In this article, we present the first demonstration of the FengYun-3E (FY3E) Global Navigation Satellite System Occultation Sounder II-Reflectometry (GNOS-R) payload's capacity to detect near-surface soil freeze/thaw (F/T) states. This study offers an initial analysis of the F/T retrieval algorithm applied to data collected from the Arctic Circle, underscoring the GNOS-R's potential to deliver long-term near-surface soil F/T products. Data for the period extending from the launch day of GNOS-R (Day of Year (DOY) 179, 2021) to DOY 270 in 2022 were analyzed using the surface reflectivity (SR) ratio factor to discriminate F/T variations. Comparisons were made with soil moisture active passive (SMAP) F/T products, serving as an auxiliary analysis. We found a strong consistency between SR ratio factor and SMAP F/T values, with the accuracy of the F/T retrieval algorithm exceeding 60%. These findings corroborate the efficacy of the GNOS-R payload aboard FY3E in monitoring F/T patterns at higher latitudes, specifically, the Arctic Circle. The outcomes of this study will be beneficial for future F/T detection efforts using spaceborne Global Navigation Satellite System-Reflectometry payloads.

The Land Surface GNSS Reflection Simulator (LAGRS)-Soil model represents a significant advancement in soil moisture detection with the aid of Global Navigation Satellite System (GNSS) Occultation Sounder-Reflectometry (GNOS-R) technology, which is one payload of the Fengyun-3E (FY-3E) satellite that was launched on 5 July 2021. To fully exploit the properties of noncoherent scattering, the LAGRS-Soil model has the capability to calculate DDM information for different observational geometries, which relies on the random surface scattering models employed in LAGRS-Soil. This will provide a comprehensive understanding of soil moisture dynamics across diverse terrains and environments. One of the most notable features of LAGRS-Soil is its ability to obtain DDMs for full polarizations, which enhances soil moisture retrievals compared to current methods that only utilize the commonly used LR polarization (left-hand circular polarization received and right-hand circular polarization transmitted). Meanwhile, the model can also capture frozen soil DDMs which holds immense potential for near-surface Freezing/Thawing (F/T) detection, opening up new research and application opportunities in cold climate regions. LAGRS-Soil is built on microwave scattering models, making it a robust and efficient theoretical model for the FY-3E GNOS-R payload. This model can support ongoing soil moisture retrieval efforts by combining physical models with investigations of diffuse scattering and polarization capabilities for soil moisture detection.

Oriented object detection in remote-sensing imagery benefits from a geometric representation that remains well-defined across localization, label assignment, and post-processing. We present Ellipse-ABC, a YOLOv8-based detector that directly predicts the coefficients of a positive-definite quadratic form while retaining the original branch for center estimation. The resulting matrix supplies Gaussian-compatible localization supervision and is analytically decoded into standardized OBB geometry for task-aligned assignment, rotated non-maximum suppression, and benchmark reporting, thereby reducing representation changes across the pipeline. Primary accuracy is evaluated with polygon area IoU between predicted and ground-truth-oriented bounding boxes. After rerunning the native baseline under the same dataset-specific training and augmentation recipes used by the ABC configurations, the complete ABC + GWD configuration changes polygon-IoU mAP50/mAP50-95 by +2.16/−1.17 points on UCAS-AOD and −1.23/+0.50 points on HRSC2016 across seeds 0, 1, and 2. An ABC + ProbIoU loss control performs better than ABC + GWD on HRSC2016 but worse on UCAS-AOD, indicating that the geometry-loss choice is dataset-dependent. The representation adds 198 parameters and no more than 0.0018 GFLOPs at the evaluated input sizes. Five repeated batch-1 measurements nevertheless show a 6.6–8.8% end-to-end latency increase and higher peak memory on DOTA-v1.5. These results support Ellipse-ABC as a geometry-consistent representation with small model-level overhead, without implying a universal accuracy gain.

Flood assessments require reproducible links between spatial products and the measurements ultimately reported to analysts, yet a large language model (LLM) agent may complete a geographic information system (GIS) workflow without generating the required product or traceable claim. We present GeoFloodAgent, an LLM-orchestrated flood-GIS workflow that combines typed planning and deterministic GIS tools with a separate evaluation of runtime disposition and post-run alignment with task-specific product, lineage, and action contracts. We evaluated 80 Chinese-language tasks from the Zhengzhou (2021) and Forlì (2023) flood cases under seven configurations and three repetitions, yielding a registry-remediated composite of 1680 assignments. In the Full configuration, 212 assignments (88.3%) were accepted and contract-aligned, whereas 16 accepted assignments did not align with their registered contracts; all 16 concerned clarification or refusal actions. Three runs were rejected despite retained contract-aligned evidence. The cluster-weighted Full-Tool Agent differences were −1.15 percentage points for accepted contract mismatch and +1.07 percentage points for contract-aligned acceptance, with empirical intervals spanning benefit and harm. Under frozen Zhengzhou inputs, deterministic outputs were sensitive to the minimum SAR object-size setting: relative to 80 pixels, candidate flood extent changed by +105.6% at 40 pixels and −30.9% at 120 pixels, whereas a 20 m simplification tolerance reproducibly failed during geometric union. These configuration-specific results do not validate hydrological accuracy or identify universal optimal defaults. These findings show that completion rate alone is inadequate for evaluating LLM-orchestrated flood GIS: runtime disposition and evidence alignment should be reported jointly. The study evaluates a finite, study-authored benchmark and does not establish hydrological accuracy, operational decision validity, or open-world error detection.