Information on land surface properties finds applications in a range of areas related to weather forecasting, environmental research, hazard management and climate monitoring. Remotely sensed observations yield the only means of supplying land surface information with adequate time sampling and a wide spatial coverage. The aim of the Satellite Application Facility for Land Surface Analysis (Land-SAF) is to take full advantage of remotely sensed data to support land, land-atmosphere and biosphere applications, with emphasis on the development and implementation of algorithms that allow operational use of data from European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) sensors. This article provides an overview of the Land-SAF, with brief descriptions of algorithms and validation results. The set of parameters currently estimated and disseminated by the Land-SAF consists of three main groups: (i) the surface radiation budget, including albedo, land surface temperature, and downward short-and longwave fluxes; (ii) the surface water budget (snow cover and evapotranspiration); and (iii) vegetation and wild-fire parameters.
[1] Here we assess established algorithms and a newly developed scheme for the estimation of downward long-wave radiation flux at the surface (DLR), i.e., the irradiance reaching the surface within 4 and 100 mm. These different methods correspond to bulk parameterization schemes, which merge the signature of clouds on Meteosat secondgeneration (MSG) data with information on atmosphere water content and near-surface air temperature available from numerical weather prediction (NWP) fields. The new formulation consists of a generalization of a method first developed for clear sky cases and now fine-tuned for a wider range of atmospheric conditions. The performance of this and three other parameterization schemes is compared with independent ground observations. Such a validation exercise is extended also to European Centre for Medium-Range Weather Forecast (ECMWF) flux forecasts, since the ECMWF model is the main source of information on air temperature and water vapor content, and to surface fluxes obtained from the Clouds and the Earth's Radiant Energy System (CERES). It is shown that the new parameterization scheme performs well when compared to other methods, with root mean square errors within 20 Wm −2 . The overall good matching between parameterized values and in situ data suggests a good performance of a relatively simple bulk scheme and also of the use of MSG-based cloud identification.Citation: Trigo, I. F., C. Barroso, P. Viterbo, S. C. Freitas, and I. T. Monteiro (2010), Estimation of downward long-wave radiation at the surface combining remotely sensed data and NWP data,
This study presents an intercalibration of Meteosat-5 11 mm channel and NOAA-14 10.8 mm and 12.0 mm channels, and their comparison for sea and land pixels. The intercalibration empirical relation is derived for clear-sky sea measurements, with similar zenith viewing angles. The root mean square difference between NOAA-14 and Meteosat-5 intercalibrated brightness temperatures is about 1.4 K (4.7 K) for all clear-sky sea (land) pixels. The discrepancies over land are analysed in terms of viewing angle, surface type, terrain elevation and exposure to sunlight. The satellite viewing geometry is responsible for two major impacts, namely: the obstruction by neighbouring clouds towards one of the satellites; and differences in surface solar illumination viewed by each sensor. It is also shown that the higher discrepancies between intercalibrated temperatures occur for the most heterogeneous surfaces (e.g. Open Shrublands). The effect of terrain variability is not linear and depends strongly on the surface type.
The vegetative development of grapevines is orchestrated by very specific meteorological conditions. In the wine industry vineyards demand diligent monitoring, since quality and productivity are the backbone of the economic potential. Regional climate indicators and meteorological information are essential to winemakers to assure proper vineyard management. Satellite data are very useful in this process since they imply low costs and are easily accessible. This work proposes a statistical modelling approach based on parameters obtained exclusively from satellite data to simulate annual wine production. The study has been developed for the Douro Demarcated Region (DDR) due to its relevance in the winemaking industry. It is the oldest demarcated and controlled winemaking region of the world and listed as one of UNESCO’s World Heritage regions. Monthly variables associated with Land Surface Temperatures (LST) and Fraction of Absorbed Photosynthetic Active Radiation (FAPAR), which is representative of vegetation canopy health, were analysed for a 15-year period (2004 to 2018), to assess their relation to wine production. Results showed that high wine production years are associated with higher than normal FAPAR values during approximately the entire growing season and higher than normal values of surface temperature from April to August. A robust linear model was obtained using the most significant predictors, that includes FAPAR in December and maximum and mean LST values in March and July, respectively. The model explains 90% of the total variance of wine production and presents a correlation coefficient of 0.90 (after cross validation). The retained predictors’ anomalies for the investigated vegetative year (October to July) from 2017/2018 satellite data indicate that the ensuing wine production for the DDR is likely to be below normal, i.e., to be lower than what is considered a high-production year. This work highlights that is possible to estimate wine production at regional scale based solely on low-resolution remotely sensed observations that are easily accessible, free and available for numerous grapevines regions worldwide, providing a useful and easy tool to estimate wine production and agricultural monitoring.
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