Limits of space-based remote sensing for methane source characterization

This analysis examines the ability of a space-based instrument to identify and quantify methane sources in the presence of temperature, humidity, and albedo uncertainty. Thus, the objective is to quantify in the context of the NASA Earth Observing System (EOS) the synergistic benefit of simultaneous observation of the Earth's surface with atmospheric attributes. The analysis is illustrative of the type of examination that should inform remote sensing policy and system configuration decisions. The retrieval technique considered is linear inversion of near-IR spectral signals. The anticipated range of methane mixing ratio enhancement due to sources at the Earth's surface is compared to the detection limit of the space-based instrument. >

[1]  Kevin E. Trenberth,et al.  An Evaluation and Intercomparison of Global Analyses from the National Meteorological Center and the European Centre for Medium Range Weather Forecasts , 1988 .

[2]  Randy E. Geiger Experimental Design and Analysis of M1A1 Commander/Gunner Performance during CONOPS (Continuous Operations) Using the U-COFT (Unit Conduct of Fire Trainer) , 1989 .

[3]  Yoram J. Kaufman,et al.  Remote sensing of water vapor in the near IR from EOS/MODIS , 1992, IEEE Trans. Geosci. Remote. Sens..

[4]  V. Zuev,et al.  Statistical Models of the Temperature and Gaseous Components of the Atmosphere , 1987 .

[5]  M. W. Smith,et al.  Bandpass optimization for total column measurements of atmospheric CO and CH/sub 4/ using a length modulated gas filter correlation radiometer , 1994, Proceedings of IGARSS '94 - 1994 IEEE International Geoscience and Remote Sensing Symposium.

[6]  D. I. Sebacher,et al.  Methane flux from northern peatlands , 1985, Nature.

[7]  T. Meyers,et al.  Measuring Biosphere‐Atmosphere Exchanges of Biologically Related Gases with Micrometeorological Methods , 1988 .

[8]  Paul J. Crutzen,et al.  Global distribution of natural freshwater wetlands and rice paddies, their net primary productivity, seasonality and possible methane emissions , 1989 .

[9]  S. Twomey Introduction to the Mathematics of Inversion in Remote Sensing and Indirect Measurements , 1997 .

[10]  S. Wofsy,et al.  The Amazon Boundary Layer Experiment (ABLE 2A) - Dry season 1985 , 1988 .

[11]  Henning Rodhe,et al.  A Comparison of the Contribution of Various Gases to the Greenhouse Effect , 1990, Science.

[12]  Otto Neall Strand,et al.  Statistical Information Content of Radiation Measurements used in Indirect Sensing , 1968 .

[13]  Piers J. Sellers,et al.  Experiment design and operations , 1990, Defense, Security, and Sensing.

[14]  L. O. Wade,et al.  Carbon monoxide and methane in the North American Arctic and Subarctic troposphere: July–August 1988 , 1992 .

[15]  Hartmut H. Aumann,et al.  Global mapping of minor atmospheric constituents with AIRS on EOS , 1990, Defense, Security, and Sensing.

[16]  Edward V. Browell,et al.  Carbon monoxide and methane over Canada: July–August 1990 , 1994 .

[17]  P. M. Lang,et al.  A dramatic decrease in the growth rate of atmospheric methane in the northern hemisphere during 1992 , 1994 .

[18]  D. Jacob,et al.  A record of the atmospheric methane sink from formaldehyde in polar ice cores , 1991, Nature.

[19]  Carlton L. Mateer,et al.  On the Information Content of Umkehr Observations. , 1965 .

[20]  E. Schanda,et al.  Physical Fundamentals of Remote Sensing , 1986 .

[21]  Drummond Measurements of Pollution in the Troposphere (Mopitt) , 1992 .