Surface Water Microwave Product Series Version 3: A Near-Real Time and 25-Year Historical Global Inundated Area Fraction Time Series From Active and Passive Microwave Remote Sensing

This letter summarizes substantial modifications made to the Surface Water Microwave Product Series (SWAMPS), a coarse-resolution (~25 km) global inundated area fraction data record derived from active and passive microwave remote sensing. SWAMPS is the most temporally dense, long-term record of global surface water dynamics publicly available today. This update improves upon the original release by: 1) incorporating a customized, consistent resampling and assembly of the Special Sensor Microwave Imager and Special Sensor Microwave Imager Sounder brightness temperature record; 2) eliminating signal contamination from ocean waters along coastlines; 3) inclusion of permanent surface waters as a component of the data record; and 4) reducing anomalous inundation retrievals over arid and semiarid regions. This update provides for the enhanced scientific utility of the full 25+ years of data records. Remaining uncertainties in the surface water fraction retrievals are principally in areas with bare, sandy surface cover and in areas with dense vegetation cover that diminishes radiometric sensitivity to surface water. This data record and associated documentation are freely available through the Alaska Satellite Facility, Fairbanks, AK, USA.

[1]  Bin Zhao,et al.  The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). , 2017, Journal of climate.

[2]  David G. Long,et al.  Vegetation studies of the Amazon basin using enhanced resolution Seasat scatterometer data , 1994, IEEE Trans. Geosci. Remote. Sens..

[3]  Fuzhong Weng,et al.  Microwave Emission and Scattering From Deserts: Theory Compared With Satellite Measurements , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Peter Bergamaschi,et al.  Three decades of global methane sources and sinks , 2013 .

[5]  Jed O. Kaplan,et al.  WETCHIMP-WSL: intercomparison of wetland methane emissions models over West Siberia , 2015 .

[6]  Kyle McDonald,et al.  Development and Evaluation of a Multi-Year Fractional Surface Water Data Set Derived from Active/Passive Microwave Remote Sensing Data , 2015, Remote. Sens..

[7]  K. Didan,et al.  MOD13C1 MODIS/Terra Vegetation Indices 16-Day L3 Global 0.05Deg CMG V006 , 2015 .

[8]  Pietro Ceccato,et al.  Utilizing Remote Sensing to Explore Environmental Factors of Visceral Leishmaniasis in South Sudan , 2014 .

[9]  J. Pekel,et al.  High-resolution mapping of global surface water and its long-term changes , 2016, Nature.

[10]  V. Brovkin,et al.  Global wetland contribution to 2000–2012 atmospheric methane growth rate dynamics , 2017 .

[11]  Makoto Saito,et al.  The Global Methane Budget: 2000–2012 , 2016 .

[12]  Maurizio Santoro,et al.  Compilation and Validation of SAR and Optical Data Products for a Complete and Global Map of Inland/Ocean Water Tailored to the Climate Modeling Community , 2017, Remote. Sens..

[13]  William R. Wieder,et al.  Regridded Harmonized World Soil Database v1.2 , 2014 .

[14]  Mark A. Trigg,et al.  Development of a global ~90m water body map using multi-temporal Landsat images , 2015, Remote Sensing of Environment.

[15]  T. Maeda,et al.  Classification of arid lands, including soil degradation and irrigated areas, based on vegetation and aridity indices , 2013 .