Floods are some of the most serious and devastating natural hazards on earth, bringing huge threats to lives, properties, and living environments. Rapid delineation of the spatial extent of flooding is of great importance for the dynamic monitoring of flood evolution and corresponding emergency strategies. Some of the current flood mapping methods mainly process single date images characterized by simple flood situations and homogenous backgrounds. Although other methods show good performance for images with harsh conditions for floods, they must be trained—many times based on pre-classified samples—or undergo complicated parameter tuning processes, which require computation efforts. The widely used change detection methods utilize multi-temporal Synthetic Aperture Radar (SAR) images for the detection of flood area, but the results are largely influenced by the quality of defined reference images. Furthermore, these methods were mostly applied for some river basin floods, which are not effective for the large scale, semi-arid regions with complex flood conditions, and various land cover types. All of these extremely limited the use of these methods for the timely and accurate extraction of the spatial distribution pattern of floods in other typical and broad areas. Based on the above considerations, this paper presents a new method for rapidly determining the extent of flooding in large, semi-arid areas with challenging environmental conditions, based on multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) data. First, a preprocessing scheme is applied to perform geometric correction and to estimate the intensity of the imagery. Second, an automatic thresholding procedure is used to generate an initial land and water classification through the integration of the probability density distribution. A fuzzy logic-based approach, combining SAR backscattering information and other auxiliary data, is then used to refine the initial classified image. The fuzzy logic-based refinement removes areas that look similar to water in the SAR images, significantly enhancing the flood mapping accuracy. Finally, a post-processing step consisting of morphological operations and extraction improves the homogeneity of the extracted flood segments, discards isolated pixels, and gives the final flood map. This method can automatically detect the extent of floods at little computational cost. As Sentinel-1 data are publicly available and have a fast repeat cycle, the procedure can provide near real time results for rapid emergency response following flash floods. The accuracy of the proposed method is assessed at three test sites in Pakistan, which covered diverse landscapes and suffered large scale serious flooding after a long and severe drought in 2015. In comparison with the more recent studies from Ohki et al., 2020, and Shahabi et al., 2020, our results indicate the best spatial agreement with GF-2 panchromatic multi-spectral (PMS) water classification, with an encouraging overall accuracy ranging from 91.1% to 96.6%, and Kappa coefficients ranging from 0.893 to 0.954. Especially for the areas with fragmented floods, heterogeneous backgrounds, and the areas where samples are highly unbalanced in the SAR images, our method combines the global statistics and local relationships of backscattering properties, terrain, and other auxiliary information, enabling to effectively preserve the detailed structures and also remove the noise.

Glacial lakes are an important component of the cryosphere in the Tibetan Plateau. In response to climate warming, they threaten the downstream lives, ecological environment, and public infrastructures through outburst floods within a short time. Although most of the efforts have been made toward extracting glacial lake outlines and detect their changes with remotely sensed images, the temporal frequency and spatial resolution of glacial lake datasets are generally not fine enough to reflect the detailed processes of glacial lake dynamics, especially for potentially dangerous glacial lakes with high-frequency variability. By using full time-series Sentinel-1A/1B imagery over a year, this study presents a new systematic method to extract the glacial lake outlines that have a fast variability in the southeastern Tibetan Plateau with a time interval of six days. Our approach was based on a level-set segmentation, combined with a median pixel composition of synthetic aperture radar (SAR) backscattering coefficients stacked as a regularization term, to robustly estimate the lake extent across the observed time range. The mapping results were validated against manually digitized lake outlines derived from Gaofen-2 panchromatic multi-spectral (GF-2 PMS) imagery, with an overall accuracy and kappa coefficient of 96.54% and 0.95, respectively. In comparison with results from classical supervised support vector machine (SVM) and unsupervised Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) methods, the proposed method proved to be much more robust and effective at detecting glacial lakes with irregular boundaries that have similar backscattering as the surroundings. This study also demonstrated the feasibility of time-series Sentinel-1A/1B SAR data in the continuous monitoring of glacial lake outline dynamics.

Winter wheat cropland is one of the most important agricultural land-cover types affected by the global climate and human activity. Mapping 30-m winter wheat cropland can provide beneficial reference information that is necessary for understanding food security. To date, machine learning algorithms have become an effective tool for the rapid identification of winter wheat at regional scales. Algorithm implementation is based on constructing and selecting many features, which makes feature set optimization an important issue worthy of discussion. In this study, the accurate mapping of winter wheat at 30-m resolution was realized using Landsat-8 Operational Land Imager (OLI), Sentinel-2 Multispectral Imager (MSI) data, and a random forest algorithm. This paper also discusses the optimal combination of features suitable for cropland extraction. The results revealed that: (1) the random forest algorithm provided robust performance using multi-features (MFs), multi-feature subsets (MFSs), and multi-patterns (MPs) as input parameters. Moreover, the highest accuracy (94%) for winter wheat extraction occurred in three zones, including: pure farmland, urban mixed areas, and forest areas. (2) Spectral reflectance and the crop growth period were the most essential features for crop extraction. The MFSs combined with the three to four feature types enabled the high-precision extraction of 30-m winter wheat plots. (3) The extraction accuracy of winter wheat in three zones with multiple geographical environments was affected by certain dominant features, including spectral bands (B), spectral indices (S), and time-phase characteristics (D). Therefore, we can improve the winter wheat mapping accuracy of the three regional types by improving the spectral resolution, constructing effective spectral indices, and enriching vegetation information. The results of this paper can help effectively construct feature sets using the random forest algorithm, thus simplifying the feature construction workload and ensuring high-precision extraction results in future winter wheat mapping research.

Previous studies have reported air pollution‐radiation interactions in the urban boundary layer (UBL), but vertical gradient variations in the radiation budget and heat flux under air pollution conditions are relatively sparse. In this study, based on gradient observations from the Beijing 325 m meteorological tower in December 2015, the characteristics of near‐surface radiation balance and energy budget at three levels under different pollution conditions were comparatively investigated. Relative to clean days, both downward and upward shortwave radiation (DSR and USR) dropped during daytime, while downward and upward longwave radiation (DLR and ULR) enhanced during nighttime on heavily polluted days, showing that with evaluated height, the drop magnitudes of DSR and USR decreased, while the enhancement magnitude of DLR (ULR) decreased (increased). The combined effects of four radiation components significantly induced the reduction in net radiation (R_n) on polluted days, leading to the near‐surface energy budget change. In addition, the monthly averaged anthropogenic heat flux (Q_f) was estimated to quantitatively calculate the heat storage (G) term in the surface energy budget. During daytime, compared to the clean episodes, the sensible heat flux (H) was reduced more than R_n in the whole near‐surface UBL during heavy polluted episodes, resulting in smaller H/(R_n + Q_f) and larger G/(R_n + Q_f). Finally, we revealed that weak thermal forcing effects caused by insufficient availability of net radiation energy at the surface and weak dynamic motion associated with weak winds were both responsible for the larger reduction (increase) in H (G) during pollution episodes in the whole near‐surface UBL.

Early recognition of glacial lake outburst floods (GLOFs) is required for timely and cost-effective remedial efforts to be implemented. Although the formation of ice-dammed lakes is known to begin as a pond or river that was blocked by ice from the glacier terminus, the relationship between glacier dynamics and lake development is not well understood. Using a time-series of Sentinel-1A synthetic aperture radar (SAR) data acquired just before and after the lake outburst event in 2018, information is presented on the dynamic characteristics of Kyagar Glacier and its ice-dammed lake. Glacier velocity data derived from interferometry show that the glacier tongue experienced an accelerated advance (maximum velocity of 20 cm/day) just one month before the lake outburst, and a decreased velocity (maximum of 13 cm/day) afterward. Interferometric and backscattering properties of this region provide valuable insight into the diverse glaciated environment. Furthermore, daily temperature and total precipitation data derived from the ECMWF re-analysis (ERA)Interim highlight the importance of the sustained high-temperature driving force, supporting empirical observations from previous studies. The spatial and temporal resolution offered by the Sentinel-1A data allows variations in the glacier surface motion and lake evolution to be detected, meaning that the interaction mechanism between the glacial lake and the associated glacier can be explored. Although the glacier surge provided the boundary conditions favorable for lake formation, the short-term high temperatures and precipitation caused the melting of ice dams and also a rapid increase in the amount of water stored, which accelerated the potential for a lake outburst.