Unpiloted Aerial Vehicle technologies have substantial potential in detecting marine oil spill incidents effective for emergency response and mitigation efforts. Offshore and onshore oil spill incidents remain under reported yet are frequently occurring and challenges remain around effective targeting of type-specific mitigation efforts. Here we demonstrate an approach of classifying imagery acquired from thermal and hyperspectral VNIR sensors mounted on UAV platforms of simulated oil spill incidents implemented in an outdoor environment under daylight conditions. Each dataset was classified using supervised pixel-based classification algorithms including Support Vector Machine, Random Trees and K-Nearest Neighbor. Six types of oil were successfully detected in seawater and in soil with SVM achieving between 74.69-76.09% overall accuracy, while RT achieving 67.77- 73.33 %, and KNN scoring 56.77-62.66%. This paper demonstrates that classification results of thermal infrared imagery had higher overall accuracy than hyperspectral imagery in detecting type specific oil which provide key insights about an oil spill incident necessary for mitigation efforts and recovery.

The SIMAGAZ gas monitoring camera was deployed during the IPOMAC and MANIFEST campaigns in 2021 and 2022. These measurement campaigns were carried out several miles off the west coast of France as part of the response to pollution at sea. Various chemicals, legally classified as HNS (Hazardous Noxious Substances), were released on the sea surface and SIMAGAZ measurements were made from the ship and by air. The camera data processing is based on a physical radiometric model, a spectroscopic database containing the compounds of interest and includes a morphological analysis of the images. SIMAGAZ has thus enabled the detection, identification and quantification of several gases evaporating from spilled slicks (acetone, butyl acetate, propyl acetate, MTBE and heptane), sometimes for over half an hour. For other products with absorption lines outside the SIMAGAZ spectral range, no gas was seen as expected. On the basis of this success, the analysis of the concentration fields obtained and their dynamics can be compared with the results of evaporation and atmospheric dispersion modelling tools for HNS.

In this work a multiplexed multi-parameter marinized sensory array is described. This was deployed in 2 different array configurations across 3 different marine sites in Orkney. The chosen deployment sites were to test the sensory array response to long period oceanic waves, the effects of passing marine traffic for harbor security, and highly energetic tidal flow generation. The sensor array is made up of 4 measurement stations which are connected in series. Across these measurement stations, a total of 16 temperature sensors, 4 attitude sensors (each consisting of 3 individual fiber sensors) and 16 flow sensors were successfully deployed. A total of 86 FBGs were interrogated simultaneously. They were addressed over 0.25km via 20 single-mode (SMF-28e) optical fibers contained in a single marine compliant ruggedized umbilical. The bio-inspired fiber optic flow sensors are designed to mimic the behavior of the superficial neuromasts naturally found as part of the lateral line sensory organ present in fish. The sensor is composed of optical fibers inscribed with Fiber Bragg Gratings glued together in a polymer matrix which are then encapsulated in a polyurethane shell. The sensors response has been tested in DC flows in a tow tank and have demonstrated the ability of measuring flow speed from 0.05ms^-1 to 2.5ms^-1. The main aim of the deployment was to demonstrate the capabilities of fiber sensor technology for oceanographic applications. The measurement periods described lasted over 1 day and the sensor system performed well in comparison with data gathered from commercial instrumentation.

Accurate bathymetry estimation is crucial for various marine and coastal applications, including resource management and environmental protection. This study presents a comparison of bathymetry estimated using optical satellite imagery, on sandy bottom, versus multibeam bathymetric data. The study area was the Gulf of Sciacca, on the southern coast of Sicily Island (Italy). High-resolution PlanetScope SuperDove satellite images were processed using a simplified version of the original radiative transfer equation from Jain and Miller (1977) was used to estimate water depth. The model was calibrated and then validated using multibeam data. Multibeam data were acquired using a Reason 8125 multibeam echosounder. Bathymetry was estimated from optical data from the shoreline down to ⁓25m water depth; while approaching this depth, the reflectance assumed an asymptotic value. The results show a strong correlation between the depths estimated from satellite images and those obtained from multibeam data, with a root mean square error lower than 11% with a confidence interval of 95%. However, some discrepancies were observed in areas in mixed pixels or reflectance surface effects.

Yield prediction is an essential task to sustain the food market and to ensure the food for the world in the upcoming decades. Potatoes (Solanum tuberosum L.) are a vital staple food for many countries in the world and the advancement of accurate yield prediction will aid in promoting the agricultural industry. Potato is one of the most exportable agricultural products in Cyprus. Artificial Intelligence (AI) and Remote Sensing (RS) based agriculture monitoring has showed a massive impact in yield estimation in recent years. Monitoring vegetation indices like Normalized Difference Vegetation Index during the phenological stages of potatoes can provide identical insights into crop growth and yield. In this study, our focus lies on robust yield prediction across varied spatial and temporal dimensions. Specifically, we explore two distinct regions in Cyprus (i.e seaside and interior), each characterized by unique local agroclimatic conditions. The dataset encompasses potato yield data, in-situ meteorological data and vegetation indices derived by Sentinel-2 for a 7-years period (2017-2023). Thus, we test invariant learning against traditional ML methods in terms of spatial robustness and data drift issues.

Durum wheat is a globally important cereal. Therefore, precise yield assessment is crucial for informed decision-making in precision agriculture. Remote sensing techniques, specifically high-resolution multispectral data from the Sentinel-2 mission, provide valuable insights. Additionally, machine learning algorithms offer a powerful alternative to traditional modeling by efficiently processing multi-dimensional datasets and extracting complex relationships from remote sensing data. This study presents two modeling approaches for the estimation of durum wheat yield based on Sentinel-2 data, covering 157 fields in Thessaly plain (Greece) across five growing periods. The first approach comprises multiple linear regression modeling based on vegetation indices (VI-MLR) that capture plant, water and soil signals (NDVI, EVI, NMDI, NDWI). The results demonstrate moderate accuracy (R²: 0.476-0.503, RMSE: 873-897 kg ha^-1). In the second approach, the reflectance data of all Sentinel-2 bands for several dates during the growth periods are used as input parameters in three machine learning model algorithms, i.e., Random Forest (RF), K-Nearest Neighbors (KNN) and Boosting Regressions (BR), resulting in significantly improved accuracy. Utilizing all 12 bands from Sentinel-2 demonstrates high accuracy (RF and KNN: R² > 0.94, RMSE < 318 kg ha^-1) throughout the growing season, that remains high when images from the start of the growing period until February, i.e., four months before harvest, are used (RF and KNN: R² > 0.92, RMSE < 357 kg ha⁻¹). Performance remains high when using only the 6 most important bands (RF and KNN: R² > 0.92, RMSE < 349 kg ha⁻¹ with all images, R² > 0.92, RMSE < 364 kg ha⁻¹ with images until February). This study underscores the potential of machine learning models for precise durum wheat yield assessment using satellite imagery, even in early stages. However, further research is necessary to evaluate their generalizability across regions with varying climatic and growing conditions.

This study focuses on predicting vegetation indices in South Korea using MODIS sensor products, which provide a temporal resolution of 16 days and a spatial resolution of 500 meters. Data preprocessing, such as outlier removal, was conducted to ensure stability. Leveraging recent advancements in artificial intelligence, the study employed deep learning models for spatio-temporal video prediction. The predicted vegetation indices were compared with the original data using error and similarity matrices to verify accuracy. The results suggest that highly accurate vegetation indices can serve as valuable input for various studies monitoring vegetation changes, offering significant insights into climate change impacts on plant life. Acknowledgments This study was carried out with the support of ´R&D Program for Forest Science Technology (Project No. RS-2024-00404128)´ provided by Korea Forest Service(Korea Forestry Promotion Institute).

This work was carried out with the support of the "Cooperative Research Program for Agriculture Science and Technology Development (Project No. PJ0162342024)" by the Rural Development Administration, Republic of Korea. The recent surge in greenhouse gas emissions has significantly accelerated global warming, making climate change more serious. In particular, Long-lived greenhouse gases such as methane (CH4) have a warming effect about 28 times stronger than carbon dioxide (CO2), making it important to calculate the amount of methane emissions generated in Korea. This study analyzed LDAPS meteorological data and FluxNet ground observations of the Cheorwon rice paddy region based on the GBM model, and generated a methane concentration map of methane emissions from rice paddies in Korea. The 1.5-kilometer spatial resolution of the data was used to capture more detailed regional variations, and daily maps were created to capture temporal details. This is expected to reveal patterns of methane generation. This helps to accurately predict methane emissions and is expected to reveal patterns of methane generation in response to changing weather conditions.

Surface water is of critical importance to the ecosystem, agricultural production and livelihoods of people in the arid regions of Africa. The surface water is characterized by significant differences in spectral features, complex morphological changes, and more fine streams, which reflects the climate and water resource conditions in Africa. To address the spectral and spatial variability of surface water of large areas, this paper proposes a new surface water extraction model. The model utilizes a VGG network to extract local information, while an MVT module is introduced to capture global contextual information in remote sensing images. Spatial attention and channel attention mechanisms are used to effectively fuse global and local information to more accurately recognize and extract continuous spatial relationships of water bodies. In this paper, Sentinel-2 multispectral remote sensing images are used to finely extract surface water in Egypt during the dry season using the proposed model. Experimental results show that the model not only improves the accuracy of fine water extraction, but also performs well in waters with complex backgrounds and boundaries.

The use of unmanned aerial spraying systems is increasing due to their many advantages, but there is a lack of research on how to evaluate their performance. In general, the spraying performance is evaluated by collecting the spray droplets with water-sensitive paper and analyzing the images. However, there is a disadvantage that the performance is affected by humidity. In this study, an image analysis program was developed to measure the spraying performance when using pigments and collectors instead of water-sensitive paper. The program was developed in Python and utilizes OpenCV related functions. To overcome the problem of binarization processing, HSV color system was used. The program is able to generate ROIs regardless of the size or shape of the collector and calculate the percentage of deposited area and droplet size distribution of the sprayed droplets.

The extensive presence of macroplastic pollutants along coastlines poses a significant environmental challenge, threatening both human health and coastal ecosystems. Accurate identification of these pollutants is essential for planning effective cleanup activities and improving overall quality of life. Traditionally, in-situ surveys have been the go-to method for locating macroplastics, but they are costly and time-consuming. To overcome these limitations, this study explores the use of Remotely Piloted Aircraft Systems (RPAS) equipped with high-resolution cameras as a cost-effective alternative for mapping macroplastic litter on beaches and distinguishing between different types of contaminants. The Brindisi shoreline was selected as a pilot site, where it was surveyed using a DJI MAVIC MINI drone with an RGB camera. The collected images were processed with Metashape software and ground control points from a GARMIN Forerunner 245 were used for georeferencing. The resulting RGB orthophoto was analyzed using Transformed Divergence and Bhattacharyya Distance criteria to assess the inter-class spectral separability and classification accuracy. The analysis identified 1,154 waste elements, demonstrating that RPAS imagery is effective for detecting macroplastic items. However, the study found that while the RPAS RGB orthophoto was suitable for manual classification, its performance in automatic classification was limited. The separability algorithms used affected the accuracy of the final classification maps, indicating that while RPAS technology is promising, improvements in spectral analysis and classification algorithms are needed for better-automated results.

A study was carried out to validate the METRIC (Mapping EvapoTranspiration at high Resolution with Internalized Calibration) model to estimate the spatial variability of water requirements for a drip and micro sprinkler irrigated European hazelnut orchard (1,500 ha) located in Maule Region, Chile (35°18’ S 71°21’ W; 219m above sea level). To estimate hazelnut water requirements (actual evapotranspiration (ET) and crop coefficient (Kc)) using the METRIC model, Landsat 7 ETM+ and 8 OLI satellite images acquired during clear sky days were used for the 2016-2017 growing season. The performance of METRIC was evaluated in an experimental block (4,5 ha) located within the orchard using measurements of ET from an eddy covariance (EC) system at the time of satellite overpass (11:30 h). The statistical analysis indicated that the original METRIC model overestimated ET values by about 42%. After calibrating sub-models for estimating leaf area index (LAI), roughness length (Z_om), and soil heat flux (G), METRIC estimated ET with errors < 10 %, root mean square error (RMSE) = 0.98mm d^-1, mean absolute = 0.90mm d^-1 and index of agreement = 0.70. Results suggested that the calibrated version of METRIC could be a valuable tool for mapping the spatial and temporal variability of ET and Kc for a drip-irrigated hazelnut orchard.

Accurate and precise measurements of microclimatic temperatures are important for understanding microclimate dynamics within forests, which are crucial in the face of climate change. Various methods exist for measuring microclimate temperatures in the field; however, for ultra-high-resolution measurements in space (< 1 m) and time (< 1 minute), optical fiber-based sensing systems become a promising option. Specifically, a Distributed Temperature Sensing (DTS) system coupled with a fiber over 100 – 1000m long could be highly suitable to collect spatial temperature data continuously in forests and other vegetation types. Although DTS is accurate in controlled lab conditions, the impacts of environmental factors such as energy fluxes from direct solar radiation on temperature measurements remain to be quantified. Given the environmental variability in forest ecosystems, it is essential to take into account the increase in fiber temperatures due to solar radiation.

Here we explore the impact of radiative fluxes from direct sunlight on the optical fibers used in DTS systems. To evaluate the impact of radiative fluxes on the thermal accuracy of a DTS, we conducted a field experiment with direct solar radiation. We put two fiber regions under the same condition, but one region is coated with aluminum while the other one is uncoated.

We observed distinct thermal responses between aluminum-coated and uncoated sections of the optical fibers when exposed to solar radiation, with the uncoated fiber exhibiting a higher temperature under the same conditions. Our results can be used to mitigate this bias in field measurements, enhancing the accuracy of DTS in environmental monitoring, and deepening our understanding of the effects of solar radiation on fiber-based remote sensing technologies.

The Mojana region in Colombia is one of the most complex inland water deltas in the world as it receives inflows from important rivers in the country, including the Cauca river. The current climate change affects this unique natural environment and alters the water balance. During the latest “El Niño-Southern Oscillation” (ENSO) cold phase (La Niña) several floods occurred. The flood happened on 2 September 2021 was the longest event ever recorded in the Mojana region and severely impacted on local agricultural systems (crops, livestock-raising areas, etc.). However, whether and how these systems have recovered and been brought back to prior production level is unknown. Given that the recent past trend suggests that in near future other and more frequent flood events may happen, an analysis of the recovery process may unveil strategies for resilience. Because the region is isolated and poorly accessible, we analysed the whole collections of Earth Observation data (optical and SAR) from the Landsat missions, Sentinel-1 and Sentinel-2 in the period 1999- 2024. The time series analysis highlighted major transformations in the agricultural systems in the area affected by the flooding. The data were processed with machine learning (ML) for mapping affected areas, recovery assessment, and analyse of how surfaces evolved temporally. Cloud denoising of optical data with ML is essential in tropical countries, in order to contrast the low availability of cloud-free optical data. From a technical point of view, one of the conclusions is that the use of ML in dual-polarization SAR data helps to achieve better land classifications and contributes to improve accuracy and mapping products.

Adverse climatic changes have increased the frequency of natural hazards, emphasizing the need for timely detection to support effective disaster management and mitigation efforts. Floods are among the most common weather-related natural disasters, causing significant damage to human settlements and the environment. While both Synthetic Aperture Radar (SAR) and Electro-Optical (EO) remote sensing data have been used to detect flooded areas, SAR is preferred due to its independence from weather and illumination conditions. However, detecting flood-inundated areas with SAR is challenging due to volume and double-bounce scattering, unlike permanent water bodies that are easily detected. To address this issue, we fuse SAR imagery with other multimodal data and introduce three novel derived features: Water-Proximity Elevation Map (PEM), Water-Terrain Binary Map (WTM), and Dual-Pol SAR Map (DPM). We also present three deep learning architectures for flood mapping that effectively fuse multi-modal data. Extensive experiments and comparisons with state-of-the-art methods demonstrate that our proposed approach achieves improved performance with significant reduction in model complexity.

In 2000, the Water Framework Directive (WFD) adopted for the first time a global approach to the risks to which EU waters are exposed, according to which the management of water resources must pay attention to land use to take into account the qualitative and quantitative aspects of water. In this context, accurate mapping of actual irrigated areas represents key information for water resource managers, both to plan sustainable use and for the prevention of unauthorized water withdrawals. Many studies have demonstrated that Earth Observation (EO) data are an effective tool for mapping irrigated areas and quantifying the irrigation water volumes at different spatial scales. This study examines an area of 39 municipalities located in the lower Volturno basin in Southern Italy. Here, integrated technologies based on the analysis of Sentinel-2 (S2) EO data, Machine Learning (ML) algorithm, and Geographic Information Systems (GIS) for the mapping of irrigated areas have been applied, outside the areas served by distribution plants of the Consortium for Land Reclamation and Irrigation of the Lower Volturno Basin (ConsBiv). By cross-referencing this geospatial information with the database of the authorized irrigation wells, the methodology for mapping the extra-consortium irrigated areas, potentially without concessions, was identified. The results highlighted areas potentially irrigated with unregistered wells, for a total area of 3,020.9 hectares. Here, the estimate of the total water volumes drawn from the aquifer reports a value of 12.39Mm³ referring to the growing season 2021.

Wetlands are the habitats for a large number of protected plant and animal species and at the same time are among the ecosystems types that are most affected by human activity, which has led to their significant reduction in their area disrtibution and the loss of biological variety within their territory. In the current survey we have analyzed the status of some of the most representative wetlands in Bulgaria (Srebarna, Shabla Lake - Ezerets, Pomorie, Mandra-Poda, Kalimok- Brashlen, Complex Kalimok, Pozharevo-Garvan, Durankulak Lake, Bourgas Lake, Atanasovsko Lake and Aheloy-Ravda- Nesebar) based on remote sensing for a period of more than 7 years to establish the availability or lack of changes mainly in wetlands’ water surface. The quantitative assessment and the degree of wetands changes have been made by calculating remotely-sensed indices NDVI, NDGI, NDWI, MNDWI and others. A methodology for ecological monitoring in different temporal intervals has been proposed and additionally the results from the application of remote sensing for the purpose of wetland monitoring have been presented. The results will contribute to developing a common model for wetlands monitoring in Bulgaria based on remote sensing which can improve the existing management practices for wetland conservation, restoration and maintenance.

The relationship between human-engineered structures and natural river systems is critical in shaping regional hydrology and land morphology. This study examines the effects of newly constructed water-retaining structures along the Mahanadi River in Chhattisgarh, India, between the Shivrinarayan and Kalam Barrages. Spanning approximately 74 km, this section has been modified with barrages, dams, and stop dams. Using Landsat 8 satellite imagery, the study tracked changes in water availability, river transformation, and Channel-to-Bank Width Ratio (CBWR) over a decade (2013–2023). Pre-monsoon (March to May) and post-monsoon (October to December) water occurrence maps were developed using the Normalized Difference Water Index (NDWI) to distinguish water from other land cover types. Thresholding and pixel removal further refined the analysis to improve the accuracy of water area calculations. Our findings reveal a significant increase in pre-monsoon water-covered areas over the study period, corresponding reduction in exposed sandbars or riverbeds. This demonstrates that these structures have increased water storage during the premonsoon period. A notable spike in water coverage in 2017 coincided with the completion of major construction, emphasizing their immediate influence on the river’s hydrological and physical features. Water occurrence maps highlight both the increased water retention due to these structures and the reduction in exposed riverbeds. This dynamic shift underscores the dual nature of these interventions: enhancing water availability while altering riverine landscapes. Additionally, the CBWR analysis revealed substantial upstream channel widening post-construction, with downstream sections showing moderate changes. However, reduced riverbed exposure has complex ecological and sediment transport implications that require thorough investigation. A local area analysis is vital to understand how increased water availability during the premonsoon period has affected neighboring regions. Insights from this study into water-retaining structures can inform sustainable river management, focusing on ecological impacts, long-term monitoring, and adaptive strategies.

Harmful algal blooms are an increasing problem for human health and ecosystems, especially in freshwater and marine coastal regions. Since 2017, cyanobacteria blooms mainly caused by Microcystis aeruginosa frequently occur in the small European inland river Moselle in late summer and autumn. Despite them being an important indicator for human safety, the temporal and spatial dynamics of these blooms are largely unknown. In order to gain a better understanding of this issue, we developed combined index-based models on Sentinel-2 data with 10 metre spatial resolution (R, G, B, NIR) and corresponding Planet SuperDove data with 3 metre spatial resolution to differentiate algal scum from water and riparian vegetation. Cloud-free almost simultaneous data of Planet SuperDove and Sentinel-2 was retrieved for portions of Moselle River in August 2022. Presence of algal scum in those areas was confirmed by field campaigns in that period. Retrieved satellite scenes were processed using ACOLITE software with the same settings to facilitate intercomparability. Then, areas visually detected in the imagery as “scum”, “ambiguous scum”, “water” or “vegetation” were digitalized and the spectral information for each class was retrieved. Based on this information, decision-tree based models were developed to differentiate algal scum and validated by analysing spatial overlap with manually digitalized areas from independent satellite scenes. Overall accuracy was substantially higher for the Planet SuperDove model (0.835 versus 0.523), yet accuracy of class “scum” was satisfying for both models (0.972 versus 0.895), thus showing the potential of 10-metre spatial resolution Sentinel-2 data in delineating algal scum.

Groundwater depletion, driven by excessive extraction and climate change, necessitates accurate prediction models. This study proposes a groundwater level prediction model using GPM Core satellite precipitation data combined with a GRU (Gated Recurrent Unit) time series prediction model. The model's performance will be compared with Korea's LDAPS (Local Data Assimilation and Prediction System) to develop an optimal groundwater level prediction model for Korea. The research focuses on a monitoring station in Shinpung-myeon, Gongju-si, Chungcheongnam-do, covering the period from 2013 to 2021. Input data includes GPM Core precipitation, Day of Year (DOY), and cumulative precipitation data. The 20-day cumulative precipitation showed high importance, improving the model's performance. This model is expected to effectively monitor groundwater level changes and predict decreases due to reduced precipitation, facilitating proactive groundwater management. This work is supported by the Korea Agency for Infrastructure Technology Advancemen(KAIA) grant funded by the Ministry of Land,Infrastructure and Transport (Grant RS-2022-00155763).

In 2013, the Apulia region in Southern Italy experienced the first outbreak of Xylella fastidiosa (Xf), a highly dangerous plant pathogenic bacterium that infects olive trees. The impact of Xf on Apulian agriculture has been profound and multifaceted. Olive orchards have suffered severe damage, with the emergence of Olive Quick Decline Syndrome (OQDS) leading to widespread tree mortality. Beyond the economic losses faced by farmers, the olive trees’ cultural and environmental significance adds another layer of concern. For these reasons, different studies have been conducted to identify olive cultivars that are more resistant to the bacterium, such as “Leccino” and “FS-17”. In recent years, a regional plan has been established to support the replanting of felled olive trees in areas interested in past eradications, allowing to plant only two olive tree varieties “Leccino” and “FS-17”, tolerant to the bacterium. For this reason, it could be particularly interesting to use proximal and remote sensing techniques to detect and classify different olive tree cultivars. This work aims to evaluate the capabilities of vis-NIR spectroscopy/hyperspectral data analysis in the classification of three olive cultivars (“Leccino”, “Cima di Bitonto”, and “FS-17”). A portable spectroradiometer (FieldSpec4 Pro), equipped with a leaf clip, was used in the field for leaf spectral measurements. 108 leaf spectral signatures were acquired at different times (0, 2 and 24 hours) after the leaf detachment phase and analyzed to evaluate the variation in overall reflectance and in 65 vegetative indices value (NDVI, EVI, …) over time. The results obtained not only demonstrate the efficacy of spectral signature in cultivar classification, but also provide important information to optimize ground measurement campaigns and to set up the hyperspectral data collection, on wide areas, acquired by sensors installed on UAV, airborne or satellite.

Hyperspectral imaging is beneficial for non-destructive agricultural inspections, and three-dimensional reconstruction modeling is a powerful tool for inspecting the phenotype of plants. This study proposes an approach to combine threedimensional reconstruction modeling and hyperspectral images into four-dimensional data. This data not only contains the three-dimensional structural information of an interesting object but also includes the spectral information of every point on the surface of this object. Firstly, the hyperspectral and visible images of an interesting object are acquired from hyperspectral and visible cameras. Secondly, high-resolution visible images are used to reconstruct a three-dimensional surface model of an interesting object. Thirdly, matching hyperspectral images with visible images establishes the correspondence between hyperspectral images and the three-dimensional model. Furthermore, the biomarker index can be derived from hyperspectral data. The biomarker index can be transformed into surface textures and combined with the three-dimensional model to form a three-dimensional biomarker model.

Selected Ecosystem Functional Properties, calculated from data collected by the flux tower stations of the ICOS network in Europe, were linked to vegetation indices extracted by satellite PRISMA hyperspectral data. Fourteen stations and sixty images were gathered for this preliminary research, in five different ecosystems including various forest types, grasslands, and wetlands. Several pre-processing steps, for both flux and PRISMA data, were needed prior to computation of correlation values and analysis of the fit for the most promising relationships. The results indicate that certain indices are highly correlated with Gross Primary Productivity, Net Ecosystem Exchange, Light Use Efficiency, and Water Use Efficiency, with the first three EFPs obtaining better values. Similar results are obtained also considering only broadleaved forest data. All the relationships resulted being linear or at the second polynomial order. Narrowband indices reporting information from the green to near infrared spectral region resulted the most useful ones. This effort shows the potential of upscaling tower station information to larger extent with PRISMA data, and additional research is planned expanding the dataset to other sites.

Submerged aquatic vegetation (SAV) forms an integral component of ecologically healthy aquatic systems. Besides providing a thriving habitat for underwater fauna, it also regulates the water flow, stabilises sediment, and improves biogeochemical cycling. However, they have been subject to different kinds of pressures ranging from grazing by invasive species, increased flow velocities, and rising temperatures leading to observable changes in their canopy structure and composition. Only fewer studies have looked into the possibility of mapping stress in SAV, mainly due to the difficulty of applying optical remote sensing techniques in submerged environments resulting from the strong attenuation of visible and particularly infrared light in water. In this project the main aim is to identify stress in SAV from multispectral imagery. To achieve this a highly detailed model of a submerged plant canopy was obtained using Structure from Motion photogrammetry (SfM).

These preliminary results show that it is possible to detect variation in spectral reflectance of SAV as a result of stress when above water radiation is corrected for submergence depth based on a 3D canopy model obtained through SfM.

The current advances in remote sensing and the availability of high-resolution and high-frequency multispectral images from satellite platforms can enhance the implementation of Precision Agriculture (PA). This study used vegetation indices (VIs) derived from Sentinel-2 and PlanetScope images and linear regression models to assess silage maize yield (Zea Mays), moisture, and qualitative spatial variability regarding Neutral Detergent Fiber (NDF), Acid Detergent Fiber (ADF), and starch content at the field scale. Maize was harvested by a combined harvester equipped with a yield monitor and a NIRS sensor on a 13-ha study field in North-East Italy, which collected more than 10,000 georeferenced yield, moisture, and quality observation points. 11 Sentinel-2 and 28 PlanetScope images were collected, covering the whole cultural cycle, from April to August 2020). Green Normalized Difference Vegetation Index (GNDVI) and Normalized Difference Vegetation Index (NDVI) were calculated for each image. Multiple regression models were tested for yield, moisture, NDF, ADF, and starch content. The capabilities of models to assess crop variability in terms of R² were evaluated throughout the growing season. PlanetScope GNDVI was the best predictor of yield variability very early in the season, 63 days after sowing (R²=0.33). PlanetScope GNDVI provided the highest R² value of 0.48 for crop moisture content at harvest day while ADF, NDF and starch content variability was best predicted by PlanetScope NDVI with R2 between 0.13 and 0.23. Sentinel-2 and PlanetScope VI’s showed similar predictive capabilities, with an early season peak for yield and quality parameters between 63 and 79 days after sowing. Based on the results, there is a potential for assessing crop moisture variability in the proximity of harvest, which is crucial for the ensilage process, while the early season yield and quality assessment can provide major information for precise fertilization management.

The significant climatic variability present in Latin America and the Caribbean (LAC) generate marked regional differences in needs, availability, and management of water resources for agriculture. The availability of large-scale technological tools for monitoring crops and environmental conditions within their surroundings, becomes imperative in scenarios with a lack of water availability. To address this challenge, irrigation specialists from INIA-Chile, INTA-Argentina, Agrosavia-Colombia, INIA-Uruguay, Irrigation-Mendoza, and the Institute of Regional Development of UCLM-Spain, with co-financial support from FONTAGRO, are developing an initiative called "New technologies for increasing water use efficiency in LAC agriculture by 2030". The main objective of the initiative is the modernization of technological tools for efficient management of water resource in agriculture. Our conceptual framework for analyzing crop water consumption is based in the standardized methodology FAO-Manual N°56 with the improvement of use satellite vegetation index information. Based on the availability of a time series of satellite imagery (NDVI index) as a technological basis, we provide detailed analysis and advice for irrigation management at plot scale, as well as at regional areas where a soil water-balance model is implemented to estimate regional water consumption. At both working scales, the validation of this conceptual-technological framework has been carried out using technological pilots. Our results confirm the operability of the proposed framework in both scales under various agricultural contexts. Therefore, the effectiveness of technologies for crop monitoring, for precise estimation of water consumption, and for the ability to improve water use efficiency has been validated.

Land use, land-use change and forestry (LULUCF) is a greenhouse gas inventory sector that evaluates greenhouse gas changes in the atmosphere from land use and land-use change. EU LULUCF Regulation (Regulation (EU) 2018/841) obliges Member States to use geographically explicit approaches to derive the land use and land use change(LULUC) statistics used for compiling and reporting greenhouse gas (GHG) emissions and removals from the LULUCF sector. These emissions/removals are subject to an inventory which must be reported annually under the United Nations Framework Convention on Climate Change. This study emphasizes on remote sensing importance for establishing the land use and land cover (LU/LC) distribution, possibilities to detect LU/LC changes associated to LULUCF sector in the Yougoiztochen (southeastern) region in Bulgaria. Data from different sources have been processed to interpret possible reasons for differences in the LU/LC categories distribution considering the impact of climate change factors. Scrutinizing the climate change impact could contribute further in the GHG emission inventory process. The remotelysensed data should be considered by stakeholders as a means to fill in gaps and possibilities to meet the needs for a more consistent approach and to support monitoring and reporting of greenhouse gas emissions and removals from LULUCF sector.

Ocean color data is a crucial tool for monitoring and understanding optical, biological, and ecological phenomena in aquatic environments. The measurement of chlorophyll-a concentration effectively facilitates the monitoring of ocean eutrophication and primary productivity. Traditional in-situ observations of the ocean are constrained by temporal and spatial scales, rendering them incapable of providing large-scale continuous monitoring of ocean color data. Satellite remote sensing has the potential to facilitate global ocean monitoring and offer extensive long-term observations of chlorophyll-a concentration products. The FY-3 Meteorological Satellite Medium Resolution Spectral Imager (MERSI) has been providing global ocean color products since 2008. However, achieving continuous daily spatial coverage of the global oceans with a single instrument is impractical due to several constraints, including the instrument's scanning width, zenith angle, solar flares, and cloud cover. In contrast, the U.S. SNPP and NOAA-20 Visible Infrared Imaging Radiometer (VIIRS) instruments offer extensive global marine aquatic products. We investigate a data-interpolating empirical orthogonal function method to composite chlorophyll-a concentration images derived from VIIRS and FY-3 MERSI satellites, aiming to produce daily global chlorophyll-a datasets. The processes of outlier detection and removal significantly enhance the overall efficacy of this interpolation technique. Chlorophyll-a data at both Level 2 and Level 3 have been utilized and reprocessed to recover missing information from cloudy images. This study demonstrates that the proposed methodology effectively addresses gaps in chlorophyll-a concentration data resulting from geophysical factors associated with limitations in inversion algorithms—such as elevated sensor zenith angles.

Chlorophyll fluorescence refers to the emission of light by chlorophyll molecules when they are excited by absorbed light. Chlorophyll is the pigment responsible for photosynthesis – the process through which plants and other photosynthetic organisms convert light energy into chemical energy. The intensity of chlorophyll fluorescence can vary based on geographical latitude as well as other environmental factors. In Antarctica, where the extreme climatic conditions define the ecosystem, mosses are one of the few land-based organisms that can survive and thrive. The Antarctic Peninsula is especially known for its sparse but resistant vegetation, including several moss species that have adapted to extreme conditions like low temperatures, intense ultraviolet (UV) radiation, and repeated freeze-thaw cycles. These mosses are essential for maintaining the ecological balance in the region and offer important insights into how extreme environments affect plant physiology. This research aims to compare the spectral properties of mosses and lichens, with a focus on differences in their fluorescence intensity on Livingston Island, Antarctica, during the summer season. Field research in Antarctica was carried out in order to validate data obtained from Sentinel 2 MSI satellite images, drone photography, and photogrammetry. A spectrometer was used to analyze the visible spectrum ranging from 380nm to 780nm, corresponding to the spectral ranges utilized by the Sentinel 2 MSI and Sentinel 3 SLSTR satellites. The main research methods involve evaluating chlorophyll fluorescence response and applying various optical indices for remote sensing, including Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Moisture Stress Index (MSI). A radar index generated from processing Sentinel 1 data is utilized as well. These methods enable a thorough analysis of photosynthetic activity and plant health in extreme conditions, providing insights into the adaptive mechanisms of mosses in polar environments.

This study focuses on the critical task of estimating wildfire-burned areas, utilizing remote sensing via cubesats to obtain high-resolution imagery for analysis. Using PlanetScope imagery and surface reflectance from visible and NIR bands, along with vegetation indices, the research mapped burned areas in two locations. A U-Net model based on CNN architecture was employed for estimations, with the data divided into training, testing, and validation sets. Model performance was assessed using metrics like IoU and F1-score, demonstrating the usefulness of vegetation indices combined with visible imagery for detecting burned areas, despite challenges from false alarms in snow and rocky terrains. The study also examined the model's transferability across different regions, indicating its adaptability despite variations in accuracy due to local conditions. This underscores the model's potential for broad geographic application in wildfire analysis.

Land cover classification is a remote sensing task that enables the visualization of different land uses. In the context of the sustainable coffee market, a land cover map is required as part of the sustainable coffee certification process. In this study, the land cover of a coffee production farm was classified into four categories: coffee, forest, civil infrastructure, and soil areas. Aerial images were acquired using a UAV equipped with visible and multispectral cameras. Image processing resulted in an orthomosaic for each camera, a vegetative index map (NDVI), and digital elevation models. Through statistical analysis and data fusion strategies, multithresholding and decision tree models—CART, Random Forest (RF), and Gradient Boosting (GB)—were trained and used to classify each pixel into one of the four categories. GB achieved the highest accuracy (94%), followed by RF (84%) and CART (83%). This study enhances the understanding of remote sensing methodologies and land use classification, specifically applied to the geographical particularities of the Colombian territory, and serves as a foundational step toward the application of agricultural technological innovation models in the country.

Hydroponic systems offer controlled environments for crop growth, demanding precise and continuous monitoring of nutrient levels to optimize plant health and yield. In-situ monitoring of crops and nutrient supply plays a pivotal role in comprehensively understanding their mineral constituents, facilitating early correction of nutritional deficiencies. In addition to detecting nutritional deficiencies, this process offers farmers guidance regarding precise crop cultivation practices. However, current monitoring techniques are often labor-intensive, laboratory-based, and require tedious sample preparation. In this context, we propose an optically modified direct spectrograph-coupled laser induced breakdown spectroscopy (LIBS) system capable of detecting and quantifying key nutrients including potassium, calcium, and magnesium. In this work, in-situ nutrient monitoring of the nutrient supply and lettuce crop was performed using singleshot LIBS. Analysis of the results elucidates that highly sensitive and reproducible LIBS spectra can be directly obtained without any sample preparation. Additionally, we assess the feasibility and accuracy of LIBS in capturing dynamic changes in nutrient concentrations, providing valuable insights into nutrient uptake kinetics and potential imbalances. This research showcases LIBS as a valuable tool for real-time monitoring in hydroponic systems, highlighting its potential for remote analysis in agriculture. It facilitates informed decision-making and enhances the overall efficiency of nutrient management practices in agricultural fields.

The following study presents results of the application of satellite optical data for eutrophication monitoring in glacial lakes located in the highest mountain in Balkans-Rila mountain. The group of the Seven Rila lakes and its second lake called Ribnoto was used as a test area in the presented research. Several differential water and vegetation indices including Normalized Differential Water Index (NDWI), Normalized Differential Vegetation Index (NDVI) and Tasseled Cap Transformation for segmentation of Sentinel-2 data were used in order to be tested, and the better ones to be implemented as a reliable method for monitoring levels of eutrophication in mountain glacial lakes areas. Results about dynamics of the eutrophicated area in the lakes are derived. Territories of the swamped areas in the lake were monitored and calculated for the selected time frame of the study. Area of the Seven Rila Lakes is endangered by the rapid eutrophication processes caused by the impact of climate change and the anthropogenic factor. Rila mountain glacial lakes are categorized as protected area included in Natura 2000 network. The results obtained in the study can be used and integrated for observations and studies of climate change at the local and regional level.

Leaf area in agricultural crops is a crucial indicator for understanding growth conditions and assessing photosynthetic efficiency. Traditional methods for measuring leaf area often involved destructive techniques, where leaves or entire plants were cut and manually measured. These methods not only reduced the yield of the destroyed crops but also required significant time and labor. In response to these challenges, this study focused on developing a method to monitor plant growth using RGBD cameras, specifically the RealSense L515 and iPhone 14 Pro, which can capture the three-dimensional structure of plants. The proposed method involves extracting plant portions from the acquired 3D point cloud data using color information characteristics and cluster classification. Subsequently, 3D models are created, and leaf area is estimated based on the surface area of these models. The experiments were conducted using artificial plants. The results showed that the method using the RealSense L515 sensor achieved an average absolute error rate as low as 6.6%, while the iPhone 14 Pro had an average absolute error rate of 10.8%. Although the RealSense L515 demonstrated better accuracy, the iPhone 14 Pro proved to be relatively usable even in outdoor environments.

Remote sensing techniques have emerged as a highly efficient means for characterizing crop conditions and forecasting water needs. This study, conducted over 20 months, from January 2021 to October 2022, investigates the correlation between the Normalized Difference Vegetation Index (NDVI), derived from data captured by a MicaSense RedEdge multispectral camera mounted on an unmanned aerial vehicle, and two growth parameters such as Canopy Cover (CC) and the Leaf Area Index (LAI). The research was carried out in a 2000m² plot in the northwest of Tenerife (Canary Islands, Spain), cultivated with bananas from the Cavendish group variety “Dwarf” local selection “Brier”. The leaf emission rate, leaf surface area and CC were determined throughout the crop cycle. The NDVI proved to be an effective tool for determining the CC, showing a correlation with an R² value greater than 0.7. However, when considering the threedimensional aspect of the LAI, obtained weekly through manual measurements of all leaves (not solely those visible from a zenithal perspective), the correlation between LAI and NDVI was comparatively weaker, with an R² value of about 0.2. The variations in the CC observed agree with the periods of development, harvest and plant cutting, although there is a moderate correlation between the CC and LAI (R²=0.40).

This study investigates the migration and stability of colloidal suspensions through quasi-surfacic resonant analyses. The stability of colloidal suspensions is crucial in various industries such as food production, pharmaceutical formulations, and petroleum fields, where ensuring product quality and longevity is essential. The investigation presented here focuses on black carbon nano-powder dispersed in aqueous solutions, both with and without the incorporation of sodium dodecyl sulfate (SDS) surfactant. These solutions result in opaque and densely darkened mixtures, presenting challenges for conventional analytical techniques. To address this challenge, sensors based on integrated photonics are developed. These sensors consist of organic UV210 Micro-Resonators (MRs) shaped and fabricated via photolithography onto oxidized silicon substrates. The photonic chip is then integrated into a test platform where detection and signal processing are performed using a spectrometer and dedicated MATLAB codes to monitor optical measurements in real-time, crucial for investigate the dynamics of colloidal stability. Meticulous experimentations enable exploring the influence of black carbon nano-powder size and concentration on colloidal dispersion stability. The findings highlight the impact of the black carbon concentration on its migration and emphasize the anionic surfactant’s effect on increasing stability by enhancing the repulsive forces between particles. These conclusions are corroborated with rheological plus zeta potential measurements to provide insights into determining colloidal dispersion stability and migration through optical resonant analysis.