- -

Potencial del producto SEVIRI/MSG GPP en la detección de zonas afectadas por estrés hídrico

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Potencial del producto SEVIRI/MSG GPP en la detección de zonas afectadas por estrés hídrico

Mostrar el registro sencillo del ítem

Ficheros en el ítem

Thumbnail
Nombre: Martínez;Sánchez- ...
Tamaño: 5.463Mb
Formato: PDF
Descripción: Versión editorial
Abrir
dc.contributor.author Martínez, B. es_ES
dc.contributor.author Sánchez-Ruiz, S. es_ES
dc.contributor.author Campos-Taberner, M. es_ES
dc.contributor.author García-Haro, F. J. es_ES
dc.contributor.author Gilabert, M. A. es_ES
dc.date.accessioned 2020-06-30T07:14:04Z
dc.date.available 2020-06-30T07:14:04Z
dc.date.issued 2020-06-23
dc.identifier.issn 1133-0953
dc.identifier.uri http://hdl.handle.net/10251/147165
dc.description.abstract [ES] Se presenta el nuevo producto de producción primaria bruta (GPP) de EUMETSAT derivado a partir de datos del satélite geoestacionario SEVIRI/MSG (MGPP LSA-411) y se evalúa su potencial para detectar zonas afectadas por estrés hídrico (hot spots). El producto GPP se basa en la aproximación de Monteith, que modela la GPP de la vegetación como el producto de la radiación fotosintéticamente activa (PAR) incidente, la fracción de PAR absorbida (fAPAR) y un factor de eficiencia de uso de la radiación (ε). El potencial del producto MGPP para detectar hot spots se evalúa, utilizando un periodo corto de tres años, a escala local y regional, comparando con datos in situ derivados de medidas en torres eddy covariance (EC) y con datos GPP derivados de satélite (producto de 8 días MOD17A2H.v6 a 500 m y producto de 10 días GDMP a 1 km). Los resultados preliminares sobre el uso del producto MGPP en la evaluación de la respuesta del ecosistema a posibles eventos de déficit de agua ponen de manifiesto que este producto, calculado íntegramente a partir de datos MSG (EUMETSAT), ofrece una alternativa prometedora para detectar y caracterizar zonas afectadas por sequía a través de la incorporación de un coeficiente de estrés hídrico. es_ES
dc.description.abstract [EN] This study aims to introduce a completely new and recently launched 10-day GPP product based on data from the geostationary MSG satellite (MGPP LSA-411) and to assess its capability to detect areas affected by water stress (hot spots). The GPP product is based on Monteith’s concept, which models GPP as the product of the incoming photosynthetically active radiation (PAR), the fractional absorption of that flux (fAPAR) and a lightuse efficiency factor (ε). Preliminary results on the use of the MGPP product in the assessment of ecosystem response to rainfall deficit events are presented in this work for a short period of three years. The robustness of this product is evaluated at both site and regional scales across the MSG disk using eddy covariance (EC) GPP measurements and Earth Observing (EO)-based GPP products, respectively. The EO-based products belong to the 8-day MOD17A2H v6 at 500 m and the 10-day GDMP at 1 km. The results reveal the MGPP product, derived entirely from MSG (EUMETSAT) products, as an efficient alternative to detect and characterize areas under water scarcity by means of a coefficient of water stress. es_ES
dc.description.sponsorship Trabajo financiado por los proyectos LSA SAF (EUMETSAT) y ESCENARIOS (CGL2012–35831). Agradecemos a los responsables de las torres EC la cesión de los datos de GPP. es_ES
dc.language Español es_ES
dc.publisher Universitat Politècnica de València es_ES
dc.relation.ispartof Revista de Teledetección es_ES
dc.rights Reconocimiento - No comercial - Compartir igual (by-nc-sa) es_ES
dc.subject MSG es_ES
dc.subject MGPP es_ES
dc.subject Monteith es_ES
dc.subject Hot spots es_ES
dc.subject Estrés hídrico es_ES
dc.subject Detection es_ES
dc.subject Water stress es_ES
dc.title Potencial del producto SEVIRI/MSG GPP en la detección de zonas afectadas por estrés hídrico es_ES
dc.title.alternative Capability assessment of the SEVIRI/MSG GPP product for the detection of areas affected by water stress es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.4995/raet.2020.13285
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//CGL2012-35831/ES/TELEDETECCION DE VARIABLES CLIMATICAS ESENCIALES: EFECTOS DEL ESTRES HIDRICO EN LA ESTIMACION DE FLUJOS DE CARBONO/ es_ES
dc.rights.accessRights Abierto es_ES
dc.description.bibliographicCitation Martínez, B.; Sánchez-Ruiz, S.; Campos-Taberner, M.; García-Haro, FJ.; Gilabert, MA. (2020). Potencial del producto SEVIRI/MSG GPP en la detección de zonas afectadas por estrés hídrico. Revista de Teledetección. 0(55):17-29. https://doi.org/10.4995/raet.2020.13285 es_ES
dc.description.accrualMethod OJS es_ES
dc.relation.publisherversion https://doi.org/10.4995/raet.2020.13285 es_ES
dc.description.upvformatpinicio 17 es_ES
dc.description.upvformatpfin 29 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 0 es_ES
dc.description.issue 55 es_ES
dc.identifier.eissn 1988-8740
dc.relation.pasarela OJS\13285 es_ES
dc.contributor.funder European Organization for the Exploitation of Meteorological Satellites es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES
dc.description.references Anyamba, A., Glennie, E., Small, J. 2018. Teleconnections and Interannual Transitions as Observed in African Vegetation: 2015-2017. Remote Sensing, 10, 1038. https://doi.org/10.3390/rs10071038 es_ES
dc.description.references Bahta, Y.T., Jordaan, A., Muyambo, F. 2016. Communal Farmers' perception of drought in South Africa: policy implication for drought risk reduction. International Journal of Disaster Risk Reduction, 20, 39-50. https://doi.org/10.1016/j.ijdrr.2016.10.007 es_ES
dc.description.references Bartholome, E. Belward, A.S. 2005. GLC2000: a new approach to global land cover mapping from Earth observation data. International Journal of Remote Sensing, 26(9), 1959-1977. https://doi.org/10.1080/01431160412331291297 es_ES
dc.description.references Bojinski, S., Verstraete, M., Peterson, T.C., Richter, C., Simmons, A., Zemp, M. 2014. The concept of essential climate variables in support of climate research, applications, and policy. American Meteorologial Society, 95(9), 1431-1443. https://doi.org/10.1175/BAMS-D-13-00047.1 es_ES
dc.description.references CGLOPS1. 2018. Copernicus Global Land Operations 'Vegetation and Energy' Product User Manual for Dry Matter Productivity (DMP) and Gross Dry Matter Productivity (GDMP). Collection 1 km, version 2- CGLOPS1_PUM_DMP1km-V2, February 2018, 47 pp. es_ES
dc.description.references Chamaillé-Jammes, S. Fritz, H. 2009. Precipitation-NDVI relationships in eastern and southern African savannas vary along a precipitation gradient. International Journal of Remote Sensing, 30(13), 3409-3422. https://doi.org/10.1080/01431160802562206 es_ES
dc.description.references Flaming, G.M. 2004. Measurement of global precipitation. In: International Geoscience and Remote Sensing Symposium. 9, Anchorage, AK, EUA. es_ES
dc.description.references Fuster, B., Sánchez-Zapero, J., Camacho, F., García- Haro, F.J., Campos-Taberner, M. 2017. Validation of the Climate Data Record of EUMETSAT LSA SAF SEVIRI/MSG LAI, FAPAR and FVC products. Proceedings of the V RAQRS conference, Torrent, September 2017. pp. 191-196. es_ES
dc.description.references Garbulsky, M.F., Peñuelas, J., Papale, D., Ardo, J., Goulden, M.L., Kiely, G., et al. 2010. Patterns and controls of the variability of radiation use efficiency and primary productivity across terrestrial ecosystems. Global Ecology and Biogeography, 19, 253-267. https://doi.org/10.1111/j.1466-8238.2009.00504.x es_ES
dc.description.references 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 es_ES
dc.description.references García-Haro, F.J., Campos-Taberner, M., Muñoz- Mari, J., Laparra, V., Camacho, F., Sánchez- Zapero, J., Camps-Valls, G. 2018. Derivation of global vegetation biophysical parameters from EUMETSAT polar system. ISPRS Journal of Photogrammetry and Remote Sensing, 139, 57-74. https://doi.org/10.1016/j.isprsjprs.2018.03.005 es_ES
dc.description.references García-Haro, F.J., Camacho, F., Martínez, B., Campos- Taberner, M., Fuster, B., Sánchez-Zapero, J., Gilabert, M.A. 2019. Climate Data Records of Vegetation Variables from Geostationary SEVIRI/MSG Data: Products, Algorithms and Applications. Remote Sensing, 11, 2103. https://doi.org/10.3390/rs11182103 es_ES
dc.description.references Gilabert, M.A., Moreno, A., Maselli, F., Martínez, B., Chiesi, M., Sánchez-Ruíz, S., et al. 2015. Daily GPP estimates in Mediterranean ecosystems by combining remote sensing and meteorological data. ISPRS Journal of Photogrammetry and Remote Sensing, 102, 184- 197. https://doi.org/10.1016/j.isprsjprs.2015.01.017 es_ES
dc.description.references Jones, L.A., Kimball, J.S., Reichle, R.H., Madani, N., Glassy, J., Ardizzone, J.V., et al. 2017. The SMAP level 4 carbon product for monitoring ecosystem land-atmosphere CO2 exchange. IEEE Transactions on Geoscience and Remote Sensing, 55(11), 6517- 6532. https://doi.org/10.1109/TGRS.2017.2729343 es_ES
dc.description.references Kummerowa, C., Simpson, J., Thielea, O., Barnesa, W., Changa, A.T.C., Stockera, E., 2000. The Status of the Tropical Rainfall Measuring Mission (TRMM) after Two Years in Orbit. Journal of Applied Meteorology, 39, 1965-1982. es_ES
dc.description.references Liu, Z., Ostrenga, D., Teng, W., Kempler, S. 2012. Tropical Rainfall Measuring Mission (TRMM) Precipitation Data and Services for Research and Applications. Bulletin of the American Meteorological Society, 93, 1317-1325. https://doi.org/10.1175/BAMS-D-11-00152.1 es_ES
dc.description.references Liu, N., Harper, R. J., Dell, B., Liu, S., Yu, Z. 2017. Vegetation dynamics and rainfall sensitivity for different vegetation types of the Australian continent in the dry period 2002-2010. International Journal of Remote Sensing, 17, 2761-2782. https://doi.org/10.1002/eco.1811 es_ES
dc.description.references Malo, A.R Nicholson, S.E. 1990. A study of rainfall and vegetation dynamics in the African Sahel using normalized difference vegetation index. Journal of Arid Environments, 19, 1-24. https://doi.org/10.1016/S0140-1963(18)30825-5 es_ES
dc.description.references Martínez, B., Sánchez-Ruiz, S., Gilabert, M.A., Moreno, A., Taberner, M.C., García-Haro, F.J., et al. 2018. Retrieval of daily gross primary production over Europe and Africa from an ensemble of SEVIRI/MSG products. International Journal of Applied Earth Observation and Geoinformation, 65, 124-136. https://doi.org/10.1016/j.jag.2017.10.011 es_ES
dc.description.references Martínez, B., Gilabert, M.A., Sánchez-Ruiz, S., Taberner, M.C., García-Haro, F.J. 2020. Evaluation of the LSA-SAF gross primary production product derived from SEVIRI/MSG data (MGPP). ISPRS Journal of Photogrammetry and Remote Sensing, 159, 220-236. https://doi.org/10.1016/j.isprsjprs.2019.11.010 es_ES
dc.description.references McKee, T.B., Doesken, N.J., Kleist, K. 1993. The relationship of drought frequency and duration to time scale. In: Proceedings of the Eighth Conference on Applied Climatology, Anaheim, California, 17- 22 January 1993. Boston, American Meteorological Society, 179-184. es_ES
dc.description.references Monteith, J. L. 1972. Solar radiation and productivity in tropical ecosystems. Journal of Applied Ecology, 9, 747-766. https://doi.org/10.2307/2401901 es_ES
dc.description.references Running, S.W., Zhao, M. 2015. Daily GPP and Annual NPP (MOD17A2/A3) Products NASA Earth Observing System MODIS Land Algorithm. User's Guide. Version 3.0 For Collection 6. es_ES
dc.description.references Sánchez-Ruiz, S., Moreno, A., Piles, M., Maselli, F., Carrara, A., Running, S., Gilabert, M.A. 2017. Quantifying water stress effect on daily light use efficiency in Mediterranean ecosystems using satellite data. International Journal of digital Earth, 10(6), 623-638. https://doi.org/10.1080/17538947.2016.1247301 es_ES
dc.description.references Simpson, J., Adler, R., North, G.A. 1988. Proposed tropical rainfall measuring mission (TRMM) satellite. Bulletin of the American Meteorological Society, 69(3), 278-295. https://doi.org/10.1175/1520- 0477(1988)069%3C0278:APTRMM%3E2.0.CO;2 es_ES
dc.description.references Vicente-Serrano, S.M., Azorín-Molina, C., Peña- Gallardo, M., Tomas-Burguera, M., Domínguez- Castro, F., et al. 2015. A high-resolution spatial assessment of the impacts of drought variability on vegetation activity in Spain from 1981 to 2015. Natural Hazards and Earth System Sciences, 19, 1189- 1213. https://doi.org/10.5194/nhess-19-1189-2019 es_ES
dc.description.references Zhang, Y., Yu, G., Yang, J., Wimberly, M.C., Zhang, X., Tao, J., et al. 2014. Climate driven global changes in carbon use efficiency. Global Ecology and Biogeography, 23, 144-155. https://doi.org/10.1111/geb.12086 es_ES
dc.description.references Zhao, M., Running, S.W., Heinsch, F.A., Nemani, R.R. 2011. MODIS derived terrestrial primary production. Land Remote Sensing and Global Environmental Change. Springer, New York, pp. 635-660. https://doi.org/10.1007/978-1-4419-6749-7_28 es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem