Analysing the advantages of high temporal resolution geostationary MSG SEVIRI data compared to Polar operational environmental satellite data for land surface monitoring in Africa

Since 1972, satellite remote sensing of the environment has been dominated by polar-orbiting sensors providing useful data for monitoring the earth’s natural resources. However their observation and monitoring capacity are inhibited by daily to monthly looks for any given ground surface which often is obscured by frequent and persistent cloud cover creating large gaps in time series measurements. The launch of the Meteosat Second Generation (MSG) satellite into geostationary orbit has opened new opportunities for land surface monitoring. The Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument on-board MSG with an imaging https://ntrs.nasa.gov/search.jsp?R=20110014298 2020-01-20T00:11:58+00:00Z

[1]  J. Townshend,et al.  African Land-Cover Classification Using Satellite Data , 1985, Science.

[2]  B. Holben Characteristics of maximum-value composite images from temporal AVHRR data , 1986 .

[3]  V. Salomonson,et al.  MODIS: advanced facility instrument for studies of the Earth as a system , 1989 .

[4]  J. Roujean,et al.  A bidirectional reflectance model of the Earth's surface for the correction of remote sensing data , 1992 .

[5]  A. Huete,et al.  Normalization of multidirectional red and NIR reflectances with the SAVI , 1992 .

[6]  G. Dedieu,et al.  SMAC: a simplified method for the atmospheric correction of satellite measurements in the solar spectrum , 1994 .

[7]  Ranga B. Myneni,et al.  Satellite‐based identification of linked vegetation index and sea surface temperature Anomaly areas from 1982–1990 for Africa, Australia and South America , 1996 .

[8]  A. Cracknell advanced very high resolution radiometer AVHRR , 1997 .

[9]  D. Fuster,et al.  VEGETATION geometrical image quality , 2000 .

[10]  Alfredo Huete,et al.  Estimating crop yields and production by integrating the FAO Crop Specific Water Balance model with real-time satellite data and ground-based ancillary data , 2000 .

[11]  C. Sear,et al.  A comparison of Meteosat rainfall estimation techniques in Kenya , 2001 .

[12]  Alan H. Strahler,et al.  Bidirectional NDVI and atmospherically resistant BRDF inversion for vegetation canopy , 2002, IEEE Trans. Geosci. Remote. Sens..

[13]  N. C. Strugnell,et al.  First operational BRDF, albedo nadir reflectance products from MODIS , 2002 .

[14]  W. Paul Menzel,et al.  The MODIS cloud products: algorithms and examples from Terra , 2003, IEEE Trans. Geosci. Remote. Sens..

[15]  Alfredo Huete,et al.  Monitoring Drought Using Coarse-Resolution Polar-Orbiting Satellite Data , 2005 .

[16]  R. Fensholt,et al.  Evaluation of AVHRR PAL and GIMMS 10‐day composite NDVI time series products using SPOT‐4 vegetation data for the African continent , 2006 .

[17]  Rasmus Fensholt,et al.  Evaluating MODIS, MERIS, and VEGETATION vegetation indices using in situ measurements in a semiarid environment , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[18]  European Space Agency, (Special Publication) ESA SP , 2006 .

[19]  Rasmus Fensholt,et al.  Estimation of diurnal air temperature using MSG SEVIRI data in West Africa , 2007 .

[20]  T. Gillespie,et al.  Assessment and prediction of natural hazards from satellite imagery , 2007, Progress in physical geography.

[21]  Rasmus Fensholt,et al.  Combining the triangle method with thermal inertia to estimate regional evapotranspiration — Applied to MSG-SEVIRI data in the Senegal River basin , 2008 .

[22]  Frank Veroustraete,et al.  Extending the SPOT-VEGETATION NDVI Time Series (1998–2006) Back in Time With NOAA-AVHRR Data (1985–1998) for Southern Africa , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[23]  N. Pergola,et al.  Advanced satellite technique for volcanic activity monitoring and early warning , 2008 .

[24]  Sergey V. Samsonov,et al.  A review of the status of satellite remote sensing and image processing techniques for mapping natural hazards and disasters , 2009 .

[25]  Isabel F. Trigo,et al.  Reference crop evapotranspiration derived from geo-stationary satellite imagery - a case study for the Fogera flood plain, NW-Ethiopia and the Jordan Valley, Jordan , 2010 .

[26]  Jonas Ardö,et al.  Mapping daily evapotranspiration and dryness index in the East African highlands using MODIS and SEVIRI data , 2010 .

[27]  R. Fensholt,et al.  Assessment of MODIS sun-sensor geometry variations effect on observed NDVI using MSG SEVIRI geostationary data , 2010 .

[28]  J. Pereira,et al.  Detection and monitoring of African vegetation fires using MSG-SEVIRI imagery , 2010 .

[29]  Rasmus Fensholt,et al.  Detecting Canopy Water Status Using Shortwave Infrared Reflectance Data From Polar Orbiting and Geostationary Platforms , 2010, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[30]  Rasmus Fensholt,et al.  A comparison of the effectiveness of 6S and SMAC in correcting for atmospheric interference of Meteosat Second Generation images , 2010 .

[31]  G. Roberts,et al.  Addressing the spatiotemporal sampling design of MODIS to provide estimates of the fire radiative energy emitted from Africa , 2011 .