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Use of satellite-derived data to improve biophysical model output: An example from Southern Kenya

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dc.contributor.authorAngerer, Jayen
dc.contributor.authorStuth, Jerryen
dc.contributor.authorWandera, F.en
dc.contributor.authorKaitho, R.en
dc.contributor.departmentSustainable Agriculture and Natural Resource Management (SANREM) Knowledgebaseen
dc.coverage.spatialSouthern Kenyaen
dc.coverage.temporal2000 - 2000en
dc.date.accessioned2016-04-19T18:09:27Zen
dc.date.available2016-04-19T18:09:27Zen
dc.date.issued2001en
dc.description.abstractThe use of satellite data products produced by the National Oceanic and Atmospheric Administration (NOAA) and the National Aeronautics and Space Administration (NASA) was explored to determine if these products could be used to provide plant growth model output for large landscapes. The use of satellite-derived data is generally advantageous because it is spatially dense (i.e., many measurements for a large landscape). Gridded daily temperature (0.1 x 0.1 degree) and rainfall (8x8 km), derived from the METEOSAT satellite, were used as inputs into the PHYGROW plant growth model for 30 pastoral households in southern Kenya. After model runs were completed, cokriging was used to determine if model output, coupled with NASAï'½s Normalized Difference Vegetation Index (NDVI) product (a greenness index), could be used to create forage production maps for a large landscape. Cokriging is a geostatistical technique that allows one to take advantage of spatial autocorrelation (i.e., things closer together in space are usually similar than those farther apart), and the similarity between a small number of data points (model output in our case) and a one that is spatially dense (NDVI). Using cokriging, the majority of ten-day averages for year 2000 had moderate to high similarity between model output and NDVI. A comparison of model output and estimates from cokriging indicated that cokriging generally did a good job of estimating forage available for the 30 pastoral households. Mapped surfaces of the cokriging output successfully identified areas of drought in year 2000. Institutions at all levels could use these mapped surfaces as part of their GIS, which can then be linked to economic models, natural resource management assessments, or used for drought early warning systems.en
dc.format.mimetypeapplication/pdfen
dc.identifier80en
dc.identifier.citationPaper presented at the SANREM CRSP Research Synthesis Conference, Athens, GA, 28-30 November 2001en
dc.identifier.other80_satelitteDataImprovedOutput.pdfen
dc.identifier.urihttp://hdl.handle.net/10919/65682en
dc.language.isoen_USen
dc.publisherWatkinsville, GA: SANREM CRSPen
dc.subjectForageen
dc.subjectDroughten
dc.subjectLand use planningen
dc.subjectEnvironmental impactsen
dc.subjectLand use managementen
dc.subjectPasture managementen
dc.subjectGISen
dc.subjectModelingen
dc.subjectEconomic modeling and analysisen
dc.subjectEconomic impactsen
dc.subjectNatural resource managementen
dc.subjectAdoption of innovationsen
dc.subjectDrought impacten
dc.subjectVegetation productivityen
dc.subjectCokrigingen
dc.subjectKrigingen
dc.subjectPhytomass growth simulator (phygrow)en
dc.subjectRainfall estimatesen
dc.subjectNdvi (normalized difference vegetation index)en
dc.subjectForage availabilityen
dc.subjectForage productionen
dc.subjectSurface mappingen
dc.subjectDrought early warningen
dc.subjectEcosystem Farm/Enterprise Scaleen
dc.titleUse of satellite-derived data to improve biophysical model output: An example from Southern Kenyaen
dc.typePresentationen
dc.type.dcmitypeTexten

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