Normalisation of directional effects in 10-day global syntheses derived from VEGETATION/SPOT:

Duchemin, B.; Berthelot, Béatrice; Dedieu, Gérard; Leroy, M.; Maisongrande, Philippe

doi:10.1016/s0034-4257(01)00337-6

Cited by 51 publications

(9 citation statements)

References 28 publications

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“…Next, total snowfall was apportioned to daily snowfall values with a snow model. SPT/VGT images are composites of 10 days of Normalized Difference Vegetation Index data: for each pixel of these images, a maximum value is found from 10 days of daily SPT/VGT images (Holben, 1986;Duchemin et al, 2002). These data have a spatial resolution of approximately 1 km at satellite nadir.…”

Section: Methodsmentioning

confidence: 99%

Spatial snowfall distribution in mountainous areas estimated with a snow model and satellite remote sensing

Asaoka

Kominami

2012

Hydrological Research Letters

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Abstract:We describe an approach for reconstructing snowfall that combines satellite observations of the snow disappearance date (SDD) with a snow model for two mountainous areas in Japan having distinct snow climatology. This approach allows assessment of the distribution of snow and topographical effects on snowfall within a catchment. We also evaluated how the reconstructed snowfall affects the catchment snow hydrology.Validation at observation sites demonstrated that a combination of the snow model and snowfall reconstructions successfully estimated the seasonal changes of snow water equivalent (SWE). In Japan, the dependence of snowfall on elevation is stronger in mountainous areas along the coast on the Sea of Japan side of Japan's central mountain spine (where snowfall triples with every 1000 m increase in elevation; C sf = 0.002 m −1 ) compared with inland locations of the same region (where snowfall doubles with every 1000 m increase; C sf = 0.001 m −1). Moreover, the reconstructed snowfall improved the estimation of maximum catchment SWE. Maximum total SWE, estimated with reconstructed snowfall, was 3.8 × 10 8 m 3 in Kurobe catchment (along the coast on the Sea of Japan side of Japan's central mountain spine), while that, estimated by convectional method with the spatially-constant C sf = 0.001 m −1 , was 2.0 × 10 8 m 3 . As a result, estimations of the snow disappearance date and of the catchment snowmelt were also improved. These results suggest that it is useful to estimate the spatial snow distribution, especially where steep topography causes large gradients of snowfall amount with respect to elevation.

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Section: Methodsmentioning

confidence: 99%

Spatial snowfall distribution in mountainous areas estimated with a snow model and satellite remote sensing

Asaoka

Kominami

2012

Hydrological Research Letters

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“…Therefore, in this study, the normalization method of reflectance was performed considering the characteristics of geostationary satellites. This method was proposed by Yeom and Kim (2013) as a modification of the VZA given by Duchemin et al (2002). In this approach, the VZA is fixed to the VZA of the pixel, and the SZA and relative azimuth angle (RAA) are modulated by the average of the synthesis period to calculate normalized reflectance.…”

Section: Brdf For Normalized Reflectancementioning

confidence: 99%

Evaluation of NDVI Estimation Considering Atmospheric and BRDF Correction through Himawari-8/AHI

Seong

Jung

Han

2020

Asia-Pacific J Atmos Sci

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Satellite-based vegetation indices are an essential element in understanding the Earth's surface. In this study, we estimated the normalized difference vegetation index (NDVI) using Himawari-8/Advanced Himawari Imager (AHI) data and analyzed the sensitivity of products to atmospheric and surface correction. We used the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) radiative transfer model for atmospheric correction, and kernel-based semi-empirical bidirectional reflectance distribution function (BRDF) model to remove surface anisotropic effects. From this, top-of-atmosphere, top-of-canopy, and normalized NDVIs were produced. A sensitivity analysis showed that the normalized NDVI had the lowest number of missing values compared with the others and almost no low peaks during the study period. These results were validated by Terra and Aqua/Moderate Resolution Imaging Spectroradiometer (MODIS) and Project for On-Board Autonomy/Vegetation (PROBA) NDVI product, showing the root mean square error (RMSE) and bias of 0.09 and + 0.04 (MODIS) and 0.09 and − 0.04 (PROBA), respectively. These results also satisfied the FP7 Geoland2/BioPar project-defined user requirements (threshold: 0.15; target: 0.10).

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“…Reflectance measurements are performed within four spectral bands. Further details on the instrument characteristics can be found in the work of Duchemin et al [2002]. The LAI products are processed by the GEOLAND Core Service Products for the years 1998 to 2003.…”

Section: Satellite-derived Leaf Area Index (Lai)mentioning

confidence: 99%

Analysis of leaf area index in the ECMWF land surface model and impact on latent heat and carbon fluxes: Application to West Africa

et al. 2008

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[1] A new version of the land surface model of the European Centre for Medium-Range Weather Forecasts (Carbon-TESSEL, or CTESSEL) includes a vegetation growth model. This study describes a leaf area index (LAI) data assimilation system (LDAS) based on CTESSEL and satellite LAI for operational Net Ecosystem Exchange (NEE) predictions. The LDAS is evaluated over West Africa. A preliminary experiment shows a significant impact of the LAI on the CTESSEL NEE. The LAI is compared to two satellite products: the predicted annual cycle is delayed over the Sahel and savannah, and the LAI values differ from the satellite products. Preliminary to their use in the LDAS, the LAI products are rescaled to the CTESSEL predictions. The LDAS simulations are confronted to measurements of biomass and LAI for a site in Mali. The LAI analysis is shown to improve the predicted biomass and the annual cycles of the water (latent heat flux, or LE) and carbon (NEE) fluxes. Afterward, the LDAS is run over West Africa with the Moderate-Resolution Imaging Spectroradiometer products (2001)(2002)(2003)(2004)(2005). The analysis of LAI shows a limited impact on LE, but it impacts strongly on NEE. Finally, the CTESSEL NEE are compared to two other models' outputs (simple biosphere (SIB) and Carnegie-Ames-Stanford (CASA)). The order of magnitude of the three data sets agrees well, and the shift in annual cycle of CTESSEL is reduced by the LDAS. It is concluded that a LAI data assimilation system is essential for NEE prediction at seasonal and interannual timescales, while a LAI satellite-based climatology may be sufficient for accurate LE predictions.

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Normalisation of directional effects in 10-day global syntheses derived from VEGETATION/SPOT:

Cited by 51 publications

References 28 publications

Spatial snowfall distribution in mountainous areas estimated with a snow model and satellite remote sensing

Spatial snowfall distribution in mountainous areas estimated with a snow model and satellite remote sensing

Evaluation of NDVI Estimation Considering Atmospheric and BRDF Correction through Himawari-8/AHI

Analysis of leaf area index in the ECMWF land surface model and impact on latent heat and carbon fluxes: Application to West Africa

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