<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0187-5779</journal-id>
<journal-title><![CDATA[Terra Latinoamericana]]></journal-title>
<abbrev-journal-title><![CDATA[Terra Latinoam]]></abbrev-journal-title>
<issn>0187-5779</issn>
<publisher>
<publisher-name><![CDATA[Sociedad Mexicana de la Ciencia del Suelo A.C.]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0187-57792018000400309</article-id>
<article-id pub-id-type="doi">10.28940/terra.v36i4.232</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Corrector atmosférico en imágenes Landsat]]></article-title>
<article-title xml:lang="en"><![CDATA[Atmospheric corrector in Landsat images]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Palacios Sánchez]]></surname>
<given-names><![CDATA[Luis Alberto]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Paz Pellat]]></surname>
<given-names><![CDATA[Fernando]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Oropeza Mota]]></surname>
<given-names><![CDATA[José Luis]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Figueroa Sandoval]]></surname>
<given-names><![CDATA[Benjamín]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Martínez Menez]]></surname>
<given-names><![CDATA[Mario]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ortiz Solorio]]></surname>
<given-names><![CDATA[Carlos A.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Exebio García]]></surname>
<given-names><![CDATA[Adolfo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Colegio de Postgraduados Campus Montecillo ]]></institution>
<addr-line><![CDATA[Montecillo Estado de México]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2018</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2018</year>
</pub-date>
<volume>36</volume>
<numero>4</numero>
<fpage>309</fpage>
<lpage>321</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0187-57792018000400309&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0187-57792018000400309&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0187-57792018000400309&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen: Los efectos atmosféricos en las imágenes satelitales distorsionan la información disponible y originan errores en la estimación de las variables biofísicas provenientes de los datos espectrales. En este trabajo se desarrolló un algoritmo de corrección atmosférica que se basó en la correlación existente entre la reflectancia de la banda 7 (infrarrojo medio, 2.2 &#956;m) y la banda 1 (Azul, 0.485 &#956;m) de la vegetación, para los sensores TM y ETM+. La ordenada al origen de la regresión entre la banda 7 y la banda 1 de imágenes Landsat es un estimador de la reflectancia de trayectoria en la banda 1. Con esta reflectancia de trayectoria y un modelo de atmósfera y aerosol definido, es posible estimar el espesor óptico de los aerosoles centrado en 0.55 &#956;m. El algoritmo se apoyó en el acoplamiento de varios modelos de simulación y bibliotecas de datos espectrales para la representación del sistema óptico suelo-vegetación-atmósfera. La vegetación en una imagen se identifica mediante un clasificador genérico de objetos en cuatro variantes: oscura densa, cobertura alta, media y baja. El algoritmo se probó en dos fases, la primera se basó en un análisis empírico sobre los resultados de la simulación del sistema óptico suelo-vegetación-atmósfera, en el que se tenían condiciones controladas. En la segunda fase se validó el algoritmo con 7 imágenes ETM+ cuyas escenas contenían un sitio de la red robótica de aerosoles (AERONET), la cual mide con gran precisión el espesor óptico de los aerosoles en diferentes intervalos de longitud de onda. El análisis de los resultados mostró que el corrector estima el espesor óptico de la escena con un ajuste: R2 = 0.97, error típico de 0.059 (20.3%) y que representa bien la variabilidad espacial de carga de aerosoles en imágenes Landsat.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Summary: Atmospheric effects in satellite imagery distort the available information and generate errors in the estimation of biophysical variables from spectral data. An atmospheric correction algorithm was developed based on the correlation between the reflectance of band 7 (medium infrared, 2.2 &#956;m) and band 1 (Blue, 0.485 &#956;m) of vegetation, for TM and ETM+ sensors. The intercept of the regression between band 7 and band 1 of Landsat imagery is an estimator of path reflectance in band 1. With this path reflectance and a defined atmosphere and aerosol model, it is possible to estimate the optical thickness of aerosols centered at 0.55 &#956;m. The algorithm was based on coupling of several simulation models and spectral data libraries to represent the soil-vegetation-atmosphere optical system. Vegetation in an image is identified by a generic object classifier in four vegetation variants: dark dense, high, medium and low coverage. The algorithm was evaluated in two phases, the first was based on empirical analysis of the results of the simulation of the soil-vegetation-atmosphere optical system, in which controlled conditions were maintained. In the second phase, the algorithm was validated with 7 ETM + images whose scenes contained a site of the aerosol robotic network (AERONET), which accurately measures the optical thickness of aerosols in different wavelengths. The analysis of results showed that the corrector estimates the optical thickness of the scene with good adjustment: R2 = 0.97, Root Mean Squared Error of 0.059 (20.3%) and represents well the spatial variability of aerosol load in Landsat imagery.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[radiancia de trayectoria]]></kwd>
<kwd lng="es"><![CDATA[aerosoles]]></kwd>
<kwd lng="es"><![CDATA[simulaciones de transferencia de radiación]]></kwd>
<kwd lng="es"><![CDATA[espesor óptico]]></kwd>
<kwd lng="en"><![CDATA[path radiance]]></kwd>
<kwd lng="en"><![CDATA[aerosols]]></kwd>
<kwd lng="en"><![CDATA[radiative simulations]]></kwd>
<kwd lng="en"><![CDATA[optical thickness]]></kwd>
</kwd-group>
</article-meta>
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