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

{"title":"CBIPDNet: A Novel Method for InSAR Deformation Interferometric Phase Filtering Using Deep Learning Network","authors":"Yandong Gao;Jiaqi Yao;Wei Zhou;Nanshan Zheng;Shijin Li;Yu Tian","doi":"10.1109/JSTARS.2024.3453071","DOIUrl":"10.1109/JSTARS.2024.3453071","url":null,"abstract":"The denoising of phase is a crucial process that impacts the accuracy of data processing in differential interferometric synthetic aperture radar. Especially in the area of large-gradient deformation, the phase filtering method is very easy to cause phase losses. This has a significant impact on the final deformation acquisition. To address this issue, here, a deep convolutional blind denoising network-based interferometric phase filtering method, named CBIPDNet, is proposed. Different from the previously proposed deep learning phase filtering methods, CBIPDNet does not add noise to the input before filtering, but adds noise to the input during the training process. Furthermore, CBIPDNet uses a CNN structure for adaptive noise estimation and uses a residual module for nonblind filtering. Therefore, CBIPDNet can be considered as an adaptive phase filtering algorithm. More importantly, the added noise is composed of heteroscedastic Gaussian noise + simulated real noise of the imaging process, which is closer to the real interferometric noise phase. Moreover, the denoising effect of targets of different scales through the asymmetric loss function has been significantly improved, which can improve the detail preservation ability of regions with substantial gradient deformations. The experimental results demonstrate that CBIPDNet is capable of enhancing phase quality and increasing phase unwrapping accuracy compared to the current interferometric filtering methods.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10670027","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193148","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 Two Spatiotemporal Reconstruction Methods for Spaceborne Sea Surface Temperature Data at Multiple Temporal Resolutions","authors":"Xuehua Ma;Junyu He;Shuangyan He;Yanzhen Gu;Anzhou Cao;Peiliang Li;Feng Zhou","doi":"10.1109/JSTARS.2024.3453508","DOIUrl":"10.1109/JSTARS.2024.3453508","url":null,"abstract":"The satellite remote sensing sea surface temperature (SST) plays a crucial role in global climate change and ocean–atmosphere interactions. With a notably severe issue of missing data due to clouds and rainfall, data reconstruction methods have been developed to effectively enhance the spatiotemporal completeness of satellite-derived SST data products in recent years. However, few studies have focused on performance comparisons between these different data reconstruction methods, which limits further improvement and application of reconstructed data products. In this study, two representative methods, the data interpolating empirical orthogonal functions (DINEOF) and a spatiotemporal geostatistical method of Bayesian maximum entropy (BME), were used to reconstruct satellite SST data in four regions, and their reconstruction performance under various temporal resolutions (hourly, daily, and monthly) and missing data rates were evaluated and compared. Our results demonstrate that BME consistently outperforms DINEOF. As the missing data rate increases from 10% to 90%, especially when it exceeds 70%, DINEOF reconstruction results exhibit significant increasing noises and reconstruction errors, while BME demonstrates stable precise reconstruction results. Compared with DINEOF method, the results of BME method are less influenced by missing data rates, spatiotemporal resolutions, temporal length, and regions of input data series by different remote sensing sensors, rendering it more applicable and robust in reconstructing multisensor SST data with different temporal resolutions. The BME method holds promising implications in reconstructing high-quality gap-filled data using noisy and high-missing-rate multisensor data in regional areas with high dynamics.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10670006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193216","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":"AI Techniques for Near Real-Time Monitoring of Contaminants in Coastal Waters on Board Future $Phi$sat-2 Mission","authors":"Francesca Razzano;Pietro Di Stasio;Francesco Mauro;Gabriele Meoni;Marco Esposito;Gilda Schirinzi;Silvia Liberata Ullo","doi":"10.1109/JSTARS.2024.3455992","DOIUrl":"10.1109/JSTARS.2024.3455992","url":null,"abstract":"Differently from conventional procedures, the proposed solution advocates for a groundbreaking paradigm in water quality monitoring through the integration of satellite Remote Sensing data, Artificial Intelligence techniques, and onboard processing. While conventional procedures present several drawbacks mainly related to late intervention capabilities, the objective of what proposed is to offer nearly real-time detection of contaminants in coastal waters addressing a significant gap in the existing literature and allowing fast alerts and intervention. In fact, the expected outcomes include substantial advancements in environmental monitoring, public health protection, and resource conservation. Namely, the specific focus of our study is on the estimation of Turbidity and pH parameters, for their implications on human and aquatic health. Nevertheless, the designed framework can be extended to include other parameters of interest in the water environment and beyond. Originating from our participation in the European Space Agency OrbitalAI Challenge, this article describes the distinctive opportunities and issues for the contaminants' monitoring on the \u0000<inline-formula><tex-math>$Phi$</tex-math></inline-formula>\u0000sat-2 mission. The specific characteristics of this mission, with the tools made available, will be presented, with the methodology proposed by the authors for the onboard monitoring of water contaminants in near real-time. Preliminary promising results are presented, along with an introduction to ongoing and future work.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10670116","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193218","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":"Deciphering the Roles of 2-D and 3-D Urban Landscape Metrics in Diurnal Surface Thermal Environment Along Urban Gradients","authors":"Jiabao Niu;Jike Chen;Yufu Zang;Kaixin Wang;Decheng Ni;Zhigang Xu;Peibin Wang","doi":"10.1109/JSTARS.2024.3455322","DOIUrl":"10.1109/JSTARS.2024.3455322","url":null,"abstract":"The urban heat island phenomenon has posed detrimental effects on urban climate and human well-being. Various influencing factors, such as urban morphology, have been applied to reveal their influences on land surface temperature (LST). However, there exists a lack of comprehension regarding how two-dimensional (2-D) and 3-D urban morphologies influence diurnal LSTs across urban gradients. In this article, Nanjing, China was taken as the study area. Using multisource remote sensing data, we investigated the relative contributions and marginal effects of 2-D/3-D urban morphology on diurnal LSTs along urban gradients. The following results have been shown. 1) The overall impact of 2-D urban morphology on daytime LST surpassed that of 3-D urban morphology. Conversely, 3-D urban morphology exhibited a greater impact on nighttime LST. 2) During the day, the percent of building (PER_B), the percent of tree (PER_T), and the sky view factor (SVF) were the main contributors in most urban gradients. At night, SVF and PER_B ranked among the top four factors for all areas. 3) PER_T was negatively related to daytime LST, and when PER_T exceed 30%, it contributed to a stronger cooling effect. PER_B was positively correlated with daytime LST, while the correlation was reversed at night. An SVF greater than 0.9 decreased daytime LST within gradients 4 to 8, while an SVF exceeding 0.8 lowered nighttime LST across all gradients. Our findings provide crucial insights for decision-makers to develop effective strategies in mitigating the diurnal urban thermal environments.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10668859","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193224","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":"Spatial Reduction Attention in Multiscale Vision Transform for Surface Water–Land Interface Zone Segmentation","authors":"Yu-Hsuan Chen;Trong-An Bui;Pei-Jun Lee;Ching-Huo Hsu","doi":"10.1109/JSTARS.2024.3455891","DOIUrl":"10.1109/JSTARS.2024.3455891","url":null,"abstract":"Water segmentation is important for applications in flood prevention, water resource management, and urban planning. The accurate identification of water–land interface zones and the delineation of edges between water and land in remote sensing satellite imagery, however, present significant challenges for traditional segmentation methods. This research aims to enhance the precision of segmentation, particularly in identifying water and land interface zones, while also reducing computational demands to enable real-time analysis on edge devices. This article introduces a novel spatial reduction attention (SRA) mechanism within the multiscale vision transform framework, which is proficient at capturing both local and global features. The proposed multiscale multihead attention mechanism, enhanced with multiscale projection and SRA, aids in learning features from various receptive fields, thereby increasing computational efficiency. The integration of dual-branch channels for multispectral imagery and color attributes significantly improves the model's recognition capabilities. In the evaluation of water segmentation, the proposed method significantly outperforms advanced models, achieving a 10.1% improvement in mean intersection over union and a 6.7% increase in mean \u0000<italic>F</i>\u00001-score. This performance underscores the model's efficacy in accurately identifying water–land interface zones and highlights its potential in improving both the accuracy and efficiency of water segmentation in satellite imagery.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10669118","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193217","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 Comparative Analysis of Remote Sensing Soil Moisture Datasets Fusion Methods: Novel LSTM Approach Versus Widely Used Triple Collocation Technique","authors":"Haojin Zhao;Carsten Montzka;Harry Vereecken;Harrie-Jan Hendricks Franssen","doi":"10.1109/JSTARS.2024.3455549","DOIUrl":"10.1109/JSTARS.2024.3455549","url":null,"abstract":"Microwave remote sensing technology has emerged to provide valuable products to monitor and assess soil moisture content at regional or global scales. However, each soil moisture product exhibits different advantages and shortcomings. Data fusion could help improve accuracy by merging information from different sources. In this research, a traditional triple collocation (TC) based method and a novel long short term memory network (LSTM) are used to merge soil moisture products from the soil moisture active passive mission, Advanced Microwave Scanning Radiometer 2 (AMSR2), and The Advanced SCATterometer for a study area located in western Europe. This research reveals that the LSTM outperforms the traditional TC based method for data fusion. The study identifies that both climate forcing and physiographic attributes significantly influence the spatial and temporal variations observed in the LSTM merging scheme. Consequently, the study highlights the considerable potential of the LSTM method for large-scale integration of remote sensing soil moisture data.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10669050","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193220","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":"1/f-Noise Estimation From Microwave Imager Data With Periodic Gaps","authors":"Sayak K. Biswas;David B. Kunkee;Gene A. Poe;Steven D. Swadley;Ye Hong","doi":"10.1109/JSTARS.2024.3456034","DOIUrl":"10.1109/JSTARS.2024.3456034","url":null,"abstract":"We describe a method to estimate coefficients \u0000<inline-formula><tex-math>${{{bm{h}}}_{bm{n}}}$</tex-math></inline-formula>\u0000 of a power spectral density of the form \u0000<inline-formula><tex-math>${bm{Sigma}}{{{bm{h}}}_{bm{n}}}/{{{bm{f}}}^{bm{n}}}$</tex-math></inline-formula>\u0000 (\u0000<inline-formula><tex-math>${bm{f}};{bm{ }}text{is}$</tex-math></inline-formula>\u0000 the frequency and integer \u0000<inline-formula><tex-math>${bm{n}} geq 0$</tex-math></inline-formula>\u0000) from corresponding measured time series with periodic gaps. This technique is applied to consistently estimate the amount of \u0000<inline-formula><tex-math>$1/{bm{f}}$</tex-math></inline-formula>\u0000 noise present in weather system follow-on microwave microwave imager's (MWI) channels from the time-series data collected with different periodic gaps during prelaunch ground tests. The method assumes that the power spectrum of \u0000<inline-formula><tex-math>$1/{bm{f}}$</tex-math></inline-formula>\u0000 noise present in MWI can be represented as a second-order frequency polynomial model of the form \u0000<inline-formula><tex-math>${bm{Sigma}}{{{bm{h}}}_{bm{n}}}/{{{bm{f}}}^{bm{n}}}{bm{ }}$</tex-math></inline-formula>\u0000 and attempts to retrieve the true spectrum by solving for the \u0000<inline-formula><tex-math>${{{bm{h}}}_{bm{n}}}$</tex-math></inline-formula>\u0000 coefficients using the power spectrum of the time-series data with periodic gaps. The method also assumes that the periodicity and duration of the data gaps are known and consistent for a given time series. The theoretical basis of the new technique is derived and tested using simulation and the new procedure is then applied to real test data to estimate the coefficients of the frequency polynomial. As a quantitative estimate for the \u0000<inline-formula><tex-math>$1/{bm{f}}$</tex-math></inline-formula>\u0000 noise, the radiometer gain fluctuation (\u0000<inline-formula><tex-math>${bm{Delta}}{bm{G}}/{bm{G}}$</tex-math></inline-formula>\u0000) at 1 Hz is then solved from the frequency polynomial of the gain fluctuation power spectrum. The 1 Hz \u0000<inline-formula><tex-math>$( {{bm{Delta}}{bm{G}}/{bm{G}}} )$</tex-math></inline-formula>\u0000 values were compared between two sets of ground test data for the same MWI channels but with large (92%) and small (4.89%) duty cycles. The similarity of the 1 Hz \u0000<inline-formula><tex-math>$( {{bm{Delta}}{bm{G}}/{bm{G}}} )$</tex-math></inline-formula>\u0000 values extracted from these two disparate datasets establishes confidence in the method. The derived noise power spectrum is then used to simulate MWIs radiometric brightness temperature images and predict the level of unwanted striping in the flight data due to \u0000<inline-formula><tex-math>$1/{bm{f}}$</tex-math></inline-formula>\u0000 noise content. This method may be applicable to solve for the polynomial coefficients of the power spectrum of any noise process, which can be modeled as a frequency polynomial, given the polynomial form is known a priori.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10669073","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193219","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":"CTST: CNN and Transformer-Based Spatio-Temporally Synchronized Network for Remote Sensing Change Detection","authors":"Shuo Wang;Wenbin Wu;Zhiqing Zheng;Jinjiang Li","doi":"10.1109/JSTARS.2024.3455261","DOIUrl":"10.1109/JSTARS.2024.3455261","url":null,"abstract":"Remote sensing change detection has achieved amazing results in recent years, especially the application of convolutional neural networks (CNN) and Transformer networks, which have revolutionized the field. However, the complex ground cover changes and the differences in lighting conditions caused by different times still pose challenges to the detection accuracy. In order to further extract spatial feature information and suppress irrelevant influences, we innovatively propose an edge-enhanced and time-synchronized remote sensing change detection network, called CNN and transformer-based spatio-temporally synchronized network (CTST). CTST designs a unique feature-integrated coding model with CNN and Transformer architectures, which enhances the model's understanding of the global dependencies and the extraction effect of the local features through the dynamic weight allocation method. We designed the edge salient feature enhancement module, which uses a dual operator fusion structure to combine the edge semantic information with the depth feature information, greatly enhancing the model's ability to recognize the edges of important terrain and features in remote sensing images. In addition, the spatio-temporally synchronized module is used to fuse the difference and superposition relationships between bitemporal features, and an innovative correlation mapping weighting algorithm is proposed to evaluate the similarity and difference of the fused features. Finally, the feature decoding complementary module is proposed to combine and complement features at different scales to further refine the already fused bichronological remote sensing features. The network results are optimized by the deep supervision (DS) strategy, which ensures the model's high efficiency and accuracy. CTST outperforms mainstream and state-of-the-art methods on all three datasets, with an F1 of 92.08% and an IoU of 85.33 on the LEVIR-CD dataset, an F1 of 93.25% and an IoU of 87.36% on the WHU-CD dataset, and an F1 of 93.25% and an IoU of 87.36% on the GZ-CD dataset. CD dataset the F1 is 85.95% and IoU is 75.37%, Param is 31.87 M, and FLOPS is 29.58 G.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10667658","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193091","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":"PPLM-Net: Partial Patch Local Masking Net for Remote Sensing Image Unsupervised Domain Adaptation Classification","authors":"Junsong Leng;Zhong Chen;Haodong Mu;Tianhang Liu;Hanruo Chen;Guoyou Wang","doi":"10.1109/JSTARS.2024.3455438","DOIUrl":"10.1109/JSTARS.2024.3455438","url":null,"abstract":"In remote sensing image classification task, it is often apply a model trained on one dataset (source domain) to another dataset (target domain). However, due to the presence of domain shift between these domains where data are not independent and identically distributed, the performance of the model typically deteriorates. Domain adaptation aims to improve the generalization performance of the model in the target domain. In response to the challenges of intricate backgrounds, domain shift, and potentially unlabeled target domain in remote sensing images, this article proposes a network specifically designed for unsupervised domain adaptation (UDA) classification of remote sensing images, named PPLM-net. The network consists of a domain adversarial training (DAT) module, a partial patch local masking (PPLM) module and a teacher–student network module. The DAT module enables the network to extract domain-invariant features. The PPLM module compels the model to focus on the global information of target domain remote sensing images with intricate backgrounds, learning contextual content to improve model performance. The teacher network generates pseudolabels for complete unlabeled target domain images. The student network trained with PPLM target domain classification loss to generate robust and discriminative features. We construct a dataset dedicated to the UDA scene classification task of remote sensing images named RSDA. We collect images from four publicly available datasets spanning seven common categories, containing over 10 000 images. Compared with the current state-of-the-art UDA model, PPLM-net achieves the best results in 12 domain adaptation classification tasks on RSDA. The average accuracy reaches 99.115%.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10668826","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193221","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":"Influence of 2-D/3-D Urban Morphology on Diurnal Land Surface Temperature From the Perspective of Functional Zones","authors":"Qianmin Zhang;Jun Yang;Xinyue Ma;Jiaxing Xin;Jiayi Ren;Wenbo Yu;Xiangming Xiao;Jianhong Xia","doi":"10.1109/JSTARS.2024.3455791","DOIUrl":"10.1109/JSTARS.2024.3455791","url":null,"abstract":"Optimizing the spatial distribution of urban functional zones (UFZs) effectively improves the thermal environment. This study utilized an enhanced regression tree model and relied on Ecosystem Spaceborne Thermal Radiometer Experiment data to analyze the relative contributions and marginal effects of 2-D/3-D urban morphological factors on the diurnal land surface temperature (LST) in Shenyang, China. The results showed that public and residential areas dominated Shenyang's UFZs. The temperature in industrial areas was the highest during the day, and residential and commercial functional areas are high-temperature concentration areas. Furthermore, the effects of the urban spatial morphology on the LST differed between diverse time points and UFZs. The digital elevation model and the normalized difference vegetation index contributed significantly to daytime and nighttime LSTs. Construction indicators, such as the normalized difference built-up index and the proportion of construction land, significantly impacted commercial services. Residential daytime LST had a large contribution value, and the sum of its contribution rates reached approximately 30%. Population greatly contributed to the nighttime LST of the industrial and residential zones, accounting for 16.77% and 22.06%, respectively. Vegetation contributed to the cooling effect on daytime LST in summer, especially in industrial areas, contributing 29.79%. In addition, 3-D indicators, such as building height and building density, contributed to diurnal LST. Finally, when the proportion of construction land reached approximately 45%, it negatively affected LST. In this study, the main factors affecting day and night LSTs were identified, and this work acts as a relevant strategic reference for alleviating the urban heat island effect.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10669042","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142224979","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}