave sensors , 2000, IEEE Trans. Geosci. Remote. Sens..

[31]  Masanobu Shimada,et al.  An Evaluation of the ALOS PALSAR L-Band Backscatter—Above Ground Biomass Relationship Queensland, Australia: Impacts of Surface Moisture Condition and Vegetation Structure , 2010, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[32]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[33]  Juan Carlos Fernandez-Diaz,et al.  Characterization of surface roughness of bare agricultural soils using LiDAR , 2010 .

[34]  Dharmendra Singh,et al.  A Fusion Approach to Retrieve Soil Moisture With SAR and Optical Data , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.