On the Airborne Spatial Coverage Requirement for Microwave Satellite Validation

With the recent launch of the Soil Moisture and Ocean Salinity (SMOS) mission, the passive microwave remote-sensing community is currently planning and undertaking airborne validation campaigns. Given the financial and logistical constraints on the size of validation area that can be covered by airborne simulators and the experiments underway that cover only a part of a satellite footprint, timely and scientifically sound advice on fractional footprint coverage requirements by campaigns for these low-resolution sensors is of paramount importance. Using high-resolution airborne data from an extensive airborne campaign in Southeast Australia, the fractional coverage requirement for L-band passive microwave satellite missions is assessed using a subsampling technique of flight lines through a passive microwave footprint. It is shown that minimum 50% coverage of the total footprint size will typically be required, given a spatial variability value of 20 K at 1-km resolution, to ensure that the footprint mean is estimated with an expected sampling error of less than 4 K, which is the design sensitivity of SMOS.

[1]  Edward J. Kim,et al.  The NAFE'06 data set: towards soil moisture retrieval at intermediate resolution , 2008 .

[2]  Iliana Mladenova,et al.  Validation of the ASAR Global Monitoring Mode Soil Moisture Product Using the NAFE'05 Data Set , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Yann Kerr,et al.  Towards validation of SMOS using airborne and ground data over the Murrumbidgee Catchment , 2009 .

[4]  Edward J. Kim,et al.  Evaluation of the SMOS L-MEB passive microwave soil moisture retrieval algorithm. , 2009 .

[5]  J. R. Eagleman,et al.  Remote sensing of soil moisture by a 21-cm passive radiometer. [onboard Skylab] , 1976 .

[6]  Jeffrey P. Walker,et al.  Soil moisture retrievals at L-band using a two-step inversion approach (COSMOS/NAFE'05 Experiment) , 2009 .

[7]  Jeffrey P. Walker,et al.  Towards deterministic downscaling of SMOS soil moisture using MODIS derived soil evaporative efficiency , 2008 .

[8]  J. Martínez-Fernández,et al.  Mean soil moisture estimation using temporal stability analysis , 2005 .

[9]  M. Drusch,et al.  Comparing ERA-40-based L-band brightness temperatures with skylab observations: a calibration/validation study using the community microwave emission model. , 2009 .

[10]  Yann Kerr,et al.  SMOS Validation and the COSMOS Campaigns , 2008, IEEE Transactions on Geoscience and Remote Sensing.

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

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

[13]  G. Ryan,et al.  ATLAS OF AUSTRALIAN SOILS , 1972 .

[14]  Edward J. Kim,et al.  The NAFE'05/CoSMOS Data Set: Toward SMOS Soil Moisture Retrieval, Downscaling, and Assimilation , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[15]  David M. Le Vine,et al.  Aquarius: An Instrument to Monitor Sea Surface Salinity From Space , 2007, IEEE Transactions on Geoscience and Remote Sensing.

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

[17]  Niels Skou,et al.  Polarimetric radiometer configurations: potential accuracy and sensitivity , 1999, IEEE Trans. Geosci. Remote. Sens..

[18]  Thomas J. Jackson,et al.  Multiple resolution analysis of L-band brightness temperature for soil moisture , 2001, IEEE Trans. Geosci. Remote. Sens..