BEGIN:VCALENDAR VERSION:2.0 PRODID:-//pretalx//pretalx.earthmonitor.org//soil-health-now-2026//ZTUQXJ BEGIN:VTIMEZONE TZID:Europe/Athens BEGIN:STANDARD DTSTART:20001029T040000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZNAME:EET TZOFFSETFROM:+0300 TZOFFSETTO:+0200 END:STANDARD BEGIN:DAYLIGHT DTSTART:20000326T030000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZNAME:EEST TZOFFSETFROM:+0200 TZOFFSETTO:+0300 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:pretalx-soil-health-now-2026-QDA9EZ@pretalx.earthmonitor.org DTSTART;TZID=Europe/Athens:20261208T143000 DTEND;TZID=Europe/Athens:20261208T144500 DESCRIPTION:Earth Observation is creating new possibilities for mapping and monitoring soil health\, yet the value of these products depends on how w ell they respond to the needs of the people who manage\, advise on\, regul ate\, or make decisions about soils. This potential will expand further wi th the advent of the Copernicus Sentinel Expansion missions CHIME and LSTM . CHIME will provide hyperspectral observations across the VIS–NIR–SWI R domain\, while LSTM will add thermal infrared information related to lan d surface temperature and emissivity. Their synergistic use is expected to broaden the range and quality of EO-derived soil health information\, inc luding indicators linked to soil organic carbon\, texture\, moisture\, min eralogy\, degradation risk\, and sustainable land management.\n\nAt the sa me time\, more advanced EO products do not automatically lead to better up take. In agricultural landscapes\, soil information is interpreted through local knowledge\, advisory networks\, policy requirements\, certification schemes\, farm constraints\, and trust in the data source. NextSoils+ add resses this challenge by placing stakeholder engagement and co-creation at the centre of EO-based service development.\n\nThe project brings togethe r farmers\, agronomists\, cooperatives\, regional authorities\, researcher s\, certification bodies\, NGOs\, and policy actors across Mediterranean p ilot regions. Engagement activities include stakeholder identification\, p ower–interest mapping\, influence mapping\, targeted consultations\, int eractive meetings\, and structured feedback. These activities help clarify which soil-related decisions matter most to different actors\, what infor mation is useful in practice\, and how EO-derived indicators should be com municated to support interpretation and uptake.\n\nEarly service outputs\, such as soil organic carbon maps\, digital interfaces\, and indicator-bas ed visual products\, are used as shared reference points for dialogue. The y allow stakeholders and technical teams to discuss local relevance\, usab ility\, uncertainty\, trust\, and integration into existing farming\, advi sory\, certification\, or reporting workflows.\n\nBy linking the emerging capabilities of CHIME and LSTM with iterative stakeholder engagement\, Nex tSoils+ contributes practical insights into how EO-based soil health produ cts can evolve into services that are scientifically robust\, understandab le\, and relevant for real soil management contexts. DTSTAMP:20260825T180353Z LOCATION:Amphitheater I SUMMARY:Shaping NextSoils+ Soil Health Services with Stakeholders for the S entinel Expansion Missions - Konstantinos Karyotis\, Eva Sarigiannidou URL:https://pretalx.earthmonitor.org/soil-health-now-2026/talk/QDA9EZ/ END:VEVENT BEGIN:VEVENT UID:pretalx-soil-health-now-2026-TZFHT8@pretalx.earthmonitor.org DTSTART;TZID=Europe/Athens:20261207T170000 DTEND;TZID=Europe/Athens:20261207T180000 DESCRIPTION:Extreme weather events\, including droughts\, heatwaves\, inten se rainfall and episodic non-seasonal frosts\, are increasingly disrupting soil functionality and agricultural production stability. In Mediterranea n agricultural landscapes\, these pressures interact with long-standing so il degradation processes such as declining organic matter\, erosion\, sali nisation\, compaction and reduced biological activity\, creating new risks for food security and land management. There is therefore a growing need for integrated\, spatially explicit tools that can assess not only the cur rent condition of soils\, but also their capacity to buffer climatic stres s\, recover after disturbance and support stable crop production over time . \n\nThe Soil Resilience and Food Security Index (SRFSI) is proposed as a composite framework for evaluating the resilience of agricultural landsca pes under climate-extreme conditions. The index integrates four complement ary dimensions: Soil Resilience Capacity\, representing the ability of soi ls to sustain carbon storage\, water regulation\, nutrient cycling\, struc tural stability and biological functioning\; Biological and Agroecological Management\, capturing the contribution of practices such as compost and manure application\, biofertilisers\, microbial inoculants\, cover crops\, crop-residue retention\, reduced tillage\, diversified rotations and land scape elements that enhance biodiversity and water regulation\; Climate-Ex treme Resilience\, assessing exposure and response to drought\, heatwaves\ , heavy rainfall\, erosion risk\, salinity stress and crop-relevant non-se asonal frosts\; and Food Security Stability\, reflecting yield stability\, stress-year yield retention\, production reliability and post-event recov ery. \n\nThrough SRFSI\, a framework is proposed that can function both as a baseline diagnostic approach and as a scenario-based tool for assessing soil and production resilience. By comparing index scores under current c onditions\, climate-extreme scenarios and post-intervention conditions\, r esilience loss and adaptation gain can be quantified across different crop s\, soil types and management systems. Remote-sensing proxies\, open soil and climate datasets\, field measurements and farm-management information are combined to support implementation at field\, farm\, municipal and reg ional scales. This approach is intended to support the identification of v ulnerable agricultural areas\, the evaluation of biological and agroecolog ical soil-management interventions\, and the design of strategies that hel p maintain productive\, resilient and food-secure agricultural systems und er increasing climate uncertainty. DTSTAMP:20260825T180353Z LOCATION:Basement (Foyer) SUMMARY:Towards a Soil Resilience and Food Security Index for Climate-Resil ient Agricultural Landscapes - Konstantinos Karyotis\, Maria Marily Christ ou URL:https://pretalx.earthmonitor.org/soil-health-now-2026/talk/TZFHT8/ END:VEVENT BEGIN:VEVENT UID:pretalx-soil-health-now-2026-YDNNYJ@pretalx.earthmonitor.org DTSTART;TZID=Europe/Athens:20261209T160000 DTEND;TZID=Europe/Athens:20261209T161500 DESCRIPTION:Accurate\, scalable estimation of soil organic carbon (SOC) and clay content from remote sensing data is crucial for monitoring soil heal th at large continental scales. Standard baseline methods such as PLSR\, S VR\, Random Forest\, Gradient Boosting etc. struggle to exploit the inhere nt relationship between spectral signatures and spatial neighborhoods of t he corresponding samples. In order to overcome this limitation\, we propos e MVHGCN\, a Multi-View Hypergraph Convolutional Network developed for joi nt SOC and clay content prediction from Sentinel-2 imagery.\n\nThe propose d architecture addresses three main drawbacks of standard approaches via t he following contributions. First\, hyperedges can capture higher-order re lationships between soil samples\, connecting pixels through both spectral or geographic similarity within a single cluster\, instead of decomposing those relationships into pairwise links. Second\, a multi-view formulatio n processes six spectral transforms of each sample (continuum-removal\, ab sorbance\, Savitzky-Golay derivatives\, SNV-normalized variants)\, encoded via a 1D-CNN\, as parallel views fused through a learned View-Based Atten tion Fusion module that adaptively weights each transform via an attention mechanism. Third\, the model operates on two parallel branches\, a global cosine-similarity branch and local multi-scale spatial branch (within-pix el and within-tile)\, which are fused at every block via a Cross-Talk blen ding unit\, so local geographic proximity and global spectral similarity c an be simultaneously exploited. Finally\, we introduce Adaptive Hypergraph Learning (AHL)\, an additional attention mechanism that refines incidence matrices during training\, and a multi-task head that predicts SOC and cl ay jointly under an uncertainty-weighted loss with learnable per-task vari ance. \n\nWe evaluate our approach on data from the LUCAS 2015 Topsoil sur vey matched to satellite-derived reflectance composites\, generated from m ulti-temporal Sentinel-2 imagery. The proposed model is compared against a series of 1) Standard machine learning methods (PLSR\, SVR\, Random Fores t\, Gradient Boosted Trees)\, a state-of-the-art 1D-CNN developed specific ally for estimation of soil-properties from remote sensing data\, and seve ral baseline graph and hypergraph networks. The results are reported on th e original target scale\, after inverting the log1p and z-score transforms . The proposed MVHGCN is consistently found to outperform all standard bas elines\, the respective graph and hypergraph base models\, as well as the state-of-the-art 1D-CNN model. DTSTAMP:20260825T180353Z LOCATION:Amphitheater I SUMMARY:A Multi-View Hypergraph Convolutional Network for Joint SOC and Cla y Prediction from Sentinel-2 Imagery - Konstantinos Karyotis\, Christos Ch adoulos URL:https://pretalx.earthmonitor.org/soil-health-now-2026/talk/YDNNYJ/ END:VEVENT END:VCALENDAR