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Vegetation drought monitoring from MODIS imagery and soil moisture data in Oklahoma Mesonet sites

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dc.creatorVanegas, Diana Ximena
dc.creatorXiao, Xiangming
dc.creatorBasara, Jeffrey
dc.date2016-10-10
dc.date.accessioned2025-08-22T21:29:28Z
dc.date.available2025-08-22T21:29:28Z
dc.descriptionDrought is a normal and recurrent climatic phenomenon, and is considered one of the most costly natural disasters in the United States. Grassland vegetation is sensitive to weather and climate, and persistent drought impacts goods and ecological services that grasslands provide (e.g., wildlife habitats, feedstock for the livestock industry, and recreational services). Droughts have extremely large spatial and temporal variations in areal coverage and intensity making drought monitoring a challenging task. Using soil and atmospheric data from the Oklahoma Mesonet and surface reflectance data from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites, this study examined the hypothesis that the satellite-derived Land Surface Water Index (LSWI) is sensitive to drought conditions and can potentially be used as an indicator or tool for drought monitoring. The sensitivity of LSWI to summer drought was first analyzed at 10 Mesonet sites that are homogeneous and representative of different types of grassland vegetation, soils and climate across Oklahoma. A summer drought event is defined, based on threshold values of LSWI and the Fractional Water Index (FWI) derived from soil moisture data at each site.Secondly, the LSWI-based drought algorithm was evaluated at103 Oklahoma Mesonet sites. Finally, the LSWI-based droughtalgorithm was used to map spatial patterns and temporal dynamicsof drought-affected land surface during 2001-2010 acrossOklahoma. The results from this study demonstrated the potentialof LSWI-based drought algorithm for tracking and mappingdrought-affected grassland vegetation in Oklahoma with 3%commission error in the Oklahoma Mesonet sites during 2001-2010.La sequía es un fenómeno climático normal y recurrente, y es considerado uno de los desastres naturales más costosos en Estados Unidos. La vegetación de pastizales es sensible al estado del tiempo y el clima, y la persistencia de la sequía afecta a los bienes y servicios ecológicos que proporcionan los pastizales (por ejemplo, son hábitats de vida silvestre, proveen materia prima para la industria ganadera, así como servicios de esparcimiento). La cobertura de área e intensidad de las sequías presentan grandes variaciones espaciales y temporales, haciendo que el monitorea de sequías sea una tarea difícil. Usando datos atmosféricos y de suelos de la Oklahoma Mesonet, y datos de reflectancia de la superficie terrestre del espectrorradiómetro de imágenes de resolución moderada (MODIS, por sus siglas en inglés) a bordo de los satélites Terra y Aqua, este estudio examinó la hipótesis de que el índice de agua de la superficie del terreno (LSWI, por sus siglas en inglés) es sensible a condiciones de sequía y potencialmente puede utilizarse como un indicador o herramienta para la monitoreo de sequías. La sensibilidad del LSWI a la sequía estival se analizó inicialmente en 10 sitios Mesonet que son homogéneos y representativos de los diferentes tipos de vegetación de pastizales, los suelos y el clima a través de Oklahoma. Un evento de sequía estival se define, en base a los valores de umbral de LSWI y el Índice de Agua fraccional (FWI) derivado de los datos de humedad del suelo en cada sitio. Posteriormente, el algoritmo de sequía basado en LSWI se evaluó en 103 sitios Oklahoma Mesonet. Por último, se utilizó el algoritmo de sequía basado en LSWI para mapear los patrones espaciales y la dinámica temporal de la superficie de la tierra afectada por la sequía durante 2001-2010 a través de Oklahoma. Los resultados de este estudio demostraron el potencial del algoritmo de sequía basado en LSWI para el seguimiento y la cartografía de vegetación de pradera afectada por la sequía en Oklahoma con un 3% de error de comisión en los sitios Oklahoma Mesonet durante 2001-2010en-US
dc.formatapplication/pdf
dc.identifierhttps://revistas.unbosque.edu.co/index.php/RevTec/article/view/1879
dc.identifier10.18270/rt.v13i2.1879
dc.identifier.urihttps://hdl.handle.net/20.500.12495/17723
dc.languagespa
dc.publisherUniversidad El Bosquees-ES
dc.relationhttps://revistas.unbosque.edu.co/index.php/RevTec/article/view/1879/1448
dc.relation/*ref*/Novick, K.A., Stoy, P.C., Katul, G.G., Ellsworth,
dc.relation/*ref*/D.S., Siqueira, M.B.S., Juang, J., & Oren, R. (2004).
dc.relation/*ref*/Carbon dioxide and water vapor exchange in a
dc.relation/*ref*/warm temperate grassland. Oecologia, 138, 259-274
dc.relation/*ref*/White, R.P., Murray, S., Rohweder, M. (2000). Pilot
dc.relation/*ref*/Analysis of Global Ecosystems: Grassland Ecosystems.
dc.relation/*ref*/In (p. 81): World Resources Institute White,
dc.relation/*ref*/R.P., Murray, S., Rohweder, M. (2000). Pilot Analysis
dc.relation/*ref*/of Global Ecosystems: Grassland Ecosystems. In
dc.relation/*ref*/(p. 81): World Resources Institute
dc.relation/*ref*/Conner, R., Seidl, A., VanTassell, L., Wilkins, N.
dc.relation/*ref*/. United States Grasslands and Related
dc.relation/*ref*/Resources: An Economic and Biological Trends
dc.relation/*ref*/Assessment. In, US Grasslands: Economic & Biological
dc.relation/*ref*/Trends (p. 170). Austin: Texas A&M University
dc.relation/*ref*/Mayeux, H. (2001). The Extent, General Characteristics,
dc.relation/*ref*/and Carbon Dynamics of U.S. Grazing Lands.
dc.relation/*ref*/The potential of U.S. grazing lands to sequester
dc.relation/*ref*/carbon and mitigate greenhouse effect: Lewis
dc.relation/*ref*/Publishers, CRC Press LLC.
dc.relation/*ref*/Foody, G.M., & Dash, J. (2010). Estimating the relative
dc.relation/*ref*/abundance of C-3 and C-4 grasses in the Great
dc.relation/*ref*/Plains from multi-temporal MTCI data: issues of
dc.relation/*ref*/compositing period and spatial generalizability.
dc.relation/*ref*/International Journal of Remote Sensing, 31, 351-362
dc.relation/*ref*/Namias, J. (1982). Anatomy of Great Plains Protracted
dc.relation/*ref*/Heat Waves (especially the 1980 U.S. summer
dc.relation/*ref*/drought). Monthly Weather Review, 110, 824-838
dc.relation/*ref*/Trenberth, K.E., Branstator, G.W., & Arkin, P.A.
dc.relation/*ref*/. Origins of the 1988 North American
dc.relation/*ref*/Drought. Science, 242, 1640-1645
dc.relation/*ref*/Hong, S.-Y., & Kalnay, E. (2000). Role of sea surface
dc.relation/*ref*/temperature and soil-moisture feedback in the 1998
dc.relation/*ref*/Oklahoma-Texas drought. Nature, 408, 842-844
dc.relation/*ref*/Schubert, S.D., Suarez, M.J., Pegion, P.J., Koster,
dc.relation/*ref*/R.D., & Bacmeister, J.T. (2004). Causes of Long-
dc.relation/*ref*/Term Drought in the U.S. Great Plains. Journal of
dc.relation/*ref*/Climate, 17, 485-503
dc.relation/*ref*/Andreadis, K.M., Clark, E.A., Wood, A.W., Hamlet,
dc.relation/*ref*/A.F., & Lettenmaier, D.P. (2005). Twentieth-Century
dc.relation/*ref*/Drought in the Conterminous United States.
dc.relation/*ref*/Journal of Hydrometeorology, 6, 985-1001
dc.relation/*ref*/McCrary, R.R., & Randall, D.A. (2009). Great Plains
dc.relation/*ref*/Drought in Simulations of the Twentieth Century.
dc.relation/*ref*/Journal of Climate, 23, 2178-2196
dc.relation/*ref*/Wang, J. (2007). FPGA Design for Real-Time Implementation
dc.relation/*ref*/of Constrained Energy Minimization for
dc.relation/*ref*/Hyperspectral Target Detection. High Performance
dc.relation/*ref*/Computing in Remote Sensing: Chapman and Hall/CRC
dc.relation/*ref*/Zhang, X.Y., Goldberg, M., Tarpley, D., Friedl, M.A.,
dc.relation/*ref*/Morisette, J., Kogan, F., & Yu, Y.Y. (2010). Droughtinduced
dc.relation/*ref*/vegetation stress in southwestern North
dc.relation/*ref*/America. Environmental Research Letters, 5
dc.relation/*ref*/Gu, Y.X., Hunt, E., Wardlow, B., Basara, J.B., Brown,
dc.relation/*ref*/J.F., & Verdin, J.P. (2008). Evaluation of MODIS
dc.relation/*ref*/NDVI and NDWI for vegetation drought monitoring
dc.relation/*ref*/using Oklahoma Mesonet soil moisture data.
dc.relation/*ref*/Geophysical Research Letters, 35
dc.relation/*ref*/Palmer, W.C. (1965). Meteorological drought. In.
dc.relation/*ref*/Washington, D.C.: U.S. Department of Commerce
dc.relation/*ref*/Weather Bureau
dc.relation/*ref*/McKee, T.B., Doesken, N.J., & Kleist, J. (1993). The
dc.relation/*ref*/relationship of drought frequency and duration. In,
dc.relation/*ref*/Preprints, 8th Conference on Applied Climatology
dc.relation/*ref*/(pp. 179-184). Anaheim, CA
dc.relation/*ref*/McKee, T.B., Doesken, N.J., & Kleist, J. (1995).
dc.relation/*ref*/Drought monitoring with multiple time scales. In,
dc.relation/*ref*/Preprints, 9th Conference on Applied Climatology
dc.relation/*ref*/(pp. 233-236). Dallas, TX
dc.relation/*ref*/Anderson, L.O., Malhi, Y., Aragao, L., Ladle, R.,
dc.relation/*ref*/Arai, E., Barbier, N., & Phillips, O. (2010). Remote sensing detection of droughts in Amazonian forest
dc.relation/*ref*/canopies. New Phytologist, 187, 733-750
dc.relation/*ref*/Tucker, C.J., & Choudhury, B.J. (1987). SATELLITE
dc.relation/*ref*/REMOTE-SENSING OF DROUGHT CONDITIONS.
dc.relation/*ref*/Remote Sensing of Environment, 23, 243-251
dc.relation/*ref*/Bayarjargal, Y., Karnieli, A., Bayasgalan, M.,
dc.relation/*ref*/Khudulmur, S., Gandush, C., & Tucker, C.J. (2006).
dc.relation/*ref*/A comparative study of NOAA-AVHRR derived
dc.relation/*ref*/drought indices using change vector analysis.
dc.relation/*ref*/Remote Sensing of Environment, 105, 9-22
dc.relation/*ref*/Gonzalez Alonso, F., Cuevas, J.M., Casanova, J.L.,
dc.relation/*ref*/Calle, A., & Illera, P. (1995). Drought monitoring in
dc.relation/*ref*/Spain using satellite remote sensing. Sensors and
dc.relation/*ref*/environmental applications of remote sensing, 87-90
dc.relation/*ref*/Jain, S.K., Keshri, R., Goswami, A., & Sarkar, A. (2010).
dc.relation/*ref*/Application of meteorological and vegetation indices
dc.relation/*ref*/for evaluation of drought impact: a case study for
dc.relation/*ref*/Rajasthan, India. Natural Hazards, 54, 643-656
dc.relation/*ref*/Kamble, M.V., Ghosh, K., Rajeevan, M., & Samui,
dc.relation/*ref*/R.P. (2010). Drought monitoring over India through
dc.relation/*ref*/Normalized Difference Vegetation Index (NDVI).
dc.relation/*ref*/Mausam, 61, 537-546
dc.relation/*ref*/Kogan, F.N. (1997). Global drought watch from
dc.relation/*ref*/space. Bulletin of the American Meteorological
dc.relation/*ref*/Society, 78, 621-636
dc.relation/*ref*/Liu, W.T., & Juarez, R.I.N. (2001). ENSO drought
dc.relation/*ref*/onset prediction in northeast Brazil using NDVI.
dc.relation/*ref*/International Journal of Remote Sensing, 22,
dc.relation/*ref*/3483-3501
dc.relation/*ref*/Murthy, C.S., Sai, M., Chandrasekar, K., & Roy, P.S.
dc.relation/*ref*/. Spatial and temporal responses of different
dc.relation/*ref*/crop-growing environments to agricultural drought:
dc.relation/*ref*/a study in Haryana state, India using NOAA AVHRR
dc.relation/*ref*/data. International Journal of Remote Sensing, 30,
dc.relation/*ref*/2897-2914
dc.relation/*ref*/Tucker, C.J. (1989). COMPARING SMMR AND
dc.relation/*ref*/AVHRR DATA FOR DROUGHT MONITORING. International
dc.relation/*ref*/Journal of Remote Sensing, 10, 1663-1672
dc.relation/*ref*/Karnieli, A., Agam, N., Pinker, R.T., Anderson, M.,
dc.relation/*ref*/Imhoff, M.L., Gutman, G.G., Panov, N., & Goldberg,
dc.relation/*ref*/A. (2010). Use of NDVI and Land Surface
dc.relation/*ref*/Temperature for Drought Assessment: Merits and
dc.relation/*ref*/Limitations. Journal of Climate, 23, 618-633
dc.relation/*ref*/Gao, B.C. (1996). NDWI - A normalized difference
dc.relation/*ref*/water index for remote sensing of vegetation liquid
dc.relation/*ref*/water from space. Remote Sensing of Environment,
dc.relation/*ref*/58, 257-266
dc.relation/*ref*/Gu, Y.X., Brown, J.F., Verdin, J.P., & Wardlow, B.
dc.relation/*ref*/. A five-year analysis of MODIS NDVI and
dc.relation/*ref*/NDWI for grassland drought assessment over the
dc.relation/*ref*/central Great Plains of the United States. Geophysical
dc.relation/*ref*/Research Letters, 34, L06407
dc.relation/*ref*/Liu, C.L., Wu, B.F., Tian, Y.C., Xu, W.B., & Huang,
dc.relation/*ref*/J.X. (2004). Crop drought monitoring using serial
dc.relation/*ref*/NDVI&NDWI in Northern China. Igarss 2004: Ieee
dc.relation/*ref*/International Geoscience and Remote Sensing
dc.relation/*ref*/Symposium Proceedings, Vols 1-7, 2264-2267
dc.relation/*ref*/Xiao, X.M., Boles, S., Liu, J.Y., Zhuang, D.F., & Liu,
dc.relation/*ref*/M.L. (2002). Characterization of forest types in
dc.relation/*ref*/Northeastern China, using multi-temporal SPOT-4
dc.relation/*ref*/VEGETATION sensor data. Remote Sensing of Environment,
dc.relation/*ref*/82, 335-348
dc.relation/*ref*/Xiao, X.M., Hollinger, D., Aber, J., Goltz, M.,
dc.relation/*ref*/Davidson, E.A., Zhang, Q.Y., & Moore, B. (2004).
dc.relation/*ref*/Satellite-based modeling of gross primary production
dc.relation/*ref*/in an evergreen needleleaf forest. Remote
dc.relation/*ref*/Sensing of Environment, 89, 519-534
dc.relation/*ref*/Kalfas, J.L., Xiao, X., Vanegas, D.X., Verma, S.B.,
dc.relation/*ref*/& Suyker, A.E. (2011). Modeling gross primary
dc.relation/*ref*/production of irrigated and rain-fed maize using
dc.relation/*ref*/MODIS imagery and CO2 flux tower data. Agricultural
dc.relation/*ref*/and Forest Meteorology, 151, 1514-1528
dc.relation/*ref*/Illston, B.G., Basara, J.B., & Crawford, K.C. (2004).
dc.relation/*ref*/Seasonal to interannual variations of soil moisture
dc.relation/*ref*/measured in Oklahoma. International Journal of
dc.relation/*ref*/Climatology, 24, 1883-1896
dc.relation/*ref*/Illston, B.G., Basara, J.B., Fiebrich, C.A., Crawford,
dc.relation/*ref*/K.C., Hunt, E., Fisher, D.K., Elliott, R., & Humes,
dc.relation/*ref*/K. (2008). Mesoscale Monitoring of Soil Moisture
dc.relation/*ref*/across a Statewide Network. Journal of Atmospheric
dc.relation/*ref*/and Oceanic Technology, 25, 167-182
dc.relation/*ref*/McPherson, R.A., Fiebrich, C.A., Crawford, K.C.,
dc.relation/*ref*/Elliott, R.L., Kilby, J.R., Grimsley, D.L., Martinez, J.E.,
dc.relation/*ref*/Basara, J.B., Illston, B.G., Morris, D.A., Kloesel, K.A.,
dc.relation/*ref*/Stadler, S.J., Melvin, A.D., Sutherland, A.J., Shrivastava,
dc.relation/*ref*/H., Carlson, J.D., Wolfinbarger, J.M., Bostic,
dc.relation/*ref*/J.P., & Demko, D.B. (2007). Statewide monitoring
dc.relation/*ref*/of the mesoscale environment: A technical update
dc.relation/*ref*/on the Oklahoma Mesonet. Journal of Atmospheric
dc.relation/*ref*/and Oceanic Technology, 24, 301-321
dc.relation/*ref*/Lillesand, T., Kiefer, R., Chipman, J. (2008). Remote
dc.relation/*ref*/sensing and image interpretation, 6th edition. (6
dc.relation/*ref*/ed.). John Wiley & Sons New York
dc.relation/*ref*/Justice, C.O., Townshend, J.R.G., Vermote, E.F.,
dc.relation/*ref*/Masuoka, E., Wolfe, R.E., Saleous, N., Roy, D.P.,
dc.relation/*ref*/and Morisette, J.T. 2002. An overview of MODIS
dc.relation/*ref*/Land data processing and product status. Remote
dc.relation/*ref*/Sensing of Environment 83 (1-2):3-15
dc.relation/*ref*/Xiao, X.M., Boles, S., Frolking, S., Li, C.S., Babu,
dc.relation/*ref*/J.Y., Salas, W., & Moore, B. (2006). Mapping paddy
dc.relation/*ref*/rice agriculture in South and Southeast Asia using
dc.relation/*ref*/multi-temporal MODIS images. Remote Sensing of
dc.relation/*ref*/Environment, 100, 95-113
dc.relation/*ref*/Xiao, X.M., Boles, S., Liu, J.Y., Zhuang, D.F., Frolking,
dc.relation/*ref*/S., Li, C.S., Salas, W., & Moore, B. (2005).
dc.relation/*ref*/Mapping paddy rice agriculture in southern China
dc.relation/*ref*/using multi-temporal MODIS images. Remote
dc.relation/*ref*/Sensing of Environment, 95, 480-492
dc.relation/*ref*/Xiao, X.M., Zhang, Q.Y., Braswell, B., Urbanski, S., Boles,
dc.relation/*ref*/S., Wofsy, S., Berrien, M., & Ojima, D. (2004). Modeling
dc.relation/*ref*/gross primary production of temperate deciduous
dc.relation/*ref*/broadleaf forest using satellite images and climate data.
dc.relation/*ref*/Remote Sensing of Environment, 91, 256-270
dc.relation/*ref*/Schneider, J.M., Fisher, D.K., Elliott, R.L., Brown, G.O.,
dc.relation/*ref*/& Bahrmann, C.P. (2003). Spatiotemporal variations
dc.relation/*ref*/in soil water: First results from the ARM SGP CART
dc.relation/*ref*/network. Journal of Hydrometeorology, 4, 106-120
dc.relation/*ref*/Dong, X., Xi, B., Kennedy, A., Feng, Z., Entin, J.K.,
dc.relation/*ref*/Houser, P.R., Schiffer, R.A., L’Ecuyer, T., Olson, W.S.,
dc.relation/*ref*/Hsu, K.-l., Liu, W.T., Lin, B., Deng, Y., & Jiang, T.
dc.relation/*ref*/. Investigation of the 2006 drought and
dc.relation/*ref*/2007 flood extremes at the Southern Great Plains
dc.relation/*ref*/through an integrative analysis of observations.
dc.relation/*ref*/Journal of Geophysical Research, 116
dc.relation/*ref*/Kogan, F.N. (1995). Droughts of the Late 1980s
dc.relation/*ref*/in the United-States as Derived from Noaa Polar-
dc.relation/*ref*/Orbiting Satellite Data. Bulletin of the American
dc.relation/*ref*/Meteorological Society, 76, 655-668
dc.relation/*ref*/Shakya, N., & Yamaguchi, Y. (2010). Vegetation,
dc.relation/*ref*/water and thermal stress index for study of drought
dc.relation/*ref*/in Nepal and central northeastern India. International
dc.relation/*ref*/Journal of Remote Sensing, 31, 903-912
dc.relation/*ref*/Basara, J.B., & Crawford, K.C. (2002). Linear relationships
dc.relation/*ref*/between root-zone soil moisture and atmospheric
dc.relation/*ref*/processes in the planetary boundary layer. Journal of
dc.relation/*ref*/Geophysical Research-Atmospheres, 107
dc.relation/*ref*/Famiglietti, J.S., Devereaux, J.A., Laymon, C.A.,
dc.relation/*ref*/Tsegaye, T., Houser, P.R., Jackson, T.J., Graham,
dc.relation/*ref*/S.T., Rodell, M., & van Oevelen, P.J. (1999). Groundbased
dc.relation/*ref*/investigation of soil moisture variability within
dc.relation/*ref*/remote sensing footprints during the Southern
dc.relation/*ref*/Great Plains 1997 (SGP97) Hydrology Experiment.
dc.relation/*ref*/Water Resources Research, 35, 1839-1851
dc.relation/*ref*/Jackson, R.B., Canadell, J., Ehleringer, J.R., Mooney,
dc.relation/*ref*/H.A., Sala, O.E., & Schulze, E.D. (1996). A global
dc.relation/*ref*/analysis of root distributions for terrestrial biomes.
dc.relation/*ref*/Oecologia, 108, 389-411
dc.relation/*ref*/Sun, G., Coffin, D.P., & Lauenroth, W.K. (1997).
dc.relation/*ref*/Comparison of root distributions of species in
dc.relation/*ref*/North American grasslands using GIS. Journal of
dc.relation/*ref*/Vegetation Science, 8, 587-596
dc.relation/*ref*/Canadell, J., Jackson, R.B., Ehleringer, J.R., Mooney,
dc.relation/*ref*/H.A., Sala, O.E., & Schulze, E.D. (1996). Maximum
dc.relation/*ref*/rooting depth of vegetation types at the global
dc.relation/*ref*/scale. Oecologia, 108, 583-595
dc.relation/*ref*/Schulze, E.D., Mooney, H.A., Sala, O.E., Jobbagy,
dc.relation/*ref*/E., Buchmann, N., Bauer, G., Canadell, J., Jackson,
dc.relation/*ref*/R.B., Loreti, J., Oesterheld, M., & Ehleringer, J.R.
dc.relation/*ref*/. Rooting depth, water availability, and vegetation
dc.relation/*ref*/cover along an aridity gradient in Patagonia.
dc.relation/*ref*/Oecologia, 108, 503-511
dc.relation/*ref*/Desilets, D., Zreda, M., & Ferre, T.P.A. (2010).
dc.relation/*ref*/Nature’s neutron probe: Land surface hydrology at
dc.relation/*ref*/an elusive scale with cosmic rays. Water Resources
dc.relation/*ref*/Research, 46
dc.relation/*ref*/Zreda, M., Desilets, D., Ferre, T.P.A., & Scott, R.L.
dc.relation/*ref*/. Measuring soil moisture content non-invasively
dc.relation/*ref*/at intermediate spatial scale using cosmic-ray
dc.relation/*ref*/neutrons. Geophysical Research Letters, 35
dc.relation/*ref*/Brown, J.F., Wardlow, B.D., Tadesse, T., Hayes,
dc.relation/*ref*/M.J., & Reed, B.C. (2008). The Vegetation Drought
dc.relation/*ref*/Response Index ( VegDRI): A new integrated
dc.relation/*ref*/approach for monitoring drought stress in vegetation.
dc.relation/*ref*/Giscience & Remote Sensing, 45, 16-46
dc.relation/*ref*/Brown, M.E., Pinzon, J.E., Didan, K., Morisette, J.T.,
dc.relation/*ref*/& Tucker, C.J. (2006). Evaluation of the consistency
dc.relation/*ref*/of long-term NDVI time series derived from AVHRR,
dc.relation/*ref*/SPOT-Vegetation, SeaWiFS, MODIS, and Landsat
dc.relation/*ref*/ETM+ sensors. Ieee Transactions on Geoscience
dc.relation/*ref*/and Remote Sensing, 44, 1787-1793
dc.relation/*ref*/Kogan, F.N. (2000). Satellite-observed sensitivity of
dc.relation/*ref*/world land ecosystems to El Nino/La Nina. Remote
dc.relation/*ref*/Sensing of Environment, 74, 445-462
dc.rightsDerechos de autor 2014 Revista de Tecnologíaes-ES
dc.sourceJournal of Technology; Vol. 13 No. 2 (2014): Hábitat que educa para la sostenibilidad; 10-27en-US
dc.sourceRevista de Tecnología (Archivo); Vol. 13 Núm. 2 (2014): Hábitat que educa para la sostenibilidad; 10-27es-ES
dc.source1692-1399
dc.subjectMODISen-US
dc.subjectvegetation drought monitoringen-US
dc.subjectgrasslanden-US
dc.subjectland surface water indexen-US
dc.titleVegetation drought monitoring from MODIS imagery and soil moisture data in Oklahoma Mesonet sitesen-US
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion

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