Reduction of ground-based sensor sites for spatio-temporal analysis of aerosols

ABSTRACT

In many remote sensing applications it is important to use multiple sensors to be able to understand the major spatio-temporal distribution patterns of an observed phenomenon. A particular remote sensing application addressed in this study is estimation of an important property of atmosphere, called Aerosol Optical Depth (AOD). Remote sensing data for AOD estimation are collected from ground and satellite-based sensors. Satellite-based measurements can be used as attributes for estimation of AOD and in this way could lead to better understanding of spatio-temporal aerosol patterns on a global scale. Ground-based AOD estimation is more accurate and is traditionally used as ground-truth information in validation of satellite-based AOD estimations. In contrast to this traditional role of ground-based sensors, a data mining approach allows more active use of ground-based measurements as labels in supervised learning of a regression model for AOD estimation from satellite measurements. Considering the high operational costs of ground-based sensors, we are studying a budget-cut scenario that requires a reduction in a number of ground-based sensors. To minimize loss of information, the objective is to retain sensors that are the most useful as a source of labeled data. The proposed goodness criterion for the selection is how close the accuracy of a regression model built on data from a reduced sensor set is to the accuracy of a model built of the entire set of sensors. We developed an iterative method that removes sensors one by one from locations where AOD can be predicted most accurately using training data from the remaining sites. Extensive experiments on two years of globally distributed AERONET ground-based sensor data provide strong evidence that sensors selected using the proposed algorithm are more informative than the competing approaches that select sensors at random or that select sensors based on spatial diversity.

REFERENCES

• 1: Aires, F., Prigent, C., Rossow, W. B., and Rothstein, M., 2001. A New Neural Network Approach Including First Guess for Retrieval of Atmospheric Watervapor, Cloud Liquid Water Path, Surface Temperature, and Emissivities Overland from Satellite Microwave Observations, J. Geophys. Res., 106, 14,887--14,908
• 2: Box, George E. P.; Cox, D. R., 1964. An Analysis of Transformations, Journal of the Royal Statistical Society, Series B 26: 211--246.
• 3: Han, B., Vucetic, S., Braverman, A., Obradovic, Z., 2006. A Statistical Complement to Deterministic Algorithms for the Retrieval of Aerosol Optical Thickness from Radiance Data, Engineering Applications of Artificial Intelligence, Vol. 19, No. 7, pp. 787--795.
• 4: Hansen, J., Sato, M., and Ruedy, R., 1997. Radiative Forcing and Climate Response, J. Geophys. Res. 102, 6831--6864.
• 5: Holben, B. N., Eck, T. F., Slutsker, I., Tanre, T., Buis, J. P., Setzer, A., Vermote, E., Reagan, J. A., Kaufman, Y. J., Nakajima, T., Lavenu, F., Jankowiak, I., and Smirnov, A., 1998. AERONET: A Federated Instrument Network and Data Archive for Aerosol Characterization, Remote Sens. Environ., 37, 2403--2412.
• 6: http://modis.gsfc.nasa.gov/, Official MODIS website
• 7: Kaufman, Y. J., Tanré, D., et al., 1997. Operational Remote Sensing of Tropospheric Aerosol Over Land From EOS Moderate Resolution Imaging Spectroradiometer", Journal of Geophysical Research - Atmospheres 102(D14): 17051--17067.
• 8: Kaufman, Y. J., Tanre, D., and Boucher, O., 2002. A Satellite View of Aerosols in the Climate System, Nature, 419, (September 2002), 215--223.
• 9: King, M. D., Kaufman, Y. J., Tanré, D., and Nakajima, T., 1999. Remote Sensing of Tropospheric Aerosols from Space: Past, Present, and Future, Bull. Amer. Meteor. Soc., 80, 2229--2259.
• 10: Li, Z., 2004. Aerosol and Climate: A Perspective from East Asia, Observation, Theory and Modeling of Atmospheric Variability, (ed. Zhu), World Scientific Pub. Co., 501--525.
• 11: Mitchell, J. F. B., Johns, T. C., Gregory, J. M., and Tett, S. F. B., 1995. Climate Response to Increasing Levels of Greenhouse Gases and Sulphate Aerosols, Nature, 376, 501--504.
• 12: Muller, M. D., Kaifel, A. K., Weber, M., Tellmann, S., Burrows, J. P., and Loyola, D., 2003. Ozone profileretrieval from Global Ozone Monitoring Experiment (GOME) Data Using a Neural Network Approach (Neural Network Ozone Retrieval System (NNORSY)), J. Geophys. Res., 108(D16), 4497.
• 13: Penner, J. E., et al., 2001. Aerosols, their Direct and Indirect Effects, Chapter 5 in Climate Change 2001, The Scientific Basis, Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change (IPCC), Cambridge University Press, 289--348.
• 14: Pope, A. C., et al., 2002. Lung Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine Particulate Air Pollution", J. Amer. Med. Assoc. 287: 1132--1141
• 15: Radosavljevic, V., Vucetic, S., Obradovic, Z., 2008. Spatio-Temporal Partitioning for Improving Aerosol Prediction Accuracy, In Proceedings of the SIAM International Conference on Data Mining, SDM2008, pp. 609--620.
• 16: Remer, L. A., Tanré, D., and Kaufman, Y. J., 2006. Algorithm for Remote Sensing of Tropospheric Aerosol from MODIS for Collection 005", http://modis-atmos.gsfc.nasa.gov/atbd02.pdf
• 17: Rosenfeld, D., and Woodley, W., 2001. Pollution and Clouds, Physics World, 33--37.
• 18: Vucetic, S., Han, B., Mi, W., Li, Z., Obradovic, Z., 2008. A Data Mining Approach for the Validation of Aerosol Retrievals, IEEE Geoscience and Remote Sensing Letters, Vol. 5 (1), 113--117.

INDEX TERMS

Primary Classification:
  J. Computer Applications
  J.2 PHYSICAL SCIENCES AND ENGINEERING
      Subjects: Earth and atmospheric sciences

General Terms:
Algorithms, Experimentation, Measurement

Keywords:
active learning, data mining, regression, remote sensing