Domain adaptation is a pivotal technique for improving the classification performance of remote sensing scenes impacted by data distribution shifts. Existing spatial-domain feature alignment methods are vulnerable to complex scene clutter and spectral variations. Considering the robustness of frequency representation in preserving edge details and structural patterns, this paper presents a novel spatial-frequency multiple alignment domain adaptation (SFMDA) method for remote sensing scene classification. First, a frequency-domain invariant feature learning module is introduced, which employs the Fourier transform and high-frequency mask strategy to derive frequency-domain features exhibiting enhanced inter-domain invariance. Subsequently, a spatial-frequency feature cross fusion module is developed to achieve more robust and domain-representative spatial-frequency fusion representations through dot product attention and interaction mechanisms. Finally, a multiple feature alignment strategy is devised to minimize both spatial-domain feature differences and fusion feature discrepancies across the source and target domains, thereby facilitating more effective inter-domain knowledge transfer. Experimental results on six cross-domain scenarios demonstrate that SFMDA outperforms eight state-of-the-art methods, achieving a 3.87%–17.98% accuracy improvement. Furthermore, SFMDA is compatible with existing spatial-domain learning frameworks, enabling seamless integration for further performance gains. Our code will be available at https://github.com/GeoRSAI/SFMDA.

Monitoring ground surface large displacements in a 3-D space is important for scientifically understanding and controlling deformation-related geohazards. It has become a promising technique to estimate surface 3-D displacements from airborne multitemporal point cloud datasets using point cloud alignment techniques, such as the typical iterative closest point (ICP) algorithm. However, the typical ICP algorithm aligns point clouds based on geometric features alone, causing a poor robustness in the absence of sufficient topographic structures. This letter presents a new framework so-called weighted hue-based ICP (WHICP) for estimating surface 3-D displacements from multitemporal colored point clouds generated from unmanned aerial vehicle (UAV) stereo photogrammetry. First, a variant of the ICP algorithm named hue-based ICP (HICP) is used, where both geometric and temporally stable color features are used to point cloud alignment. Then, a multiwindow weighted framework is proposed to further process the HICP-aligned point cloud to generate robust estimates of surface 3-D displacements. The WHICP algorithm was tested in a coal mine in Tangshan city, China, where large displacements occurred in flatten terrains. The result shows that the mean accuracy of the WHICP-estimated 3-D displacements is 0.03 m, indicating an improvement by 84% than that of the typical ICP-estimated 3-D displacements.

GNSS tomography is acknowledged as one of the most attractive techniques to accurately retrieve three-dimensional distribution of atmospheric water vapor with high-resolution. Here the development of a wide-area tomography technique for the retrieval of water vapor fields by jointly using GNSS observations and numerical weather prediction forecasts is described. We present an improved node parameterization tomography to retrieve the high-resolution wet refractivity fields over the continent of USA. This method does not depend on numerical integration by Newton-Cotes quadrature and considerably reduces computational burden in linearization. To refine the tomographic modeling, vertical variation parameter of water vapor for each voxel is estimated dynamically from the updated wet refractivity profiles after each iteration, towards achieving a self-adaptive design matrix. Global Forecast System products from NCEP are applied to initialize the tomographic solution for a simulation of real time mode. Tomography experiment is demonstrated with GPS data collected from 1440 stations over a one-month period of June 2020. Compared with the traditional node parameterization method, the improved method can enhance the performance by 6% and reduce the computational burden by 30%, respectively.

The potential of precipitable water vapor (PWV) maps retrieved by remote sensing satellites can address the geometry defect of global navigation satellite system (GNSS) observations in tropospheric tomography. The second-generation geostationary meteorological satellite Fengyun-4A (FY-4A) of China can provide PWV products with high spatial (4 km) and temporal (15 min) resolutions. This article presents the first study on water vapor tomography by integrating GNSS measurements and FY-4A products using the node-based parameterized method. Layer PWV (LPW) products of FY-4A instead of the total PWV are adopted, which can increase the rank of the tomographic equation significantly. The integrated tomography model is validated with observational data collected over the three-month period of June to August 2020 from 124 GNSS stations in Hunan province, China. Assessments using radiosonde and European Centre for Medium-Range Weather Forecasts ReAnalysis 5 (ERA5) data demonstrate the better performance of the integrated model against the traditional model using GNSS data alone. In the assessment with radiosonde profiles, the integrated model improves the tomographic solutions upon the traditional model by 40.58% and 36.33% for 30- and 15-min resolutions, respectively. Root mean square errors (RMSEs) of density differences between ERA5 and the integrated model vary from 1.24 to 2.82 g/$\text{m}^{3}$throughout the study area. RMSEs vertically decrease from$\sim 5$g/$\text{m}^{3}$at the bottom to$\sim 0.5$g/$\text{m}^{3}$at the top layer of about 10 km. This work demonstrates the benefit of high-quality FY-4A products to GNSS tomography because they can effectively mitigate the ill-posed problem of inverse process.

Precipitable water vapor (PWV) products from the second generation of China’s geostationary meteorological satellite Fengyun-4A (FY-4A) have the advantage of high spatiotemporal resolution and can play an increasingly important role in the study of atmosphere and climate. Using the radiosonde, the global navigation satellite system (GNSS), and the European Centre for Medium-Range Weather Forecasts (ECMWF) ReAnalysis 5 (ERA5) reanalysis data, this study presented a comprehensive evaluation of PWV products from FY-4A for a one-year period from January 2019 to January 2020. Results indicated that FY-4A PWV data have a good agreement with radiosonde and GNSS measured ones with the same correlation coefficient of 0.976, and the root mean square errors (RMSEs) are 3.95 and 3.73 mm, respectively. Compared with the radiosonde and GNSS, the FY-4A Advanced Geostationary Radiation Imager (AGRI) was found to underestimate the water vapor during humid conditions when the PWV is greater than 50 mm. The magnitude of underestimation increases with the growth in water vapor content. In terms of spatial variability, the RMSE of FY-4A PWV decreases with the increase in latitude, while the relative RMSE (R-RMSE) displays an opposite pattern. RMSE from the comparison between FY-4A and ERA5 PWV varies from 0 to 6 mm depending upon the location. Statistics showed that 55.08%, 59.79%, and 83.13% RMSE values are less than 4 mm in the evaluation by radiosonde, GNSS, and ERA5, respectively. The seasonal and diurnal variations of RMSE showed that: 1) summer exhibited larger RMSE than winter and 2) daytime obtained slightly worse performance than nighttime.

The advanced Himawari imager (AHI) onboard Himawari-8 can provide full-disk observations with high temporal resolution (10 min), which has outstanding advantages for dynamic real-time aerosol monitoring in East Asia. In this study, a new aerosol retrieval algorithm for AHI by integrating regional PM2.5concentrations (IRPAR) was proposed. The IRPAR algorithm constructed the surface reflectance library by integrating regional PM2.5levels as a quantitative indicator of atmospheric aerosol loadings. The IRPAR algorithm was used to obtain the aerosol optical depth (AOD) retrievals over Beijing–Tianjin–Hebei (BTH) region from March 2019 to February 2020, and its performance was preliminarily evaluated by aerosol robotic network (AERONET) measurements. The results showed that the IRPAR algorithm was able to obtain more highly accurate AOD retrievals compared to the JAXA L2 algorithm during the autumn in BTH region, with a large$R$of 0.87 (0.71 for JAXA L2 AOD) and a global climate observing system fraction (GCOSF) percentage of 28% (21% for JAXA L2 AOD). During different daytime hours, the IRPAR AOD showed a stable retrieval performance, while the JAXA L2 AOD exhibited a worst performance from 12:00 to 14:00 Beijing standard time (BST). These results demonstrated that the IRPAR algorithm was relatively less affected by the viewing angle. Future work will require a comprehensive evaluation of the IRPAR algorithm on a larger spatial scale.

The hydrologic monitoring is of great importance to the water resources management, ecological environment protection. The equipment and methods used in hydrological monitoring are various, the data types are complex and diverse. There are various causes of the hydrological disasters, and they are interrelated and interactional. This paper summarizes the main hydrological monitoring sensors, observations and events, and builds a preliminary hydrological ontology model for hydrological monitoring, which solves the problem of hydrological monitoring complexity to a certain extent.

In this paper, we joint autoencoder with active learning for hyperspectral imagery classification. Specifically, we learn the classifier via autoencoder, where the most informative samples are acitvely selected through the interaction between the autoencoder and active learning. Experimental results, conducted using both the Kennedy Space Center and the Indian Pines hyperspectral images, show that driven by active learning, the performance of autoencoder can be greatly improved.

Precipitation stations are important components of a hydrological monitoring network. Given their critical role in rainfall forecasting and flood warnings, along with limited observation resources, determining the optimal locations to deploy precipitation stations presents an important problem. In this paper, we use a maximal covering location problem to identify the best precipitation station sites. Considering the terrain conditions and the characteristics of a rainfall network, the original maximal covering location model is modified with the introduction of a set of additional constraints. The minimum density requirement is used to determine a precipitation station’s coverage range, and three weighting schemes are used to evaluate each demand object’s covering priority. As a typical mountainous watershed with high annual precipitation, the Jinsha River Basin is selected as the study area to test the applicability of the proposed method. Results show that the proposed method is effective for precipitation station configuration optimization, and the model solution achieves higher coverage than the real-world deployment. Compared with the commercial solver CPLEX, a genetic algorithm-based heuristic can significantly reduce the computation time when the problem size is large. Several deployment strategies are also discussed for establishing the optimal configuration of precipitation stations.

The Sensor Web is a coordinated observation infrastructure composed of distributed resources that can behave as a single, autonomous, task-able, reconfigurable observing system that provides observed and derived data along with the associated metadata by using a set of standards-based service oriented interfaces. But these resources, including sensor, data, platform etc., with different characteristics are hard to be fused well. This paper analyzes concepts of various types of sensor web resources for atmospheric observing, focusing on how to access different types of resources expediently, abstract essential features of these sensor web resources, and construct a Sensor Web Resources Ontology for Atmospheric Observation (SWRO-AO) represented by Web Ontology Language. The SWRO-AO could serve as a knowledge repository of sensor web resources for the research and application community in atmospheric science.

The OGC Web Service (OWS) schemas have the characteristics of a complex element structure, are distributed and large scale, have differences in element naming, and are available in different versions. Applying conventional matching approaches may lead to not only poor quality, but also bad performance. In this article, the OWS schema file decomposition, fragment presentation, fragment identification, fragment element match, and combination of match results are developed based on the extended FRAG-BASE (fragment-based) schema-matching method. Different versions of Web Feature Service (WFS) and Web Coverage Service (WCS) schema-matching experiments show that the average recall of the extended FRAG-BASE matching for the schemas is above 80%, the average precision reaches 90%, the average overall achieves 85%, and the matching efficiency increases by 50% as compared with that of the COMA and CONTEXT matcher. The multi-version WFS retrieval under the Antarctic Spatial Data Infrastructure (AntSDI) data service environment demonstrates the feasibility and superiority of the extended FRAG-BASE method.

With the emerging of multi-frequency InSAR system, extraction of 3-D and variation information of earth surface using multi-frequency InSAR data has drawn more attention. Methods based on Maximum-Likelihood Estimation (MLE), combining with some conventional phase unwrapping algorithm can be used to achieve this purpose. In this paper, MLE integrated with weighted multigrid phase-unwrapping method for high-sloped terrain profile reconstruction is presented and studied in detail. The performance of this method is also analyzed. Experiment result using simulated InSAR data shows that the method can be used to reconstruct high-sloped terrain with relatively high accuracy.

This paper introduces a method for building location information extraction using airborne C-band polarimetric SAR data. The method is based on the analysis of building feature. The method decomposes the scattering covariance matrix into three simple mechanism, i.e., odd-bounce; even-bounce; cross-bounce. Taking into account that the typical strong T-shaped echoes from quite large buildings are visible, a method is introduced to extract the location of the buildings.

SAR interferometry allows height reconstruction of the earth surface. A method based on the use of multi-frequency interferograms and Maximum Likelihood Estimation (MLE) has recently been proposed. However without a priori knowledge of the terrain, the result of the reconstruction is unsatisfied in practical cases. In this paper, we present a novel method to reconstruct highly sloped and discontinuous terrain height profiles using multi-frequency interferograms. It is based on MLE using multi-frequency interferograms joint statistic property, combining with some conventional signal frequency phase unwrapping algorithm. The method can not only improve efficiency of the MLE, but also ensure reliability of the estimation.