IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing最新文献

{"title":"Hyperspectral Band Selection via Heterogeneous Graph Convolutional Self-Representation Network","authors":"Junde Chen;Wenzhao Li;Surendra Maharjan;Hesham El-Askary","doi":"10.1109/JSTARS.2025.3589866","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3589866","url":null,"abstract":"Hyperspectral image (HSI) band selection (BS) plays a crucial role in HSI dimensionality reduction, aiming to identify a representative subset of bands with minimal redundancy. However, conventional BS approaches primarily operate in the Euclidean domain, often overlooking the structural characteristics of pixels and spectral bands, such as spatial continuity and spectral dependencies. In addition, they handle each HSI as an integrated unit to harness implicit spatial information, disregarding spatial distribution variations across different homogeneous regions. To fully leverage structural information, this study introduces a novel BS method, termed the dual heterogeneous graph convolutional network with enhanced self-representation (ESR-HGCN), for HSI BS. The heterogeneous graph convolutional network (HGCN) and enhanced self-representation (ESR) serve as the two essential components of the proposed ESR-HGCN. To explore spatial features and the potential hidden interactions among spectral bands, we use the HGCN as the backbone network for heterogeneous graph-based HSI BS. Dual graphs at the pixel and band levels are separately constructed and integrated into the ESR module, where a sparsity constraint is enforced and the original Frobenius norm is replaced with <inline-formula><tex-math>$ell _{1}$</tex-math></inline-formula>- and <inline-formula><tex-math>$ell _{2,1}$</tex-math></inline-formula>-norm regularizations to achieve robust BS. Meanwhile, dual graph convolution operations are performed to separately extract spatial and spectral features, thereby seamlessly integrating spectral, spatial, and geometric information, offering significant advantages for HSI BS. Finally, an effective optimization scheme is developed to refine the proposed approach. Experimental findings on representative HSI datasets highlight the superiority of ESR-HGCN over state-of-the-art methods.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"18543-18560"},"PeriodicalIF":5.3,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11081469","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144758326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Satellite-Derived Bathymetry Combined With Sentinel-2 and ICESat-2 Datasets Using Deep Learning","authors":"Weidong Zhu;Yanying Huang;Tiantian Cao;Xiaoshan Zhang;Qidi Xie;Kuifeng Luan;Wei Shen;Ziya Zou","doi":"10.1109/JSTARS.2025.3589289","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3589289","url":null,"abstract":"Accurate bathymetric data are critical for marine ecological balance and resource management. Deep learning algorithms, known for their capacity to model complex, multivariate, and nonlinear relationships, have been increasingly applied to satellite-derived bathymetry. However, existing deep learning models are limited by simple architectures and low efficiency in hyperparameter optimization, resulting in suboptimal training performance. This article proposes a convolutional neural network and bidirectional long short-term memory hybrid model based on the Bayesian optimization algorithm (BOA-CNN-BILSTM) to enhance bathymetric inversion accuracy and efficiency. The model employs BOA to optimize the key hyperparameters of the CNN-BILSTM architecture, thereby improving inversion performance. Bathymetric inversion experiments were conducted using fused ICESat-2 and Sentinel-2 data, focusing on Coral Island and Dong Island in the South China Sea, as well as Midway Island and Oahu Island in the Pacific Ocean. Comparative experiments demonstrated that BOA significantly outperforms conventional random search by achieving near-optimal hyperparameter configurations with fewer evaluations, accelerating convergence and reducing computational costs. The BOA-CNN-BILSTM model reduced the root mean square error by 28.6%–56.5%, 29.6%–53.7%, 34.1%–52.6%, and 28.9%–57.1% across the study areas compared with the CNN, BILSTM, and CNN-BILSTM models. Other evaluation metrics also showed varying degrees of improvement. These results demonstrate that the proposed approach is effective and highly accurate for bathymetric inversion in shallow waters.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"18376-18390"},"PeriodicalIF":5.3,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11080361","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144758442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrections to “In-Season Mapping of Sugarcane Planting Based on Sentinel-2 Imagery”","authors":"Hui Li;Liping Di;Chen Zhang;Li Lin;Liying Guo;Ruopu Li;Haoteng Zhao","doi":"10.1109/JSTARS.2025.3581645","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3581645","url":null,"abstract":"This addresses errors in (1).","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"15957-15957"},"PeriodicalIF":4.7,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11079755","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144640967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Soybean Yield Estimation Using Improved Deep Learning Models With Integrated Multisource and Multitemporal Remote Sensing Data","authors":"Jian Li;Junrui Kang;Ji Qi;Jian Lu;Hongkun Fu;Baoqi Liu;Xinglei Lin;Jiawei Zhao;Hengxu Guan;Jing Chang;Zhihan Liu","doi":"10.1109/JSTARS.2025.3588917","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3588917","url":null,"abstract":"Accurate soybean yield estimation is critically imperative for modern agricultural systems amid escalating global food security pressures, yet conventional methodologies are constrained for large-scale high-frequency monitoring. To address this, an innovative deep learning framework, TransBiHGRU-PSO, is proposed for precise large-scale soybean yield estimation via effective fusion of multisource multitemporal remote sensing data, emphasizing robust and accurate estimation even with anomalous yield data. This framework synergistically integrates an optimized bidirectional hierarchical gated recurrent unit (BiHGRU), a Transformer encoder, and a novel Greenness and Water Content Composite Index, with critical parameters optimized by particle swarm optimization (PSO). County-level yield data from 12 U.S. states were used, supplemented by multitemporal remote sensing datasets (MODIS surface reflectance, vegetation indices, and environmental variables). Empirical analyses showed that TransBiHGRU-PSO demonstrated improved estimation capability and generalizability compared to multiple benchmark models. Notably, with anomalous yield data retained, the model achieved solid test set performance [coefficient of determination (<italic>R</i>²) of 0.71 and root-mean-square error (RMSE) of 4.2812 bushels/acre]. Compared to the best traditional machine learning model (support vector regression), <italic>R</i>² increased by 52.96% and RMSE decreased by 26.05%, and relative to the best deep learning baseline model (long short-term memory), <italic>R</i>² and RMSE improved by 7.04% and 7.04%, respectively. Furthermore, validation of interannual stability (2008–2018, anomalies retained) revealed a mean <italic>R</i>² of 0.70 and a mean RMSE of 4.4701 bushels/acre, affirming its consistency under complex real-world conditions. This TransBiHGRU-PSO algorithmic framework, combined with multisource and multitemporal remote sensing data, offers a valuable exploration for large-scale soybean yield estimation.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"18450-18477"},"PeriodicalIF":5.3,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11079987","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144758473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deformation Monitoring for Mountain Excavation and City Construction Area in Yan’an New District by Improved MT-InSAR Method","authors":"Guangrong Li;Chaoying Zhao;Bin Li;Ming Yan;Baohang Wang;Jianqi Lou;Najeebullah Kakar;Jiangbo Xi","doi":"10.1109/JSTARS.2025.3588158","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3588158","url":null,"abstract":"The increasing demand for land in the cities of Loess Plateau, China has intensified the conflict between people and land. Therefore, large-scale mountain excavation and city creation (MECC) projects have emerged in many densely populated cities, where Yan’an New District in China was one of the largest MECC projects. Interferometric synthetic aperture radar (InSAR) technology can be used to monitor historical uneven deformation in MECC areas. However, it is difficult to acquire high-precision and high-density deformation field by the traditional InSAR technique due to the serious DEM errors, incoherence errors caused by human engineering activities and longtime baselines, and phase unwrapping errors. Therefore, an improved MT-InSAR method is proposed to estimate the ground height changes, correct the phase unwrapping errors and densify the deformation monitoring points. Moreover, bias adjustment and deformation time series fusion are used to improve the accuracy and temporal resolution of the deformation time series. The results show that the ground height changes in Yan’an New district are between –100 and 100 m. The improved method can improve the point density by 1.4 times compared with the distributed scatterers InSAR method. The fusion results improve the temporal resolution twice and increase the accuracy about 20% compared with the individual track monitoring results. Finally, 60% of high fill areas in Yan’an New district are predicted to be stable within 5 years. In the next 30 years, Yan’an New District will also undergo deformation of more than 450 mm. This research extends the InSAR technique in terms of height changes estimation. The application of this method can be beneficial to the prevention and controlling of geological hazards corresponding to the MECC projects.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"18019-18030"},"PeriodicalIF":4.7,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11079797","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Phase Error Compensation Method for High-Rise Targets in High-Resolution SAR Images","authors":"JiaDong Deng;Wei Yang;HongCheng Zeng;YaMin Wang;Jiayi Guo;Wei Xiong;Min Liu;Jie Chen;Wei Liu","doi":"10.1109/JSTARS.2025.3588656","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3588656","url":null,"abstract":"High-resolution images of urban areas generated by spaceborne synthetic aperture radar (SAR) have a wide range of applications. However, with the improvement in resolution, the slant range error caused by the height of buildings leads to high-order and complex phase errors during the imaging process, which in turn causes image defocusing. To address this issue, an optimization algorithm is proposed based on the conjugate gradient method to estimate and compensate for the phase error. It uses image entropy as the optimization objective function, which is applicable to segmented images, effectively solving the problem of variation of phase error at different heights. In addition, the algorithm improves the computational efficiency by introducing a restart strategy and a momentum term in the 1-D line search. Experimental results using both simulation and actual SAR data show that the proposed algorithm outperforms the existing methods in terms of both efficiency and focusing quality.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"18181-18196"},"PeriodicalIF":5.3,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11079293","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of in Situ Plant Area Index and Remotely Sensed Leaf Area Index of Northeastern American Deciduous, Mixed, and Coniferous Forests for SMAPVEX19-22","authors":"Christopher L. Cook;Laura Bourgeau-Chavez;Dorthea Vander Bilt;Mary Ellen Miller;Simon Kraatz;Michael H. Cosh;Victoria R. Kelly;Andreas Colliander","doi":"10.1109/JSTARS.2025.3588091","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3588091","url":null,"abstract":"Leaf area index (LAI) and plant area index (PAI) are critical biophysical parameters for quantifying foliage density of crops and trees. As such they may also be effective metrics to estimate the impact of leafy vegetation on microwave signals, such as the radiometer on board NASA’s soil moisture active passive (SMAP) satellite. This study presents a comprehensive analysis of PAI and LAI measurements acquired through in situ and remotely sensed (RS) methods in diverse forest ecosystems of northeastern America, including deciduous, mixed, and coniferous forests. We compare RS LAI and ground-truth PAI data to understand the variation between the RS LAI products and the impacts of RS spatial resolution on analysis. We perform these comparisons in the context of understanding SMAP’s vegetation optical depth (VOD) retrievals, and compare RS LAI with SMAP VOD measurements. We find strong (<italic>R</i>² > 0.83) positive relationships between in situ PAI and the tested RS LAI products, with improved positive relationships (<italic>R</i>² > 0.92) for higher spatial resolution (<30>y</i>-intercept values (< -0.56) associated with the higher resolution products relative to the coarse resolution products (> -0.29) could indicate greater influence of woody biomass on higher resolution RS LAI algorithms relative to low resolution RS LAI algorithms. Comparisons of RS LAI and VOD showed generally positive relationships that varied by satellite sensor.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"18251-18263"},"PeriodicalIF":5.3,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11078448","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Preliminary Results of Marine Gravity Anomaly and Bathymetry From SWOT Wide-Swath Altimeter Data","authors":"Mingzhi Sun;Wei Feng;Dechao An;Xiaodong Chen;Meng Yang;Min Zhong","doi":"10.1109/JSTARS.2025.3588419","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3588419","url":null,"abstract":"In this article, we assess the accuracy of the surface water and ocean topography (SWOT) mission for deriving marine gravity anomalies and bathymetry in the Philippine Sea using wide-swath L3 Ka-band radar interferometer data. Our evaluation includes determining the deflection of vertical (DOV) using the least-squares collocation method, recovering gravity anomalies with the inverse Vening–Meinesz formula, and predicting bathymetry through the gravity-geological method. The accuracy of the east–west DOV component has improved but remains lower than that of the north–south component. SWOT-derived (from 14-cycle data) gravity anomalies exhibit stronger signals in the short-wave ranges (< 20 km), with a precision of 2.37 mGal compared to shipborne measurements. The SWOT-derived bathymetry shows an accuracy of 124.02 m, representing an 8.36-m improvement compared to the conventional SIO altimeter-based model. We show that 14 cycles (10 months) of SWOT data provide more detailed information than 30 years of traditional nadir altimetry in both gravity anomaly and bathymetry. We have optimized the SWOT data processing strategy to enable the retrieval of high-quality marine gravity anomalies. With advancements in data processing and the accumulation of observations, the SWOT mission is progressing toward its marine geophysics objectives.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"18107-18116"},"PeriodicalIF":5.3,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11078148","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144725141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A GRU-Based Model Using GNSS-PWV and Meteorological Data for Forecasting Rainfalls","authors":"Longjiang Li;Kefei Zhang;Hong Zhang;Suqin Wu;Dongsheng Zhao;Xiaoming Wang;Andong Hu;Minghao Zhang;Mardina Abdullah","doi":"10.1109/JSTARS.2025.3588524","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3588524","url":null,"abstract":"As one of the most important parameters for the atmospheric water vapor contents, precipitable water vapor derived from global navigation satellite systems (GNSS) has been a valuable source of information for forecasting rainfall events in recent years due to the distinctive advantages of high accuracy, high temporal–spatial resolution, wide coverage, and low cost of GNSS data. In this study, a new gated recurrent unit (GRU) based model for forecasting rainfall events was developed using training dataset in the nine-year period of 2010−2018 at 54 GNSS stations located in the USA. Moreover, the length of input windows and the effectiveness of various combinations of seven meteorological parameters were also investigated for the determination of the optimal ones to be used in the new model. The performance of the new model was evaluated using the test dataset in the two-year period of 2019 and 2020, and its results were also compared with those of the threshold-based model developed in our previous study. It is showed that the optimal lengths of input windows at different stations were different; thus, they need to be specifically determined for each station; the model was improved by incorporating four meteorological parameters into the input data; and the mean values of probability of detection and false alarm rate resulting from the new model at all the above 54 stations were 93% and 45%, respectively, which were significantly improved over a threshold-based model. These results suggest that the GRU-based model can effectively forecast most rainfall events due to its utilization of more meteorological data in the input data.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"18316-18329"},"PeriodicalIF":5.3,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11078771","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cross-Modality Consistency Network for Remote Sensing Text-Image Retrieval","authors":"Yuchen Sha;Yujian Feng;Miao He;Yichi Jin;Shuai You;Yimu Ji;Fei Wu;Shangdong Liu;Shaoshuai Che","doi":"10.1109/JSTARS.2025.3586914","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3586914","url":null,"abstract":"Remote sensing cross-modality text-image retrieval aims to retrieve a specific object from a large image gallery based on a natural language description, and vice versa. Existing methods mainly capture local and global context information within each modality for cross-modality matching. However, these methods are prone to interference from redundant information, such as background noises and irrelevant words, and neglect the capture of co-occurrence semantic relations between modalities (i.e., the probability of semantic information co-occurring with other information). To filter out intramodality redundant information and capture intermodality co-occurrent relations, we propose a cross-modality consistency network including a text-image attention-conditioned module (TAM) and a co-occurrent features module (CFM). First, TAM interacts with visual and textual feature representations by employing the cross-modality attention mechanism to focus on semantically similar fine-grained image features and then generate aggregated visual representations. Second, CFM is designed to estimate co-occurrence probability by measuring fine-grained feature similarity, thereby reinforcing the relations of target-consist features across modalities. In addition, we propose the cross-modality distinction loss function to learn semantic consistency between modalities by compacting intraclass samples and separating interclass samples. Extensive benchmark experiments on three benchmarks demonstrate that our approach outperforms state-of-the-art methods.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"17539-17551"},"PeriodicalIF":4.7,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11075559","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144695632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}