Remote Sensing of Environment最新文献

{"title":"Preface: Advancing deep learning for remote sensing time series data analysis","authors":"Hankui K. Zhang ,&nbsp;Gustau Camps-Valls ,&nbsp;Shunlin Liang ,&nbsp;Devis Tuia ,&nbsp;Charlotte Pelletier ,&nbsp;Zhe Zhu","doi":"10.1016/j.rse.2025.114711","DOIUrl":"10.1016/j.rse.2025.114711","url":null,"abstract":"<div><div>This special issue explores the burgeoning field of deep learning for remote sensing time series analysis. The 20 contributed papers showcase diverse applications, including land cover mapping, change detection, atmospheric and biophysical/biochemical parameter retrieval, and disaster monitoring. The articles demonstrate a variety of approaches to address the challenges of irregular time series, such as data compositing, harmonic modeling, and direct ingestion of irregular data using recurrent and attention-based networks (e.g., LSTMs and Transformers). Several studies highlight the potential of integrating physical models with deep learning to improve model trustworthiness and interpretability. Looking ahead, we identify key future directions: the development of globally representative benchmark datasets with time series labels; the creation of readily available, operational time series products and models; the exploration of multi-modal and foundation models tailored to remote sensing time series; and more sophisticated integration of physical knowledge within deep learning frameworks. This collection highlights current progress and fosters innovation in time-aware deep learning for Earth observation.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"322 ","pages":"Article 114711"},"PeriodicalIF":11.1,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143675514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hydrological proxy derived from InSAR coherence in landslide characterization","authors":"Yuqi Song ,&nbsp;Xie Hu ,&nbsp;Xuguo Shi ,&nbsp;Yifei Cui ,&nbsp;Chao Zhou ,&nbsp;Yueren Xu","doi":"10.1016/j.rse.2025.114712","DOIUrl":"10.1016/j.rse.2025.114712","url":null,"abstract":"<div><div>Quantifying landslide susceptibility saves lives, especially in populous areas exposed to wet climates. However, available hydrological data sets such as precipitation and soil moisture are usually from reanalysis with a few to tens of kilometers' coarse resolution compared to the dimensions of landslides. Here we aim to seek substitutes to characterize hydrological features with finer spacing for landslide susceptibility assessment encompassing the tectonically active California. We synergize remote sensing big data and derivatives including topographic characteristics, vegetation index, hydrological variables, land cover, and geological units in different machine learning architectures. Our results illuminate that the interferometric coherence derived from synthetic aperture radar (SAR) can be an effective hydrological proxy, providing enhanced resolution by three orders of magnitude to tens of meters and presenting satisfactory performance, with recalls &gt;85 % and AUCs &gt;90 % in our landslide susceptibility models. The consequent spatially continuous landslide susceptibility map further demonstrates the effectiveness of high-resolution SAR products in compensating for limitations in traditional hydrological data sets. The map and our inferred relationship with the mélange and the distance to faults improve our ability in landslide hazard mitigation.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"322 ","pages":"Article 114712"},"PeriodicalIF":11.1,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143675515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to “Non-linear spectral unmixing for monitoring rapidly salinizing coastal landscapes” [Remote Sensing of Environment Volume 319, 15 March 2025, 114642]","authors":"Manan Sarupria ,&nbsp;Rodrigo Vargas ,&nbsp;Matthew Walter ,&nbsp;Jarrod Miller ,&nbsp;Pinki Mondal","doi":"10.1016/j.rse.2025.114710","DOIUrl":"10.1016/j.rse.2025.114710","url":null,"abstract":"","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"322 ","pages":"Article 114710"},"PeriodicalIF":11.1,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143661032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Urban thermal anisotropies by local climate zones: An assessment using multi-angle land surface temperatures from ECOSTRESS","authors":"Yue Chang ,&nbsp;Qihao Weng ,&nbsp;James A. Voogt ,&nbsp;Jingfeng Xiao","doi":"10.1016/j.rse.2025.114705","DOIUrl":"10.1016/j.rse.2025.114705","url":null,"abstract":"<div><div>Knowledge of anisotropy-induced spatial and temporal variations of land surface temperature (LST) is crucial for enhancing the quality of remote sensing products, refining land surface process modeling, and optimizing climate models. However, the limited availability of simultaneous multi-angle LST observations from space has hindered the exploration of this topic. NASA's latest ECOSTRESS sensor deployed on the International Space Station (ISS) generates multi-angle LST measurements at a 70-m spatial resolution for different times of day/night, providing a new avenue for investigating urban thermal anisotropy. In this study, we presented an initial examination of the performance of ECOSTRESS LST observations in unraveling the fine-grained urban thermal anisotropy, by taking the City of Phoenix, Arizona, United States, as the study area. We proposed a method to generate a quasi-simultaneous multi-angle ECOSTRESS LST dataset over the course of the diurnal cycle with the assistance of air temperature data from weather stations and hourly LST observations from a geostationary satellite, GOES-R. We then examined the thermal anisotropic patterns and their diurnal and seasonal variations across different Local Climate Zones (LCZs) at a spatial resolution of 200 m. Based on quasi-simultaneous multi-angle ECOSTRESS observations, Vinnikov and Vinnikov-RL models were employed to generate LCZ-scale anisotropy profiles of the study area to quantify and correct the LST directional effect. The results revealed that ECOSTRESS observations manifest unique angular patterns, featuring substantial variations in sensor viewing azimuth angles (VAA) and limited changes in sensor viewing zenith angles (VZA) within a 30° range. The angular effect led to notable variations in the observed LST, with potential deviations at the city scale of up to 10 K during winter and around 5 K during summer, relative to the nadir LST. Furthermore, the LST anisotropy exhibited distinct diurnal and seasonal patterns across LCZs, characterized by prominent variations in the intensity and width of hot/cold spots. LCZ 6, 9, and D typically displayed higher hotspot intensity and width than other LCZs at varying times of day in both summer and winter. In addition, the Vinnikov-RL model had good performance in simulating diurnal LST anisotropy over LCZs. This study reveals the potential of multi-angle ECOSTRESS LST observations in exploring urban thermal anisotropy, and contributes to better utilization of ECOSTRESS LST products. The integration of ECOSTRESS LST data with other satellite derived LST data have important implications for studying urban climate and improving long-term surface climate record, contributing to global climate studies.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"322 ","pages":"Article 114705"},"PeriodicalIF":11.1,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143629603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Unveiling soil salinity patterns in soda saline-alkali regions using Sentinel-2 and SDGSAT-1 thermal infrared data","authors":"Zirui Gao ,&nbsp;Xiaojie Li ,&nbsp;Lijun Zuo ,&nbsp;Bo Zou ,&nbsp;Bin Wang ,&nbsp;Wen J. Wang","doi":"10.1016/j.rse.2025.114708","DOIUrl":"10.1016/j.rse.2025.114708","url":null,"abstract":"<div><div>Soil salinization, a critical form of global soil degradation, threatens agricultural productivity and ecosystem functions. Accurate mapping of soil salinity is essential for sustainable land management and informed decision-making. However, conventional optical or radar satellite sensors are often limited in detecting key salinity spectral signatures due to their insufficient thermal infrared (TIR) coverage. TIR remote sensing offers unique advantages for soil salinity assessment, owing to its sensitivity to the emissivity of saline soils within the TIR spectrum, but its application remains underexplored. This study evaluated the suitability and robustness of SDGSAT-1 TIS data for large-scale soil salinity mapping in the Songnen Plain, China, one of the world's three largest soda saline-alkali soil regions. We compared the performance of soil salinity models integrating SDGSAT-1 TIS data with those using optical (Sentinel-2) and radar (Sentinel-1 and GF-3) data across several machine learning techniques. Our results demonstrated that incorporating SDGSAT-1 TIS data significantly enhanced soil salinity modeling accuracy, consistently outperforming models based solely on Sentinel-2 optical or Sentinel-1/GF-3 radar data. The combination of SDGSAT-1 TIS and Sentinel-2 data, optimized using the Gaussian Process Regression model, achieved the highest accuracy (R² = 0.75, RMSE = 0.65 dS/m). The resulting salinity maps revealed widespread soil salinization across the region, with the majority of the area exhibiting slight to moderate salinity levels, posing substantial challenges to plant growth and ecosystem resilience. This study offers a robust, data-driven validation of TIR's unique sensitivity to soil salinity, emphasizing its potential for integration into large-scale soil salinity mapping frameworks.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"322 ","pages":"Article 114708"},"PeriodicalIF":11.1,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143618772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 model based on spectral invariant theory for correcting topographic effects on vegetation canopy reflectance","authors":"Weihua Li ,&nbsp;Guangjian Yan ,&nbsp;Jun Geng ,&nbsp;Yuhan Guo ,&nbsp;Tian Xie ,&nbsp;Xihan Mu ,&nbsp;Donghui Xie ,&nbsp;Jean-Louis Roujean ,&nbsp;Guoqing Zhou ,&nbsp;Jean-Philippe Gastellu-Etchegorry","doi":"10.1016/j.rse.2025.114695","DOIUrl":"10.1016/j.rse.2025.114695","url":null,"abstract":"<div><div>Topography alters both the incident radiation and radiative transfer (RT) processes within the canopy, leading to changes in the canopy bidirectional reflectance factor (BRF). Most traditional semi-physical terrain correction (TC) methods for vegetation canopy BRFs rely on simplifying physically-based analytical RT models. However, these analytical RT models are not comprehensively parameterized for all RT computations, leading to the neglect of crucial processes, such as multiple scattering processes during the derivation of semi-physical TC methods. The spectral invariants theory (<em>p</em>-theory) offers an efficient approach to model canopy BRFs by simplifying RT computations. We extended <em>p</em>-theory to sloping terrain, considering the variation of the terrain-induced incident radiation and RT processes, and developed a canopy BRF TC model, termed the <em>p</em>-C method. The <em>p</em>-C method applies not only to spectral bands with lower multiple scattering within the canopy (e.g., visible bands) but also to near-infrared (NIR) bands, where multiple scattering effects may be more pronounced than in the visible bands within the canopy. We used the three-dimensional RT model DART (Discrete Anisotropic Radiative Transfer) to simulate BRFs of homogeneous, realistic canopies of the RAMI (RAdiation transfer Model Intercomparison) experiment, and BRF images with real DEM (Digital Elevation Model) to evaluate the <em>p</em>-C method and to compare it with traditional empirical and semi-physical TC methods (CC, SCS, SCS+C, DS, PLC-S, and SE). The <em>p</em>-C method reduced the RMSE (root mean square error) by 67 %, 64 %, 64 %, 85 %, 83 %, and 54 % respectively over these methods. Furthermore, when applied to Landsat 8 OLI remote sensing BRF images, the <em>p</em>-C method effectively eliminated terrain texture, as confirmed by visual interpretation and the linear regression between the corrected BRF images and the local solar incidence angle. Currently, the <em>p</em>-C method only considers illuminated slopes, and corrections for shaded slopes need to be studied in the future.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"322 ","pages":"Article 114695"},"PeriodicalIF":11.1,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143608529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterizing leaf-scale fluorescence with spectral invariants","authors":"Wendi Lu ,&nbsp;Yelu Zeng ,&nbsp;Nastassia Vilfan ,&nbsp;Jianxi Huang ,&nbsp;Shari Van Wittenberghe ,&nbsp;Yachang He ,&nbsp;Yongyuan Gao ,&nbsp;Laura Verena Junker-Frohn ,&nbsp;Jennifer E. Johnson ,&nbsp;Wei Su ,&nbsp;Qinhuo Liu ,&nbsp;Bastian Siegmann ,&nbsp;Dalei Hao","doi":"10.1016/j.rse.2025.114704","DOIUrl":"10.1016/j.rse.2025.114704","url":null,"abstract":"<div><div>Sun-induced chlorophyll fluorescence (SIF) is increasingly recognized as a non-destructive probe for tracking terrestrial photosynthesis. Emerging developments in spectral invariants theory provide an innovative and efficient approach for representing SIF radiative transfer processes at the canopy scale. However, modeling leaf-scale fluorescence based on the spectral invariants properties (SIP) remains underexplored. In this study, the spectral invariants theory is employed for the first time to model the leaf-scale total, backward and forward fluorescence (leaf-SIP SIF). The leaf-SIP SIF model separates the leaf-scale radiative transfer process into two distinct components: the wavelength-dependent one associated with leaf biochemical properties, and the wavelength-independent component linked to leaf structural characteristics. The leaf structure-related effects are characterized by two spectrally invariant parameters: the photon recollision probability (<em>p</em>) and the scattering asymmetry parameter (<em>q</em>), which are parameterized using the directly measurable leaf dry matter. Evaluation against field measurements shows that the proposed leaf-SIP SIF model has a good performance, with coefficient of determination (<em>R</em>²) of 0.89, 0.89, 0.90 and root mean squared errors (RMSE) of 1.28, 0.69, 0.74 Wm⁻²μm⁻¹sr⁻¹, respectively for the total, backward, and forward fluorescence (660–800 nm). The leaf-SIP SIF model with a more concise formulation demonstrates comparable performance with the widely used Fluspect model. The leaf-SIP SIF model provides a simple and efficient approach for simulating leaf-scale fluorescence, with the potential to be integrated into a unified SIP-based model framework for simulating the radiative transfer processes across the soil-leaf-canopy-atmosphere continuum.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"322 ","pages":"Article 114704"},"PeriodicalIF":11.1,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143608530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unveiling coastal change across the Arctic with full Landsat collections and data fusion","authors":"Tua Nylén ,&nbsp;Mikel Calle ,&nbsp;Carlos Gonzales-Inca","doi":"10.1016/j.rse.2025.114696","DOIUrl":"10.1016/j.rse.2025.114696","url":null,"abstract":"<div><div>Arctic communities urgently need regional to local-scale information on the rapid coastal changes, caused by thawing permafrost, melting glaciers, and declining sea ice. We introduce a procedure for mapping coastal land cover change from satellite images in the challenging Arctic conditions (and beyond). Our approach utilizes data fusion and cloud computing in Google Earth Engine to process the full Landsat collections for the entire Arctic. It merges information from multiple Landsat sensors and utilizes complementary spatial data and two algorithms to enhance classification accuracy and processing efficiency. This mitigates issues with local illumination conditions and the low availability and quality of satellite data in the Arctic before 2010s. Calculating post-classification composites of coastal land cover over five-year time-steps effectively reduces the impacts of clouds, suspended sediment, and the tide. The procedure was iteratively developed in calibration sites with contrasting physical characteristics. Validation of the final product indicates an overall classification accuracy of more than 98 % (against manually labelled data) and a median shoreline error distance of c. 20 and 10 m in mesotidal and microtidal coasts, respectively. The resulting Arctic Coastal Change dataset presents coastal dynamics from 1984 to 2023 at a 30-m resolution, and highlights hotspots that experience coastal erosion or accretion at a rate of more than 10 m/a. The overall coherence of our results with 61 other studies across the Arctic shows the robustness of the procedure. However, exploring the dataset may uncover localized errors that call for procedure improvements through new collaborative Arctic coastal dynamics studies.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"322 ","pages":"Article 114696"},"PeriodicalIF":11.1,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143593444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Training sensor-agnostic deep learning models for remote sensing: Achieving state-of-the-art cloud and cloud shadow identification with OmniCloudMask","authors":"Nicholas Wright ,&nbsp;John M.A. Duncan ,&nbsp;J. Nik Callow ,&nbsp;Sally E. Thompson ,&nbsp;Richard J. George","doi":"10.1016/j.rse.2025.114694","DOIUrl":"10.1016/j.rse.2025.114694","url":null,"abstract":"<div><div>Deep learning models are widely used to extract features and insights from remotely sensed imagery. However, these models typically perform optimally when applied to the same sensor, resolution and imagery processing level as used during their training, and are rarely used or evaluated on out-of-domain data. This limitation results in duplication of efforts in collecting similar training datasets from different satellites to train sensor-specific models. Here, we introduce a range of techniques to train deep learning models that generalise across various sensors, resolutions, and processing levels. We applied this approach to train OmniCloudMask (OCM), a sensor-agnostic deep learning model that segments clouds and cloud shadow. OCM demonstrates robust state-of-the-art performance across various satellite platforms when classifying clear, cloud, and shadow classes, with balanced overall accuracy values across: Landsat (91.5 % clear, 91.5 % cloud, and 75.2 % shadow); Sentinel-2 (92.2 % clear, 91.2 % cloud, and 80.5 % shadow); and PlanetScope (96.9 % clear, 98.8 % cloud, and 97.4 % shadow). OCM achieves this accuracy while only being trained on a single Sentinel-2 dataset, employing spectral normalisation and mixed resolution training to address the spectral and spatial differences between satellite platforms. This approach allows the model to effectively handle imagery from different sensors within the 10 m to 50 m resolution range, as well as higher resolution imagery that has been resampled to 10 m. The OCM library is available as an open source Python package on PyPI.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"322 ","pages":"Article 114694"},"PeriodicalIF":11.1,"publicationDate":"2025-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143576336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Estuarine temperature variability: Integrating four decades of remote sensing observations and in-situ sea surface measurements","authors":"Ashfaq Ahmed ,&nbsp;Baylor Fox-Kemper ,&nbsp;Daniel M. Watkins ,&nbsp;Daniel Wexler ,&nbsp;Monica M. Wilhelmus","doi":"10.1016/j.rse.2025.114643","DOIUrl":"10.1016/j.rse.2025.114643","url":null,"abstract":"<div><div>Characterizing sea surface temperature (SST) variability is a critical aspect of studying long-term changes in estuarine environments. However, the scales of estuarine variability and change can be quite small (10 m–10 km). In this study, we present the first combined analysis of an estuary using the 39-year-long SST evolution from the multi-satellite Landsat data (<span><math><mrow><mo>∼</mo><mn>18</mn></mrow></math></span> day average sampling), over a decade of in-situ buoy records (15 min. sampling), and tide gauges (60 min. sampling). We retrieved the seasonal-to-decadal sea surface and tidal temperature variabilities and trends over four decades in Narragansett Bay and its arm, Mt. Hope Bay. The seasonal solar heating, river run-off, and resulting salinity stratification, and bathymetry determine the dominant (<span><math><mrow><mo>∼</mo><mn>80</mn><mtext>%</mtext></mrow></math></span>) temperature variance in the bay. The warming trend of the annual mean SST is 0.057 <span><math><mo>±</mo></math></span> 0.024<!--> <!-->°C<!--> <!-->yr⁻¹ for Narragansett Bay and 0.015 <span><math><mo>±</mo></math></span> 0.018<!--> <!-->°C<!--> <!-->yr⁻¹ for Mt. Hope Bay. We classified each Landsat image by tidal phase using tide gauge measurements in order to produce composite SST anomaly maps corresponding to each tidal phase, but non-tidal noise made the signal trustworthy in only a few regions. High-frequency measurements reveal that tidal temperature changes are detectable and consistent at buoy sites but secondary to the temperature changes by season in the bay. The shallower, fresher upper bay shows greater SST variability than the lower bay, whose temperature approaches the more oceanic, less seasonal temperatures at the mouth. Importantly, our study represents the synergistic advantages of utilizing Landsat and in-situ buoy data to offer new and deeper insights into the changing conditions of global estuaries.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"322 ","pages":"Article 114643"},"PeriodicalIF":11.1,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143576122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}