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Soil surveys with line-scanning platforms appear to have great advantages over the traditional methods used to collect soil information for the development of field-scale soil mapping and applications. These carry VNIR (visible and near infrared) spectrometers and have been used in recent years extensively for the assessment of soil fertility at the field scale, and the delineation of site-specific management zones (MZ). A challenging feature of VNIR applications in precision agriculture (PA) is the massiveness of the derived datasets that contain point predictions of soil properties, and the interpolation techniques involved in incorporating these data into site-specific management plans. In this study, fixed-rank kriging (FRK) geostatistical interpolation, which is a flexible, non-stationary spatial interpolation method especially suited to handling huge datasets, was applied to massive VNIR soil scanner data for the production of useful, smooth interpolated maps, appropriate for the delineation of site-specific MZ maps. Moreover, auxiliary Sentinel-2 databased biophysical parameters NDVI (normalized difference vegetation index) and fAPAR (fraction of photosynthetically active radiation absorbed by the canopy) were included as covariates to improve the filtering performance of the interpolator and the ability to generate uniform patterns of spatial variation from which it is easier to receive a meaningful interpretation in PA applications. Results from the VNIR prediction dataset obtained from a pivot-irrigated field in Albacete, southeastern Spain, during 2019, have shown that FRK variants outperform ordinary kriging in terms of filtering capacity, by doubling the noise removal metrics while keeping the computation cost reasonably low. Such features, along with the capacity to handle a large volume of spatial information, nominate the method as ideal for PA applications with massive proximal and remote sensing datasets.

The terroir effect refers to the interactions between the grapes and their natural surroundings and has been recognized as an important factor in wine quality. The identification and mapping of viticultural terroir have long been relying on expert opinion coupled with land classification and soil/climate mapping. In this study, the data-driven approach has been implemented for mapping natural terroir units based on spatial modeling of public-access geospatial information regarding the three most important environmental factors that make up the terroir effect on different scales, climate, soil, and topography. K-means cluster analysis was applied to the comprehensive databases of relevant spatial information, and the optimum number of clusters was identified by the Dunn and CCC indices. The results have revealed ten clusters that cover the agricultural area of Drama (Greece), where it was applied, and displayed variable conditions on the climate, soil, and topographic factors. The implications of the resulting natural terroir units on the vini-viticultural management of the most common vine varieties are discussed. As more accurate and detailed input spatial data become available, the potential of such an approach is highlighted and paving the way toward a true understanding of the drivers of terroir.

Monitoring soil erosion risk is an important part of soil conservation practices. It is usually estimated with the Universal Soil Loss Equation, and the C-factor (vegetation cover) is derived from optical satellite images. However, because of lack of data and resources, or in rapid assessments, C-factor is estimated using one or a few satellite observations, despite being temporally variable according to plants' phenology. The aim of this work was to study the effect of seasonality in estimating C-factor. This was achieved by demonstrating first that there is a difference when estimating soil erosion with Universal Soil Loss Equation at variable time steps in a year, namely once, seasonally and monthly. Using Moderate Resolution Imaging Spectroradiometer normalized difference vegetation index images and statistical analysis at subcatchment scale, it was shown that there is a significant difference when estimating mean annual soil loss with the aforementioned temporal options. The highest differences were observed between monthly and annual time steps. The second objective was to identify which is the optimum time to estimate C-factor in a year. The results show that November, October and March are the optimum months for single image estimation of annual soil erosion. Statistical analysis with a random point dataset suggested that the spatial variability of the results was influenced by the land cover type, especially in areas with variable leaf cover where a single date estimation of C-factor was not representative of the whole year, such as annual crops and deciduous trees. Copyright © 2013 John Wiley & Sons, Ltd.

Water resources management is a comprehensive planning process dimensioning all water related components. With this in mind, the EU Water Framework Directive (WFD) requires reliable and continuous water monitoring and collection of qualitative information in order to support decision making concerning water pollution events, focusing primarily on civil protection. The present research study initiates the coupling of water quality - quantity monitoring techniques and telemetric systems with geographic information system (GIS) based map server technologies for the near real time warning about potential water pollution events. The study is being implemented in the Greek part of the transboundary Nestos river basin and forms part of the Bulgarian and Greek European Territorial Cooperation Programme 2007-2013. The aim of this partnership is to develop an operational supportive mechanism for the Competent Authorities of water management in both countries in order to facilitate the planning and implementation of sustainable water management along with civil protection.