Conference Agenda

Accurate estimation of Soil Organic Carbon (SOC) is essential for sustainable land management, agricultural productivity, and climate change mitigation. This study presents a novel framework for SOC estimation using machine learning models and diverse predictors, including spectral bands, vegetation and soil indices, topographical features, soil texture components, and HSV-derived soil color proxies. SOC data from 180 samples collected between 2007 and 2020 across 21 fields in Manitoba, Canada, were used for model training and validation. Landsat 5, 7, and 8 data were utilized to extract spectral and soil indices, while SoilGrids and SRTM DEM provided texture and topographical features. Random Forest (RF), Extreme Gradient Boosting (XGB), and a BC-VW-based ensemble model were evaluated across five feature scenarios. The ensemble model achieved the highest accuracy, with an R² of 0.57, RMSE of 0.25, and RMSPE of 7.87%, outperforming individual models. SHAP-based feature selection identified Clay%, SWIR1, and Value (HSV) as the most critical predictors. Independent validation using data from 2021 and 2023 confirmed the model's robustness, with RMSPE values of 10.93% and 12.83%, respectively. This study demonstrates the importance of integrating soil-specific indices, texture, and color features with ensemble modeling to improve SOC predictions. The framework offers a scalable and reliable approach for large-scale SOC monitoring, contributing to sustainable agriculture and carbon sequestration efforts. The findings underscore the need for robust uncertainty analysis and independent validation, setting a benchmark for future SOC modeling studies. 

Soil salinization is a major form of land degradation in drylands, where closed hydrological systems, shallow water tables, and strong evaporative demand favor the recurrent buildup of salts at the surface. Accurate and spatially explicit salinity assessment is crucial for guiding agricultural management and land rehabilitation, yet conventional soil sampling remains spatially restrictive and most remote-sensing approaches insufficiently capture the hydrological and pedological processes that drive seasonal salt redistribution. This study evaluates whether post-rainfall spectral information can improve soil salinity mapping in a large endorheic depression in central Morocco (Sehb El Masjoune). A dataset of 121 ECe-measured topsoil samples was combined with multi-sensor optical imagery from Sentinel-2, Landsat-9, and PlanetScope. In addition to standard salinity, soil, vegetation, and moisture indices, two new post-rainfall predictors were developed: a Depression Proxy (DP), delineating moisture-retentive micro-depressions where salts accumulate, and a Soil Cluster Proxy (SCP), capturing soil textural and compositional contrasts from spectral responses. These predictors were integrated into Random Forest and Gradient Boosting Regressor models and evaluated using repeated cross-validation on Box–Cox-transformed ECe. The combination of DP and SCP with Sentinel-2 predictors yielded the highest performance (R² = 0.92; RMSE = 20.53 dS·m⁻¹), outperforming models relying only on spectral indices and topographic covariates. Seasonal salinity maps revealed strong intra-annual dynamics associated with rainfall events and subsequent evaporative concentration. The proposed DP–SCP framework offers transferable, physically interpretable predictors for dryland salinity assessment and provides a scalable step toward process-informed remote-sensing approaches supporting climate-resilient land-use planning.

Soil total nitrogen (TN) is a critical nutrient for sustainable agricultural management, yet large-scale mapping remains constrained by high laboratory analysis costs. Spaceborne hyperspectral remote sensing offers a promising alternative, but its effectiveness is limited by spectral gaps caused by atmospheric water-vapor absorption in nitrogen-sensitive NIR and SWIR regions. This study evaluates the contribution of reconstructing missing spectral domains to improve soil TN estimation from PRISMA hyperspectral imagery. A spectral gap-filling framework combining a conditional generative adversarial network (cGAN) with a self-supervised masked autoencoder pretraining strategy was developed to reconstruct reflectance spectra across water-vapor absorption intervals (950–990 nm, 1320–1500 nm, and 1780–2050 nm), achieving R² = 0.95 on PRISMA test data and R² = 0.91 against ASD FieldSpec III measurements. Applied to 1,037 samples across three Moroccan agricultural regions, incorporating reconstructed bands consistently improved TN prediction: R² increased from 0.83 to 0.89 in Al Haouz, 0.73 to 0.79 in Doukkala, with R² = 0.73 in Khouribga. Feature-selection analyses identified reconstructed water-vapor bands among the most informative predictors (1050–1450 nm, 1800–2100 nm, and 2300–2400 nm). These findings demonstrate that spectral gap filling enhances spaceborne hyperspectral data usability for operational soil TN monitoring and precision agriculture.

This study investigates the performance of a novel L-band passive microwave radiometer mounted on a Remotely Piloted Aerial System (RPAS) for high-resolution soil moisture retrieval. Soil moisture is a critical variable for predicting crop stress, scheduling field operations, and optimizing irrigation, yet traditional measurement approaches have limitations. Satellite radiometers provide broad spatial coverage but coarse resolution, while in situ sensors offer high accuracy with limited spatial representativeness. RPAS-based sensing offers an intermediate solution, enabling fine-scale mapping with flexible deployment. The sensor evaluated in this research, developed by Skaha Remote Sensing Ltd., measures brightness temperature (Tb) at 1.4 GHz, a frequency where soil emissivity varies strongly with moisture content.

Field campaigns were conducted from May to October 2025 at the Elora Research Station in Ontario, with weekly flights over plots containing different crops and tillage conditions. Concurrent ground measurements of soil moisture, leaf area index (LAI), and vegetation water content (VWC) supported evaluation of vegetation impacts. Statistical analyses, including Pearson correlation and linear regression, revealed the relationships between microwave emissions, soil moisture, and vegetation properties.

Results show a strong inverse relationship between microwave emissions and soil moisture, with vertically polarized signals exhibiting the highest sensitivity. Vegetation effects were crop-dependent due to the unique canopy structures. These findings demonstrate that RPAS-mounted radiometers can provide reliable, high-resolution soil moisture measurements and highlight the importance of crop geometry in interpreting microwave observations.

Drought represents one of the most devastating natural hazards, causing billions in economic losses and threatening global food security. Conventional single-variable drought indices often fail to capture drought's multifaceted nature, while existing composite indices are frequently constrained by linear assumptions or operate as 'black boxes,' obscuring physical drivers. This study introduces the State-Space Gradient Drought Index (SSGDI), developed via a novel Gaussian Mixture Model–Minimum Spanning Tree (GMM–MST) framework that re-conceptualizes drought as a trajectory within a physical system. By modeling a 3D state-space composed of the Standardized Precipitation Index (SPI), Standardized Soil Moisture Index (SSMI), and Standardized Runoff Index (SRI) with a Gaussian Mixture Model (GMM), the framework learns distinct hydro-climatic archetypes; a Minimum Spanning Tree (MST) then imposes physically plausible connections among these archetypes to define the principal wet-to-dry gradient. The final SSGDI is derived from a data point's probabilistic position along this gradient and is complemented by a classification system that diagnoses the drought's physical type. Applied to the Central China Triangle, the framework successfully uncovered the hydro-climatic system's intrinsic, non-linear structure. Validation showed the SSGDI provides a significantly more robust measure, with SSGDI-6 achieving a spatially-averaged Pearson correlation of r = 0.80 against the PDSI benchmark—a marked improvement over any single component. The SSGDI framework bridges robust statistical aggregation with clear physical interpretation, offering a powerful tool that provides not just a severity score but a diagnostic narrative for proactive drought management.

Synthetic Aperture Radar (SAR) has become a popular form of remotely sensed data for agricultural management due to its ability to acquire cloud-free images at extremely high temporal resolutions. A particularly useful product that can be derived from SAR imagery is coherence, which visualizes structural target changes over time based on phase decorrelation. In a crop management context, coherence is largely unexplored. This is in part due to the fine resolution image requirements that field-scale vegetation monitoring demands. Within agricultural fields, high image coherence should correlate to areas with minimal to no crop growth, whereas low image coherence should correlate to areas where crops are consistently growing. Based upon this hypothesis, our research investigates the applications an ICEYE fine spatial resolution X-band SAR imagery time series has for detecting low yielding regions within corn fields using coherent change detection.