This study proposes a three-stage, flexible and adaptable protocol for the establishment of field-scale agricultural management zones (AMZs) using remote sensing, ground truthing (apparent electrical conductivity and soil sampling), the IRRIGOPTIMAL^® system and machine learning. The methodology to develop this protocol was applied to olive and alfalfa plots in Heraklion (Crete, Greece) to monitor soil and plant responses for the period 2022–2024. However, the actual time for the implementation of this protocol varies between 3 and 6 months. The first step of this protocol involves the use of soil and vegetation reflectance mapping (moisture, photosynthetic activity) by satellites and unmanned aerial systems, together with geophysical electromagnetic induction mapping (apparent electrical conductivity) to verify soil variability, which is strongly linked to the delineation of management zones. In the second step, a machine learning-based prediction of the spatial distribution of soil electrical conductivity is made, considering the data obtained in the first step. Furthermore, in the second step, the IRRIGOPTIMAL^® system provides real-time monitoring of a variety of weather (such as air temperature, dew point, solar radiation, relative humidity, precipitation) and soil (temperature, moisture) parameters to support the optimal cultivation strategy for the plants. Once the data have been analysed, the soil variability of the plot and the presence or absence of cultivation zones are determined and the decision on the cultivation strategy is made based on targeted soil sampling and further soil analyses. This protocol could contribute significantly to the rational use of inputs (water, seeds, fertilizers and pesticides) and support variable rate technology in the agricultural sector of Crete.

Urban streams undergo significant ecological alterations due to urbanization, including hydrological changes, water contamination, and biodiversity loss. This research employs a combination of satellite and drone imagery alongside traditional chemical and geophysical methods, facilitating a multi-dimensional assessment of Almyros and Gazanos urban stream health in Heraklion (Crete, Greece). The satellite imagery, obtained from the Copernicus program, allows for monitoring land use and impervious surface density around the streams, while drone surveys capture high-resolution images and calculate various water quality indices. In addition, chemical analyses of water samples for pollutants, as well as geophysical measurements using spectral induced polarization (SIP) and electromagnetic scanning (GEM-2), provide insight into the integrity of aquatic and riparian ecosystems. The study reflects on the different types of anthropogenic pressure faced by these two ecosystems. Almyros stream exhibits signs of eutrophication, characterized by elevated levels of chlorophyll and the presence of algal blooms, possibly due to runoff from adjacent agricultural activities. Conversely, the Gazanos stream shows signs of pollution mostly related to urbanization. The findings emphasize that both streams are under increasing anthropogenic pressure, thus highlighting the importance of employing comprehensive methods for effective stream management and policy implementation. This study ultimately advocates for ongoing monitoring initiatives that embrace technological advancements to safeguard urban water ecosystems.

This study proposes a flexible and adaptable protocol for the establishment of agricultural management zones that utilises remote sensing, ground truthing (apparent electrical conductivity and soil sampling), the IRRIGOPTIMAL® system and machine learning. This protocol could contribute significantly to the rational use of inputs (water, fertilizers and pesticides) and to the further efficient development of an optimal irrigation system with variable irrigation rates. The methodology to develop this protocol was applied to olive and alfalfa plots in Heraklion (Crete, Greece) to monitor soil and crop responses for the period 2022-2024. Spatial and temporal assessment of selected soil and plant parameters (moisture, photosynthetic activity) using ground and vegetation reflectance mapping by satellites and unmanned aerial systems provides important information in both the pre- and main phases of the management zone delineation. Geophysical methods such as electromagnetic induction, applied in the main phase of management zone delineation, provide a robust technique to determine the spatial and temporal distribution of apparent electrical conductivity. The Random Forest machine learning model was found to be most suitable for predicting soil electrical conductivity based on satellite-derived salinity indices and ground electromagnetic induction. Finally, the IRRIGOPTIMAL® system provides real-time monitoring of a variety of weather and soil parameters to determine the optimal cultivation of crops based on the creation of agricultural management zones.

Eutrophication is a form of pollution caused by elevated nutrient concentrations in water bodies, leading to excessive algal growth and subsequent oxygen depletion. This process poses significant risks to aquatic ecosystems and overall water quality. This study investigates the spatial distribution of eutrophication in the Almyros Stream, aiming to develop a rapid and high-resolution approach for identifying eutrophication patterns and selecting representative sampling sites. Almyros is an urban stream in the western Heraklion Basin (Crete, Greece) that is subjected to considerable pressures from agricultural, industrial, urban, and tourism-related activities. Data for this study were collected using a drone equipped with a multispectral sensor. The multispectral bands, together with remote sensing indices associated with chlorophyll presence, served as input data. Chlorophyll presence is a key indicator of phytoplankton biomass and is widely used as a proxy for nutrient enrichment and eutrophication intensity in aquatic ecosystems. The k-means clustering algorithm was then applied to classify the data and reveal the eutrophication spatial patterns of the study area. The results show that the methodology successfully identified spatial variations in eutrophication-related conditions and generated robust eutrophication pattern maps. These findings underscore the potential of integrating remote sensing and machine learning techniques for efficient monitoring and management of water bodies.