Radiant heat flux map of El Salvador using satellite remote sensing

Authors

  • Carlos Pocasangre Universidad de El Salvador
  • Luis Castillo Universidad de El Salvador
  • Carlos Martínez Universidad de El Salvador
  • Andrés García Universidad de El Salvador
  • Douglas Rivas Universidad de El Salvador
  • Rubén Henríquez Universidad de El Salvador

Keywords:

Landsat, Satellite Image, Terrestrial Surface Temperature, Emissivity, Heat Flow, ASTER GED, Earth Explorer, Google Earth Engine

Abstract

El Salvador is in a region populated with volcanos in the Pacific Ring of Fire, this gives rise to the presence of geothermal phenomena, which are of interest for the study of the implementation of applications that make use of this geothermal resource in the country. In this research, the use of remote sensing data obtained from satellite images and other sources is implemented with the goal of obtaining information related to the Earth, like terrain emissivity, land surface temperature, ambient temperature, among various types of spectral information provided by these sources which were implemented. Obtaining this data is highly valuable for calculating various parameters, like vegetation indexes, land surface temperature, atmospheric transmissivity, and radiative heat flux, which is the main goal of this research. With the data obtained, a map of El Salvador will be made, in which the result will show and classify the information regarding the heat flux values. In addition, an analysis of these values in points of geothermal interest is also presented. The heat flux map generated will be useful to identify areas that present surface geothermal manifestations that are of possible geothermal interest, the heat fluxes in these areas will be obtained for each satellite scene captured by Landsat 8 in 2019 and the average annual heat flux. Volcanic areas that present high heat fluxes will be used as a reference for surface manifestations

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Author Biographies

  • Carlos Pocasangre, Universidad de El Salvador

    Facultad de Ingeniería y Arquitectura, Escuela de Ingeniería Eléctrica

  • Luis Castillo, Universidad de El Salvador

    Facultad de Ciencias Naturales y Matemática, Escuela de Geofísica

  • Carlos Martínez, Universidad de El Salvador

    Facultad de Ingeniería y Arquitectura, Escuela de Ingeniería Eléctrica

  • Andrés García, Universidad de El Salvador

    Facultad de Ingeniería y Arquitectura, Escuela de Ingeniería Eléctrica

  • Douglas Rivas, Universidad de El Salvador

    Facultad de Ingeniería y Arquitectura, Escuela de Ingeniería Eléctrica

     

  • Rubén Henríquez, Universidad de El Salvador

    Facultad de Ingeniería y Arquitectura, Escuela de Ingeniería Eléctrica

References

Alvarenga Artiga, K., Amaya Mata, J., & Sibrián Carballo, M. (2004). Evaluación y análisis de los beneficios de la ecoeficiencia en los procesos de la perforación de pozos geotérmicos. Ciudad Universitaria: Universidad de El Salvador

Baldridge, A. M., Hook, S. J., Grove, C. I., & Rivera, G. (2009). The ASTER spectral library version 2.0. Remote Sensing of Environment, 114(4), 711-715

Bromley, C. J., van Manen, S. M., & Mannington, W. (2011). Heat flux from steaming ground: reducing uncertainties. En Thirty-Sixth WOrkshop on Geothermal Reservoir Engineering. Stanford, California: Stanford Geothermal Program

Campos, T. (1988). Geothermal Resources of El Salvador. Preliminary Assessment. Great Britain: Pergamon Press plc.

Carlson, T. N., & Ripley, D. A. (1997). On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sensing, 62(3), 241-252.

Caselles, V., Rubio, E., & Badenas, C. (1997). Emissivity measurements of several soils and vegetation types in the 8–14, μm Wave band: Analysis of two field methods. Remote Sensing of Environment, 59(3), 490-521

ERA5-Land_Hourly. (s.f.). ECMWF climate reanalysis. Obtenido de https://developers. google.com/earth-engine/datasets/ catalog/ECMWF_ERA5_LAND_HOURLY

Ermida, S. L., Soares, P., Mantas, V., Göttsche, F.-M., & Trigo, I. F. (2020). Google Earth Engine Open-Source Code for Land Surface Temperature Estimation from the Landsat Series. Remote Sensing, 12(9), 1471.

FlujoDeCalor-SV. (s.f.). GeoTIFF de las bandas NDVI, LST, EM y Temperatura Ambiental. Obtenido de https://code.earthengine. google.com/?accept_repo=users/gm15001/ FlujoDeCalor-SV

GEE. (s.f.). Landsat smw list. Obtenido de https:// code.earthengine.google.com/?accept_ repo=users/sofiaermida/landsat_smw_lst

Hulley, G. C., & Hook, S. J. (2010). Generating Consistent Land Surface Temperature and Emissivity Products Between ASTER and MODIS DAta for Earth Science Research. IEEE Transactions on Geoscience and Remote Sensing, 49(4), 1304 - 1315.

Hulley, G. C., Hook, S. J., & Baldridge, A. M. (2009). Validation of the North American ASTER Land Surface Emissivity Database (NAALSED) version 2.0 using pseudoinvariant sand dune sites. Remote Sensing of Environment, 113(10), 2224-2233

Hulley, G., Hook, S., Abbott, E., Malakar, N., Islam, T., & Abrams, M. (2018). The ASTER Global Emissivity Dataset (ASTER GED): Mapping Earth’s emissivity at 100 m spatial scale. Geophysical Research Letters, 42(19), 7966- 7976.

Jiménez-Muñoz, J. C., Sobrino, J. A., Plaza, A., Guanter, L., Moreno, J., & Martínez, P. (2009). Comparison Between Fractional Vegetation Cover Retrievals from Vegetation Indices and Spectral Mixture Analysis: Case Study of PROBA/CHRIS Data Over an Agricultural Area. Sensors, 9(2), 768-793.

Kaneko, T., & Wooster, M. J. (1999). Landsat infrared analysis of fumarolic activity at Unzen Volcano: time-series comparison with gas and magma fluxes. Journal of Volcanology and Geothermal Research, 89, 81-94

Kustas, W., & Norman, J. (1996). Use of remote sensing for evapo-transpiration monitoring over land surfaces. Hydrological Sciences Journal, 41(4), 495-516.

Lewis., F. M. (1998). Principles and applications of imaging radar

LSIB. (2017). Large Scale International Boundaru Polygons Symplified. Obtenido de https://developers.google.com/earthengine/ datasets/catalog/USDOS_LSIB_ SIMPLE_2017

Malakar, N. K., Hulley, G. C., Hook, S. J., Laraby, K. G., Cook, M., & Schott, J. R. (2018). An Operational Land Surface Temperature Product for Landsat Thermal Data: Methodology and Validation. IEEE Transactions on Geoscience and Remote Sensing, 1-19.

Mia, M. B., & Fujimitsu, Y. (2011). Study on satellite images based spectral emissivity, land surface temperature and land-cover in and around Kuju Volcano, Central Kyushu, Japan. Journal of Advanced Science and Engineering Research, 1, 177-191

Mia, M. B., Bromley, C. J., & Fujimitsu, Y. (2012). Monitoring heat flux using Landsat TM/ ETM + thermal infrared data - A case study at Karapiti (‘Craters of the Moon’) thermal area, New Zealand. Journal of Volcanology and Geothermal Research, 235, 1-10

Mia, M. B., Nishijima, J., & Fujimitsu, Y. (2014). Exploration and monitoring geothermal activity using Landsat ETM+ images. A case study at Aso volcanic area in Japan. Journal of Volcanology and Geothermal Research, 275, 14-221

Mokhari, A., Noory, H., Purshakouri, F., Haghighatmehr, P., Afrasiabian, Y., Razavi, M., . . . Naeni, A. S. (2019). Calculating potential evapotranspiration and single crop coefficient based on energy balance equation using Landsat 8 and Sentinel-2. Sensing, 154, 231-241.

Montanaro, M., Gerace, A., & Rohrbach, S. (2015). Toward an operational stray light correction for the Landsat 8 Thermal Infrared Sensor. Applied Optics, 54(13), 3963-3978.

NASA. (s.f.). Atmospheric Parameter Calculator. Obtenido de https://atmcorr.gsfc.nasa.gov/

NASA. (s.f.). NASA. Obtenido de https://landsat. gsfc.nasa.gov/

Ogawa, K., & Schmugge, T. (2004). Mapping Surface Broadband Emissivity of the Sahara Desert Using ASTER and MODIS Data. Earth Interactions, 8(7), 1-14.

Peng, J., Jia, J., Liu, Y., Li, H., & Wu, J. (2018). Seasonal contrast of the dominant factors for spatial distribution of land surface temperature in urban areas. Remote Sensing of Environment, 215, 255-267.

Peres, L., & DaCamara, C. (2005). Emissivity maps to retrieve land-surface temperature from MSG/SEVIRI. IEEE Transactions on Geoscience and Remote Sensing, 43(8), 1834-1844.

Planck, M. (1914). The Theory of Heat Radiation (Segunda ed.). Philadelphia, P. Blakiston’s Son & Co.

Prihodko, L., & Goward, S. N. (1997). Estimation of air temperature from remotely sensed surface observations. Remote Sensing of Environment, 60(3), 335-346.

Prol-Ledesma, R. M., & Morán-Zenteno, D. J. (2018). Heat flow and geothermal provinces in Mexico. Cd. Universitaria: Elsevier Ltd.

Ren, H., Liu, R., Qin, Q., Fan, W., Yu, L., & Du, C. (2017). Mapping finer‐resolution land surface emissivity using Landsat images in China. Journal of Geophysical Research: Atmospheres, 122(13), 6764-6781.

Richards, J. A. (2012). Remote Sensing Digital Image Analysis: An Introduction. (5th ed.). Springer Publishing Company, Incorporated

Ruíz, J. A. (2019). Tensor Decomposition and Deep Learning Neural Networks for Multispectral Image Compression and Semantic Segmentation.

Thomas, C. (May 2008). Synthesis of Multispectral Images to High Spatial Resolution: A Critical Review of Fusion Methods Based on Remote Sensing Physics”. IEEE Transactions on Geoscience and Remote Sensing 46.5

USGS. (Noviembre de 2019). Landsat 8 Data Users Handbook. Recuperado el 25 de Enero de 2021, de https://www.usgs.gov/ core-science-systems/nli/landsat/landsat- 8-data-users-handbook

Valor, E., Caselles, V., Coll, C., & Rubio, E. (1997). Thermal band selection for the PRISM instrument: 1. Analysis of emissivitytemperature separation algorithms. Journal of Geophysical Research: Atmospheres, 102(10), 11145-11164.

Wang, F., Qin, Z., Song, C., Tu, L., Karnieli, A., & Zhao, S. (2015). An Improved Mono-Window Algorithm for Land Surface Temperature Retrieval from Landsat 8 Thermal Infrared Sensor Data. Remote Sensing, 7(4), 4268- 4289

Wehner, D. R. (1994). High-Resolutions Radar. 2nd.

Weier, J., & Herring, D. (2000). Measuring Vegetation. Recuperado el 1 de Febrero de 2021, de https://earthobservatory.nasa.gov/ features/MeasuringVegetation

Weier, J., & Herring, D. (2000). Normalized Difference Vegetation Index (NDVI). Recuperado el 1 de Febrero de 2021, de https://earthobservatory.nasa.gov/ features/MeasuringVegetation/measuring_ vegetation_2.php

Published

2023-09-29

Issue

Section

Artículos Científicos

How to Cite

Radiant heat flux map of El Salvador using satellite remote sensing. (2023). Revista Minerva, 4(3), 34-52. https://revistas.ues.edu.sv/index.php/minerva/article/view/2616