Development of an operational land water mask for MODIS Collection 6, and influence on downstream data products

ABSTRACT Data from the Moderate Resolution Imaging Spectro-radiometer (MODIS) on-board the Earth Observing System Terra and Aqua satellites are processed using a land water mask to determine when an algorithm no longer needs to be run or when an algorithm needs to follow a different pathway. Entering the fourth reprocessing (Collection 6 (C6)) the MODIS team replaced the 1 km water mask with a 500 m water mask for improved representation of the continental surfaces. The new water mask represents more small water bodies for an overall increase in water surface from 1% to 2% of the continental surface. While this is still a small fraction of the overall global surface area the increase is more dramatic in certain areas such as the Arctic and Boreal regions where there are dramatic increases in water surface area in the new mask. MODIS products generated by the on-going C6 reprocessing using the new land water mask show significant impact in areas with high concentrations of change in the land water mask. Here differences between the Collection 5 (C5) and C6 water masks and the impact of these differences on the MOD04 aerosol product and the MOD11 land surface temperature product are shown.

[1]  L. Remer,et al.  The Collection 6 MODIS aerosol products over land and ocean , 2013 .

[2]  D. Tanré,et al.  Remote sensing of aerosol properties over oceans using the MODIS/EOS spectral radiances , 1997 .

[3]  E. Vermote,et al.  Second‐generation operational algorithm: Retrieval of aerosol properties over land from inversion of Moderate Resolution Imaging Spectroradiometer spectral reflectance , 2007 .

[4]  D. Roy,et al.  An overview of MODIS Land data processing and product status , 2002 .

[5]  A. Smirnov,et al.  AERONET-a federated instrument network and data archive for aerosol Characterization , 1998 .

[6]  Jeff Dozier,et al.  A generalized split-window algorithm for retrieving land-surface temperature from space , 1996, IEEE Trans. Geosci. Remote. Sens..

[7]  N. C. Hsu,et al.  Implications of MODIS bow-tie distortion on aerosol optical depth retrievals, and techniques for mitigation , 2015 .

[8]  Jin Huang,et al.  Enhanced Deep Blue aerosol retrieval algorithm: The second generation , 2013 .

[9]  David P. Roy,et al.  MODIS Land Data Products: Generation, Quality Assurance and Validation , 2010 .

[10]  James Voelz,et al.  Chapter 25 , 2011, Wide Neighborhoods.

[11]  V. Caselles,et al.  A split‐window algorithm for land surface temperature from advanced very high resolution radiometer data: Validation and algorithm comparison , 1997 .

[12]  Robert C. Levy,et al.  MODIS Collection 6 aerosol products: Comparison between Aqua's e‐Deep Blue, Dark Target, and “merged” data sets, and usage recommendations , 2014 .

[13]  Andrew M. Sayer,et al.  Validation and uncertainty estimates for MODIS Collection 6 “Deep Blue” aerosol data , 2013 .

[14]  John R. Townshend,et al.  A new global raster water mask at 250 m resolution , 2009, Int. J. Digit. Earth.

[15]  Alan H. Strahler,et al.  Global land-water mask derived from MODIS Nadir BRDF-adjusted reflectances (NBAR) and the MODIS land cover algorithm , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[16]  P. Micklin.  The Aral Sea Disaster , 2007 .