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Landsat imagery is one of the world’s longestrunning and most widely used high-resolution collections. To make extensive use of this vast archive, platforms such as Google Earth Engine are necessary to reduce processing time and facilitate analyses. This study aimed to identify the Landsat scenes acquired between 1972 and 2017 covering the Mexican territory that are available through Google Earth Engine. The query was conducted on the Tier 1 raw scenes imagery collection (as these are the images with the lowest geospatial error between scenes) using a Javascript program in the Google Earth Engine platform. For each scene the query obtained information on the sensor, acquisition date, Landsat key (path/row), and cloudiness percentage over the Earth's surface. The information obtained was processed in R 3.5.1. J. Vidal Solórzano, J. A. Gallardo-Cruz y C. Peralta-Carreta Potencial del acervo de imágenes Landsat disponible en Google... 2 • Investigaciones Geográficas • eissn: 2448-7279 • doi: 10.14350/rig.59821 • ARTÍCULOS • Núm. 101 • Abril • 2020 • e59821 INTRODUCCIÓN Con la aparición de las imágenes satelitales, el avance y alcance de los estudios ambientales en el mundo se potenció enormemente (Campbell y Wynne, 2011; Cohen y Goward, 2004). Esto se debió a que las imágenes satelitales contienen información sobre algunas características de la superficie terrestre y permiten contar con un registro histórico de estas (Aplin, 2005; Chambers et al., 2007; Melesse, Weng, Thenkabail y Senay, 2007; Miller y Rogan, 2007; Wang, Franklin, Guo y Cattet, 2010b). Por ello, este tipo de información consiste en la principal fuente de información para hacer estudios de mediano-largo plazo y a escalas desde locales hasta mundiales (Foody, 2003; Pettorelli et al., 2018; Turner et al., 2003). Dentro de la gama completa de imágenes satelitales que actualmente existen, la misión Landsat representa un hito en el uso de imágenes satelitales multiespectrales para el estudio de la Tierra, ya que representa el registro histórico de imágenes más longevo (Markham y Helder, 2012). Adicionalmente, la liberación del archivo Landsat en 2008 aseguró que se convirtiera en uno de los conjuntos de imágenes más utilizados (Wulder, Masek, Cohen, Loveland y Woodcock, 2012; Wulder et al., 2008). La misión Landsat comenzó con el lanzamiento del satélite Landsat 1 en 1972; hoy en día cuenta con los satélites Landsat 7 y 8, y se planea continuar con la puesta en órbita en 2023 del Landsat 9 (NASA, 2018; Wulder et al., 2016). Las aplicaciones de las imágenes Landsat incluyen el monitoreo de bosques (Banskota et al., 2014; Butler, Mouchot, Barale y LeBlanc, 1990; Cohen y Goward, 2004); de especies invasoras (GavierPizarro et al., 2012); de biodiversidad (Gillespie, Foody, Rocchini, Giorgi y Saatchi, 2008; Turner et al., 2003); de actividad volcánica (Mia, Fujimitsu y Nishijima, 2017), salud de los bosques (Wang et al., 2010a); de la atmósfera (Moawad, Youssief y Madkour, 2017); de glaciares (Man, Guo, Liu, Dong y 2014); de incendios (Schroeder, Wulder, Healey y Moisen, 2012); de cambio de uso de suelo (Hermosilla, Wulder, White, Coops y Hobart, 2017), de cuerpos de agua (Anderson, Allen, Morse y Kustas, 2012), suelo (Li, Ti, Zhao y Yan, 2016), de sedimentos (Boettinger et al., 2008); de la fenología vegetal (Baumann, Ozdogan, Richardson y Radeloff, 2017); de enfermedades transmitidas por vectores (Berrozpe et al., 2018), del crecimiento Data acquisition took approximately 10 seconds, which shows the enormous processing power of Google Earth Engine. A total of 146 Landsat keys are necessary to encompass the entire Mexican territory with scenes acquired by the sensors Landsat 1-MSS through Landsat 3-MSS, and 134 keys are necessary for scenes recorded by sensors Landsat 4 MSS, TM and Landsat 8 OLI. We gathered a total of 89,649 scenes acquired between 1972 and 2017 covering the country. However, the number of scenes available for a given year varied widely; only 9 scenes were found for 1972 (0.06 images per path/row, on average), and 5403 for 2017 (40.32 images per path/row, on average). Over this period, the number of scenes available increased in those years when a new sensor started operations (e.g., 1984, 1999, and 2013) and when the Landsat archive was centralized by the USGS (in 1993). By contrast, the number of scenes available decreased in those years when a satellite ceased operations (e.g., 2012). The sensors that acquired the greatest number of scenes were Landsat 5 TM (38,897 scenes), followed by Landsat 7 ETM+ SLC-off (31,254) and Landsat 8, although with a significantly smaller number (12,796). In addition, almost half of scenes had less than 50% cloudiness over the Earth's surface, although when examined by path/row, it is clear that the driest areas of the country had a lower cloudiness vs. more humid zones. By design, Landsat images should cover the entire Earth's surface since 1972; however, for many areas of the world, including Mexico, the oldest scenes are usually very scarce for the following reasons: lack of reception and/or storage facilities in those regions; lack of proper storage practices; the scenes did not meet the quality standards for inclusion in the Tier-1 collection. Therefore, characterizing the collection of Landsat scenes actually available for the country contributes to understand the true possibilities that this archive provides for studying the Mexican territory. The results of this study are expected to guide future investigations using Landsat imagery to explore the Mexican territory, by providing information on the number of scenes actually available per year and their cloudiness condition. Finally, in order to foster the Google Earth Engine for image processing, a JavaScript routine to build annual mosaics of the highest quality Tier-1 surface reflectance data available was written and made available to any user. This routine is supplemented by a brief tutorial in Spanish that aims to provide an introduction to Google Earth Engine and promote its use in Spanish-speaking countries.

Potencial del acervo de imágenes Landsat disponible en Google Earth Engine para el estudio del territorio mexicano