Surface soil moisture (SSM) is an essential component of surface ecosystems. However, the existing microwave SSM retrieval methods are beleaguered with issues such as insufficient decoupling of terrestrial scattering characteristics and excessive reliance on empirical parameters within the models. This paper proposes a novel generalized SAR backscattering model (GSBM) for time-series retrieval of SSM, considering different land-cover influences. Initially, the GSBM is introduced, categorizing land cover into built-up areas, water bodies, vegetation-covered areas, and soils. Subsequently, we establish a unique SAR Water Cloud Model (SWCM) with the dual-polarization SAR vegetation index (DRVIs). A high-quality soil backscatter coefficient is obtained by employing the SWCM to eliminate vegetation's influence. Ultimately, the dry and wet reference values of soil backscatter are calculated to retrieve the relative SSM time series. Based on Sentinel-1 data, we select the Golmud area for a three-year spatiotemporal monitoring of SSM. Experimental results show that our method improves the correlation coefficients (r) between SAR backscatter and in situ soil moisture data from 0.63 to 0.73, improving about 16%. The r between SSM retrieval results and in situ data was above 0.66 at 500 m, 1 km and 3 km spatial resolutions. Consequently, the proposed method underscores the advantages of simplicity in parameters, high estimation precision, and robust adaptability, thereby augmenting the potential for large-scale global monitoring applications.

Affected by the surrounding environment of the station and GPS satellite signal receiving conditions, there are significant fluctuations in the snow depth retrieval results of the Global Positioning System Interferometric Reflection (GPS-IR) based on GPS data acquired in different trajectories or frequency bands, resulting in the loss of snow depth retrieval accuracy and reliability. Therefore, this contribution proposes a novel snow depth retrieving approach using time-series clustering (TSC) in GPS-IR data. For improving the adaptability of the algorithm to different application scenarios, Dynamic Time Warping (DTW) is introduced to perform K-Medoids clustering on the snow depth retrieval sequence of all available satellite trajectories, and the daily time-series snow depth retrieval results are obtained based on the clustering center evaluation index. For validation purposes, multi-frequency GPS data was selected for snow depth retrieval. The results indicate that, compared with traditional approaches, the proposed TSC method demonstrated higher robustness and accuracy, with correlation coefficients reaching above 0.975 in all frequency bands. The root mean square difference (RMSD) for snow depth retrieval at P351 and AB39 stations, using GPS L1 frequency data, were 8.772 and 6.095cm, respectively. The TSC algorithm proposed in this article classifies the snow depth retrieval sequences of different trajectories, achieving an adaptive acquisition of the optimal snow depth retrieval sequence, thus greatly reducing the workload of manual intervention and preliminary data quality assessment in GPS-IR snow depth retrieval. The proposed GPS-IR snow depth retrieval algorithm may offer a new technical approach for future related research.

Monitoring changes in glacial lake (GL) area is of great significance for revealing climate change and analyzing the risk of GL outburst floods (GLOFs). However, in the case of Southeastern Tibet Plateau (SETP), the availability of optical images in high mountain areas is low, and Synthetic Aperture Radar (SAR) amplitude images have significant geometric distortions. Most GL extraction studies are mainly focused on autumn and winter seasons. In order to obtain accurate GL boundaries at any time and to capture seasonal changes of GLs in a wide area, this letter proposes a high mountain GL extraction framework based on the coupling of Google Earth Engine (GEE) and historical GL catalog data, which focuses on local GL extraction problems. GL extraction and validation were performed using various clustering and adaptive threshold segmentation methods, all of which showed strong stability and reliability of the proposed scheme. Finally, we employed a superpixel clustering algorithm to estimate the area of GLs as of August 1, 2019, and then compared the results with two widely used spatially referenced datasets. The results indicate that our method achieves a comprehensive Intersection over Union (IoU) of up to 95%. The proposed method can effectively support the extraction of wide-area summer GLs and the monitoring of seasonal changes in GL area, thus enabling the dynamic updating of GL information at a high temporal frequency.

Synthetic aperture radar (SAR) is a crucial technique in Earth observation, providing vast amounts of data for monitoring the Earth’s surface. However, SAR’s side-looking imaging characteristics often result in significant geometric distortions in complex terrains such as mountainous gorges. Current methods struggle to accurately compute both active and passive geometric distortions when orbital state vector information is not available. This study aims to address this challenge by concentrating on the Southeastern Tibetan Plateau (SETP) and introducing a DEM-based radar incidence angle-tracking method (Angle-Tracking) based on ray tracing principles. The fundamental aspects of this method include constructing the discrete range direction vector (DRDV) to establish calculation directions, refining grid distribution via cubic-patch cells, and identifying potential topographic occluder points (PTOPs) to minimize redundancy in iterative computations. Through the utilization of this approach, we have acquired and disclosed the distribution of geometric distortion in ascending and descending Sentinel-1 data over the SETP region. Cross validation with results computed from precise orbital data showcases the efficacy of the angle-tracking method in identifying geometric distortions in the absence of satellite state vector information. Furthermore, the angle-tracking method demonstrates effectiveness when implemented on cloud computing platforms such as Google Earth Engine (GEE), thereby enhancing the feasibility of SAR-based research in mountainous areas.

Forest fire not only seriously affects the stability of the forest ecosystem but also threatens the safety of human life and property. The previously burned areas extracting method mainly focuses on optical images, susceptible to cloud and fog objective environmental factors. Although there are related studies on the threshold segmentation of single SAR feature types, further information mining for SAR image data, e.g., backscattering intensity, polarization decomposition, texture, and other features, is still insufficient. Therefore, this letter proposes a burned area extracting method using polarization and texture features of Sentinel-1A images to combine and comprehensively mine various SAR feature change information caused by forest fires with a random forest (RF) model. For validation purposes, we compared the burned areas’ extracted results with the reference data acquired based on Sentinel-2A optical imagery. The comparative results show that the SAR extraction results highly agree with the reference data, with an accuracy of 87.12%, and the commission and omission errors were 20.44% and 12.88%, respectively. The proposed machine learning method helps extract fire areas covered by thick smoke or persistent clouds and provides a reference to related research.

The global positioning system interferometric reflectometry (GPS-IR) method has the advantage of acquiring observations continuously in all weather conditions, which has great application potential in vegetation remote sensing. However, L-band electromagnetic wave signals are susceptible to various environmental factors, resulting in deviations in GPS-IR observation data at certain times. The accuracy and reliability of the vegetation index retrieving results are challenging to achieve by using single-frequency GPS data. This letter proposed a novel method to combine dual-frequency data for retrieving normalized difference vegetation index (NDVI). We integrated the multipath observations based on the theory of information entropy. Subsequently, a unary linear regression model was established to retrieve the NDVI from the calculated normalized microwave reflection index (NMRI). For validation purposes, the comparative analysis was conducted between the proposed model and the previous single-frequency NDVI retrieving model in terms of retrieval accuracy, based on the continuous observation data acquired by four GPS reference stations in the past five years. The experimental results indicated that the proposed model is available for retrieving the NDVI, with the correlation coefficient of 0.749–0.815 and the root mean square error (RMSE) of 0.056–0.081. Compared with the results acquired by the previous single-frequency NDVI retrieving model, the correlation coefficient of the retrieved NDVI was increased by an average of 18.5%, and the RMSE was reduced by 30.3%. The proposed method in this letter helps further improve the accuracy and continuity of NDVI observation data in some local areas, which contributes to grasping the growth status of vegetation comprehensively.

An emerging microwave remote sensing technology, global navigation satellite system interferometry reflectometry (GNSS-IR) shows excellent application potential in vegetation remote sensing due to its all-weather and high temporal resolution characteristics. Previous studies for GNSS-IR have mainly concentrated on the Global Positioning System (GPS) SNR data.Because of the similarity between GPS and BDS (Beidou Navigation Satellite System), the signal of BDS can also be impacted by vegetation, which however, has not been comprehensively researched. Therefore, this letter acquires normalized amplitude of the BDS SNR data based on triple-frequency SNR observations collected by two stations with different vegetation types. To reveal the impact of different vegetation growth conditions on the BDS multi-frequency signals, we conduct a comparative analysis with normalized difference vegetation index (NDVI) data obtained by Sentinel-2 and Moderate-resolution Imaging Spectroradiometer (MODIS) imagery, respectively. The outcomes demonstrate that the normalized amplitude of the BDS signal exhibits a strong association with Sentinel-2 NDVI, with correlation coefficients varying from 0.69 to 0.83 and 0.78 to 0.84 at P041 and P105, respectively. In addition, it is the first time discovered that harvesting vegetation around the stations leads to a cliff-like decrease in normalized amplitude. Compared with optical remote sensing vegetation monitoring methods, BDS-IR shows significant temporal resolution and sensitivity advantages. This finding may promote vegetation monitoring, vegetation protection, and other related research fields.