Spatiotemporal patterns of Sentinel-2 observations at image- and pixel-level of the Mexican territory between 2015 and 2019

Sentinel-2 imagery has the highest temporal, spectral and spatial resolution to monitor land surface among the freely available multispectral collections. However, the possibility to use these images in different applications is conditioned by the number of cloudless observations available for a cer...

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Published in:Revista de Teledetección
Main Authors: Solórzano, J.V., Mas, J.F., Gao, Y., Gallardo-Cruz, J.A.
Other Authors: CONACyT
Format: Article in Journal/Newspaper
Language:Spanish
Published: Universitat Politècnica de València 2020
Subjects:
Online Access:https://polipapers.upv.es/index.php/raet/article/view/14044
https://doi.org/10.4995/raet.2020.14044
id ftunpvalenciaojs:oai:ojs.upv.es:article/14044
record_format openpolar
institution Open Polar
collection Universitat Politècnica de València: PoliPapers
op_collection_id ftunpvalenciaojs
language Spanish
topic Mexico
ecoregions
cloudless observations
Sentinel-2
optical satellite imagery
México
Ecorregiones
Observaciones sin nubes
Sentinel-2 1C
imágenes satelitales ópticas
spellingShingle Mexico
ecoregions
cloudless observations
Sentinel-2
optical satellite imagery
México
Ecorregiones
Observaciones sin nubes
Sentinel-2 1C
imágenes satelitales ópticas
Solórzano, J.V.
Mas, J.F.
Gao, Y.
Gallardo-Cruz, J.A.
Spatiotemporal patterns of Sentinel-2 observations at image- and pixel-level of the Mexican territory between 2015 and 2019
topic_facet Mexico
ecoregions
cloudless observations
Sentinel-2
optical satellite imagery
México
Ecorregiones
Observaciones sin nubes
Sentinel-2 1C
imágenes satelitales ópticas
description Sentinel-2 imagery has the highest temporal, spectral and spatial resolution to monitor land surface among the freely available multispectral collections. However, the possibility to use these images in different applications is conditioned by the number of cloudless observations available for a certain spatiotemporal window. Thus, the objective of this article is to analyze the number of Sentinel-2 observations available for the Mexican territory at image and pixel level. In the first case, the total number of available images and its cloud cover percentage was calculated; while in the second case, the number of cloudless observations was estimated for each pixel. Additionally, in order to take into account the territory diversity, the monthly mean number of cloudless observations, as well as the proportion of its surface with at least one cloudless observation in monthly, bimonthly, trimonthly and annual intervals, was computed for each one of the seven ecoregions of the country. The results show that annually, the number of valid observations per pixel is between 0 and 121 observations, while in monthly evaluations, between 0 and 6.58 observations. Additionally, in the 2017-2019 period annual observations can be obtained for the entire Mexican land surface, while in 2018-2019, monthly or trimonthly evaluations can be achieved, depending on the ecoregion. We consider that these results will provide useful information for researchers that are interested in using Sentinel-2 imagery for different applications. Actualmente, las imágenes Sentinel-2 son uno de los acervos multiespectrales y gratuitos de mayor resolución temporal, espectral y espacial para monitorear la superficie terrestre. Sin embargo, la posibilidad de utilizar este acervo para distintas aplicaciones está condicionada por el número de observaciones sin nubes disponibles para una ventana espacio-temporal determinada. Por ello, este artículo tuvo el objetivo de analizar el número de observaciones de Sentinel-2 disponibles para el territorio mexicano a nivel de imagen y de pixel. En el primer caso, se contabilizó el total de imágenes disponibles por año y su porcentaje de nubosidad; mientras que, en el segundo, se calculó el número de observaciones despejadas por pixel. Además, para tomar en cuenta la diversidad del territorio, se evaluó el promedio mensual de las observaciones por pixel de cada una de las siete ecorregiones del país, así como la proporción de su superficie con por lo menos una observación despejada en intervalos mensuales, bimestrales, trimestrales y anuales. Los resultados mostraron que el número de observaciones válidas por pixel variaron entre 0 y 121 observaciones al año y entre 0 y 6.58 al mes. Adicionalmente, se observó que en el periodo 2017 – 2019 se pueden obtener observaciones de todo el país en ventanas anuales, mientras que en el periodo 2018 – 2019, se pueden obtener observaciones en intervalos mensuales o trimestrales, dependiendo de la ecorregión. Finalmente, consideramos que los resultados de este trabajo servirán de guía para los usuarios interesados en utilizar estas imágenes para distintos estudios.
author2 CONACyT
format Article in Journal/Newspaper
author Solórzano, J.V.
Mas, J.F.
Gao, Y.
Gallardo-Cruz, J.A.
author_facet Solórzano, J.V.
Mas, J.F.
Gao, Y.
Gallardo-Cruz, J.A.
author_sort Solórzano, J.V.
title Spatiotemporal patterns of Sentinel-2 observations at image- and pixel-level of the Mexican territory between 2015 and 2019
title_short Spatiotemporal patterns of Sentinel-2 observations at image- and pixel-level of the Mexican territory between 2015 and 2019
title_full Spatiotemporal patterns of Sentinel-2 observations at image- and pixel-level of the Mexican territory between 2015 and 2019
title_fullStr Spatiotemporal patterns of Sentinel-2 observations at image- and pixel-level of the Mexican territory between 2015 and 2019
title_full_unstemmed Spatiotemporal patterns of Sentinel-2 observations at image- and pixel-level of the Mexican territory between 2015 and 2019
title_sort spatiotemporal patterns of sentinel-2 observations at image- and pixel-level of the mexican territory between 2015 and 2019
publisher Universitat Politècnica de València
publishDate 2020
url https://polipapers.upv.es/index.php/raet/article/view/14044
https://doi.org/10.4995/raet.2020.14044
long_lat ENVELOPE(13.782,13.782,67.054,67.054)
geographic Tuvo
geographic_facet Tuvo
genre Antarctic and Alpine Research
Arctic
genre_facet Antarctic and Alpine Research
Arctic
op_source Revista de Teledetección; Núm. 56 (2020): Número especial: Applications of Copernicus Sentinel Satellites; 103-115
1988-8740
1133-0953
op_relation https://polipapers.upv.es/index.php/raet/article/view/14044/13216
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https://polipapers.upv.es/index.php/raet/article/view/14044
doi:10.4995/raet.2020.14044
op_rights Copyright (c) 2020 Jonathan V. Solórzano, Jean-Francois Mas, Yan Gao, José Alberto Gallardo-Cruz
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spelling ftunpvalenciaojs:oai:ojs.upv.es:article/14044 2023-05-15T14:14:43+02:00 Spatiotemporal patterns of Sentinel-2 observations at image- and pixel-level of the Mexican territory between 2015 and 2019 Patrones espaciotemporales de las observaciones de Sentinel-2 a nivel de imagen y píxel sobre el territorio mexicano entre 2015 y 2019 Solórzano, J.V. Mas, J.F. Gao, Y. Gallardo-Cruz, J.A. CONACyT 2020-11-27 application/pdf https://polipapers.upv.es/index.php/raet/article/view/14044 https://doi.org/10.4995/raet.2020.14044 spa spa Universitat Politècnica de València https://polipapers.upv.es/index.php/raet/article/view/14044/13216 Agapiou, A., Alexakis, D. D., Sarris, A., Hadjimitsis, D. G. 2014. Evaluating the potentials of sentinel-2 for archaeological perspective. Remote Sensing, 6(3), 2176-2194. https://doi.org/10.3390/rs6032176 Berger, M., Moreno, J., Johannessen, J. A., Levelt, P. F., Hanssen, R. F. 2012. ESA's sentinel missions in support of Earth system science. Remote Sensing of Environment, 120, 84-90. https://doi.org/10.1016/j.rse.2011.07.023 Boyd, D. S., Danson, F. M. 2005. Satellite remote sensing of forest resources: three decades of research development. Progress in Physical Geography, 29(1), 1-26. https://doi.org/10.1191/0309133305pp432ra Caballero, I., Fernández, R., Moreno Escalante, O., Mamán, L., Navarro, G. 2020. New Capabilities of Sentinel-2A/B Satellites Combined with in Situ Data for Monitoring Small Harmful Algal Blooms in Complex Coastal Waters. Scientific Reports, 10, 1-14. https://doi.org/10.1038/s41598-020-65600-1 Carrasco, L., O’Neil, A.W., Morton, R.D., Rowland, C.S. 2019. Evaluating Combinations of Temporally Aggregated Sentinel-1, Sentinel-2 and Landsat 8 for Land Cover Mapping with Google Earth Engine. Remote Sens., 11, 288. https://doi.org/10.3390/ rs11030288 Claverie, M., Ju, J., Masek, J.G., Dungan, J.L., Vermote, E.F., Roger, J.-C., Skakun, S.V., Justice, C. 2018. The Harmonized Landsat and Sentinel-2 Surface Reflectance Data Set. Remote Sensing of Environment, 219, 145-61. https://doi.org/10.1016/j. rse.2018.09.002 Coluzzi, R., Imbrenda, V., Lanfredi, M., Simoniello, T. 2018. A first assessment of the Sentinel-2 Level 1-C cloud mask product to support informed surface analyses. Remote Sensing of Environment, 217, 426-443. https://doi.org/10.1016/j.rse.2018.08.009 Comber, A., Wulder, M. A. 2019. Considering spatiotemporal processes in big data analysis: Insights from remote sensing of land cover and land use. Transactions in GIS, 23, 879-891. https://doi.org/10.1111/tgis.12559 Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., . . . Bargellini, P. 2012. Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services. Remote Sensing of Environment, 120, 25-36. https://doi.org/10.1016/j.rse.2011.11.026 ESA (European Space Agency). (2016). Sentinel Data Access Annual Report. Disponible en https://sentinel.esa.int/documents/247904/2955773/Sentinel-Data-Access-Annual-Report-2016 ESA (European Space Agency). (2018). Sentinel Data Access Annual Report. Disponible en https://sentinels.copernicus.eu/web/sentinel/news/-/article/2018-sentinel-data-access-annual-report ESA (European Space Agency). Sentinel-2 MSI Technical Guide. Último acceso 25/05/2020, de https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-2-msi ESA (European Space Agency). Sentinel-2 MSI User Guide. Último acceso 28/05/2020, de https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi ESA (European Space Agency). SNAP. Último acceso 20/05/2020, de https://step.esa.int/main/toolboxes/snap/ Espinosa, D., Ocegueda, S., Aguilar, C., Flores, O, Llorente-Bousquets, J. 2008. El conocimiento biogeográfico de las especies y su regionalización natural, En: Capital natural de México, vol. I: Conocimiento actual de la biodiversidad. Conabio, México, pp. 33-65. Filipponi, F. 2018. BAIS2: Burned Area Index for Sentinel-2. Proceedings 2nd International Electronic Conference on Remote Sensing, 22 March–5 April 2018, 2, 364. https://doi.org/10.3390/ecrs-2-05177 GEE (Google Earth Engine). Sentinel-2 MSI: MultiSpectral Instrument, Level-2A. Último acceso 05/03/2020, https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., Moore, R. 2017. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18-27. https://doi.org/10.1016/j.rse.2017.06.031 Griffiths, P., Nendel, C., Hostert, P. 2019. Intra-Annual Reflectance Composites from Sentinel-2 and Landsat for National-Scale Crop and Land Cover Mapping. Remote Sensing of Environment, 220, 135-51. https://doi.org/10.1016/j.rse.2018.10.031 Heckel, K., Urban, M., Schratz, P., Mahecha, M. D., Schmullius, C. 2020. Predicting Forest Cover in Distinct Ecosystems: The Potential of Multi-Source Sentinel-1 and -2 Data Fusion. Remote Sensing, 12, 302. https://doi.org/10.3390/rs12020302 INEGI (Instituto Nacional de Estadística), CONABIO (Comisión Nacional para el Conocimiento y Uso de la Biodiversidad), INE (Instituto Nacional de Ecología). 2008. Ecorregiones terrestres de México 1:1000000. Disponible en http://www.conabio.gob.mx/informacion/metadata/gis/ecort08gw.xml?_xsl=/db/metadata/xsl/fgdc_html.xsl&_indent=no Li, J., Roy, D. P. 2017. A global analysis of Sentinel-2a, Sentinel-2b and Landsat-8 data revisit intervals and implications for terrestrial monitoring. Remote Sensing, 9, 902. https://doi.org/10.3390/rs9090902 Lima, T.A., Beuchle, R., Langner, A., Griess, V.C., Achard, F. 2019. Comparing Sentinel-2 MSI and Landsat 8 OLI Imagery for Monitoring Selective Logging in the Brazilian Amazon. 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Solórzano, Jean-Francois Mas, Yan Gao, José Alberto Gallardo-Cruz http://creativecommons.org/licenses/by-nc-sa/4.0 CC-BY-NC-SA Revista de Teledetección; Núm. 56 (2020): Número especial: Applications of Copernicus Sentinel Satellites; 103-115 1988-8740 1133-0953 Mexico ecoregions cloudless observations Sentinel-2 optical satellite imagery México Ecorregiones Observaciones sin nubes Sentinel-2 1C imágenes satelitales ópticas info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article 2020 ftunpvalenciaojs https://doi.org/10.4995/raet.2020.14044 https://doi.org/10.3390/rs6032176 https://doi.org/10.1016/j.rse.2011.07.023 https://doi.org/10.1191/0309133305pp432ra https://doi.org/10.1038/s41598-020-65600-1 https://doi.org/10.1016/j https://doi.org/ 2022-01-07T06:51:06Z Sentinel-2 imagery has the highest temporal, spectral and spatial resolution to monitor land surface among the freely available multispectral collections. However, the possibility to use these images in different applications is conditioned by the number of cloudless observations available for a certain spatiotemporal window. Thus, the objective of this article is to analyze the number of Sentinel-2 observations available for the Mexican territory at image and pixel level. In the first case, the total number of available images and its cloud cover percentage was calculated; while in the second case, the number of cloudless observations was estimated for each pixel. Additionally, in order to take into account the territory diversity, the monthly mean number of cloudless observations, as well as the proportion of its surface with at least one cloudless observation in monthly, bimonthly, trimonthly and annual intervals, was computed for each one of the seven ecoregions of the country. The results show that annually, the number of valid observations per pixel is between 0 and 121 observations, while in monthly evaluations, between 0 and 6.58 observations. Additionally, in the 2017-2019 period annual observations can be obtained for the entire Mexican land surface, while in 2018-2019, monthly or trimonthly evaluations can be achieved, depending on the ecoregion. We consider that these results will provide useful information for researchers that are interested in using Sentinel-2 imagery for different applications. Actualmente, las imágenes Sentinel-2 son uno de los acervos multiespectrales y gratuitos de mayor resolución temporal, espectral y espacial para monitorear la superficie terrestre. Sin embargo, la posibilidad de utilizar este acervo para distintas aplicaciones está condicionada por el número de observaciones sin nubes disponibles para una ventana espacio-temporal determinada. Por ello, este artículo tuvo el objetivo de analizar el número de observaciones de Sentinel-2 disponibles para el territorio mexicano a nivel de imagen y de pixel. En el primer caso, se contabilizó el total de imágenes disponibles por año y su porcentaje de nubosidad; mientras que, en el segundo, se calculó el número de observaciones despejadas por pixel. Además, para tomar en cuenta la diversidad del territorio, se evaluó el promedio mensual de las observaciones por pixel de cada una de las siete ecorregiones del país, así como la proporción de su superficie con por lo menos una observación despejada en intervalos mensuales, bimestrales, trimestrales y anuales. Los resultados mostraron que el número de observaciones válidas por pixel variaron entre 0 y 121 observaciones al año y entre 0 y 6.58 al mes. Adicionalmente, se observó que en el periodo 2017 – 2019 se pueden obtener observaciones de todo el país en ventanas anuales, mientras que en el periodo 2018 – 2019, se pueden obtener observaciones en intervalos mensuales o trimestrales, dependiendo de la ecorregión. Finalmente, consideramos que los resultados de este trabajo servirán de guía para los usuarios interesados en utilizar estas imágenes para distintos estudios. Article in Journal/Newspaper Antarctic and Alpine Research Arctic Universitat Politècnica de València: PoliPapers Tuvo ENVELOPE(13.782,13.782,67.054,67.054) Revista de Teledetección 56 103