A new method, which integrates multivariable consisting of soil moisture (SM) active passive (SMAP)-derived SM and vegetation optical depth, the water seasonality, geolocation, digital elevation model (DEM), slope, and biomass as inputs and adopts the technique of bootstrap aggregation of regression trees (BARTs), is proposed for retrieving monthly surface water fraction (SWF) at a spatial resolution of 0.025° from cyclone global navigation satellite system (CYGNSS) data. The model is trained using surface water microwave product series (SWAMPS) data with a coarser resolution of 25 km and then applied to CYGNSS data with an enhanced resolution of 0.025° to generate high-resolution water maps. The resulting CYGNSS SWF (CSWF) maps are evaluated by comparing them with other water data sources, namely, SWAMPS, global surface water (GSW), and global surface water dynamics (GLADs), as well as ground measurements. A quadruple collocation (QC) analysis indicates that the CSWF results exhibit the lowest error variance among the four SWF datasets. Furthermore, additional testing with water level (WL) measurements demonstrates a strong correlation with station data and clear seasonal patterns. Notably, the CSWF estimates significantly improve spatial coverage compared to both optical data (GSW and GLAD) with enhanced spatial resolution and the coarser SWAMPS data. This study underscores the effectiveness and efficiency of CSWF estimates, highlighting their potential as a valuable complement to existing microwave- and optical-based surface water products.

The application of empirical methods for satellite-derived bathymetry is limited by the lack of in situ bathymetric data in remote, inaccessible areas. This challenge has been addressed with the launch of ICESat-2 (Ice, Cloud, and land Elevation Satellite-2). This study provides an accurate bathymetric photon extraction process for ICESat-2 ATL03 data, and the R ² value of the bathymetric photons obtained using this process and airborne bathymetric LiDAR data is up to 99%. Next, based on two types of remote sensing data, ICESat-2 and Sentinel-2, machine learning (ML) models, including linear regression (LR), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost), were trained to obtain bathymetric maps. The experimental results show that the mean RMSE (root mean square error), mean MAE (mean absolute error), and mean MRE (mean relative error) values of the LR models are less than 3.02 m, 2.38 m, and 86.03%, respectively. The mean RMSE, MAE, and MRE values of the LightGBM and CatBoost models are less than 0.91 m, 0.66 m, and 23.17%, respectively. It is concluded that the proposed denoising process for ICESat-2 ATL03 data is effective, and the results of the bathymetric maps obtained using these data are satisfactory. Thus, the proposed approach is effective, and this strategy can be used to replace conventional bathymetric inversion methods to obtain high-accuracy bathymetric maps.

This work discusses the accuracy of C-2PO (C-band cross-polarized ocean backscatter) and CMOD4 (C-band model) geophysical model functions (GMF) for sea surface wind speed retrieval from satellite-born Synthetic Aperture Radar (SAR) images over in the Northwest Pacific off the coast of China. In situ observations are used for comparison of the retrieved wind speed using two established wind retrieval models: C-2PO model and CMOD4 GMF. Using 439 samples from 92 RADARSAT-2 fine quad-polarization SAR images and corresponding reference winds, we created two subset wind speed databases: the training and testing subsets. From the training data subset, we retrieve ocean surface wind speeds (OSWSs) from different models at each polarization and compare with reference wind speeds. The RMSEs of SAR-retrieved wind speeds are: 2.5 m/s: 2.11 m/s (VH-polarized), 2.13 m/s (HV-polarized), 1.86 m/s (VV-polarized) and 2.26 m/s (HH-polarized) and the correlation coefficients are 0.86 (VH-polarized), 0.85(HV-polarized), 0.87(VV-polarized) and 0.83 (HH-polarized), which are statistically significant at the 99.9% significance level. Moreover, we found that OSWSs retrieved using C-2PO model at VH-polarized are most suitable for moderate-to-high winds while CMOD4 GMF at VV-polarized tend to be best for low-to-moderate winds. A hybrid wind retrieval model is put forward composed of the two models, C-2PO and CMOD4 and sets of SAR test data are used in order to establish an appropriate wind speed threshold, to differentiate the wind speed range appropriate for one model from that of the other. The results show that the OSWSs retrieved using our hybrid method has RMSE of 1.66 m/s and the correlation coefficient are 0.9, thereby significantly outperforming both the C-2PO and CMOD4 models.

In the era of big data, how to utilize synthetic aperture radar (SAR) and passive microwave radiometer (MWR) data for better sea ice monitoring, by deep learning (DL) technology has recently attracted wide attention. In this paper, we first propose a universal and lightweight multiscale cascade network (MCNet) for Sentinel-1 SAR-based sea ice classification. In comparison to previous local inference methods that split SAR images to small patches, our proposed global inference method MCNet is able to segment whole SAR images directly. Then taking MCNet as a basis, we investigate four different fusion methods for Sentinel-1 SAR and the Advanced Microwave Scanning Radiometer-2 (AMSR-2) data. These are the early fusion, deep fusion, late fusion and the hybrid method, which fuse data at the input level, feature level, decision level, as well as both feature and decision levels, respectively. Experiments demonstrate that MCNet performs better than the commonly used U-Net in terms of accuracy, memory usage, inference speed and in capturing small-scale local details. As for data fusion, compared to MCNet, significant improvements have been achieved for all data fusion methods, except the early fusion method. Both deep fusion and late fusion methods have their own advantages in classifying certain sea ice types. By combining them together, the proposed hybrid method achieves optimal performance. Lastly, with regard to the class imbalance problem, we recommend the application of self-supervised learning, to mine the value of massively unlabeled SAR images.

The detection of collapsed buildings based on post-earthquake remote sensing images is conducive to eliminating the dependence on pre-earthquake data, which is of great significance to carry out emergency response in time. The difficulties in obtaining or lack of elevation information, as strong evidence to determine whether buildings collapse or not, is the main challenge in the practical application of this method. On the one hand, the introduction of double bounce features in synthetic aperture radar (SAR) images are helpful to judge whether buildings collapse or not. On the other hand, because SAR images are limited by imaging mechanisms, it is necessary to introduce spatial details in optical images as supplements in the detection of collapsed buildings. Therefore, a detection method for collapsed buildings combining post-earthquake high-resolution optical and SAR images was proposed by mining complementary information between traditional visual features and double bounce features from multi-source data. In this method, a strategy of optical and SAR object set extraction based on an inscribed center (OpticalandSAR-ObjectsExtraction) was first put forward to extract a unified optical-SAR object set. Based on this, a quantitative representation of collapse semantic knowledge in double bounce (DoubleBounceCollapseSemantic) was designed to bridge a semantic gap between double bounce and collapse features of buildings. Ultimately, the final detection results were obtained based on the improved active learning support vector machines (SVMs). The multi-group experimental results of post-earthquake multi-source images show that the overall accuracy (OA) and the detection accuracy for collapsed buildings (P_cb) of the proposed method can reach more than 82.39% and 75.47%. Therefore, the proposed method is significantly superior to many advanced methods for comparison.