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Träfflista för sökning "WFRF:(Sandholt Inge) "

Search: WFRF:(Sandholt Inge)

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1.
  • Olsen, Jorgen L., et al. (author)
  • Relation between Seasonally Detrended Shortwave Infrared Reflectance Data and Land Surface Moisture in Semi-Arid Sahel
  • 2013
  • In: Remote Sensing. - : MDPI AG. - 2072-4292. ; 5:6, s. 2898-2927
  • Journal article (peer-reviewed)abstract
    • In the Sudano-Sahelian areas of Africa droughts can have serious impacts on natural resources, and therefore land surface moisture is an important factor. Insufficient conventional sites for monitoring land surface moisture make the use of Earth Observation data for this purpose a key issue. In this study we explored the potential of using reflectance data in the Red, Near Infrared (NIR), and Shortwave Infrared (SWIR) spectral regions for detecting short term variations in land surface moisture in the Sahel, by analyzing data from three test sites and observations from the geostationary Meteosat Second Generation (MSG) satellite. We focused on responses in surface reflectance to soil-and surface moisture for bare soil and early to mid-growing season. A method for implementing detrended time series of the Shortwave Infrared Water Stress Index (SIWSI) is examined for detecting variations in vegetation moisture status, and is compared to detrended time series of the Normalized Difference Vegetation Index (NDVI). It was found that when plant available water is low, the SIWSI anomalies increase over time, while the NDVI anomalies decrease over time, but less systematically. Therefore SIWSI may carry important complementary information to NDVI in terms of vegetation water status, and can provide this information with the unique combination of temporal and spatial resolution from optical geostationary observations over Sahel. However, the relation between SIWSI anomalies and periods of water stress were not found to be sufficiently robust to be used for water stress detection.
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2.
  • Stisen, Simon, et al. (author)
  • Estimation of diurnal air temperature using MSG SEVIRI data in West Africa
  • 2007
  • In: Remote Sensing of Environment. - : Elsevier BV. - 0034-4257. ; 110, s. 262-274
  • Journal article (peer-reviewed)abstract
    • Spatially distributed air temperature data with high temporal resolution are desired for several modeling applications. By exploiting the thermal split window channels in combination with the red and near infrared channels of the geostationary MSG SEVIRI sensor, multiple daily air temperature estimates can be achieved using the contextual temperature-vegetation index method. Air temperature was estimated for 436 image acquisitions during the 2005 rainy season over West Africa and evaluated against in situ data from a field test site in Dahra, Northern Senegal. The methodology was adjusted using data from the test site resulting in RMSE=2.55 K, MBE=-0.30 K and R-2=0.63 for the estimated versus observed air temperatures. A spatial validation of the method using 12 synoptic weather stations from Senegal and Mali within the Senegal River basin resulted in overall values of RMSE=2.96 K, MBE=-1.11 K and R-2=0.68. The daytime temperature curve is interpolated using a sine function based on the multiple daily air temperature estimates from the SEVIRI data. These estimates (covering the 8:00-20:00 UCT time window) were in good agreement with observed values with RMSE=2.99 K, MBE=-0.70 K and R-2=0.64. The temperature-vegetation index method was applied as a moving window technique to produce distributed maps of air temperature with 15 min intervals and 3 km spatial resolution for application in a distributed hydrological model. (c) 2007 Elsevier Inc. All rights reserved.
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3.
  • Tagesson, Torbern, et al. (author)
  • Ecosystem properties of semiarid savanna grassland in West Africa and its relationship with environmental variability
  • 2015
  • In: Global Change Biology. - : Wiley. - 1354-1013 .- 1365-2486. ; 21:1, s. 250-264
  • Journal article (peer-reviewed)abstract
    • The Dahra field site in Senegal, West Africa, was established in 2002 to monitor ecosystem properties of semiarid savanna grassland and their responses to climatic and environmental change. This article describes the environment and the ecosystem properties of the site using a unique set of in situ data. The studied variables include hydroclimatic variables, species composition, albedo, normalized difference vegetation index (NDVI), hyperspectral characteristics (350-1800nm), surface reflectance anisotropy, brightness temperature, fraction of absorbed photosynthetic active radiation (FAPAR), biomass, vegetation water content, and land-atmosphere exchanges of carbon (NEE) and energy. The Dahra field site experiences a typical Sahelian climate and is covered by coexisting trees (similar to 3% canopy cover) and grass species, characterizing large parts of the Sahel. This makes the site suitable for investigating relationships between ecosystem properties and hydroclimatic variables for semiarid savanna ecosystems of the region. There were strong interannual, seasonal and diurnal dynamics in NEE, with high values of similar to-7.5g Cm(-2)day(-1) during the peak of the growing season. We found neither browning nor greening NDVI trends from 2002 to 2012. Interannual variation in species composition was strongly related to rainfall distribution. NDVI and FAPAR were strongly related to species composition, especially for years dominated by the species Zornia glochidiata. This influence was not observed in interannual variation in biomass and vegetation productivity, thus challenging dryland productivity models based on remote sensing. Surface reflectance anisotropy (350-1800nm) at the peak of the growing season varied strongly depending on wavelength and viewing angle thereby having implications for the design of remotely sensed spectral vegetation indices covering different wavelength regions. The presented time series of in situ data have great potential for dryland dynamics studies, global climate change related research and evaluation and parameterization of remote sensing products and dynamic vegetation models.
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