Remote Sensing of Environment最新文献

{"title":"Internal solitary waves in the Banda Sea, a pathway between Indian and Pacific oceans: Satellite observations and physics-AI hybrid forecasting","authors":"Xudong Zhang ,&nbsp;Haoyu Wang ,&nbsp;Xiaofeng Li ,&nbsp;Adi Purwandana ,&nbsp;I Wayan Sumardana Eka Putra","doi":"10.1016/j.rse.2025.114733","DOIUrl":"10.1016/j.rse.2025.114733","url":null,"abstract":"<div><div>Internal solitary waves (ISW) are widespread in global oceans, and satellite/in-situ observations showed that the Banda Sea has frequent ISW activities, characterized by long-wave crests, fast propagation speeds, and large amplitudes exceeding 100 m. In this paper, we conducted a comprehensive ISW study in the Banda Sea to reveal ISW characteristics by collecting 417 synthetic aperture radar and optical images from 2013 to 2019. The constructed dataset comprises 134 pairs of matched satellite images and a total of 12,021 ISW propagation vectors were extracted. Satellite observation reveals that ISWs in the Banda Sea mainly originate from the Ombai Strait and propagate northward, with an average propagation speed of over 2.50 m/s and with seasonal variation of less than 20 %. To forecast ISW propagations, we developed a physics-informed neural network ISW forecast model combining the classic Eikonal Eq. (EE) and the data-driven AI algorithms following a two-step transfer learning scheme. The forecast model employs a three-hidden-layer structure with 512 nodes in each layer. Firstly, the hybrid model includes ISW physics by setting the EE as the loss function. The second step is the data-driven process, which exploits a fully connected neural network and collected ISW dataset to improve EE-based model performance by 61 % with a loss function of the mean squared error. Through the two-step training, the forecast model adopts ISW physics and also benefits from the high accuracy of the data-driven process. We randomly selected 188/118 satellite images from the built dataset to serve as the training/test dataset for the data-driven process. After the second-step training, the root mean square (average) error of the model-predicted ISW propagation time reduced from 2.59 (2.37) h to 1.01 (−0.01) h. Error analysis shows that the data-driven process can efficiently correct the systematic error in the first-step model, which stems from errors in determining the ISW source and the propagation speed distribution map. Using the developed model, we predicted the propagation time of the ISWs and compared these predictions with satellite observations and in-situ observations. The comparison showed a high degree of agreement regarding the ISWs' location and their wave crests' geometry between model predictions and satellite/in-situ observations. Key differences between the proposed model and previous models are discussed.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"323 ","pages":"Article 114733"},"PeriodicalIF":11.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143745215","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":"Combining geometric-optical and spectral invariants theories for modeling canopy fluorescence anisotropy","authors":"Yachang He ,&nbsp;Yelu Zeng ,&nbsp;Dalei Hao ,&nbsp;Nikolay V. Shabanov ,&nbsp;Jianxi Huang ,&nbsp;Gaofei Yin ,&nbsp;Khelvi Biriukova ,&nbsp;Wendi Lu ,&nbsp;Yongyuan Gao ,&nbsp;Marco Celesti ,&nbsp;Baodong Xu ,&nbsp;Si Gao ,&nbsp;Mirco Migliavacca ,&nbsp;Jing Li ,&nbsp;Micol Rossini","doi":"10.1016/j.rse.2025.114716","DOIUrl":"10.1016/j.rse.2025.114716","url":null,"abstract":"<div><div>The spectral invariants theory (<span><math><mi>p</mi></math></span>-theory) has received much attention in the field of quantitative remote sensing over the past few decades and has been adopted for modeling of canopy solar-induced chlorophyll fluorescence (SIF). However, the spectral invariant properties (SIP) in simple analytical formulae have not been applied for modeling canopy fluorescence anisotropy primarily because they are parameterized in terms of leaf total scattering, which precludes the differentiation between forward and backward leaf SIF emissions. In this study, we have developed the canopy-SIP SIF model by combining geometric-optical (GO) theory to account for asymmetric leaf SIF forward and backward emissions at the first-order scattering and by modeling multiple scattering based on the <span><math><mi>p</mi></math></span>-theory, thus avoiding the dependence on radiative transfer models. The applicability of the model simulations especially over 3D heterogeneous canopies was improved by incorporating canopy structure through multi-angular clumping index, and by modeling single scattering from the four components of the scene in view according to the GO approach. The results show good consistency with both the state-of-the-art SIF models and multi-angular field SIF observations over grass and chickpea canopies. The coefficient of determination (<em>R</em>²) between the simulated SIF and field measurements was 0.75 (red) and 0.74 (far-red) for chickpea, and 0.65 (both red and far-red) for grass. The average relative error was approximately 3 % for 1D homogeneous scenes when comparing the canopy-SIP SIF model simulations to the SCOPE model simulations, and around 4 % for the 3D heterogeneous scene when comparing to the LESS model simulations. The results indicate that the proposed approach for separating asymmetric leaf SIF emissions is a robust way to keep a balance between satisfactory simulation accuracy and efficiency. Model simulations suggest that neglecting the leaf SIF asymmetry can lead to an underestimation of canopy red SIF by 6.3 % to 42.6 % for various leaf biochemical and canopy structural parameters. This study presents a simple but efficient analytical approach for canopy fluorescence modeling, with potential for large-scale canopy fluorescence simulations.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"323 ","pages":"Article 114716"},"PeriodicalIF":11.1,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738651","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":"Spatially continuous mapping of pre-fire fuel characteristics with imaging spectroscopy and lidar for fire emissions modeling","authors":"Clare M. Saiki ,&nbsp;Dar A. Roberts ,&nbsp;E. Natasha Stavros ,&nbsp;Andrew T. Hudak ,&nbsp;Nancy H.F. French ,&nbsp;Olga Kalashnikova ,&nbsp;Michael J. Garay ,&nbsp;T. Ryan McCarley ,&nbsp;Mark Corrao","doi":"10.1016/j.rse.2025.114721","DOIUrl":"10.1016/j.rse.2025.114721","url":null,"abstract":"<div><div>Fuels are a large source of uncertainty in fire emissions estimates due to variability in the physical and chemical properties of fuels and how they are represented. These uncertainties can be addressed using imaging spectroscopy and lidar data, that provide observations of the chemical and physical traits and spatial distribution of vegetation. Combined with ground fuel measurements, these data provide information on fuel distribution and quantity important for mapping and modeling fire effects. In this study, we present a methodology to develop models and continuous maps of pre-fire fuel characteristics for use in fire emissions modeling. We first addressed any spatial gaps over fire areas for Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) chemical trait data using Random Forests regression and for derived fractional cover. We used the AVIRIS fractional cover and chemical traits or AVIRIS estimates alongside lidar, multispectral, and topographic variables to build fuel characteristic models informed by ground measurements with partial least squares regression. We derived maps of predictive uncertainty alongside a suite of uncertainty statistics for each fuel characteristic that inform the use of fuels data within fire effects models. We used two study sites: the Williams Flats wildfire in eastern Washington state, USA and three prescribed crown fires in Utah, USA. The results show similar error between calibration and validation sets and NRMSE of around 20 % or lower for a majority of the fuel models. We present fuel characteristic and uncertainty maps for all fires. This study shows that the use of imaging spectroscopy and lidar data have the potential to represent fuel heterogeneity and continuously map fuel characteristics for fire effects modeling.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"323 ","pages":"Article 114721"},"PeriodicalIF":11.1,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725614","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":"Application of satellite and proximal hyperspectral sensing to target lithium mineralization in volcano-sedimentary deposits: A case study from the McDermitt caldera, USA","authors":"F. Corrado ,&nbsp;F. Putzolu ,&nbsp;R.N. Armstrong ,&nbsp;N. Mondillo ,&nbsp;R. Chirico ,&nbsp;B. Casarotto ,&nbsp;M. Massironi ,&nbsp;D. Fuller ,&nbsp;R. Ball ,&nbsp;R.J. Herrington","doi":"10.1016/j.rse.2025.114724","DOIUrl":"10.1016/j.rse.2025.114724","url":null,"abstract":"<div><div>This study provides satellite and proximal hyperspectral analyses of lithium (Li)-bearing volcano-sedimentary environments aimed at determining the target absorption features of alteration assemblages to be used as exploration vectors towards analogous Li-mineralized systems.</div><div>The study was applied at the McDermitt caldera (USA), which hosts volcano-sedimentary Li mineralization in the form of clay minerals originated from the alteration of glass-rich extrusive igneous rocks in endorheic lacustrine basins. The surface-exposed areas of the caldera were investigated using satellite hyperspectral imagery acquired by the German Environmental Mapping and Analysis Program (EnMAP) mission. Satellite data were validated via ground spectroscopy, performed through hyperspectral imaging, complemented by mineralogical and geochemical analyses on specimens deriving from the Jindalee McDermitt Li deposit.</div><div>The Li mineralization in the Jindalee McDermitt deposit is dominated by a Mg(Li)-smectite (hectorite) + amorphous silica assemblage, showing absorption features at 2306 nm and 2200 nm that can be detected in the spectral range covered by the EnMAP sensor. The analysis of the corresponding hyperspectral feature distribution maps and the comparison with ground control samples, confirmed that the above features together can be effectively used as mineralogical-hyperspectral vectors for Li-prospective areas on a caldera-scale. This spectral footprint was used in the analysis of a similar system that lacks Li mineralization (High Rock caldera complex, USA). Results of this test show that the distinctive 2200 + 2306 nm bands association is lacking at the High Rock caldera complex, which suggests that this spectral footprint can be employed as a mappable criteria to target lacustrine sequences with mineralogical features analogous to those of Li-mineralized volcano-sedimentary deposits.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"323 ","pages":"Article 114724"},"PeriodicalIF":11.1,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143723679","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":"Sensitivity of sun-induced chlorophyll fluorescence (SIF) and hyperspectral reflectance to drought response in soybean genotypes with contrasting affinities for arbuscular mycorrhizal fungi","authors":"Christine Y. Chang ,&nbsp;Jinyoung Y. Barnaby ,&nbsp;Jude E. Maul","doi":"10.1016/j.rse.2025.114722","DOIUrl":"10.1016/j.rse.2025.114722","url":null,"abstract":"<div><div>Increasing frequency and severity of drought events impact global and domestic agricultural productivity. Monitoring drought in agricultural fields with remote sensing can provide faster, lower-cost decision management support for critical field management activities. We evaluated the application of sun-induced chlorophyll fluorescence (SIF) emitted at red (SIF_Red) and far-red (SIF_FarRed) wavelengths in comparison with chlorophyll- and xanthophyll-sensitive reflectance-based remote sensing indices (NDVI, NIR_V, NIR_VP and PRI) for drought stress monitoring at the canopy scale. To do so, we evaluated impacts of drought stress on two soybean varieties with similar phenology but contrasting affinities for arbuscular mycorrhizal fungi (AMF), which can provide host plants with extended access to water and nutrients in exchange for carbohydrates. Drought response physiology of the two genotypes was further explored using leaf level photosynthetic gas exchange, chlorophyll fluorescence, water potential and phenology. We observed distinct responses, with the low-affinity genotype exhibiting lower SIF_Red and more negative midday leaf water potential, as well as reduced growth and development rate compared with the high-affinity genotype. SIF_FarRed and NIR_VP exhibited the strongest correlation with canopy photosynthesis followed by NIR_V. We also observed different timing of drought response parameters associated with different remote sensing signals. Our findings demonstrate the particular sensitivity of SIF to physiological drought responses, conferred here through AMF associations in the soil, and provide insight to the physiological drought responses tracked by different remote sensing signals.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"323 ","pages":"Article 114722"},"PeriodicalIF":11.1,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143705771","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":"STARS: A novel gap-filling method for SDGSAT-1 nighttime light imagery using spatiotemporal and spectral synergy","authors":"Congxiao Wang ,&nbsp;Wei Xu ,&nbsp;Zuoqi Chen ,&nbsp;Shaoyang Liu ,&nbsp;Wei Li ,&nbsp;Lingxian Zhang ,&nbsp;Shimin Gao ,&nbsp;Yan Huang ,&nbsp;Jianping Wu ,&nbsp;Bailang Yu","doi":"10.1016/j.rse.2025.114720","DOIUrl":"10.1016/j.rse.2025.114720","url":null,"abstract":"<div><div>The Sustainable Development Goals Satellite 1 (SDGSAT-1), equipped with the Glimmer Imager (GLI), provides high-resolution nighttime light (NTL) data across multiple spectral bands. Thus, it can notably monitor human dynamics and light pollution with enhanced spectral and spatial resolution. However, cloud cover and low-quality observations often contaminate the SDGSAT-1 GLI NTL data, limiting its effectiveness. This challenge is addressed by the development of a novel method, namely the SpatioTemporal And spectRal gap-filling method for Sdgsat-1 (STARS) GLI NTL images, which combines spatiotemporal and spectral information to generate cloud-free NTL images with satisfactory pixel brightness and continuity. STARS is the first method to effectively address gap-filling in multiband NTL data using RGB spectral information, even with irregular time intervals and limited image inputs. Compared with traditional methods such as the temporal gap-filling method and the mean-weighted gap-filling method, the Cloud Removing bY Synergizing spatioTemporAL information (CRYSTAL) method, and the spatial and temporal adaptive reflectance fusion model (STARFM), which do not specifically account for the differences in light source variations in multi-band NTL data, STARS demonstrates superior performance (higher R-squared (R²) and lower root-mean-square error (RMSE)) in simulations across seven global cities, demonstrating its effectiveness in filling cloud-induced gaps in multi-band NTL data. On average, STARS achieves R² values for the gap-filling results compared to the actual values of 0.79, 0.78, and 0.70 in the RGB bands, respectively. The cloud-free images produced by STARS extend the time series of the SDGSAT-1 GLI NTL data, supporting multitemporal quantitative analysis. In cloudy regions like Tianjin, China, STARS effectively captures dynamic changes in NTL before and after the Spring Festival, closely matching human activity patterns from Baidu Maps, both spatially and temporally. Overall, STARS offers an innovative and effective approach for gap-filling multiband NTL data, with potential applications in similar datasets.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"322 ","pages":"Article 114720"},"PeriodicalIF":11.1,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697400","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":"Quantified positive radiative forcing at a greening Canadian boreal-Arctic transition over the last four decades","authors":"Florent Dominé ,&nbsp;Arthur Bayle ,&nbsp;Maria Belke-Brea ,&nbsp;Esther Lévesque ,&nbsp;Ghislain Picard","doi":"10.1016/j.rse.2025.114715","DOIUrl":"10.1016/j.rse.2025.114715","url":null,"abstract":"<div><div>Climate warming in northern and Arctic regions drives vegetation growth and shifts species distribution. In northern Quebec's Boreal-Arctic transition (forest-tundra ecotone), this is seen in the replacement of lichen by shrubs, primarily dwarf birch. These changes impact surface albedo, contributing to climate forcings with broad consequences. This study measures vegetation changes in Tasiapik valley near Umiujaq, Quebec, using a combination of (1) hyperspectral data (347–2400 nm) collected from 62 vegetation assemblages, including lichen, dwarf birch, willow, and spruce, to calculate broadband albedo, and (2) remote sensing data from Landsat satellites over 1984–2023. By combining these data, the proportion of vegetation type for each pixel was determined at the beginning and end of the 40-year period. The areal coverage of six main vegetation types was quantified over the 9.25 km² valley. The most significant change was lichen replacement by dwarf birch with lichen understory, leading to an albedo reduction from 0.233 to 0.168 and a summer shortwave forcing of 11.17 W m⁻². At the valley scale, the spatially-averaged summer forcing was 2.16 W m⁻² when considering all observed vegetation changes. These values, lower than those in previous Norwegian studies, highlight the spatial variability of shortwave forcing due to lichen replacement. We observed that the vegetation change producing the greatest positive radiative forcing also caused the strongest greening. This suggests that Landsat-based greening may be used as a proxy for surface albedo change on an Arctic scale. This unique combination of ground and satellite data allows quantification of a direct, first-order effect of Arctic shrubification.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"322 ","pages":"Article 114715"},"PeriodicalIF":11.1,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143677933","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":"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}