arXiv:2606. 25174v1 Announce Type: new Abstract: Field-scale retrieval of surface soil moisture (SM), leaf area index (LAI), and plant height (PH) is essential for precision agriculture, yet it remains an ill-posed inverse problem.
arXiv:2607. 03663v1 Announce Type: cross Abstract: The accurate estimation of Above-Ground Biomass (AGB) in mature tropical forests remains a critical challenge in remote sensing, primarily due to the saturation of Synthetic Aperture Radar (SAR) signals in high-density areas and persistent cloud cover affecting optical imagery.
Agricultural monitoring faces unique challenges, arising from the landscape's complex temporal, phenological, and climate dynamics, yet monitoring them is critical for ensuring food security. Synthetic Aperture Radar (SAR) satellites offer all-weather day-night imaging capability supporting key monitoring tasks including crop type mapping, yield prediction and phenological event detection.
EigenCL is a contrastive learning framework that stages crop stress by embedding Sentinel‑2 NDRE trajectories, producing four physiologically coherent clusters—Healthy, Mild, Moderate, and Severe—without retraining across different states. Trained on 10,000 maize NDRE patches from Iowa in 2020 and validated on Nebraska data in 2023, EigenCL outperformed baselines such as K‑Means, SimCLR, and ProtoCLR, achieving high silhouette, DBI, and CHI scores. The resulting clusters align with maize growth stages, correlate strongly with soil moisture and yield anomalies, and provide interpretable outputs like heatmaps and scouting priorities for decision‑support systems.
AgroBench is a reproducible benchmark that converts U.S. county-level crop yield statistics into weakly supervised pixel‑level crop time series. The data generation pipeline fuses USDA yield data with land cover masks, Sentinel‑2 and Sentinel‑1 imagery, climatic variables, and terrain information to produce multimodal sequences for individual crop pixels across the growing season. The benchmark includes over 13 million observations from 788,654 crop pixels, covering 5,107 county‑year combinations for five major U.S. crops from 2017 to 2024, and establishes a Leave‑One‑Year‑Out evaluation protocol with baseline machine learning results.
arXiv:2606. 05731v1 Announce Type: new Abstract: In-season crop type mapping is critical for food security in the face of increasingly extreme climate-related threats to crops.
arXiv:2608. 00870v1 Announce Type: cross Abstract: Panoptic crop mapping requires both delineating individual agricultural parcels and assigning a crop type to each parcel from satellite image time series.
arXiv:2608. 11638v1 Announce Type: new Abstract: Spatially continuous quantification of forest above-ground biomass (AGB) is what makes carbon accounting credible and mitigation strategies actionable.
arXiv:2608. 07801v1 Announce Type: cross Abstract: Precision nitrogen (N) management (PNM) for cotton requires in-season monitoring of crop growth parameters and N status indicators to decide fertilizer timing, placement, and application rates for optimal canopy development and yield.
TRNet is a multimodal segmentation network that maps paddy rice in mountainous and hilly regions using 0.5 m RGB imagery, a 5 m DEM, and slope data. It introduces a Topographic Energy Spectral Rectification module to suppress steep‑slope clutter and a Topography Guided Paddy Structure Decoder to refine predictions with topographic context. On two test areas, TRNet achieves Rice IoU scores of 85.10 % and 80.68 %, outperforming a Dual Encoder U‑Net by 9.15 and 18.83 percentage points, and maintains strong performance on new August 2024 imagery.
arXiv:2608. 04154v1 Announce Type: cross Abstract: Mapping paddy rice from very-high-resolution imagery in mountainous and hilly regions is difficult because terrain alters optical appearance and increases confusion with visually similar vegetation.
The paper presents a unified compression framework for Vision Transformers aimed at on‑device plant disease detection in resource‑constrained agricultural settings. It combines Hessian‑Balanced Adaptive Block Pruning, quantization, and attention‑based knowledge distillation, evaluating each component separately before integrating the best performers into a deployment pipeline. On a chilli disease dataset, the compressed models achieve accuracy comparable to the FP32 baseline while reducing model size by 74‑98 %, and the full pipeline attains a 54.5× size reduction to 6.01 MB with 95.13 % accuracy.
arXiv:2608. 00608v1 Announce Type: new Abstract: Visible and near-infrared (vis-NIR) and mid-infrared (MIR) spectroscopy enable rapid, cost-effective prediction of soil properties.