Estimación de la temperatura mensual del aire usando imágenes satelitales en una zona de topografía muy variable en los Andes del sur del Ecuador
ilustraciones, gráficas
- Autores:
-
Orellana Samaniego, Maria Lorena
- Tipo de recurso:
- Fecha de publicación:
- 2020
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/78826
- Palabra clave:
- 550 - Ciencias de la tierra::551 - Geología, hidrología, meteorología
Imágenes por satélites
Pronóstico meteorológico
Temperatura del aire
satellite imagery
Weather forecasting
air temperature
Temperatura del aire mensual
Andes
Modelos de regresión
Altitud
Variables auxiliares
Andes
Regression models
Altitude
Auxiliary variables
Monthly air temperature
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional
| id |
UNACIONAL2_a1c80419ae30342548bee20ad4c3fc40 |
|---|---|
| oai_identifier_str |
oai:repositorio.unal.edu.co:unal/78826 |
| network_acronym_str |
UNACIONAL2 |
| network_name_str |
Universidad Nacional de Colombia |
| repository_id_str |
|
| dc.title.spa.fl_str_mv |
Estimación de la temperatura mensual del aire usando imágenes satelitales en una zona de topografía muy variable en los Andes del sur del Ecuador |
| title |
Estimación de la temperatura mensual del aire usando imágenes satelitales en una zona de topografía muy variable en los Andes del sur del Ecuador |
| spellingShingle |
Estimación de la temperatura mensual del aire usando imágenes satelitales en una zona de topografía muy variable en los Andes del sur del Ecuador 550 - Ciencias de la tierra::551 - Geología, hidrología, meteorología Imágenes por satélites Pronóstico meteorológico Temperatura del aire satellite imagery Weather forecasting air temperature Temperatura del aire mensual Andes Modelos de regresión Altitud Variables auxiliares Andes Regression models Altitude Auxiliary variables Monthly air temperature |
| title_short |
Estimación de la temperatura mensual del aire usando imágenes satelitales en una zona de topografía muy variable en los Andes del sur del Ecuador |
| title_full |
Estimación de la temperatura mensual del aire usando imágenes satelitales en una zona de topografía muy variable en los Andes del sur del Ecuador |
| title_fullStr |
Estimación de la temperatura mensual del aire usando imágenes satelitales en una zona de topografía muy variable en los Andes del sur del Ecuador |
| title_full_unstemmed |
Estimación de la temperatura mensual del aire usando imágenes satelitales en una zona de topografía muy variable en los Andes del sur del Ecuador |
| title_sort |
Estimación de la temperatura mensual del aire usando imágenes satelitales en una zona de topografía muy variable en los Andes del sur del Ecuador |
| dc.creator.fl_str_mv |
Orellana Samaniego, Maria Lorena |
| dc.contributor.advisor.spa.fl_str_mv |
Ospina Noreña, Jesús Efren Ballari, Daniela |
| dc.contributor.author.spa.fl_str_mv |
Orellana Samaniego, Maria Lorena |
| dc.subject.ddc.spa.fl_str_mv |
550 - Ciencias de la tierra::551 - Geología, hidrología, meteorología |
| topic |
550 - Ciencias de la tierra::551 - Geología, hidrología, meteorología Imágenes por satélites Pronóstico meteorológico Temperatura del aire satellite imagery Weather forecasting air temperature Temperatura del aire mensual Andes Modelos de regresión Altitud Variables auxiliares Andes Regression models Altitude Auxiliary variables Monthly air temperature |
| dc.subject.agrovoc.spa.fl_str_mv |
Imágenes por satélites Pronóstico meteorológico Temperatura del aire |
| dc.subject.agrovoc.eng.fl_str_mv |
satellite imagery Weather forecasting air temperature |
| dc.subject.proposal.spa.fl_str_mv |
Temperatura del aire mensual Andes Modelos de regresión Altitud Variables auxiliares |
| dc.subject.proposal.eng.fl_str_mv |
Andes Regression models Altitude Auxiliary variables Monthly air temperature |
| description |
ilustraciones, gráficas |
| publishDate |
2020 |
| dc.date.issued.spa.fl_str_mv |
2020-12-04 |
| dc.date.accessioned.spa.fl_str_mv |
2021-01-19T20:48:11Z |
| dc.date.available.spa.fl_str_mv |
2021-01-19T20:48:11Z |
| dc.type.spa.fl_str_mv |
Trabajo de grado - Maestría |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/masterThesis |
| dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
| dc.type.content.spa.fl_str_mv |
Text |
| dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/TM |
| status_str |
acceptedVersion |
| dc.identifier.uri.none.fl_str_mv |
https://repositorio.unal.edu.co/handle/unal/78826 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Nacional de Colombia |
| dc.identifier.reponame.spa.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
| dc.identifier.repourl.none.fl_str_mv |
https://repositorio.unal.edu.co/ |
| url |
https://repositorio.unal.edu.co/handle/unal/78826 https://repositorio.unal.edu.co/ |
| identifier_str_mv |
Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
| dc.language.iso.spa.fl_str_mv |
spa |
| language |
spa |
| dc.relation.references.spa.fl_str_mv |
Ahrens, D. (2008). Essentials of Meteorology. An invitation to the Atmosphere. Time (Vol. 67). https://doi.org/10.1111/j.1467-8535.2007.00763.x Appelhans T, Mwangomo E, Hardy D.R, Hemp A, Nauss T (2015) Evaluating machine learning approaches for the interpolation of monthly air temperature at Mt. Kilimanjaro Tanzania. Spat Stat-Neth 14:91-113. https://doi.org/10.1016/j.spasta.2015.05.008 Bahi H, Rhinane H, Bensalmia A (2016) Contribution of MODIS satellite image to estimate the daily air temperature in the Casablanca City, Morocco. Int. arch. photogramm. remote sens. spat. inf. Sci 42:3-11. https://doi.org/10.5194/isprs-archives-XLII-2-W1-3-2016 Ballari D, Castro E, Campozano L (2016) Validation of satellite precipitation (TRMM 3B43) in Ecuadorian coastal plains, Andean highlands and Amazonian rainforest. Int. arch. photogramm. remote sens. spat. inf. Sci 41:305-311. https://doi.org/10.5194/isprsarchives-XLI-B8-305-2016 Benali A, Carvalho A.C, Nunes J.P, Carvalhais N, Santos, A (2012) Estimating air surface temperature in Portugal using MODIS LST data. Remote Sens. Environ 124:108-121. https://doi.org/10.1016/j.rse.2012.04.024 Benavides R, Montes F, Rubio A Osoro K (2007) Geostatistical modelling of air temperature in a mountainous region of Northern Spain. Agric. For. Meteorol 146:173-188. https://doi.org/10.1016/j.agrformet.2007.05.014 Bendix J (2014) Adjustment of the Convective- Stratiform Technique (CST) to estimate 1991 / 93 El Nino rainfall distribution in Ecuador and Peru by means of Meteosat-3 IR data. Int. J. Remote Sens International Journal of Remote Sensing 8:1387-1394. https://doi.org/10.1080/014311697218502 Breiman L (2001) Random Forests. Mach Learn 45: 5–32. Campozano L, Sánchez E, Avilés Á, Samaniego E (2014) Evaluation of infilling methods for time series of daily precipitation and temperature: The case of the Ecuadorian Andes. Maskana 5:99-115. https://doi.org/10.18537/mskn.05.01.07. Chen, F., Liu, Y., & Qin, F. (2015). A statistical method based on remote sensing for the estimation of air temperature in China, 2143(July 2014), 2131–2143. https://doi.org/10.1002/joc.4113 Chowdhury, F. R., Shihab, Q., Ibrahim, U., Bari, S., Alam, J., Dunachie, S. J., … Patwary, I. (2018). The association between temperature , rainfall and humidity with common climate-sensitive infectious diseases in Bangladesh, 1–17. Córdova, M., Célleri, R., Shellito, C. J., Orellana-alvear, J., Abril, A., Carrillo-rojas, G., … Carrillo-rojas, G. (n.d.). Near-Surface Air Temperature Lapse Rate Over Complex Terrain in the Southern Ecuadorian Andes : Implications for Temperature Mapping Near-surface air temperature lapse rate over complex terrain in the Southern Ecuadorian Andes : implications for temperature mapping, 48(4), 678–684. Cuesta, F., Bustamante, M., Becerra, M. T., Postigo, J., & Peralvo, M. (2012). Panorama andino sobre cambio climático. Condesa, 1, 167. Retrieved from http://www20.iadb.org/intal/catalogo/PE/2013/12414.pdf Didan, K., Munoz, A. B., & Huete, A. (2015). MODIS Vegetation Index User ’ s Guide ( MOD13 Series ), 2015(June). Emamifar, S., & Akbar, A. (2013). Daily mean air temperature estimation from MODIS land surface temperature products based on M5 model tree, 3181(February), 3174–3181. https://doi.org/10.1002/joc.3655 Florio, E. N., Lele, S. R., Chang, Y. C., Sterner, R., & Glass, G. E. (2004). Integrating AVHRR satellite data and NOAA ground observations to predict surface air temperature: A statistical approach. International Journal of Remote Sensing, 25(15), 2979–2994. https://doi.org/10.1080/01431160310001624593 Forest, C. E., Stone, P. H., Sokolov, A. P., Allen, M. R., & Webster, M. D. (2002). Quantifying Uncertainties in Climate System Properties with the Use of Recent Climate Observations, 295(January), 113–118. Fu, G., Shen, Z., Zhang, X., Shi, P., Zhang, Y., & Wu, J. (2011). Estimating air temperature of an alpine meadow on the Northern Tibetan Plateau using MODIS land surface temperature. Acta Ecologica Sinica, 31(1), 8–13. https://doi.org/10.1016/j.chnaes.2010.11.002 Geophysics, A. (2015). The Role of Auxiliary Variables in Deterministic and Deterministic-Stochastic Spatial Models of Air Temperature in Poland. https://doi.org/10.1007/s00024-015-1199-2 Gornall, J., Betts, R., Burke, E., Clark, R., Camp, J., Willett, K., … Wiltshire, A. (2010). Implications of climate change for agricultural productivity in the early twenty-first century Implications of climate change for agricultural productivity in the early twenty-first century. https://doi.org/10.1098/rstb.2010.0158 Irmak, A., Ranade, P. K., Marx, D., Irmak, S., Hubbard, K. G., Meyer, G. E., & Martin, D. L. (2010). S i c v n, 53(6), 1759–1771. Janatian, N., Sadeghi, M., Sanaeinejad, S. H., Bakhshian, E., Farid, A., Hasheminia, S. M., & Ghazanfari, S. (2017). A statistical framework for estimating air temperature using MODIS land surface temperature data. International Journal of Climatology, 37(3), 1181–1194. https://doi.org/10.1002/joc.4766 Jang, J., Viau, A. A., & Anctil, F. (n.d.). International Journal of Remote Neural network estimation of air temperatures from AVHRR data, (October 2014), 37–41. https://doi.org/10.1080/01431160310001657533 Jin, M., & Dickinson, R. E. (2010). Land surface skin temperature climatology: Benefitting from the strengths of satellite observations. Environmental Research Letters, 5(4), 044004. Li, L., & Zha, Y. (2019). ScienceDirect Estimating monthly average temperature by remote sensing in China. Advances in Space Research, 63(8), 2345–2357. https://doi.org/10.1016/j.asr.2018.12.039 Marzban, F., Conrad, T., Marzban, P., & Sodoudi, S. (2018). Estimation of the Near-Surface Air Temperature during the Day and Nighttime from MODIS in Berlin , Germany, 7(1), 2478–2517. Meyer, H., Katurji, M., Appelhans, T., Müller, M. U., Nauss, T., Roudier, P., & Zawar-reza, P. (n.d.). Mapping Daily Air Temperature for Antarctica Based on MODIS LST, 1–16. https://doi.org/10.3390/rs8090732 Mora, D. E., & Willems, P. (2012). Decadal oscillations in rainfall and air temperature in the Paute River Basin-Southern Andes of Ecuador. Theoretical and Applied Climatology, 108(1–2), 267–282. https://doi.org/10.1007/s00704-011-0527-4 Mutiibwa, D., Strachan, S., & Albright, T. (2015). Land surface temperature and surface air temperature in complex terrain. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(10), 4762-4774. Naseer, A., Koike, T., Mohamad, R., & Ushiyama, T. (2019). Distributed hydrological modelling framework for quantitative and spatial bias correction for rainfall , snowfall , and mixed-phase precipitation using Vertical Profile of Temperature, 0–2. https://doi.org/10.1029/2018JD029811 Nieto, H., Sandholt, I., Aguado, I., Chuvieco, E., & Stisen, S. (2011). Remote Sensing of Environment Air temperature estimation with MSG-SEVIRI data : Calibration and validation of the TVX algorithm for the Iberian Peninsula. Remote Sensing of Environment, 115(1), 107–116. https://doi.org/10.1016/j.rse.2010.08.010 Ort, D., Thomson, A. M., & Wolfe, D. (2011). Climate Impacts on Agriculture :, 351–370. https://doi.org/10.2134/agronj2010.0303 Prata, A. J., Caselles, V., Coll, C., Sobrino, J. A., & Ottle, C. (1995). Thermal remote sensing of land surface temperature from satellites: Current status and future prospects. Remote Sensing Reviews, 12(3-4), 175-224. Prihodko, L., & Goward, S. N. (1997). Estimation of air temperature from remotely sensed observations. Remote Sensing of Environment, 60(3), 335–346. R., L. D., & Willmott, C. J. (1990). Mean Seasonal and Spatial Variability in Global Surface Air Temperature. Theoretical and Applied Climatology, 41, 11–21. Rollenbeck, R., & Bendix, J. (2011). Rainfall distribution in the Andes of southern Ecuador derived from blending weather radar data and meteorological fi eld observations. Atmospheric Research, 99(2), 277–289 Shi, Y., Jiang, Z., Dong, L., & Shen, S. (2017). Statistical Estimation of High-Resolution Surface Air Temperature from MODIS over the Yangtze River Delta , China, 31(April). https://doi.org/10.1007/s13351-017-6073-y.1. Sun, H., Chen, Y., Gong, A., Zhao, X., Zhan, W., & Wang, M. (2014). Estimating mean air temperature using MODIS day and night land surface temperatures. Theoretical and Applied Climatology, 118(1–2), 81–92. https://doi.org/10.1007/s00704-013-1033-7 Trapasso, L. M. (2005). Temperature Distribution. In J. E. Oliver (Ed.), Encyclopedia of World Climatology (pp. 711–716). Dordrecht: Springer Netherlands. https://doi.org/10.1007/1-4020-3266-8_203 Tomlinson, C. J., Chapman, L., Thornes, J. E., & Baker, C. (2011). Remote sensing land surface temperature for meteorology and climatology: a review. Meteorological Applications, 18(3), 296-306. Ulloa, J., Ballari, D., Campozano, L., & Samaniego, E. (2017). Two-step downscaling of TRMM 3b43 V7 precipitation in contrasting climatic regions with sparse monitoring: The case of Ecuador in tropical South America. Remote Sensing, 9(7), 1–23. https://doi.org/10.3390/rs9070758 Vancutsem, C., Ceccato, P., Dinku, T., & Connor, S. J. (2010). Evaluation of MODIS land surface temperature data to estimate air temperature in different ecosystems over Africa. Remote Sensing of Environment, 114(2), 449–465. https://doi.org/10.1016/j.rse.2009.10.002 Wan, Z. (2009). MODIS Land Surface Temperature Products Users’ Guide. Contract, (April), 30. https://doi.org/10.5067/MODIS/MOD11B3.006 Xu, Y., Knudby, A., & Ho, H. C. (2014). International Journal of Remote Estimating daily maximum air temperature from MODIS in British Columbia , Canada, (December), 37–41. https://doi.org/10.1080/01431161.2014.978957 Xu, Y., Qin, Z., & Shen, Y. (2012). International Journal of Remote Study on the estimation of near-surface air temperature from MODIS data by statistical methods, (November 2014), 37–41. Yang, D., Wang, Z., Xu, L., & Liu, Y. (2019). Estimation and distribution of near-surface meteorological elements over complex terrains : a case study in the Tibetan areas of West Sichuan. International Journal of Remote Sensing, 40(23), 8811–8837. https://doi.org/10.1080/01431161.2019.1624859 Zheng, X., Jiao-jun, Z., & Yan, Q. (2013). Monthly Air Temperatures over Northern China Estimated by Integrating MODIS Data with GIS Techniques Monthly Air Temperatures over Northern China Estimated by Integrating MODIS Data with GIS Techniques, (September). https://doi.org/10.1175/JAMC-D-12-0264.1 |
| dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
| dc.rights.license.spa.fl_str_mv |
Atribución-NoComercial-SinDerivadas 4.0 Internacional |
| dc.rights.uri.spa.fl_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
| rights_invalid_str_mv |
Atribución-NoComercial-SinDerivadas 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ http://purl.org/coar/access_right/c_abf2 |
| eu_rights_str_mv |
openAccess |
| dc.format.extent.spa.fl_str_mv |
xv, 129 páginas |
| dc.format.mimetype.spa.fl_str_mv |
application/pdf |
| dc.coverage.country.spa.fl_str_mv |
Ecuador |
| dc.coverage.tgn.spa.fl_str_mv |
http://vocab.getty.edu/page/tgn/1000051 |
| dc.publisher.spa.fl_str_mv |
Universidad Nacional de Colombia |
| dc.publisher.program.spa.fl_str_mv |
Bogotá - Ciencias Agrarias - Maestría en Geomática |
| dc.publisher.department.spa.fl_str_mv |
Escuela de posgrados |
| dc.publisher.faculty.spa.fl_str_mv |
Facultad de Ciencias Agrarias |
| dc.publisher.place.spa.fl_str_mv |
Bogotá, Colombia |
| dc.publisher.branch.spa.fl_str_mv |
Universidad Nacional de Colombia - Sede Bogotá |
| institution |
Universidad Nacional de Colombia |
| bitstream.url.fl_str_mv |
https://repositorio.unal.edu.co/bitstream/unal/78826/1/0104530761.2020.pdf https://repositorio.unal.edu.co/bitstream/unal/78826/2/license.txt https://repositorio.unal.edu.co/bitstream/unal/78826/3/license_rdf https://repositorio.unal.edu.co/bitstream/unal/78826/4/0104530761.2020.pdf.jpg |
| bitstream.checksum.fl_str_mv |
c02befff2ae0835c8936851e3a2c6549 cccfe52f796b7c63423298c2d3365fc6 217700a34da79ed616c2feb68d4c5e06 2f4b3caaf8084287061fc5420d4aa25d |
| bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 MD5 |
| repository.name.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
| repository.mail.fl_str_mv |
repositorio_nal@unal.edu.co |
| _version_ |
1814089208234508288 |
| spelling |
Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Ospina Noreña, Jesús Efren783f7d11cffaf0b8fae3d1e7128c7b34Ballari, Danielaf40596cc-978f-490b-b58a-79be45b2f487Orellana Samaniego, Maria Lorenae2cf244ddabdc880b986b92dea7ec2532021-01-19T20:48:11Z2021-01-19T20:48:11Z2020-12-04https://repositorio.unal.edu.co/handle/unal/78826Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, gráficasEl monitoreo de la temperatura del aire (Ta) tiene implicaciones en una amplia gama de aplicaciones ambientales. La Ta se mide comúnmente con estaciones meteorológicas, que proporcionan una alta precisión y una alta resolución temporal en un sitio específico. Sin embargo, estos datos in situ proporcionan información limitada sobre patrones espaciales. Dicha limitación se magnifica en regiones con topografía muy variable y con una red de monitoreo escasa, como es el caso de los Andes del sur del Ecuador. Es por eso que, debido a la continuidad espacial de la información, los datos de teledetección tienen un gran potencial para estimar la distribución espacial de las variables climatológicas. Esta investigación tiene como objetivo estimar la distribución espacial de la Ta mensual en la cuenca del río Paute utilizando métodos estadísticos y geoestadísticos como: regresión lineal LR (por sus siglas en inglés), regresión de bosques aleatorios (RF, por sus siglas en inglés) y regresión Kriging (RK); además se evalúa el uso de la altitud y otras variables auxiliares (temperatura de la superficie terrestre -LST- por sus siglas en inglés, latitud y longitud) en los modelos de regresión. Los resultados mostraron que la altitud y LST fueron las variables auxiliares más efectivas para estimar la temperatura del aire. La validación cruzada mostró que la RF tuvo un mejor desempeño que la LR, así como el uso de variables auxiliares en relación al uso solo de la altitud (basados en la mediana, LR-altitud: RMSE= 1.325°C, P-Bias= -0.150%, r= 0.775; LR-variables auxiliares: RMSE= 1.265°C, P-Bias= 0.000% r=0.795; RF-altitud: RMSE= 1.235°C, P-Bias=0.200%, r= 0.810; RF-variables auxiliares RMSE= 1.205°C, P-Bias=0.2%, r=0.820). La aplicación de RK fue limitada debido a que en menos del 50% de los meses de estudio existió autocorrelación espacial en los residuos de los modelos de regresión lineal y de bosques aleatorios. Sin embargo, en estos meses, RK aumentó ligeramente el rendimiento de las estimaciones. Estos resultados permiten obtener mapas mensuales de la Ta en la cuenca del rio Paute con exactitudes aceptables, siendo esencial en la aplicación de modelos y en actividades que requieren del uso de la Ta como variable de entrada. (Texto tomado de la fuente).Monitoring of air temperature (Ta) has implications in a wide range of environmental applications. Ta is commonly measured with weather stations, which provide a high accuracy and high temporal resolution for the specific monitoring sites. However, these in-situ data provide limited information about spatial patterns. Such limitation is magnified in regions with highly variable topography ad scarce monitoring, such as the case of the southern Ecuadorian Andes. Thus, remote sensing data has a great potential to estimate the spatial distribution of climatological variables due to the spatial continuity of the information. This research aims to estimate the spatial distribution of the monthly Ta in the Paute river basin using statistical and geostatistical methods, such as linear regression (LR), random forest regression (RF) and regression Kriging (RK); while evaluating the use of altitude and other auxiliary variables (land surface temperature (LST), latitude, and longitude). The results showed that altitude and LST were the most effective auxiliary variables. Cross-validation showed that RF performed better than linear regression, as well as when using auxiliary variables compared to only the altitude (LR-altitude: RMSE= 1.325°C, P-Bias= -0.150%, r= 0.775; LR-auxiliary variables: RMSE= 1.265°C, P-Bias= 0.000% r=0.795; RF-altitude: RMSE= 1.235°C, P-Bias=0.200%, r= 0.810; RF-auxiliary variables RMSE= ±1.205°C, P-Bias=0.2%, r=0.820). The application of RK was limited since less than 50% of the study months had spatial autocorrelation in the regression model residuals. Nevertheless, in these months RK increased the estimation performance.Incluye anexosMaestríaMagíster en GeomáticaTecnologías geoespacialesCiencias Agronómicasxv, 129 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ciencias Agrarias - Maestría en GeomáticaEscuela de posgradosFacultad de Ciencias AgrariasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá550 - Ciencias de la tierra::551 - Geología, hidrología, meteorologíaImágenes por satélitesPronóstico meteorológicoTemperatura del airesatellite imageryWeather forecastingair temperatureTemperatura del aire mensualAndesModelos de regresiónAltitudVariables auxiliaresAndesRegression modelsAltitudeAuxiliary variablesMonthly air temperatureEstimación de la temperatura mensual del aire usando imágenes satelitales en una zona de topografía muy variable en los Andes del sur del EcuadorTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMEcuadorhttp://vocab.getty.edu/page/tgn/1000051Ahrens, D. (2008). Essentials of Meteorology. An invitation to the Atmosphere. Time (Vol. 67). https://doi.org/10.1111/j.1467-8535.2007.00763.xAppelhans T, Mwangomo E, Hardy D.R, Hemp A, Nauss T (2015) Evaluating machine learning approaches for the interpolation of monthly air temperature at Mt. Kilimanjaro Tanzania. Spat Stat-Neth 14:91-113. https://doi.org/10.1016/j.spasta.2015.05.008Bahi H, Rhinane H, Bensalmia A (2016) Contribution of MODIS satellite image to estimate the daily air temperature in the Casablanca City, Morocco. Int. arch. photogramm. remote sens. spat. inf. Sci 42:3-11. https://doi.org/10.5194/isprs-archives-XLII-2-W1-3-2016Ballari D, Castro E, Campozano L (2016) Validation of satellite precipitation (TRMM 3B43) in Ecuadorian coastal plains, Andean highlands and Amazonian rainforest. Int. arch. photogramm. remote sens. spat. inf. Sci 41:305-311. https://doi.org/10.5194/isprsarchives-XLI-B8-305-2016Benali A, Carvalho A.C, Nunes J.P, Carvalhais N, Santos, A (2012) Estimating air surface temperature in Portugal using MODIS LST data. Remote Sens. Environ 124:108-121. https://doi.org/10.1016/j.rse.2012.04.024Benavides R, Montes F, Rubio A Osoro K (2007) Geostatistical modelling of air temperature in a mountainous region of Northern Spain. Agric. For. Meteorol 146:173-188. https://doi.org/10.1016/j.agrformet.2007.05.014Bendix J (2014) Adjustment of the Convective- Stratiform Technique (CST) to estimate 1991 / 93 El Nino rainfall distribution in Ecuador and Peru by means of Meteosat-3 IR data. Int. J. Remote Sens International Journal of Remote Sensing 8:1387-1394. https://doi.org/10.1080/014311697218502Breiman L (2001) Random Forests. Mach Learn 45: 5–32.Campozano L, Sánchez E, Avilés Á, Samaniego E (2014) Evaluation of infilling methods for time series of daily precipitation and temperature: The case of the Ecuadorian Andes. Maskana 5:99-115. https://doi.org/10.18537/mskn.05.01.07.Chen, F., Liu, Y., & Qin, F. (2015). A statistical method based on remote sensing for the estimation of air temperature in China, 2143(July 2014), 2131–2143. https://doi.org/10.1002/joc.4113Chowdhury, F. R., Shihab, Q., Ibrahim, U., Bari, S., Alam, J., Dunachie, S. J., … Patwary, I. (2018). The association between temperature , rainfall and humidity with common climate-sensitive infectious diseases in Bangladesh, 1–17.Córdova, M., Célleri, R., Shellito, C. J., Orellana-alvear, J., Abril, A., Carrillo-rojas, G., … Carrillo-rojas, G. (n.d.). Near-Surface Air Temperature Lapse Rate Over Complex Terrain in the Southern Ecuadorian Andes : Implications for Temperature Mapping Near-surface air temperature lapse rate over complex terrain in the Southern Ecuadorian Andes : implications for temperature mapping, 48(4), 678–684.Cuesta, F., Bustamante, M., Becerra, M. T., Postigo, J., & Peralvo, M. (2012). Panorama andino sobre cambio climático. Condesa, 1, 167. Retrieved from http://www20.iadb.org/intal/catalogo/PE/2013/12414.pdfDidan, K., Munoz, A. B., & Huete, A. (2015). MODIS Vegetation Index User ’ s Guide ( MOD13 Series ), 2015(June).Emamifar, S., & Akbar, A. (2013). Daily mean air temperature estimation from MODIS land surface temperature products based on M5 model tree, 3181(February), 3174–3181. https://doi.org/10.1002/joc.3655Florio, E. N., Lele, S. R., Chang, Y. C., Sterner, R., & Glass, G. E. (2004). Integrating AVHRR satellite data and NOAA ground observations to predict surface air temperature: A statistical approach. International Journal of Remote Sensing, 25(15), 2979–2994. https://doi.org/10.1080/01431160310001624593Forest, C. E., Stone, P. H., Sokolov, A. P., Allen, M. R., & Webster, M. D. (2002). Quantifying Uncertainties in Climate System Properties with the Use of Recent Climate Observations, 295(January), 113–118.Fu, G., Shen, Z., Zhang, X., Shi, P., Zhang, Y., & Wu, J. (2011). Estimating air temperature of an alpine meadow on the Northern Tibetan Plateau using MODIS land surface temperature. Acta Ecologica Sinica, 31(1), 8–13. https://doi.org/10.1016/j.chnaes.2010.11.002Geophysics, A. (2015). The Role of Auxiliary Variables in Deterministic and Deterministic-Stochastic Spatial Models of Air Temperature in Poland. https://doi.org/10.1007/s00024-015-1199-2Gornall, J., Betts, R., Burke, E., Clark, R., Camp, J., Willett, K., … Wiltshire, A. (2010). Implications of climate change for agricultural productivity in the early twenty-first century Implications of climate change for agricultural productivity in the early twenty-first century. https://doi.org/10.1098/rstb.2010.0158Irmak, A., Ranade, P. K., Marx, D., Irmak, S., Hubbard, K. G., Meyer, G. E., & Martin, D. L. (2010). S i c v n, 53(6), 1759–1771.Janatian, N., Sadeghi, M., Sanaeinejad, S. H., Bakhshian, E., Farid, A., Hasheminia, S. M., & Ghazanfari, S. (2017). A statistical framework for estimating air temperature using MODIS land surface temperature data. International Journal of Climatology, 37(3), 1181–1194. https://doi.org/10.1002/joc.4766Jang, J., Viau, A. A., & Anctil, F. (n.d.). International Journal of Remote Neural network estimation of air temperatures from AVHRR data, (October 2014), 37–41. https://doi.org/10.1080/01431160310001657533Jin, M., & Dickinson, R. E. (2010). Land surface skin temperature climatology: Benefitting from the strengths of satellite observations. Environmental Research Letters, 5(4), 044004.Li, L., & Zha, Y. (2019). ScienceDirect Estimating monthly average temperature by remote sensing in China. Advances in Space Research, 63(8), 2345–2357. https://doi.org/10.1016/j.asr.2018.12.039Marzban, F., Conrad, T., Marzban, P., & Sodoudi, S. (2018). Estimation of the Near-Surface Air Temperature during the Day and Nighttime from MODIS in Berlin , Germany, 7(1), 2478–2517.Meyer, H., Katurji, M., Appelhans, T., Müller, M. U., Nauss, T., Roudier, P., & Zawar-reza, P. (n.d.). Mapping Daily Air Temperature for Antarctica Based on MODIS LST, 1–16. https://doi.org/10.3390/rs8090732Mora, D. E., & Willems, P. (2012). Decadal oscillations in rainfall and air temperature in the Paute River Basin-Southern Andes of Ecuador. Theoretical and Applied Climatology, 108(1–2), 267–282. https://doi.org/10.1007/s00704-011-0527-4Mutiibwa, D., Strachan, S., & Albright, T. (2015). Land surface temperature and surface air temperature in complex terrain. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(10), 4762-4774.Naseer, A., Koike, T., Mohamad, R., & Ushiyama, T. (2019). Distributed hydrological modelling framework for quantitative and spatial bias correction for rainfall , snowfall , and mixed-phase precipitation using Vertical Profile of Temperature, 0–2. https://doi.org/10.1029/2018JD029811Nieto, H., Sandholt, I., Aguado, I., Chuvieco, E., & Stisen, S. (2011). Remote Sensing of Environment Air temperature estimation with MSG-SEVIRI data : Calibration and validation of the TVX algorithm for the Iberian Peninsula. Remote Sensing of Environment, 115(1), 107–116. https://doi.org/10.1016/j.rse.2010.08.010Ort, D., Thomson, A. M., & Wolfe, D. (2011). Climate Impacts on Agriculture :, 351–370. https://doi.org/10.2134/agronj2010.0303Prata, A. J., Caselles, V., Coll, C., Sobrino, J. A., & Ottle, C. (1995). Thermal remote sensing of land surface temperature from satellites: Current status and future prospects. Remote Sensing Reviews, 12(3-4), 175-224.Prihodko, L., & Goward, S. N. (1997). Estimation of air temperature from remotely sensed observations. Remote Sensing of Environment, 60(3), 335–346.R., L. D., & Willmott, C. J. (1990). Mean Seasonal and Spatial Variability in Global Surface Air Temperature. Theoretical and Applied Climatology, 41, 11–21.Rollenbeck, R., & Bendix, J. (2011). Rainfall distribution in the Andes of southern Ecuador derived from blending weather radar data and meteorological fi eld observations. Atmospheric Research, 99(2), 277–289Shi, Y., Jiang, Z., Dong, L., & Shen, S. (2017). Statistical Estimation of High-Resolution Surface Air Temperature from MODIS over the Yangtze River Delta , China, 31(April). https://doi.org/10.1007/s13351-017-6073-y.1.Sun, H., Chen, Y., Gong, A., Zhao, X., Zhan, W., & Wang, M. (2014). Estimating mean air temperature using MODIS day and night land surface temperatures. Theoretical and Applied Climatology, 118(1–2), 81–92. https://doi.org/10.1007/s00704-013-1033-7Trapasso, L. M. (2005). Temperature Distribution. In J. E. Oliver (Ed.), Encyclopedia of World Climatology (pp. 711–716). Dordrecht: Springer Netherlands. https://doi.org/10.1007/1-4020-3266-8_203Tomlinson, C. J., Chapman, L., Thornes, J. E., & Baker, C. (2011). Remote sensing land surface temperature for meteorology and climatology: a review. Meteorological Applications, 18(3), 296-306.Ulloa, J., Ballari, D., Campozano, L., & Samaniego, E. (2017). Two-step downscaling of TRMM 3b43 V7 precipitation in contrasting climatic regions with sparse monitoring: The case of Ecuador in tropical South America. Remote Sensing, 9(7), 1–23. https://doi.org/10.3390/rs9070758Vancutsem, C., Ceccato, P., Dinku, T., & Connor, S. J. (2010). Evaluation of MODIS land surface temperature data to estimate air temperature in different ecosystems over Africa. Remote Sensing of Environment, 114(2), 449–465. https://doi.org/10.1016/j.rse.2009.10.002Wan, Z. (2009). MODIS Land Surface Temperature Products Users’ Guide. Contract, (April), 30. https://doi.org/10.5067/MODIS/MOD11B3.006Xu, Y., Knudby, A., & Ho, H. C. (2014). International Journal of Remote Estimating daily maximum air temperature from MODIS in British Columbia , Canada, (December), 37–41. https://doi.org/10.1080/01431161.2014.978957Xu, Y., Qin, Z., & Shen, Y. (2012). International Journal of Remote Study on the estimation of near-surface air temperature from MODIS data by statistical methods, (November 2014), 37–41.Yang, D., Wang, Z., Xu, L., & Liu, Y. (2019). Estimation and distribution of near-surface meteorological elements over complex terrains : a case study in the Tibetan areas of West Sichuan. International Journal of Remote Sensing, 40(23), 8811–8837. https://doi.org/10.1080/01431161.2019.1624859Zheng, X., Jiao-jun, Z., & Yan, Q. (2013). Monthly Air Temperatures over Northern China Estimated by Integrating MODIS Data with GIS Techniques Monthly Air Temperatures over Northern China Estimated by Integrating MODIS Data with GIS Techniques, (September). https://doi.org/10.1175/JAMC-D-12-0264.1EstudiantesInvestigadoresPúblico generalORIGINAL0104530761.2020.pdf0104530761.2020.pdfTesis de Maestría en Geomáticaapplication/pdf5011188https://repositorio.unal.edu.co/bitstream/unal/78826/1/0104530761.2020.pdfc02befff2ae0835c8936851e3a2c6549MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-83964https://repositorio.unal.edu.co/bitstream/unal/78826/2/license.txtcccfe52f796b7c63423298c2d3365fc6MD52CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8811https://repositorio.unal.edu.co/bitstream/unal/78826/3/license_rdf217700a34da79ed616c2feb68d4c5e06MD53THUMBNAIL0104530761.2020.pdf.jpg0104530761.2020.pdf.jpgGenerated Thumbnailimage/jpeg5533https://repositorio.unal.edu.co/bitstream/unal/78826/4/0104530761.2020.pdf.jpg2f4b3caaf8084287061fc5420d4aa25dMD54unal/78826oai:repositorio.unal.edu.co:unal/788262023-07-09 23:05:07.971Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.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 |