Remote Sensing of Environment最新文献

{"title":"An extended vector inclination method for inferring detailed slip surfaces beneath landslides from SAR and optical satellite remote sensing image","authors":"Ya Kang ,&nbsp;Zhong Lu ,&nbsp;Chaoying Zhao ,&nbsp;Yufen Niu ,&nbsp;Liquan Chen ,&nbsp;Wei Qu ,&nbsp;Menghua Li","doi":"10.1016/j.rse.2025.115225","DOIUrl":"10.1016/j.rse.2025.115225","url":null,"abstract":"<div><div>The slip-surface geometry of a landslide is crucial for stability analysis and early warning, making its accurate and detailed characterization essential for hazard assessment and mitigation. However, traditional methods for obtaining slip-surface geometry are resource-intensive, requiring significant manpower and materials. Some studies have used the three-dimensional (3D) deformation from remote sensing data to infer detailed slip-surface geometry of landslides using the mass conservation method. However, this method usually requires prior data to calibrate the model parameters, and its underlying assumptions may not be valid for all landslide scenarios. In this study, we designed an Extended Vector Inclination Method (EVIM) to determine detailed landslide slip-surface geometry and established a framework for deriving landslide thickness based on 3D deformation derived from SAR and optical remote sensing data. The experiments based on actual events (Hooskanaden, Shangxintian, and Daopo landslide) and simulations demonstrate that EVIM can reliably estimate landslide thickness in a manner consistent with in-situ measurements. Furthermore, simulations indicate that the magnitude of deformation and the error in the constrained 3D deformation affect the accuracy of our method. We conclude that in cases where the mass conservation method is difficult to apply (e.g., landslides lacking prior information), the EVIM may serve as an alternative method. The proposed framework enables rapid slip-surface inversion, making it well-suited for regional-scale landslide stability assessments.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115225"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145897541","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":"Hyperspectral BRDF based on UAV measurements can characterize optical properties of flat desert surfaces: A comprehensive comparison with laboratory and satellite data","authors":"Yuechao Sheng ,&nbsp;Xiuqing Hu ,&nbsp;Xingwei He ,&nbsp;Lin Chen ,&nbsp;Zhongqiu Sun ,&nbsp;Shan Lu","doi":"10.1016/j.rse.2025.115228","DOIUrl":"10.1016/j.rse.2025.115228","url":null,"abstract":"<div><div>Desert is a crucial component of terrestrial ecosystems, and its optical reflection properties are essential for understanding land surface radiative balance. Combining the hyperspectral and angular measurements has been considered as an effective method to characterize optical reflection properties of desert surfaces. However, the ground and satellite-based instruments cannot obtain both hyperspectral and angular reflectance of desert surfaces at appropriate scales due to the limitations of their measurement configurations. We employed an unmanned aerial vehicle (UAV) equipped with a spectrometer to perform hyperspectral BRDF measurements of six desert areas in Northwest China to overcome the limitations. In the comparison with those obtained from laboratory and satellite observations at the same geometries, it is found that UAV-measured hyperspectral reflectance has a spectral shape similar to that of spectra measured in laboratory settings, and UAV-measured angular reflectance shows a good match of BRDF characterization as the satellite observations in the hemispherical space. Moreover, we compared the modeled angular reflectance, which are derived from BRDF models, with measured angular reflectance of deserts at selected wavelengths from MODIS. The BRDF models using UAV-measured hyperspectral BRDF have superior performance in spectral BRDF simulation. The uncertainty analysis based on a Bayesian inversion further confirmed the reliability of the UAV data in effectively characterizing optical properties of the desert surface. These results demonstrate the robustness of UAV-based spectral and angular measurements in quantifying the hyperspectral BRDF, which provide comprehensive in-situ optical properties of desert surfaces. These data can serve as valuable validation for future studies on radiative balance of desert surfaces.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115228"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145897259","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":"Leveraging additional VIIRS information to improve wildfire tracking in the western US","authors":"Shane R. Coffield ,&nbsp;Tempest D. McCabe ,&nbsp;Wilfrid Schroeder ,&nbsp;Yang Chen ,&nbsp;Rebecca C. Scholten ,&nbsp;Elijah Orland ,&nbsp;Tianjia Liu ,&nbsp;Elizabeth Wiggins ,&nbsp;James T. Randerson ,&nbsp;Melanie Follette-Cook ,&nbsp;Douglas C. Morton","doi":"10.1016/j.rse.2025.115156","DOIUrl":"10.1016/j.rse.2025.115156","url":null,"abstract":"<div><div>Recent record-breaking fire activity in the western US poses clear threats to humans, ecosystems, and climate. Larger and faster fires increase the challenges for fire managers and further motivate the need for improved tracking of extreme fire behavior. There are also known limitations to our current ability to monitor fires from space. These include infrequent coverage from moderate resolution (<span><math><mo>≤</mo></math></span> 1 km) sensors, smoke and cloud obscuration, omission of small or low-intensity fires, and atmospheric attenuation of fire radiative power (FRP). These effects diminish our ability to quantify fire behavior and emissions, including persistent burning behind the flaming fire front, particularly in ecosystems with high fuel loads. In this study, we examined the Visible Infrared Imaging Radiometer Suite (VIIRS) imagery and data products to assess the utility of candidate fire pixels in addition to the low/nominal/high confidence 375-m fire detections already included in the active fire product. We found that these candidate pixels added 45% more daytime detections and 12% more nighttime detections for large fires in the western US 2020 fire season. Candidate fires were highly consistent with areas of flaming and smoldering fire activity identified by near-coincident airborne data as well as patterns of known active or candidate fires in sequential VIIRS overpasses, without significantly increasing false detections (commission errors). The candidate fire detections helped fill data gaps due to cloud obscuration during large fires that generated pyrocumulonimbus (pyroCb) clouds. Including this additional information also impacted estimates of fire activity, increasing fire persistence by 20% and FRP by 7% across our sample. Although the contribution from candidate fire detections to total FRP was relatively small, including these additional pixels could provide a more consistent estimate of fire emissions for smoke models and air quality forecasts by filling gaps in active fire information and improving the representation of smoldering fire activity. These results demonstrate the potential to augment the standard VIIRS product with candidate fire information for known large fire events to improve fire tracking and downstream products. Such approaches to leverage additional VIIRS information may be suitable for other biomass burning regions where global fire detection algorithms provide incomplete information for specific fire types and observing conditions.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115156"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145652002","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":"Soil spectral simulation and soil parameter retrieval with an optimized four-flux MLG radiative transfer model","authors":"Weihao Wang ,&nbsp;Xia Zhang ,&nbsp;Kun Shang ,&nbsp;Yanli Sun ,&nbsp;Youxin Sun ,&nbsp;Wenliang Chen","doi":"10.1016/j.rse.2025.115210","DOIUrl":"10.1016/j.rse.2025.115210","url":null,"abstract":"<div><div>Soil moisture (SM), with its strong water absorption characteristics, profoundly alters the soil reflectance spectra and reduces the reliability of predicting other soil parameters. To achieve high-precision spectral simulation and remove SM effects efficiently, we propose a novel method based the optimized four-flux Maheu-Letoulouzan-Gouesbet (MLG) radiative transfer model. The model incorporates key soil properties (e.g., SM, organic matter, iron oxides, texture fractions) to better characterize soil spectral formation mechanisms. Additionally, the genetic algorithm, with a spectral continuity constraint, is developed for global parameter optimization, thereby simplifying the calculation process and improving the simulation reliability. Based on the optimal parameters, the SM factor is independently excluded and then the spectra are reconstructed to remove SM effects. Results demonstrated that the optimized MLG model achieved the highest accuracy in soil spectral simulation under both dry and wet conditions. Compared with the Kubelka-Munk (KM) model, the coefficient of determination (R²) improved from 0.87 to 0.94 in the ICRAF dry soil dataset and from 0.87 to 0.94 in the Nong'an wet soil dataset. The model maintained high accuracy (R² = 0.81) even when the SM content exceeded 30 %, with strong effects in the water absorption bands around 1400 and 1900 nm. After removing SM effects from in-situ field spectra, corrected spectra exhibited stronger correlations with organic matter, iron oxides, and texture fractions, significantly enhancing prediction accuracy (R² &gt; 0.90, the relative percent difference (RPD) increase by 0.88–3.00). This physically-grounded method underscores the potential for accurate soil spectral simulation and SM effects removal, thereby strengthening the foundation for large-scale soil monitoring via satellite remote sensing.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115210"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145907736","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":"A novel hybrid approach for mapping global surface solar radiation with DSCOVR/EPIC: Combining deep learning with physical algorithm","authors":"Wenjun Tang ,&nbsp;Jinwen Qi ,&nbsp;Junmei He ,&nbsp;Fuxin Zhu ,&nbsp;Chuanming Yuan ,&nbsp;Jinyan Yang ,&nbsp;Bing Hu","doi":"10.1016/j.rse.2025.115200","DOIUrl":"10.1016/j.rse.2025.115200","url":null,"abstract":"<div><div>Surface solar radiation (SSR, also known as global radiation, R_g), is critical for Earth's energy, water, and carbon cycles, yet existing satellite-derived global R_g products suffer from spatial inconsistencies due to multi-source data fusion. To address this, we propose a novel hybrid approach integrating deep learning with physical algorithms using observations from the Deep Space Climate Observatory (DSCOVR/EPIC), positioned at the Sun-Earth Lagrange-1 point and continuously observed the entire portion of the Earth with sunshine. Unlike traditional physical algorithms or machine learning algorithms, this method estimates cloud transmittance via a DenseNet-based convolutional neural network (CNN), calculates clear-sky R_g using a physical parameterization scheme, and combines these to derive all-sky R_g. Meanwhile, the direct and diffuse components (R_dir and R_dif) are further separated from the estimated R_g using a Light Gradient Boosting Machine (LightGBM) model. The method was trained with in-situ observations from the Baseline Surface Radiation Network (BSRN), and further independently evaluated against in-situ observations from three networks of the Solar Radiation (SOLRAD), China Meteorological Administration (CMA) radiation stations and Global Energy Balance Archive (GEBA). Independent evaluation demonstrates that our hybrid method exhibits excellent spatial scalability. Comparative validation against the product of <span><span>Hao et al. (2020)</span></span> derived from DSCOVR/EPIC observations demonstrates our method can generate more accurate global products of R_g, R_dir and R_dif. The innovation of our method lies in integrating machine learning with physical algorithms to leverage their complementary strengths, while overcoming the limitations of high uncertainty associated with cloud optical property retrievals from DSCOVR/EPIC observations. This approach will contribute to the mapping of global spatially consistent radiation products, overcoming the limitations of geostationary and polar-orbiting satellites.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115200"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785694","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":"Self-supervised representation learning for cloud detection using Sentinel-2 images","authors":"Yawogan Jean Eudes Gbodjo ,&nbsp;Lloyd Haydn Hughes ,&nbsp;Matthieu Molinier ,&nbsp;Devis Tuia ,&nbsp;Jun Li","doi":"10.1016/j.rse.2025.115205","DOIUrl":"10.1016/j.rse.2025.115205","url":null,"abstract":"<div><div>The unavoidable presence of clouds and their shadows in optical satellite imagery hinders the true spectral response of the Earth’s underlying surface. Accurate cloud and cloud shadow detection is therefore a crucial preprocessing step for optical satellite images and any downstream analysis. Various methods have been developed to address this critical task and can be broadly categorized into physical rule-based methods and learning based methods. In recent years, machine learning based methods, particularly deep learning frameworks, have proven to outperform physical rule-based models. However, these approaches are mostly fully supervised and require a large amount of pixel-level annotations whose acquisition is costly and time consuming. In this work, we propose to address cloud and cloud shadow detection in optical satellite images using self-supervised representation learning, a machine learning paradigm that focuses on extracting relevant representations from unlabeled data, which can then be used as an effective starting point to fine-tune models with few labeled data in a supervised fashion. These approaches have been shown to perform competitively with fully supervised methods without the requirement of large annotation datasets. Specifically, we assessed two self-supervised representation learning methods that use different philosophies about self-supervision: Momentum Contrast (MoCo), based on contrastive learning and DeepCluster, based on clustering. Using two publicly available Sentinel-2 cloud datasets, namely WHUS2–CD+ and CloudSEN12, we show that MoCo and DeepCluster, trained with only 25 % of the annotated data, can perform better than physical rule-based methods such as FMask and Sen2Cor, weakly supervised methods and even several fully supervised methods. These results highlight the strong applicability of self-supervised representation learning methods to the task of cloud and cloud shadow detection with self-supervised pretraining leading to fine-tuned models that outperform industry standards and achieve near state-of-the-art performance with a fraction of the data.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115205"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823484","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":"A global intercomparison of SWOT and traditional nadir radar altimetry for monitoring river water surface elevation","authors":"Yue Xu ,&nbsp;Frédéric Frappart ,&nbsp;Guoqiang Tang ,&nbsp;Guoqing Zhang ,&nbsp;Peirong Lin ,&nbsp;Liguang Jiang ,&nbsp;Simon Papalexiou ,&nbsp;Fangfang Yao ,&nbsp;Xiaoran Han ,&nbsp;Jun Xia","doi":"10.1016/j.rse.2025.115219","DOIUrl":"10.1016/j.rse.2025.115219","url":null,"abstract":"<div><div>The water surface elevation (WSE) of rivers serves as fundamental data for various hydrological research and applications. The recently launched Surface Water and Ocean Topography (SWOT) satellite offers a revolutionary altimetry approach by providing wide-swath elevation mapping using a SAR Interferometer (InSAR) operating at Ka-band. While SWOT provides unprecedented spatio-temporal coverage of WSE, it has not been systematically compared with reference water stage databases. Currently, due to difficulties in accessing recent and globally homogenous gauge station records, established WSE derived from radar altimetry (RA) missions is the most suitable dataset to perform global validation of WSE. This study presents the first global-scale intercomparison of the two altimetry systems, the wide-swath InSAR technique used for the first time by SWOT and the classical along-track RA using the SAR technique, and identifies several representative factors influencing their consistency. SWOT WSE are compared with virtual stations derived from Sentinel-3 and Sentinel-6 missions, across five different node quality categories (“good”, “suspect”, “degraded”, “bad” and a combined “all” group without “bad” data). The analysis further examines the potential influences from river width, river ice, backscattering coefficients (sigma0), and dark water fraction in modulating data consistency. The root mean square error (and correlation coefficient) between WSE from SWOT and RA in “good” and “suspect” data are 0.80 m (0.85) and 1.62 m (0.78), respectively, while those for “degraded” and “bad” data rise significantly to 8.80 m (0.60) and 16.91 m (0.50). The combined “all” category yields an overall <em>RMSE</em> (<em>CC</em>) of 5.15 m (0.65). For rivers wider than 160 m, SWOT measurements with “good” and “suspect” quality demonstrate notably improved consistency with RA compared to narrower rivers. Under frozen conditions, the reduced consistency between SWOT and RA is most evident in the “degraded” and “bad” quality data, with average reductions in <em>CC</em> of 0.17 and 0.21, respectively. In addition, radar backscatter strongly impacts the quality of SWOT-based WSE, as both extremely low values (dark water) and very high values (specular ringing) can lead to unrealistic estimates. Overall, this study offers important insights into the global performance of SWOT-based WSE estimation and informs the future refinement and application of SWOT data in hydrological research.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115219"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145836211","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":"A novel UAV lidar-derived shrub structural index for estimating above-ground biomass","authors":"Jiaming Wu ,&nbsp;Yaxin Wang ,&nbsp;Liang Hong ,&nbsp;Bin Sun ,&nbsp;Zhenping He ,&nbsp;Zejiang Li ,&nbsp;Zhijie Ma","doi":"10.1016/j.rse.2025.115189","DOIUrl":"10.1016/j.rse.2025.115189","url":null,"abstract":"<div><div>Precise estimation of shrub above-ground biomass (AGB) in arid regions is crucial for carbon cycle research and ecosystem assessment. Unmanned aerial vehicle (UAV) -borne light detection and ranging (LiDAR) has become a key tool for quantifying three-dimensional vegetation structure and estimating AGB. However, the short stature of arid zone vegetation, combined with sparse and low-quality point clouds acquired by UAV, limits high-accuracy shrub AGB estimation. To address this issue, this study selected <em>Caragana korshinskii</em>, a typical psammophytic shrub in Ordos City, as the research object. By integrating UAV-based multispectral and LiDAR data, a biomass estimation method based on a novel Shrub Structure Index (SSI) was proposed. The SSI workflow reconstructs the three-dimensional shrub structure under sparse point cloud conditions and improves AGB estimation accuracy. This workflow comprises Object-based image analysis (OBIA) classification for individual shrub extraction, Delaunay linear up-sampling, voxel-based partitioning, and dynamic stratification by height percentiles. Experimental results demonstrate that: (1) The individual shrub extraction method utilizing the large-scale mean shift (LSMS) segmentation algorithm and support vector machine (SVM) classification achieved a total quadrat segmentation accuracy of over 90.61 %, an overall classification accuracy of 91.51 % (Kappa = 0.86). (2) In SSI construction, the height-percentile stratification thickness, point-cloud sampling, and voxel edge length together set <em>Caragana korshinskii</em> stratification accuracy and density scale; the 5 % height percentile interval, a sampling size of 100 points, and 0.04 m voxel edge length proved optimal. (3) Comparative experiments showed that the three-dimensional feature integrated SSI significantly outperformed single-feature, two-feature, traditional allometric equation, and random forest (RF) models, with the SSI-based model achieving R², RMSE, MAE, and rRMSE of 0.90, 529.01 g, 432.58 g, and 26.54 %, respectively. These results indicate that SSI more effectively captures shrub spatial structure and improves AGB prediction under sparse UAV-LiDAR conditions.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115189"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729070","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":"Bridging the thermal gap: Generating 10 m, 3-day land surface temperature via Landsat–Sentinel-2 fusion","authors":"Yuan Qi ,&nbsp;Bo Huang ,&nbsp;Min Zhao ,&nbsp;Xiaolu Jiang ,&nbsp;Wenfei Mao","doi":"10.1016/j.rse.2025.115227","DOIUrl":"10.1016/j.rse.2025.115227","url":null,"abstract":"<div><div>High spatiotemporal resolution land surface temperature (LST) is essential for climate-impact studies, particularly for urban thermal environment analyses and vegetation phenology tracking. However, current satellite sensors exhibit inherent hardware trade-offs: Sentinel-2 (S2) provides high-resolution (10 m/5 day) optical observations without thermal capability, whereas Landsat-8/9 (L8/9), equipped with both optical and thermal sensors, suffers from coarser resolution (30 m/16 day). This configuration fails to meet the demand for fine spatial and dense temporal surface thermal monitoring. To address this gap, we propose a novel fusion framework for high spatiotemporal resolution LST generation (<span><math><mi>f</mi></math></span>HiSTR-LST). It first applies a deep-learning-based spatiotemporal fusion to densify reflectance data, then performs a spatial-spectral fusion to generate high-resolution LST. By synergizing L8/9 and S2 data, our approach reliably produces 10 m spatial-resolution LST across three overpass scenarios (joint, L8/9-only, S2-only), thus achieving an effective ∼3-day temporal resolution. Cross-validations between upscaled LST predictions and native L8/9 LST demonstrate <span><math><mi>f</mi></math></span>HiSTR-LST's robust performance across eight study areas worldwide (mean <em>R</em> = 0.90, RMSE = 1.17 K). More significantly, ground-truth validation—previously unaddressed—confirms its satisfactory accuracy (mean <em>R</em> = 0.97, RMSE = 3.45 K). The combined validation shows that <span><math><mi>f</mi></math></span>HiSTR-LST outperforms the state-of-the-art by 13 % in R and reduces RMSE by 9 %. Finally, we illustrate two applications—small-area vegetation-phenology tracking and fine-scale urban thermal-pattern delineation—which collectively showcase <span><math><mi>f</mi></math></span>HiSTR-LST's capability to resolve subtle surface thermal variations. Our study bridges a critical gap in generating high spatiotemporal resolution LST from satellite imagery, a capability crucial for investigating the nuanced effects of global warming.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115227"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145881023","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":"Determinants of L-band backscatter in dry tropical ecosystems: Implications for biomass mapping","authors":"João M.B. Carreiras ,&nbsp;Thomas Higginbottom ,&nbsp;John L. Godlee ,&nbsp;Sam Harrison ,&nbsp;Lorena Benitez ,&nbsp;Penelope J. Mograbi ,&nbsp;Aurora Levesley ,&nbsp;Karina Melgaço ,&nbsp;David Milodowski ,&nbsp;Georgia Pickavance ,&nbsp;Geoff Wells ,&nbsp;Edmar Almeida de Oliveira ,&nbsp;Luzmila Arroyo ,&nbsp;Sam Bowers ,&nbsp;Roel J.W. Brienen ,&nbsp;Domingos Cardoso ,&nbsp;António Alberto Jorge Farias Castro ,&nbsp;Ezequiel Chavez ,&nbsp;Ítalo A.C. Coutinho ,&nbsp;Tomás F. Domingues ,&nbsp;Casey M. Ryan","doi":"10.1016/j.rse.2025.115213","DOIUrl":"10.1016/j.rse.2025.115213","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Accurate characterization of the role of the dry tropics in the global carbon cycle requires precise estimation of woody biomass changes due to ecological and anthropogenic change, including deforestation, forest degradation, regrowth, mortality and enhanced tree growth due to climate change. L-band Synthetic Aperture Radar (SAR) backscatter observations offer a reliable option to consistently map these processes as they are (i) available globally since 2007 (JAXA ALOS-1, ALOS-2 and ALOS-4), and (ii) sensitive to woody structure, such as aboveground biomass density (&lt;span&gt;&lt;math&gt;&lt;mi&gt;AGBD&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt;) up to ∼100 t ha&lt;sup&gt;−1&lt;/sup&gt;. However, we lack multi-site empirical understanding of the scattering processes that determine the relationship between L-band SAR and woody vegetation structure in the dry tropics, and how this is mediated by soil properties.&lt;/div&gt;&lt;div&gt;This study used observations from ground plots in Africa (&lt;em&gt;n&lt;/em&gt; = 171), Australia (&lt;em&gt;n&lt;/em&gt; = 6), and South America (&lt;em&gt;n&lt;/em&gt; = 44) to understand the impact of vegetation structure and soil properties on spatially and temporally coincident fully-polarimetric L-band SAR data. Fully-polarimetric L-band SAR single-look complex data were converted to scattering mechanisms/parameters using van Zyl, Cloude-Pottier, and Freeman-Durden polarimetric decompositions to elucidate the physical mechanisms involved. Multivariate SAR-vegetation-soil relationships were analysed using a theory-informed structural equation modelling approach. The strongest positive effects on volume scattering come from stem density (stems ha&lt;sup&gt;−1&lt;/sup&gt;) and mean stem biomass of trees, and soil water and sand content (standardized regression coefficients of 0.3, 0.1, 0.2 and 0.1, respectively). The only significant effect on surface scattering is from stem density (0.1). Significant effects on double bounce scattering are from stem density (0.3) and soil sand content (−0.2). Since &lt;span&gt;&lt;math&gt;&lt;mi&gt;AGBD&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt; is the product of stem density and mean stem biomass, this modelling framework points to a stronger effect from the number of trees rather than their size/biomass. Therefore, &lt;span&gt;&lt;math&gt;&lt;mi&gt;AGBD&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt; maps relying solely on radar intensity may not reflect significant changes when &lt;span&gt;&lt;math&gt;&lt;mi&gt;AGBD&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt; is increasing due to the growth of existing stems. Additionally, such maps might overestimate changes in &lt;span&gt;&lt;math&gt;&lt;mi&gt;AGBD&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt; when driven by the recruitment of new stems or loss of existing stems. Full-polarimetric observations allow the decomposition of the radar signal into volume scattering, surface scattering, and double bounce, enabling the inversion of structural equation models to retrieve both stem density and mean stem biomass. This provides a more comprehensive description of forest structure compared to retrieving only &lt;span&gt;&lt;math&gt;&lt;mi&gt;AGBD&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt;. As this approach depends on full-polarimetric data, its effective","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115213"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145939014","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}