The LUCAS (Land Use/Cover Area frame Statistical Survey) database currently contains about 20,000 topsoil samples of 15 soil properties. It is the largest harmonised soil survey field database currently available for Europe. Soil Organic Carbon (SOC) levels have been successfully determined using both proximal and airborne/spaceborne reflectance spectroscopy. In this paper, Cyprus was selected as a study area for estimating SOC content from multispectral remotely sensed data. The estimation of SOC was derived by comparing field measurements with a set of spatially exhaustive covariates, including DEM-derived terrain features, MODIS Vegetation indices (16 days) and Landsat ETM+ data. In particular, the SOC levels in the LUCAS database were compared with the covariate values in the collocated pixels and their eight surrounding neighbours. The regression model adopted made use of Support Vector Machines (SVM) regression analysis. The SVM regression proved to be very efficient in mapping SOC with an R2 fitting of 0.81 and an R2 k-fold cross-validation of 0.68. This study proves that the inference of SOC levels is possible at regional or continental scales using available remote sensing and Earth observation data.
Under the European Union’s Thematic Strategy for Soil Protection, the European Commission’s Directorate-General for the Environment (DG Environment) has identified the mitigation of soil losses by erosion as a priority area. Policy makers call for an overall assessment of soil erosion in their geographical area of interest. They have asked that risk areas for soil erosion be mapped under present land use and climate conditions, and that appropriate measures be taken to control erosion within the legal and social context of natural resource management. Remote sensing data help to better assessment of factors that control erosion, such as vegetation coverage, slope length and slope angle. In this context, the data availability of remote sensing data during the past decade facilitates the more precise estimation of soil erosion risk. Following the principles of the Universal Soil Loss Equation (USLE), various options to calculate vegetative cover management (C-factor) have been investigated. The use of the CORINE Land Cover dataset in combination with lookup table values taken from the literature is presented as an option that has the advantage of a coherent input dataset but with the drawback of static input. Recent developments in the Copernicus programme have made detailed datasets available on land cover, leaf area index and base soil characteristics. These dynamic datasets allow for seasonal estimates of vegetation coverage, and their application in the G2 soil erosion model which represents a recent approach to the seasonal monitoring of soil erosion. The use of phenological datasets and the LUCAS land use/cover survey are proposed as auxiliary information in the selection of the best methodology.
The Joint Research Centre of the European Commission has developed Interpolated Meteorological Datasets available
on a regular 25x25km grid both to the scientific community and the general public. Among others, the Interpolated
Meteorological Datasets include daily maximum/minimum temperature, cumulated daily precipitation,
evapotranspiration and wind speed. These datasets can be accessed through a web interface after a simple registration
procedure. The Interpolated Meteorological Datasets also serve the Crop Growth Monitoring System (CGMS) at
European level. The temporal coverage of the datasets is more than 30 years and the spatial coverage includes EU
Member States, neighboring European countries, and the Mediterranean countries. The meteorological data are highly
relevant for the development, implementation and assessment of a number of European Union (EU) policy areas:
agriculture, soil protection, environment, agriculture, food security, energy, climate change.
An online user survey has been carried out in order to assess the impact of the Interpolated Meteorological Datasets on
research developments. More than 70% of the users have used the meteorological datasets for research purposes and
more than 50% of the users have used those sources as main input for their models. The usefulness of the data scored
more than 70% and it is interesting to note that around 25% of the users have published their scientific outputs based on
the Interpolated Meteorological Datasets. Finally, the user feedback focuses mostly on improving the data distribution
process as well as the visibility of the web platform.
The Soil and Water Assessment Tool (SWAT) was evaluated while modeling daily stream flow in Limnatis basin, Cyprus over a period of seven years. Stream flow data from 2006-2008 were used as a warm up period, the period 2008- 2010 was used to calibrate the model and stream flow, data from 2008-2012 were used for the validation. The model could adequately predict daily stream flow trends with Nash-Sutcliffe values of 0.68. Overall the results of the simulation indicate that SWAT model can be an effective tool for the modeling of stream flow in intermittent rivers like Limnatis, and could contribute valuable information for successful catchment management.
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