Correcciones atmosféricas usando patrones invariantes en el espacio del rojo e infrarojo cercano
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Paz, F. (2020). Correcciones atmosféricas usando patrones invariantes en el espacio del rojo e infrarojo cercano. Revista Latinoamericana De Recursos Naturales, 2(1), 3-16. Recuperado a partir de https://revista.itson.edu.mx/index.php/rlrn/article/view/76
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Baret, F. y Guyot, G., 1991. Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment, 35:161-174.
Baret, F.; Jacquemoud, S. y Hanocq, J.F., 1983. The soil line concept in remote sensing. Remote Sensing Reviews, 7: 65-82.
Bausch, W.C., 1993. Soil background effects on reflectance based crop coefficients for corn. Remote Sensing of Environment , 46: 213-222.
Chandrasekhar, S., 1960. Radiative transfer. Dover, New York Chavez, P. Jr., 1996. Image based atmospheric corrections -revisited and improved. Photogrammetric Engineering and Remote Sensing, 62: 1025-1036
de Haan, J.F.; Hovenier, J.W.; Kokke, J.M.M. y van Stokkon, H.T.C., 1993. Removal of atmospheric influences on satellite-borne imagery: a radiative transfer approach. Remote Sensing of Environment, 37: 1-21.
Fox, G. A. y Sabbagh, G.J., 2002. Estimation of soil organic matter from red and near-infrared remotely sensed data using a soil line euclidean distance technique. Soil Science Society of America Journal, 66: 1922-1929.
Fox, G.A., Sabbagh, G.J. y Searly, S.W., 2003. Radiometric normalization of multi-temporal images based on image soil lines, Transactions of the ASAE, 46: 851-859.
Fox, G.A., Sabbagh, G.J., Searly, S.W. y Yang, C., 2004. An automated soil line identification routine for remotely sensed images. Soil Science Society of America Journal, 68:1326-1331.
Gausman, H.W., Allen, W.A., Wiegand, C.L., Escobar, D.E., Rodriguez, R.R. y Richardson, A.J., 1973. The leaf mesophills of twenty crops, their light spectra, optical and geometrical parameters. USDA Technical Bulletin, 59 pp.
Gilabert, M.A., González-Piqueras, J., García-Haro, F.J. y Melia, J., 2002. A generalized soil-adjusted vegetation index. Remote Sensing of Environment, 82: 303-310.
Huete, A.R. y Jackson, R.D., 1987. Suitability of spectral índices for evaluating vegetation characteristics on rangelands. Remote Sensing of Environment, 23: 213-232.
Huete, A.R., Jackson, R.D., y Post, D.F., 1985. Spectral response of a plant canopy with different soil backgrounds. Remote Sensing of Environment, 17: 37-53.
Kaufman, Y.J., 2002. Relationship between surface reflectance in the visible and mid-IR used in MODIS aerosol algorithm - theory. Geophysical Research Letters, 29: 2116-2119.
Kaufman, Y.J. y Sendra, C., 1988. Automatic atmospheric correction. International Journal of Remote Sensing, 9:1357-1381.
Kaufman, Y.J., Wald, A., Lorraine, L.A., Gao, B.C., Li, R.R. y Flynn, L., 1997. Remote sensing of aerosol over the continents with the aid of a 2.2 μm channel. IEEE Transactions on Geosciences and Remote Sensing, 35:1286-1298.
Kauth, R.J. y Thomas, G.S., 1976. The tasseled cap - a graphic description of the spectral-temporal development of agricultural crops as seen by Landsat, Proceedings of the Simposium on Machine Processing of Remotely Sensed Data. Purdue University, West Lafayette, Indiana, Estados Unidos. pp. 4B41-4B51.
Liang G.S., Fallah-Adl, H., Kalluri, S., JaJa, J.; Kaufman, Y. y Townshend, J., 1997. Development of an operational atmospheric correction algorithm for TM Imagery. Journal of Geophysical Research, 102:17174-17186.
Liang G.S., Fang, H. y Chen, M., 2001. Atmospheric correction of Landsat ETM+ land surface imagery – Part I: methods, IEEE Transactions on Geoscience and Remote Sensing, 39:2490-2498.
Meza-Díaz, B. y Blackburn, G.A., 2003. Remote sensing of mangrove biophysical properties: evidence from a laboratory simulation of the possible effects of background variation on spectral indices. International Journal of Remote Sensing, 24: 53-75.
Moran, S., Jackson, R.D., Slater, P.N. y Teillet, P.N., 1992. Evaluating of simplified procedures for retrieval of land surface reflectance factors from satellite sensor output. Remote Sensing of Environment, 41: 169-182.
Palacios, L.A., Paz, F., Oropeza, J.L., Figueroa, B., Martínez, M., Ortiz, C.A. y García, E., 2005, Clasificador genérico de objetos en imágenes ETM+. Agrociencia (en prensa).
Paz, F., Palacios, E., Mejía, E., Martínez, M. y Palacios, L.A., 2005a, Análisis de los espacios espectrales de la reflectividad del follaje de los cultivos, Agrociencia, 39:293-301.
Paz, F., Palacios, E., Mejía, E., Martínez, M. y Palacios, L.A., 2005b. Determinación del estado de crecimiento de cultivos sanos usando la transformada de Hough de las reflectividades del follaje. Agrociencia (en prensa).
Price, J.C., 1992. Estimating vegetation amount from visible and near infrared reflectances. Remote Sensing of Environment, 41: 29-34.
Qi, J., Chehbouni, A., Huete, A.R., Kerr, Y.H. y Sorooshian, S., 1994. A modified soil adjusted vegetation index. Remote Sensing of Environment, 48: 119-126.
Richardson, A.J. y Wiegand, C.L., 1977. Distinguishing vegetation from soil background information. Photogrammetric Engineering, 43: 1541-1552.
Richardson, A.J. y Wiegand, C.L.,1991. Comparison of two models for simulating the soil vegetation composite reflectance of a developing cotton canopy. International Journal of Remote Sensing, 11: 447-459.
Richter, R., 1996a. A spatially adaptive fast atmospheric correction algorithm. International Journal of Remote Sensing, 17: 1201-1214.
Richter, R., 1996b. Atmospheric correction of satellite data with haze removal including a haze/clear transition region. Computers and Geosciences, 22: 675-681.
Richter, R., 1996c. Atmospheric correction of DAIS hyperspectral image data. Computers and Geosciences, 22: 785-793.
Tanre, D., Deschamps, P.Y., Devaux, C. y Herman, M., 1988. Estimation of Saharan aerosol optical thickness from blurring effects in thematic mapper data. Journal of Geophysical Research, 93: 15955-15964.
Tanre, D. y Legrand, M., 1991. On the satellite retrieval of Saharan dust optical thickness over land: two different approaches. Journal of Geophysical Research, 96: 5221-5227.
Teillet, P.N. y Fedosejevs, G., 1995. On the dark target approach to atmospheric correction of remotely sensed data. Canadian Journal of Remote Sensing, 21: 374-387.
Thome, K.J., 2001. Absolute radiometric calibration of Landsat 7 ETM+ using the reflectance-based method. Remote Sensing of Environment, 78: 27-38.
Verhoef, W., 1984. Light scattering by leaf layers with application to canopy reflectance modeling: the SAIL
model. Remote Sensing of Environment, 16: 125-141. Vermote, E. F., Tanre, D., Deuze, J.L., Herman, M. y Morcrete, J.J., 1997. Second simulation of the satellite signal in the solar spectrum, 6S: an overview. IEEE Transactions on Geoscience and Remote Sensing, 35: 675-686.
Wen, G., Tsay, S., Cahalan, R. y Oreopolus, L., 1999. Path radiance technique for retrieving aerosol optical thickness over land. Journal of Geophysical Research, 104: 31321-31332.
Yoshioka, H., Miura, T.; Huete, A.R. y Ganapol, B.D., 2000. Analysis of vegetation isolines in red-nir reflectance space. Remote Sensing of Environment, 74: 313-326.
Baret, F.; Jacquemoud, S. y Hanocq, J.F., 1983. The soil line concept in remote sensing. Remote Sensing Reviews, 7: 65-82.
Bausch, W.C., 1993. Soil background effects on reflectance based crop coefficients for corn. Remote Sensing of Environment , 46: 213-222.
Chandrasekhar, S., 1960. Radiative transfer. Dover, New York Chavez, P. Jr., 1996. Image based atmospheric corrections -revisited and improved. Photogrammetric Engineering and Remote Sensing, 62: 1025-1036
de Haan, J.F.; Hovenier, J.W.; Kokke, J.M.M. y van Stokkon, H.T.C., 1993. Removal of atmospheric influences on satellite-borne imagery: a radiative transfer approach. Remote Sensing of Environment, 37: 1-21.
Fox, G. A. y Sabbagh, G.J., 2002. Estimation of soil organic matter from red and near-infrared remotely sensed data using a soil line euclidean distance technique. Soil Science Society of America Journal, 66: 1922-1929.
Fox, G.A., Sabbagh, G.J. y Searly, S.W., 2003. Radiometric normalization of multi-temporal images based on image soil lines, Transactions of the ASAE, 46: 851-859.
Fox, G.A., Sabbagh, G.J., Searly, S.W. y Yang, C., 2004. An automated soil line identification routine for remotely sensed images. Soil Science Society of America Journal, 68:1326-1331.
Gausman, H.W., Allen, W.A., Wiegand, C.L., Escobar, D.E., Rodriguez, R.R. y Richardson, A.J., 1973. The leaf mesophills of twenty crops, their light spectra, optical and geometrical parameters. USDA Technical Bulletin, 59 pp.
Gilabert, M.A., González-Piqueras, J., García-Haro, F.J. y Melia, J., 2002. A generalized soil-adjusted vegetation index. Remote Sensing of Environment, 82: 303-310.
Huete, A.R. y Jackson, R.D., 1987. Suitability of spectral índices for evaluating vegetation characteristics on rangelands. Remote Sensing of Environment, 23: 213-232.
Huete, A.R., Jackson, R.D., y Post, D.F., 1985. Spectral response of a plant canopy with different soil backgrounds. Remote Sensing of Environment, 17: 37-53.
Kaufman, Y.J., 2002. Relationship between surface reflectance in the visible and mid-IR used in MODIS aerosol algorithm - theory. Geophysical Research Letters, 29: 2116-2119.
Kaufman, Y.J. y Sendra, C., 1988. Automatic atmospheric correction. International Journal of Remote Sensing, 9:1357-1381.
Kaufman, Y.J., Wald, A., Lorraine, L.A., Gao, B.C., Li, R.R. y Flynn, L., 1997. Remote sensing of aerosol over the continents with the aid of a 2.2 μm channel. IEEE Transactions on Geosciences and Remote Sensing, 35:1286-1298.
Kauth, R.J. y Thomas, G.S., 1976. The tasseled cap - a graphic description of the spectral-temporal development of agricultural crops as seen by Landsat, Proceedings of the Simposium on Machine Processing of Remotely Sensed Data. Purdue University, West Lafayette, Indiana, Estados Unidos. pp. 4B41-4B51.
Liang G.S., Fallah-Adl, H., Kalluri, S., JaJa, J.; Kaufman, Y. y Townshend, J., 1997. Development of an operational atmospheric correction algorithm for TM Imagery. Journal of Geophysical Research, 102:17174-17186.
Liang G.S., Fang, H. y Chen, M., 2001. Atmospheric correction of Landsat ETM+ land surface imagery – Part I: methods, IEEE Transactions on Geoscience and Remote Sensing, 39:2490-2498.
Meza-Díaz, B. y Blackburn, G.A., 2003. Remote sensing of mangrove biophysical properties: evidence from a laboratory simulation of the possible effects of background variation on spectral indices. International Journal of Remote Sensing, 24: 53-75.
Moran, S., Jackson, R.D., Slater, P.N. y Teillet, P.N., 1992. Evaluating of simplified procedures for retrieval of land surface reflectance factors from satellite sensor output. Remote Sensing of Environment, 41: 169-182.
Palacios, L.A., Paz, F., Oropeza, J.L., Figueroa, B., Martínez, M., Ortiz, C.A. y García, E., 2005, Clasificador genérico de objetos en imágenes ETM+. Agrociencia (en prensa).
Paz, F., Palacios, E., Mejía, E., Martínez, M. y Palacios, L.A., 2005a, Análisis de los espacios espectrales de la reflectividad del follaje de los cultivos, Agrociencia, 39:293-301.
Paz, F., Palacios, E., Mejía, E., Martínez, M. y Palacios, L.A., 2005b. Determinación del estado de crecimiento de cultivos sanos usando la transformada de Hough de las reflectividades del follaje. Agrociencia (en prensa).
Price, J.C., 1992. Estimating vegetation amount from visible and near infrared reflectances. Remote Sensing of Environment, 41: 29-34.
Qi, J., Chehbouni, A., Huete, A.R., Kerr, Y.H. y Sorooshian, S., 1994. A modified soil adjusted vegetation index. Remote Sensing of Environment, 48: 119-126.
Richardson, A.J. y Wiegand, C.L., 1977. Distinguishing vegetation from soil background information. Photogrammetric Engineering, 43: 1541-1552.
Richardson, A.J. y Wiegand, C.L.,1991. Comparison of two models for simulating the soil vegetation composite reflectance of a developing cotton canopy. International Journal of Remote Sensing, 11: 447-459.
Richter, R., 1996a. A spatially adaptive fast atmospheric correction algorithm. International Journal of Remote Sensing, 17: 1201-1214.
Richter, R., 1996b. Atmospheric correction of satellite data with haze removal including a haze/clear transition region. Computers and Geosciences, 22: 675-681.
Richter, R., 1996c. Atmospheric correction of DAIS hyperspectral image data. Computers and Geosciences, 22: 785-793.
Tanre, D., Deschamps, P.Y., Devaux, C. y Herman, M., 1988. Estimation of Saharan aerosol optical thickness from blurring effects in thematic mapper data. Journal of Geophysical Research, 93: 15955-15964.
Tanre, D. y Legrand, M., 1991. On the satellite retrieval of Saharan dust optical thickness over land: two different approaches. Journal of Geophysical Research, 96: 5221-5227.
Teillet, P.N. y Fedosejevs, G., 1995. On the dark target approach to atmospheric correction of remotely sensed data. Canadian Journal of Remote Sensing, 21: 374-387.
Thome, K.J., 2001. Absolute radiometric calibration of Landsat 7 ETM+ using the reflectance-based method. Remote Sensing of Environment, 78: 27-38.
Verhoef, W., 1984. Light scattering by leaf layers with application to canopy reflectance modeling: the SAIL
model. Remote Sensing of Environment, 16: 125-141. Vermote, E. F., Tanre, D., Deuze, J.L., Herman, M. y Morcrete, J.J., 1997. Second simulation of the satellite signal in the solar spectrum, 6S: an overview. IEEE Transactions on Geoscience and Remote Sensing, 35: 675-686.
Wen, G., Tsay, S., Cahalan, R. y Oreopolus, L., 1999. Path radiance technique for retrieving aerosol optical thickness over land. Journal of Geophysical Research, 104: 31321-31332.
Yoshioka, H., Miura, T.; Huete, A.R. y Ganapol, B.D., 2000. Analysis of vegetation isolines in red-nir reflectance space. Remote Sensing of Environment, 74: 313-326.