Artículo

Grimson, R.; Bali, J.L.; Rajngewerc, M.; Martin, L.S.; Salvia, M."A Statistical Inverse Method for Gridding Passive Microwave Data with Mixed Measurements" (2019) IEEE Transactions on Geoscience and Remote Sensing. 57(3):1347-1357
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Abstract:

When a passive microwave footprint intersects objects on the ground with different spectral characteristics, the corresponding observation is mixed. The retrieval of geophysical parameters is limited by this mixture. We propose to partition the study region into objects following an object-based image analysis procedure and then to refine this partition into small cells. Then, we introduce a statistical method to estimate the brightness temperature (TB) of each cell. The method assumes that TB of the cells corresponding to the same object is identically distributed and that the TB heterogeneity within each cell can be neglected. The implementation is based on an iterative expectation-maximization algorithm. We evaluated the proposed method using synthetic images and applied it to grid the TBs of sample AMSR -2 real data over a coastal region in Argentina. © 1980-2012 IEEE.

Registro:

Documento: Artículo
Título:A Statistical Inverse Method for Gridding Passive Microwave Data with Mixed Measurements
Autor:Grimson, R.; Bali, J.L.; Rajngewerc, M.; Martin, L.S.; Salvia, M.
Filiación:Instituto de Investigacion e Ingenieria Ambiental, Universidad Nacional de San Martín, San Martín, 1650, Argentina
Consejo Nacional de Investigaciones Cientificas y Tecnologicas, CABA1425, Argentina
Instituto de Investigaciones Científicas y Tecnicas para la Defensa, Villa Martelli, 1603, Argentina
Instituto de Investig. e Ingenieria Ambiental, Universidad Nacional de San Martín, San Martín, 1650, Argentina
Instituto de Astronomia y Fisica Del Espacio, Universidad de Buenos Aires, Buenos Aires, 1053, Argentina
Palabras clave:Expectation-maximization (EM) algorithms; inverse problems; passive microwave remote sensing; Cells; Cytology; Image segmentation; Iterative methods; Maximum principle; Remote sensing; Brightness temperatures; Expectation-maximization algorithms; Geophysical parameters; Object based image analysis; Passive microwave data; Passive microwave remote sensing; Passive microwaves; Spectral characteristics; Inverse problems
Año:2019
Volumen:57
Número:3
Página de inicio:1347
Página de fin:1357
DOI: http://dx.doi.org/10.1109/TGRS.2018.2866196
Handle:http://hdl.handle.net/20.500.12110/paper_01962892_v57_n3_p1347_Grimson
Título revista:IEEE Transactions on Geoscience and Remote Sensing
Título revista abreviado:IEEE Trans Geosci Remote Sens
ISSN:01962892
CODEN:IGRSD
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01962892_v57_n3_p1347_Grimson

Referencias:

  • Ulaby, F.T., Moore, R.K., Fung, A.K., (1981) Microwave Remote Sensing: Active and Passive, 1-3. , Norwood, MA, USA: Artech House
  • Choudhury, B.J., Monitoring global land surface using Nimbus-7 37 GHz data theory and examples (1989) Int. J. Remote Sens., 10 (10), pp. 1579-1605
  • Njoku, E.G., Jackson, T.J., Lakshmi, V., Chan, T.K., Nghiem, S.V., Soil moisture retrieval from AMSR-E (2003) IEEE Trans. Geosci. Remote Sens., 41 (2), pp. 215-229. , Feb
  • Jones, L.A., Kimball, J.S., McDonald, K.C., Chan, S.T.K., Njoku, E.G., Oechel, W.C., Satellite microwave remote sensing of boreal and arctic soil temperatures from AMSR-E (2007) IEEE Trans. Geosci. Remote Sens., 45 (7), pp. 2004-2018. , Jul
  • Zhang, L., Zhao, T., Jiang, L., Zhao, S., Estimate of phase transition water content in freeze-thaw process using microwave radiometer (2010) IEEE Trans. Geosci. Remote Sens., 48 (12), pp. 4248-4255. , Dec
  • Sippel, S.J., Hamilton, S.K., Melack, J.M., Choudhury, B.J., Determination of inundation area in the Amazon River floodplain using the SMMR 37 GHz polarization difference (1994) Remote Sens. Environ., 48 (1), pp. 70-76
  • Min, Q., Lin, B., Li, R., Remote sensing vegetation hydrological states using passive microwave measurements (2010) IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., 3 (1), pp. 124-131. , Mar
  • Descriptions of GCOM-W1 AMSR2 Level 1R and Level 2 Algorithms. Japan Aerosp. Explor. Agency, Earth Observ. Res. Center, Tokyo, Japan, 2013; Barraza, V., Monitoring vegetation moisture using passive microwave and optical indices in the dry Chaco forest, Argentina (2014) IEEE J. Sel. Topics Appl. Earth Observat. Remote Sens., 7 (2), pp. 421-430. , Feb
  • Backus, G., Gilbert, F., Uniqueness in the inversion of inaccurate gross earth data (1970) Philos. Trans. Roy. Soc. London A, Math. Phys. Sci., 266 (1173), pp. 123-192. , Mar
  • Stogryn, A., Estimates of brightness temperatures from scanning radiometer data (1978) IEEE Trans. Antennas Propag., AP-26 (5), pp. 720-726. , Sep
  • Poe, G.A., Optimum interpolation of imaging microwave radiometer data (1990) IEEE Trans. Geosci. Remote Sens., 28 (5), pp. 800-810. , Sep
  • Farrar, M.R., Smith, E.A., Spatial resolution enhancement of terrestrial features using deconvolved SSM/I microwave brightness temperatures (1992) IEEE Trans. Geosci. Remote Sens., 30 (2), pp. 349-355. , Mar
  • Bellerby, T., Retrieval of land and sea brightness temperatures from mixed coastal pixels in passive microwave data (1998) IEEE Trans. Geosci. Remote Sens., 36 (6), pp. 1844-1851. , Nov
  • Limaye, A.S., Crosson, W.L., Laymon, C.A., Njoku, E.G., Land cover-based optimal deconvolution of PALS L-band microwave brightness temperatures (2004) Remote Sens. Environ., 92 (4), pp. 497-506
  • Blaschke, T., Object based image analysis for remote sensing (2010) ISPRS J. Photogram. Remote Sens., 65 (1), pp. 2-16. , Jan
  • Blaschke, T., Geographic object-based image analysis-Towards a new paradigm (2014) ISPRS J. Photogramm. Remote Sens., 87, pp. 180-191. , Jan
  • Rees, W.G., (2013) Physical Principles of Remote Sensing, , Cambridge, U.K.: Cambridge Univ. Press
  • Rodgers, C.D., (2000) Inverse Methods for Atmospheric Sounding: Theory and Practice, 2. , Singapore: World scientific
  • Murphy, K.P., (2012) Machine Learning-A Probabilistic Perspective, , Cambridge, MA, USA: MIT Press
  • Dempster, A.P., Laird, N.M., Rubin, D.B., Maximum likelihood from incomplete data via the em algorithm (1977) J. Roy. Statist. Soc., B (Methodol.), 39 (1), pp. 1-38
  • Brodzik, M., Long, D., Hardman, M., Paget, A., Armstrong, R., (2018) MEaSUREs Calibrated Enhanced-Resolution Passive Microwave Daily EASE-Grid 2.0 Brightness Temperature ESDR, Version 1, , NASA Nat. Snow Ice Data Center Distrib. Act. Arch. Center, Washington, DC, USA
  • Long, D.G., Daum, D.L., Spatial resolution enhancement of SSM/I data (1998) IEEE Trans. Geosci. Remote Sens., 36 (2), pp. 407-417. , Mar

Citas:

---------- APA ----------
Grimson, R., Bali, J.L., Rajngewerc, M., Martin, L.S. & Salvia, M. (2019) . A Statistical Inverse Method for Gridding Passive Microwave Data with Mixed Measurements. IEEE Transactions on Geoscience and Remote Sensing, 57(3), 1347-1357.
http://dx.doi.org/10.1109/TGRS.2018.2866196
---------- CHICAGO ----------
Grimson, R., Bali, J.L., Rajngewerc, M., Martin, L.S., Salvia, M. "A Statistical Inverse Method for Gridding Passive Microwave Data with Mixed Measurements" . IEEE Transactions on Geoscience and Remote Sensing 57, no. 3 (2019) : 1347-1357.
http://dx.doi.org/10.1109/TGRS.2018.2866196
---------- MLA ----------
Grimson, R., Bali, J.L., Rajngewerc, M., Martin, L.S., Salvia, M. "A Statistical Inverse Method for Gridding Passive Microwave Data with Mixed Measurements" . IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 3, 2019, pp. 1347-1357.
http://dx.doi.org/10.1109/TGRS.2018.2866196
---------- VANCOUVER ----------
Grimson, R., Bali, J.L., Rajngewerc, M., Martin, L.S., Salvia, M. A Statistical Inverse Method for Gridding Passive Microwave Data with Mixed Measurements. IEEE Trans Geosci Remote Sens. 2019;57(3):1347-1357.
http://dx.doi.org/10.1109/TGRS.2018.2866196