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.

Climate change intensifies extreme weather, which in turn influences infectious disease transmission. As a dengue fever (DF) hotspot, Guangzhou lacks research on how extreme weather characteristics and spatial factors interact to shape DF patterns. This study analyzed DF distribution in Guangzhou from 2017 to 2019, using a zero‐inflated negative binomial spatial lag (ZINB‐SAR) regression model to assess the effects of daytime heatwaves (DHW), nighttime heatwaves (NHW) and extreme precipitation (EP) on DF. Results revealed that DF cases were predominantly clustered in central urban areas, with an epidemic season from May to November. The ZINB‐SAR model outperformed negative binomial regression and spatial econometric models, with all spatial effect coefficients significantly positive. Analysis of lagged effects showed that each additional DHW event increased DF cases by up to 10.80% (95% CI: 6.22%–15.59%) at a 2‐month lag, while NHW events increased DF by 2.73% (95% CI: −1.59%–7.23%). Threshold analysis indicated DHW intensity shifted from promoting to inhibiting DF between 0.66°C and 0.76°C, while NHW intensity transitioned between 0.95°C and 2.28°C. EP demonstrated the strongest effects at a 3‐month lag, increasing DF cases by 12.05% (95% CI: 9.03%–15.17%), although its intensity was not statistically significant. Seasonal and spatial variations in DF incidence were evident. In conclusion, DHW and EP impacts were primarily driven by event frequency rather than intensity, whereas NHW effects were more dependent on intensity. These findings highlight the complex spatiotemporal interplay between extreme weather and DF in Guangzhou, providing critical evidence for developing targeted climate‐adaptive disease control strategies.