Conference Agenda

Gorgan Bay in the southeast of the Caspian Sea faces significant issues with water volume reduction and water quality deterioration. The turbidity levels of this water body have increased recently owing to the ongoing decline in the Caspian Sea level and the increase in human activity. In this study, to monitor water quality of the bay, various machine learning models were used to retrieve turbidity levels from Sentinel-2 satellite imagery. In situ turbidity measurements acquired throughout the bay were correlated with Sentinel-2 reflectance data. A statistical evaluation was conducted to ascertain the prospective band combinations for estimating turbidity. Four regression methods, including Multiple Linear Regression (MLR), Random Forest (RF), Classification and Regression Trees (CART), and Gradient Tree Boost (GTB), were implemented to estimate turbidity levels using six different input scenarios. These models were tested on unseen test data, and it was found that the CART model with RMSE = 7.89 FTU, R² = 0.93, and Nash-Sutcliffe efficiency (NSE) = 0.74 exhibited superior performance. The generated turbidity maps across the bay showed sediment plumes next to southeastern river mouths, indicating increased turbidity levels in these areas compared to the rest of the bay, revealing intra-bay variability due to tidal and discharge dynamics. The applied methodology demonstrated superior performance compared to conventional empirical models in turbid coastal environments. The results indicated that the machine learning approaches coupled with satellite data provides water resource managers with a cost-effective and real-time tool for coastal water quality monitoring.

The formation of pit lakes in decommissioned open-pit mines has raised concerns regarding long-term water quality. Turbidity, a key indicator of suspended particulate matter, influences water clarity and aquatic ecological processes. This study estimates surface turbidity in the Águas Claras Mine (MAC) pit lake in Nova Lima, Brazil, using satellite imagery and in situ data to generate a continuous time series and assess compliance with thresholds established by current Brazilian environmental legislation (CONAMA Resolution No. 357/2005). Landsat 5 and 8 imagery were used to derive a spectral turbidity index. Based on the temporal overlap between satellite and field data, a linear regression model (R² = 0.77) was developed and applied to extend the turbidity time series. The results indicate that turbidity values remained below the legal limits for Class 1 freshwater. Higher turbidity levels were observed during the initial filling phase, associated with exposed slopes, as well as episodic increases during the rainy season due to sediment runoff. Over time, progressive revegetation and minimal anthropogenic disturbance contributed to the stabilization of water quality conditions. The integration of in situ measurements and remote sensing proved to be an effective approach for monitoring water quality in post-mining environments, supporting both environmental liability assessment and closure management.

Harmful algal blooms caused by Margalefidinium polykrikoides have intensified in Korean coastal waters, yet existing bio-optical models are not able to reproduce the species-specific spectral features required for quantitative bloom assessment. This study develops a dedicated semi-analytical bio-optical model by integrating multi-year field measurements collected from six campaigns between 2013 and 2022, including hyperspectral above-water radiometry, laboratory absorption spectra, and chlorophyll-a (Chla) observations.

The model formulation follows a standard absorption–backscattering reflectance framework, in which total absorption is decomposed into water, phytoplankton, NAP, and CDOM components, while phytoplankton backscattering is parameterized using two optimized species-dependent parameters. An iterative inversion procedure identifies the optimal backscattering structure by minimizing the spectral mismatch between modeled and measured hyperspectral Rrs. In addition, an empirical red-edge term is introduced to capture the distinct fluorescence-associated peak near ~700 nm that characterizes high-biomass M. polykrikoides waters.

The resulting model accurately reconstructs observed Rrs across low to high Chla conditions, reproducing key features such as strong blue absorption, the secondary blue rebound, and the pronounced red-edge peak. Comparisons with GIOP and Karenia-based models show substantially improved performance, particularly under extreme bloom conditions.

This work provides the first validated species-specific bio-optical parameterization for M. polykrikoides and offers a practical pathway for satellite-based HAB monitoring using upcoming hyperspectral missions such as PACE and GLIMR. The framework is extendable to additional HAB species and supports future development of physics-based, species-resolved coastal water-quality retrievals.

Coastal zones are highly vulnerable to multiple hazards, including tsunami, shoreline erosion, coral reef degradation, and escalating impacts of sea-level rise. These concerns illustrate the urgent need for accurate and high-resolution geospatial data in coastal areas are required to support coastal risk assessment and management. A seamless and accurate coastal digital elevation model (DEM) is a foundational dataset to support these needs.

However, the development of a seamless land-sea elevation surface remains challenging. The intertidal zone often forms a critical data gap between land DEMs and bathymetry grids. To address these limitations, the use of multi-sensor geospatial data has grown considerably in coastal science and hydrography, such as structure-from-motion (SfM) photogrammetry and satellite-derived bathymetry (SDB). Assimilating SfM and SDB offers a viable pathway for constructing seamless coastal DEM.

Therefore, understanding the quality of SfM and SDB data before integration is a critical step. This study addresses this gap by evaluating the vertical accuracy, effective spatial resolution, and internal consistency of SfM-derived coastal topography and SDB-derived shallow-water bathymetry in a challenging coastal environment commonly found in Indonesian waters, i.e., coral reef islands. In this study, the area of interest is Karimunjawa and Kemujan Islands, located in the Java Sea, Indonesia, approximately 80-90 km from the mainland.

Based on our preliminary results, SfM provides high spatial detail depths but introduces short wavelength oscillation while SDB shows smoother depth gradients. In addition, several depths derived from SfM and SDB indicate different vertical reference levels.

This study evaluates the classification performance of Sentinel-2 multispectral and EnMAP hyperspectral imagery across three Canadian agricultural sites. Using Random Forest with recursive feature elimination, we assess whether EnMAP’s spectral richness provides measurable improvements for operational crop mapping. Results show comparable overall accuracies, with EnMAP offering advantages for spectrally complex crop types. Findings highlight the potential and practical limitations of incorporating satellite hyperspectral data into national agricultural monitoring workflows.

Agricultural land in Slovakia has undergone a significant transformation over the past two decades, but the extent and direction of this change remain insufficiently quantified at the national level. This study provides the first spatially explicit assessment of agricultural land loss using Land Parcel Identification System (LPIS) records (2004–2022) in combination with Sentinel-2-based land cover classification. By differentiating the LPIS, we identified polygons that were lost from the agricultural land register; these polygons could represent abandoned agricultural land as well as areas converted to other uses. These polygons were classified into four land cover classes using a Random Forest model trained on Sentinel-2 spectro-temporal metrics and cleaned LUCAS 2022 samples (F1 = 0.867), with additional filtering applied to separate grasslands from shrubs based on vegetation height. The results show that more than 1,000 km² of originally suitable agricultural land has been converted to other land cover types. Forest expansion accounts for 834 km², while 298 km² has been converted to shrubland and 553 km² remains as grassland. Non-forested areas, including buildings and infrastructure, cover an area of 258 km². Only 17 km² of formerly agricultural land remained as actively utilised arable land. These findings indicate that agricultural land abandonment, ecological succession, and urbanisation are the primary causes of agricultural land loss in Slovakia. The research presented provides important data confirming that the loss of agricultural land is extensive and largely threatens habitats with high biodiversity, highlighting the urgent need to harmonise strategies across agriculture, the environment, and land-use planning.

Agricultural drought affects crop productivity and threatens food security. This study presents the Hierarchical Robust Combined Drought Index (HRCDI) for operational agricultural drought monitoring, based on Earth Observation (EO) data freely available in Google Earth Engine. The HRCDI integrates four complementary indicators—Standardized Precipitation Evapotranspiration Index (SPEI3), Soil Moisture (SM), Land Surface Temperature (LST), and Normalized Difference Vegetation Index (NDVI)—using a hierarchical fuzzy logic framework that reflects the progression of drought impacts from climatic anomalies to vegetation stress. To ensure robustness, monthly anomalies of SM, LST, and NDVI were computed through a robust z-score formulation based on median and NMAD, which reduces the influence of outliers. The HRCDI was applied to the Province of Foggia (southern Italy), one of the main durum wheat production areas in Italy, over the period 2017–2022. HRCDI outputs were aggregated at the municipality scale and validated against independent datasets, including durum wheat yield statistics (2006–2022), SPEI3 provided by the Institute of Services for Agricultural and Food Market (ISMEA), and reports from the European Drought Impact Database. The HRCDI effectively captured the severity and spatial extent of major drought events, particularly in 2017 and 2022, which corresponded to documented yield losses of −5% and −22%, respectively. Results highlight the scalability, operational relevance, and transferability of the HRCDI for supporting drought early warning and agricultural risk management. The HRCDI framework could be applied to other regions and integrated with higher-resolution satellite data to enhance drought monitoring in line with the objectives of the Common Agricultural Policy.

Fall Armyworm (FAW) is a major pest, threatening maize production and food security. FAW preferentially attacks nitrogen-rich maize due to its nutritional composition, which accelerates larval development. Understanding the effects of FAW infestation on maize growth and nutrient status is critical for effective crop management. This study (i) examines the impact of FAW infestation on maize using key physiological metrics, including fresh/dry weight, chlorophyll content (as measured by SPAD), leaf area index (LAI), stem length, and leaf dimensions (length, width, and area); (ii) investigates differences in carbon, hydrogen, nitrogen, and sulphur concentrations between healthy and infested maize, and (iii) analyses spectral variability between healthy and infested maize using leaf-level hyperspectral data. Hyperspectral data at the leaf scale were used to identify and distinguish the spectral reflectance between healthy and infested FAW maize crops. Results indicate that infested crops exhibit lower fresh weight, reduced LAI, shorter stems, and smaller leaves compared to healthy crops, highlighting a substantial negative effect on above-ground biomass and overall crop vigour. Infested crops showed higher nitrogen levels than healthy crops. This trend could be attributed to nitrogen redistribution following FAW damage, where nitrogen decreases in attacked leaves and increases in roots, with partial recovery after the pest moves on to another crop. The study establishes a methodological framework for linking laboratory, field, and remote sensing approaches, providing a foundation for future predictive modelling of pest impacts on maize nutrient content and productivity.