In recent years, global forest fires have occurred more frequently, seriously destroying the structural functions of forest ecosystem. Mapping the burn severity after forest fires is of great significance for quantifying fire’s effects on landscapes and establishing restoration measures. Generally, intensive field surveys across burned areas are required for the effective application of traditional methods. Unfortunately, this requirement could not be satisfied in most cases, since the field work demands a lot of personnel and funding. For mapping severity levels across burned areas without field survey data, a semi-supervised transfer component analysis-based support vector regression model (SSTCA-SVR) was proposed in this study to transfer knowledge trained from other burned areas with field survey data. Its performance was further evaluated in various eco-type regions of southwestern United States. Results show that SSTCA-SVR which was trained on source domain areas could effectively be transferred to a target domain area. Meanwhile, the SSTCA-SVR could maintain as much spectral information as possible to map burn severity. Its mapped results are more accurate (RMSE values were between 0.4833 and 0.6659) and finer, compared to those mapped by ∆NDVI-, ∆LST-, ∆NBR- (RMSE values ranged from 0.7362 to 1.1187) and SVR-based models (RMSE values varied from 1.7658 to 2.0055). This study has introduced a potentially efficient mechanism to map burn severity, which will speed up the response of post-fire management.

Water use efficiency (WUE) has garnered considerable attention at global and regional levels. However, spatio-temporal variations of WUE and related influencing factors in the complex karst landforms of southwest China require further elucidation. Herein, the ratio of gross primary productivity (GPP) to evapotranspiration (ET) obtained through the PML-V2 product was used to characterize ecosystem WUE, the spatio-temporal variations to ecosystem WUE, and responses to temperature, precipitation, and the enhanced vegetation index (EVI) in southwest China. The results showed that: (1) The ecosystem WUE in southwest China decreased with increasing latitude and altitude. Spatially, the ecosystem WUE fluctuates in a “W” pattern with increasing longitude because of the karst landforms’ distribution patterns. (2) The non-significant trend in increased ecosystem WUE during 2003–2017 may be associated with significant increases in the ET offsetting part of the GPP contribution to ecosystem WUE. Spatial distribution of changes in WUE is similar to GPP owing to the dominant role of GPP in changes to ecosystem WUE. (3) The multi-year average ecosystem WUE was lower in karst than in non-karst landforms; however, vegetation restoration projects have contributed in significantly increasing variation rate of ecosystem WUE in karst than that in non-karst landforms. (4) Temperature, precipitation, and EVI were generally positively correlated with ecosystem WUE and were important factors for the increase in ecosystem WUE. EVI characterized vegetation restoration indicators showed that the ecological engineering construction in the study area was effective and was the dominant factor of ecosystem WUE change in 59.59% of the study area. The results of this study are important for further understanding carbon and water cycling processes in karst regions.

Under the influence of space weather, abnormal disturbances in the ionosphere will distort the ionosphere model seriously and affect the global navigation satellite system negatively. This study analyzes the ionospheric activity characteristics and the ionospheric model performance in low latitude during a strong geomagnetic storm from 7 to 9 September 2017. The research goals are to determine the abnormal behavior of the ionosphere during the geomagnetic storm and to refine the ionosphere model in the low latitude. In the experiment, the vertical total electron content (VTEC) peak value at low latitudes caused by this geomagnetic storm was significantly higher than that on the geomagnetic quiet day, and the VTEC peak value increased by approximately 75%. In the main phase of the geomagnetic storm, the degree of VTEC variation with longitude is significantly higher than that of the geomagnetic quiet day. The VTEC variation trend in the northern hemisphere is more severe than that in the southern hemisphere. In the region where VTEC decreases with longitude, the VTEC in the northern hemisphere is higher than that in the southern hemisphere on the same longitude at low latitudes, and this phenomenon is not significantly affected by the geomagnetic disturbance of the recovery phase. During the geomagnetic storm, the daily minimum value of VTEC at different latitudes was basically the same, approximately 5 TECU, indicating that the nighttime VTEC of the ionosphere in low latitudes was weakly affected by latitude and geomagnetic storms. Geomagnetic disturbances during geomagnetic storms will lead to anomalous features of the “Fountain effect” in the ionosphere at low latitudes. In addition, this geomagnetic storm event caused the accuracy of spherical harmonics (SH), polynomial, and ICE models to decrease by 7.12%, 27.87%, and 48.56%, respectively, and caused serious distortion, which is negative VTEC values fitted by the polynomial model.

The Global Navigation Satellite System (GNSS) refraction pattern technique makes use of two pairs of GNSS receivers and antennas to collect the refracted signal in the medium and the direct signal in the air respectively. Due to the sensitive response of the refracted signal to variation of the dielectric constant, the technique is suitable for measuring medium dielectric constant related parameters such as snowpack density, vegetation water content, and soil moisture. In this paper, a forward model related to power ratio of the refracted signal to the direct one is developed to elucidate the mechanism of soil moisture induced refracted signal strength attenuation. By making use of the simulating results derived from the model, a second order polynomial function is established to describe the relationship between the power ratio, soil temperature, and the soil moisture. Based on the function, an inversion algorithm, which takes the GNSS carrier-to-noise observations under high elevation angle (50° to 60°) and soil temperature as the inputs, is proposed for the near-surface soil moisture estimation. The proposed algorithm is validated through a data set collected in an experimental campaign over 2 years. The results demonstrate that there exists good agreement between the proposed method derived soil moisture estimations and ground-truth ones; and root-mean-square error (RMSE) of the proposed algorithm derived soil moisture estimation is 0.009cm ³ cm ^-3 , when the ground-truth soil moisture is in the range from 0.150 to 0.550cm ³ cm ^-3 . Because the observations were collected by using the consumer-grade GNSS chips and antennas, this study also provides a basis for design and development of the low-cost GNSS soil moisture sensor in the future.

Revealing the spatiotemporal patterns and controlling factors of atmospheric CO₂ in the Sichuan Basin (SCB) is a prerequisite for the scientific regulation of regional carbon sources and sinks. This study investigates surface and column-averaged CO₂ concentrations in SCB and surrounding regions during August to December 2024, using in situ measurements, Orbiting Carbon Observatory-3 (OCO-3) column-averaged CO₂ retrievals, and a Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) simulations and footprint analysis. At the national atmospheric background site JinFoShan (JFS), observed CO₂ concentrations rose from ~404 ppm in mid-August to around 440 ppm by late December. Comparisons between observations at JFS and WRF-Chem show that simulations driven by Open-source Data Inventory for Anthropogenic CO₂ (ODIAC) fossil fuel emissions and Vegetation-Global-Atmosphere-Soil (VEGAS) biosphere fluxes yield the highest Pearson correlation coefficient (r = 0.77). The diurnal and seasonal variabilities at JFS are primarily controlled by biospheric fluxes, with fossil fuel contributions weak in August to September and slightly enhanced in October to December, partly due to regional transport from downtown Chongqing revealed by footprint analysis. By contrast, Yongchuan station, closer to urban Chongqing, shows mean CO₂ levels ~10 ppm higher than JFS. For satellite observations, OCO-3 Snapshot Area Map (SAM) measurements over the SCB suffer from substantial missing samples and high spatial noise. We find no spatial correlation exists between SAM retrievals and simulations over Chongqing, and only weak positive correlations appear over Chengdu using ODIAC and Gridded Fossil Emissions Datasets (GridFEDs), whereas results based on the Multi-resolution Emission Inventory for China (MEIC) show no correlation, likely related to biased suburban emission spatial distributions. Overall, the spatial correlations between SAM retrievals and model simulations are weak, ranging from −0.22 to 0.31. To our knowledge, this is the first study to systematically compare in situ observations and OCO-3 SAM retrievals with WRF-Chem simulations in the Sichuan Basin.