Drought monitoring in El Salvador through remotely sensed variables using the Google Earth Engine platform

O. Córdova, V. Venturini, E. Walker

Abstract

Drought is a phenomenon that causes great economic losses in the society and is being observed more frequently due to climate change. In Central America this event is related to the anomalous distribution of precipitation (P) in a short period, within the rainy season. Specifically, in El Salvador, the phenomenon socalled “canícula” is associated to a significant decrease in P that lasts few days, making difficult to monitor it with P alone, as it is currently done. At present, many indicators have been developed to characterize droughts. In particular, the standardized precipitation and the condition indices proposed by Kogan (1995) that use various sources of information, stand out. In this work, five indicators of water deficit were applied - the standardized P, evapotranspiration (ET), the soil moisture condition index (HSCI), the vegetation condition index (VCI) and water stress (EH)- to assess droughts in El Salvador. For this, satellite information, climate database and the application programming interface available on the Google Earth Engine platform were used. The behaviour of the indexes in the period 2015-2019 was analysed, particularly the extremely dry year 2015, to determine the monitoring capacity of the indicators used. The results obtained suggest that the proposed set of indicators allows monitoring the drought, by identifying the onset, impact and territorial extension of it in El Salvador.


Keywords

water deficit; monitoring; remote sensing; Google Earth Engine; water stress

Full Text:

PDF

References

Cea, C., Cristóbal, J., Pons, X. 2007. An improved methodology to map snow cover by means of Landsat and MODIS imagery. En: Geoscience and Remote Sensing Symposium, 2007. IGARSS 2007. Barcelona. IEEE International, 4217-4220. https://doi.org/10.1109/IGARSS.2007.4423781

Elvidge, C.D., Sutton, P.C., Wagner, T.W., Ryznar, R., Goetz, S.J., Smith, A.J., Jantz, C., Seto, K., Imhoff, M.L., Vogelmann, J., wang, Y.Q., Milesi, C., Nemani, R. 2004. Urbanization. En: Gutman, G. et al. (ed.). Land change science: Observing, monitoring, and understanding trajectories of change on the earth's surface. Dordrecht, Países Bajos: Kluwer Academic Publishers, pp. 315-328. https://doi.org/10.1007/978-1-4020-2562-4_18

ESA. 2015. Sentinel-2 User Handbook. Recuperado de https://sentinel.esa.int/documents/247904/685211/ Sentinel-2_User_Handbook Último acceso: 4 de febrero, 2020.

González-Guerrero, O., Pons-Fernández, X., Bassols- Morey, R., Camps-Fernandez, F.J. 2019. Dinàmica de les superfícies de conreu a Catalunya mitjançant Teledetecció en el període 1987-2012. Quaderns Agraris, 46, 59-91.

Hansen, M.C., Loveland, T.R. 2012. A review of large area monitoring of land cover change using Landsat data. Remote Sensing of Environment, 122, 66-74. https://doi.org/10.1016/j.rse.2011.08.024

ICC. 1992. Mapa d'usos del sòl de Catalunya. Institut Cartogràfic de Catalunya. Barcelona. 118 p.

ICGC (2017). Datos lidar. Institut Cartogràfic i Geològic de Catalunya. Recuperado de https://www.icgc.cat/ es/Descargas/Elevaciones/Datos-lidar Último acceso: 1 de mayo, 2020.

Loveland, T.R., Dwyer, J.L. 2012. Landsat: Building a strong future. Remote Sensing of Environment, 122, 22-29. https://doi.org/10.1016/j.rse.2011.09.022

Moré, G., Pons, X. 2007. Influencia del número de imágenes en la calidad de la cartografía detallada de vegetación forestal. Revista de Teledetección, 28, 61- 68. Recuperado de http://www.aet.org.es/revistas/ revista28/7-111_More_revisado.pdf Último acceso: 1 de mayo, 2020.

Padial, M., Vidal-Macua, J.J., Serra, P., Ninyerola, M., Pons, X. 2019. Aplicación de filtros multicriterio basados en NDVI para la extracción de áreas de entrenamiento desde la base de datos SIOSE. Ruiz L.A., Estornell J., Calle A., Antuña-Sánchez J.C. (eds) Teledetección: hacia una visión global del cambio climático, pp. 311-314. ISBN: 978- 84-1320-038-5. Libro de actas XVIII Congreso de la Asociación Española de Teledetección, 24 - 27 Septiembre, Valladolid (Spain).

Padró, J.C., Pons, X., Aragonés, D., Díaz-Delgado, R., García, D., Bustamante, J., Pesquer, L., Domingo- Marimon, C., González-Guerrero, O., Cristóbal, J., Doktor, D., Lange, M. 2017. Radiometric Correction of Simultaneously Acquired Landsat-7/Landsat-8 and Sentinel-2A Imagery Using Pseudoinvariant Areas (PIA): Contributing to the Landsat Time Series Legacy. Remote Sensing, 9(12), 1319. https://doi.org/10.3390/rs9121319

Padró, J.C., Muñoz, F.J., Ávila, L.A., Pesquer, L., Pons, X. 2018. Radiometric Correction of Landsat-8 and Sentinel-2A Scenes Using Drone Imagery in Synergy with Field Spectroradiometry. Remote Sensing, 10(11), 1687. https://doi.org/10.3390/rs10111687

Pons, X. 2004. MiraMon. Sistema de Información Geográfica y software de Teledetección. Centre de Recerca Ecològica i Aplicacions Forestals, CREAF. Bellaterra. ISBN: 84-931323-4-9. Recuperado de http://www.miramon.cat/Index_es.htm Último acceso: 1 de mayo, 2020.

Pons, X., Ninyerola, M. 2008. Mapping a topographic global solar radiation model implemented in a GIS and refined with ground data. International Journal of Climatology, 28(13), 1821-1834. https://doi.org/10.1002/joc.1676

Pons, X., Pesquer, L., Cristóbal, J., González- Guerrero, O. 2014. Automatic and improved radiometric correction of Landsat imagery using reference values from MODIS surface reflectance images. International Journal of Applied Earth Observation and Geoinformation, 33, 243-254. https://doi.org/10.1016/j.jag.2014.06.002

Pons X., Masó J. 2016. A comprehensive open package format for preservation and distribution of geospatial data and metadata. Computers & Geosciences, 97, 89-97. https://doi.org/10.1016/j.cageo.2016.09.001

Townshend, J.R., Masek, J.G., Huang, C., Vermote, E.F., Gao, F., Channan, S., Sexton, J.O., Feng, M., Narasimhan, R., Kim, D., Song, K., Song, D., Song, X. P., Noojipady, P., Tan, B., Hansen, M.C., Li, M., Wolfe, R.E. 2012. Global characterization and monitoring of forest cover using Landsat data: Opportunities and challenges. International Journal of Digital Earth, 5(5), 373-397. https://doi.org/10.1080/17538947.2012.713190

Woodcock, C.E., Allen, R., Anderson, M., Belward, A., Bindschadler, R., Cohen, W., Gao, F., Goward, S.N., Helder, D., Helmer, E., Nemani, R., Oreopoulos, L., Schott, J., Thenkabail, P.S., Vermote, E.F., Vogelmann, J., Wulder, M.A., Wynne, R. 2008. Free access to Landsat imagery. Science, 320(5879), 1011. https://doi.org/10.1126/science.320.5879.1011a

Wulder, M.A., Masek, J.G., Cohen, W.B., Loveland, T.R., Woodcock, C.E. 2012. Opening the archive: How free data has enabled the science and monitoring promise of Landsat. Remote Sensing of Environment, 122, 2-10. https://doi.org/10.1016/j.rse.2012.01.010

Chang, K.Y., Xu, L., Starr, G., Paw U, K.T. 2018. A Drought Indicator Reflecting Ecosystem Responses to Water Availability: The Normalized Ecosystem Drought Index. Agricultural and Forest Meteorology, 250-251, 102-117. https://doi.org/10.1016/j.agrformet.2017.12.001

Dada, H., Sevilla M. 2009. IV censo agropecuario 2007-2008. Resumen de Resultados [On line]. San Salvador: Ministry of Economy, Vice-ministry of Industry (SV) 2009 Dec [cited 2013 Aug 18]. 74 p.

Didan, K., Munoz, A. B., Solano, R., Huete, A. 2015. MODIS vegetation index user's guide (MOD13 series). University of Arizona: Vegetation Index and Phenology Lab.

Du, L., Tian, Q., Yu, T., Meng, Q., Jancso, T., Udvardy, P., Huang, Y. 2013. A comprehensive drought monitoring method integrating MODIS and TRMM data. International Journal of Applied Earth Observation and Geoinformation, 23, 245-253. https://doi.org/10.1016/j.jag.2012.09.010

Entekhabi, D., Yueh, S., O'Neill, P.E., Kellog, K.H., Allen, A., Bindlish, R., Das, N., et al. 2014. SMAP Handbook-Soil Moisture Active Passive: mapping Soil Moisture and Freeze/Thaw from space. National Aeronautic Space Administration.

Fensholt, R., Sandholt, I. 2003. Derivation of a shortwave infrared water stress index from MODIS near- and shortwave infrared data in a semiarid environment. Remote Sensing Environment, 87, 111-121. https://doi.org/10.1016/j.rse.2003.07.002

Food and Agriculture Organization of the United Nations, 1974. Soil Map of the World, 1:5 000 000: Volume I: Legend.

García-Haro, F.J., Campos-Taberner, M., Sabater, N., Belda, F., Moreno, A., Gilabert, M.A., Martínez, B., Pérez-Hoyos A., Meliá, J. 2014. Vulnerabilidad de la vegetación a la sequía en España, Revista de Teledetección, 42, 29-37. https://doi.org/10.4995/raet.2014.2283

Girolimetto, D., Venturini, V. 2013. Water Stress Estimation from NDVI-Ts Plot and the Wet Environment Evapotranspiration. Advances in Remote Sensing, 2, 283-291. https://doi.org/10.4236/ ars.2013.24031

Girolimetto, D., Venturini, V. 2014. Evapotranspiration and water stress estimation from TIR and SWIR bands. Agriculture, Forestry and Fisheries, 3(6-1), 36-45.

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

Holzman, M.E., Carmona, F., Rivas, R., Niclòs, R. 2018. Early assessment of crop yield from remotely sensed water stress and solar radiation data. ISPRS journal of photogrammetry and remote sensing, 145, 297-308. https://doi.org/10.1016/j.isprsjprs.2018.03.014

Kattan, C., Menjívar, L., Molina, G., Peñate, Y., Estrada A., Moran, I., Chávez T., Arriola B., Cruz D., Vides R., Erazo A., Beltrán H., Rivas C., Barrera G., Cañas, A. 2017. Informe Nacional del Estado de los Riesgos y Vulnerabilidades, San Salvador. Ministerio de Medio Ambiente y Recursos Naturales.

Kim, D., Rhee J. 2016. A drought index based on actual evapotranspiration from the Bouchet hypothesis. Geophysical Research Letters, 43, 10277-10285. https://doi.org/10.1002/2016GL070302

Kogan, F.N. 1990. Remote sensing of weather impacts on vegetation in non-homogeneous areas. International Journal of Remote Sensing, 11(8), 1405-1419. https://doi.org/10.1080/01431169008955102

Kogan, F.N. 1995. Application of vegetation index and brightness temperature for drought detection. Advances in space research, 15(11), 91-100. https://doi.org/10.1016/0273-1177(95)00079-T

Mishra, A., Vu, T., Veettil, A. V., Entekhabi, D. 2017. Drought monitoring with soil moisture active passive (SMAP) measurements. Journal of Hydrology, 552, 620-632. https://doi.org/10.1016/j.jhydrol.2017.07.033

Mladenova, I.E., Bolten, J.D., Crow, W.T., Sazib, N., Cosh, M.H., Tucker, C.J., Reynolds, C. 2019. Evaluating the Operational Application of SMAP for Global Agricultural Drought Monitoring. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(9), 3387-3397. https://doi.org/10.1109/JSTARS.2019.2923555

Mo, K.C. 2008. Model-based drought indices over the United States. Journal of Hydrometeorology, 9(6), 1212-1230. https://doi.org/10.1175/2008JHM1002.1

Moran, M.S., Clarke, T.R., Inoue, Y., Vidal, A. 1994. Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index. Remote Sensing of Environment, 49(3), 246- 263. https://doi.org/10.1016/0034-4257(94)90020-5

Priestley, C.H.B., Taylor, R.J. 1972. On the assessment of surface heat flux and evaporation using large-scale parameters. Monthly Weather Review, 100, 81-92. https://doi.org/10.1175/1520-0493(1972)100%3C0 081:OTAOSH%3E2.3.CO;2

Rodell, M., Houser, P.R., Jambor, U.E.A., Gottschalck, J., Mitchell, K., Meng, C.J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin, J.K., Walker, J.P., Lohmann, D., Toll, D. 2004. The global land data assimilation system. Bulletin of the American Meteorological Society, 85(3), 381-394. https://doi.org/10.1175/BAMS-85-3-381

Sánchez, N., González-Zamora, Á., Martínez- Fernández, J., Piles, M., Pablos, M. 2018. Integrated remote sensing approach to global agricultural drought monitoring. Agricultural and forest meteorology, 259, 141-153. https://doi.org/10.1016/j.agrformet.2018.04.022

Valladares, F., Vilagrosa, A., Peñuelas, J., Ogaya, R., Camarero, J. J., Corcuera, L., Sisó, S., Gil-Pelegrín, E. 2004. Estrés hídrico: ecofisiología y escalas de la sequía. En Valladares, F. Ecología del bosque mediterráneo en un mundo cambiante. 163-190. Ministerio de Medio Ambiente, EGRAF, S. A., Madrid.

Van der Zee Arias, A., Van der Zee, J., Meyrat, A., Poveda, C., Picado, L. 2012. Estudio de caracterización del Corredor Seco Centroamericano (Países CA-4): Tomo I. FAO, Roma (Italia).

Venturini, V., Islam, S., Rodríguez, L. 2008. Estimation of evaporative fraction and evapotranspiration from MODIS products using a complementary based model. Remote Sensing of Environment, 112(1), 132-141. https://doi.org/10.1016/j.rse.2007.04.014

Vicente-Serrano, S.M., Beguería, S., López- Moreno, J.I. 2010. A multiscalar drought index sensitive to global warming: The Standardized Precipitation Evapotranspiration Index (SPEI). Journal of Climate, 23, 1696-1718. https://doi.org/10.1175/2009JCLI2909.1

Walker, E., García, G.A., Venturini, V., Carrasco, A. 2019. Regional evapotranspiration estimates using the relative soil moisture ratio derived from SMAP products. Agricultural Water Management, 216, 254- 263. https://doi.org/10.1016/j.agwat.2019.02.009

Walker, E., Venturini, V. 2019. Land surface evapotranspiration estimation combining soil texture information and global reanalysis datasets in Google Earth Engine. Remote Sensing Letters, 10, 929-938. https://doi.org/10.1080/2150704X.2019.1633487

Zhang, L., Jiao, W., Zhang, H., Huang, C., Tong, Q. 2017. Studying drought phenomena in the Continental United States in 2011 and 2012 using various drought indices. Remote sensing of environment, 190, 96- 106. https://doi.org/10.1016/j.rse.2016.12.010

Zhou, L., Zhang, J., Wu, J., Zhao, L., Liu, M., Lü, A., Wu, Z. 2012. Comparison of remotely sensed and meteorological data-derived drought indices in mid-eastern China. International journal of remote sensing, 33(6), 1755-1779. https://doi.org/10.1080/01431161.2011.600349

Abstract Views

1458
Metrics Loading ...

Metrics powered by PLOS ALM




 

This journal is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International

Universitat Politècnica de València

Official Journal of the Spanish Association of Remote Sensing

e-ISSN: 1988-8740    ISSN: 1133-0953           https://doi.org/10.4995/raet