The accuracy of machine learning models in plant disease detection significantly relies on large volumes of knowledge-based labeled data; the acquisition of annotation remains a significant bottleneck in domain-specific research such as plant disease detection. While unsupervised learning alleviates the need for labeled data, its effectiveness is constrained by the intrinsic separability of feature clusters. These limitations underscore the need for approaches that enable supervised early disease detection without extensive annotation. To this end, we propose a self-supervised learning (SSL) framework for the early detection of soybean’s sudden death syndrome (SDS) using hyperspectral data acquired from an unmanned aerial vehicle (UAV). The methodology employs a novel distance-based spectral pairing technique that derives intermediate labels directly from the data. In addition, we introduce an adapted contrastive loss function designed to improve cluster separability and reinforce discriminative feature learning. The proposed approach yields an 11% accuracy gain over agglomerative hierarchical clustering and attains both classification accuracy and F1 score of 0.92, matching supervised baselines. Reflectance frequency analysis further demonstrates robustness to label noise, highlighting its suitability in label-scarce settings.

Depth-resolved estimation of soil organic carbon (SOC) remains challenging because optical measurements originate at the surface while carbon dynamics vary vertically. We propose a physics-aware uncrewed aerial vehicle (UAV) framework that integrates multispectral imagery (MSI) and hyperspectral imagery (HSI) to estimate SOC concentration (%) across five depths. The experiment was conducted at Plantheaven Farms, Missouri, with ten sorghum genotypes across three replicates. Feature construction combined spectral derivatives from HSI with texture features from MSI, compressed via principal component analysis (PCA). Physics-based regularization was implemented through: 1) a second-difference penalty to enforce vertical smoothness and 2) a profile-integral consistency constraint to preserve whole-profile balance. Four model configurations evaluated on local data showed progressive improvements: MSI-only, MSI + HSI, MSI + HSI with smoothness, and MSI + HSI with full physics constraints. In addition, transfer learning from the open soil spectral library (OSSL) was tested to address data limitations. Model fitting on the available data achieved ${R} ^{2} = 0.72$ at 0–30 cm, with physics-aware constraints notably improving vertical coherence. The physics-aware model reduced variance and improved plausibility. In-sample, transfer learning achieved ${R} ^{2}=0.60$ at 0–30 cm, with conservative interpretation below 90 cm due to reduced optical sensitivity. Exploratory genotype patterns suggested higher surface SOC percent for PI 656 029 and PI 656 057, and lower values for PI 276 837 and PI 656 044.

Interferometric Synthetic Aperture Radar (InSAR) uses two or more SAR images over the same region for mapping ground surface displacements through time. InSAR displacements are relative to a reference point due to the 2π phase ambiguity that from unwrapping, and as a result ground control points (GCPs) are often used to calibrate the displacement model and obtain absolute measurements. Errors in InSAR time-series are typically measured with respect to the reference point, but absolute errors are not currently well constrained. To develop absolute error models for InSAR, we generate Sentinel-1 InSAR time-series for Belleville, Illinois and compare time-series with Global Navigation Satellite System (GNSS) and Light Detection and Ranging (LiDAR) displacements. Both GNSS and LiDAR data showed complementary results with the InSAR measurement, demonstrating the advantages of combining InSAR with small scale LiDAR or GPS measurements to improve InSAR accuracy.

Archaeological feature detection at complex, mixed-environment sites requires accurate, efficient methods for identifying subtle morphological signatures. This study presents a semi-automated remote sensing pipeline for the detection and delineation of archaeological earthworks at Cahokia Mounds (Illinois, USA), a major Mississippian urban center and UNESCO World Heritage Site. Three LiDAR datasets, two collected via UAV-mounted sensors and one from a piloted aircraft survey, were processed into Digital Terrain Models and transformed into Local Relief Models (LRM). K-means clustering was applied to segment the LRMs into feature classes, followed by contour bounding using the OpenCV library to outline mounds and borrow pits. Additionally, SAR-derived Local Incidence Angle (LIA) rasters from PALSAR-3 and Sentinel-1 were processed through angular deviation mapping to identify slope anomalies associated with archaeological features. Results across all five datasets demonstrate the complementary strengths of LiDAR and SAR: LiDAR excels at resolving elevation-defined features such as mound footprints, while LIA captures directional slope behavior that highlights mound edges, borrow pit rims, and linear features such as causeways. Comparative analysis of LiDAR acquisition frequencies reveals minimal differences in archaeological feature recovery between pulse settings, suggesting that sensor platform choice matters more than power-density tradeoffs for this application. Despite the need for human review to filter modern disturbances and natural false positives, the integrated workflow meaningfully accelerates prospection and reduces interpretive subjectivity. The methods are scalable, site-invariant, and work with open-access data, making them applicable to archaeological landscapes worldwide.