Remote Sensing of Environment最新文献

{"title":"A gradient-based nonlinear multi-pixel physical method for simultaneously separating component temperature and emissivity from nonisothermal mixed pixels with DART","authors":"Zhijun Zhen ,&nbsp;Shengbo Chen ,&nbsp;Nicolas Lauret ,&nbsp;Abdelaziz Kallel ,&nbsp;Tiangang Yin ,&nbsp;Jonathan León-Tavares ,&nbsp;Biao Cao ,&nbsp;Jean-Philippe Gastellu-Etchegorry","doi":"10.1016/j.rse.2025.114738","DOIUrl":"10.1016/j.rse.2025.114738","url":null,"abstract":"<div><div>Component temperature and emissivity are crucial for understanding plant physiology and urban thermal dynamics. However, existing thermal infrared unmixing methods face challenges in simultaneous retrieval and multi-component analysis. We propose Thermal Remote sensing Unmixing for Subpixel Temperature and emissivity with the Discrete Anisotropic Radiative Transfer model (TRUST-DART), a gradient-based multi-pixel physical method that simultaneously separates component temperature and emissivity from non-isothermal mixed pixels over urban areas. TRUST-DART utilizes the DART model and requires inputs including at-surface radiance imagery, downwelling sky irradiance, a 3D mock-up with component classification, and standard DART parameters (<em>e.g.</em>, spatial resolution and skylight ratio). This method produces maps of component emissivity and temperature. The accuracy of TRUST-DART is evaluated using both vegetation and urban scenes, employing Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images and DART-simulated pseudo-ASTER images. Results show a residual radiance error is approximately 0.05 W/(m²·sr). In absence of the co-registration and sensor noise errors, the median residual error of emissivity is approximately 0.02, and the median residual error of temperature is within 1 K. This novel approach significantly advances our ability to analyze thermal properties of urban areas, offering potential breakthroughs in urban environmental monitoring and planning. The source code of TRUST-DART is distributed together with DART (<span><span>https://dart.omp.eu</span><svg><path></path></svg></span>).</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"324 ","pages":"Article 114738"},"PeriodicalIF":11.1,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838423","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":"Slip surface, volume and evolution of active landslide groups in Gongjue County, eastern Tibetan Plateau from 15-year InSAR observations","authors":"Bo Chen ,&nbsp;Zhenhong Li ,&nbsp;Chuang Song ,&nbsp;Chen Yu ,&nbsp;Roberto Tomás ,&nbsp;Jiantao Du ,&nbsp;Xinlong Li ,&nbsp;Adrien Mugabushaka ,&nbsp;Wu Zhu ,&nbsp;Jianbing Peng","doi":"10.1016/j.rse.2025.114763","DOIUrl":"10.1016/j.rse.2025.114763","url":null,"abstract":"<div><div>Landslides stand as a prevalent geological risk in mountainous areas, presenting substantial danger to human habitation. The slip surface (SSF), volume, type and evolution of landslides constitute crucial information from which to understand landslide mechanisms and assess landslide risk. However, current methods for obtaining this information, relying primarily on field surveys, are usually time-consuming, labor-intensive and costly, and are more applicable to individual landslides than large-scale landslide groups. To tackle these challenges, we present a novel method utilizing multi-orbit Synthetic Aperture Radar (SAR) data to deduce the SSF, volume and type of active landslides. In this method, the SSF of landslides over a wide area is determined from three-dimensional deformation fields by assuming that the most authentic direction of the landslide movement aligns parallel to the SSF, on the basis of which the volume and type of active landslides can also be inferred. This approach was utilized with landslide groups in Gongjue County (LGGC), situated in the eastern Tibetan Plateau, which pose grave peril to community members and critical construction along the upstream/downstream of the Jinsha River. Firstly, SAR images were gathered and interferometrically processed from four separate platforms, spanning the period from July 2007 to August 2022. Then, three-dimensional displacement time series were inverted based on Interferometric Synthetic Aperture Radar (InSAR) observations and a topography-constrained model, from which the SSF, volume and type were determined using our proposed method. Finally, the Tikhonov regularization method was applied to reconstruct 15-year displacement time series along the sliding surface, and potential driving factors of landslide motion were identified. Results indicate that 53 landslides were detected in the LGGC region, of which ∼70 % were active and complex landslides with maximum cumulative displacement along the sliding surface reaching 1.5 m over the past ∼15 years. In addition, the deepest SSF of these landslides was found to reach 114 m, with volumes ranging from 1.66 × 10⁵ m³ to 1.72 × 10⁸ m³. Independent in-situ measurements validate the reliability of the SSF obtained in this study. More particularly, we found that the 2018 failure of the Baige landslide (approximately 50 km from LGCC) had caused persistent acceleration to those wading landslides, highlighting the prolonged impact of external factors on landslide evolution. These insights provide a deeper understanding of landslide dynamics and mechanisms, which is crucial when implementing early warning systems and forecasting future failure events.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"324 ","pages":"Article 114763"},"PeriodicalIF":11.1,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143833479","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":"The Harmonized Landsat and Sentinel-2 version 2.0 surface reflectance dataset","authors":"Junchang Ju ,&nbsp;Qiang Zhou ,&nbsp;Brian Freitag ,&nbsp;David P. Roy ,&nbsp;Hankui K. Zhang ,&nbsp;Madhu Sridhar ,&nbsp;John Mandel ,&nbsp;Saeed Arab ,&nbsp;Gail Schmidt ,&nbsp;Christopher J. Crawford ,&nbsp;Ferran Gascon ,&nbsp;Peter A. Strobl ,&nbsp;Jeffrey G. Masek ,&nbsp;Christopher S.R. Neigh","doi":"10.1016/j.rse.2025.114723","DOIUrl":"10.1016/j.rse.2025.114723","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Frequent multispectral observations of sufficient spatial detail from well-calibrated spaceborne sensors are needed for large-scale terrestrial monitoring. To meet this demand, the NASA Harmonized Landsat and Sentinel-2 (HLS) project was initiated in early 2010s to produce comparable 30-m surface reflectance from the US Landsat 8 Operational Land Imager (OLI) and the European Copernicus Sentinel-2A MultiSpectral Instrument (MSI), and currently from two OLI and two MSI sensors, by applying atmospheric correction to top-of-atmosphere (TOA) reflectance, masking out clouds and cloud shadows, normalizing bi-directional reflectance view angle effects, adjusting for sensor bandpass differences with the OLI as the reference, and providing the harmonized data in a common grid. Several versions of HLS dataset have been produced in the last few years. The recent improvements on almost all the harmonization algorithms had prompted a production of a new HLS dataset, tagged Version 2.0, which was completed in the summer of 2023 and for the first time takes on a global coverage (except for Antarctica). The HLS V2.0 data record starts in April 2013, two months after Landsat 8 launch. For 2022, the first whole year two Landsat and two Sentinel-2 satellites were available, HLS provides a global median of 66 cloud-free observations over land, substantially more than from a single sensor. This paper describes the HLS algorithm improvements and assesses the harmonization efficacy by examining how the reflectance difference between contemporaneous Landsat and Sentinel-2 observations was successively reduced by each harmonization step. The assessment was conducted on 545 pairs of globally distributed same-day Landsat/Sentinel-2 images from 2021 to 2022. Compared to the TOA data, the HLS atmospheric correction slightly increased the reflectance relative difference between Landsat and Sentinel-2 for most of the spectral bands, especially for the two blue bands and the green bands. The subsequent bi-directional reflectance view angle effect normalization effectively reduced the between-sensor reflectance difference present in the atmospherically corrected data for all the spectral bands, and notably to a level below the TOA differences for the red, near-infrared (NIR), and the two shortwave infrared (SWIR) bands. The bandpass adjustment only had a modest effect on reducing the between-sensor reflectance difference. In the final HLS products, the same-day reflectance difference between Landsat and Sentinel-2 was below 4.2% for the red, NIR, and the two SWIR bands, all smaller than the difference in the TOA data. However, the between-sensor differences for the two blue and the green bands remain slightly higher than in TOA data, and this reflects the difficulty in accurately correcting for atmospheric effects in the shorter wavelength visible bands. The data consistency evaluation on a suite of commonly used vegetation indices (VI) calculated from the HLS V2.0 ref","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"324 ","pages":"Article 114723"},"PeriodicalIF":11.1,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143833480","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 and vegetation cover estimation for global imaging spectroscopy using spectral mixture analysis","authors":"Francisco Ochoa ,&nbsp;Philip G. Brodrick ,&nbsp;Gregory S. Okin ,&nbsp;Eyal Ben-Dor ,&nbsp;Thoralf Meyer ,&nbsp;David R. Thompson ,&nbsp;Robert O. Green","doi":"10.1016/j.rse.2025.114746","DOIUrl":"10.1016/j.rse.2025.114746","url":null,"abstract":"<div><div>The Earth surface Mineral dust source InvesTigation (EMIT) is a visible-to-shortwave infrared imaging spectrometer currently aboard the International Space Station. Derivations of fractional cover from spectral unmixing algorithms have provided insights into various ecosystem functions. In the case of EMIT, they will be used by multiple global Earth systems models to constrain the sign of dust-related radiative forcing. This study aims to evaluate the efficacy of different approaches for estimating fractional cover and quantifying the corresponding uncertainty, and serves as a model to encapsulate the true error budget for EMIT. We simulated surface reflectance from a spectral library compiled from various drylands to generate millions of candidate spectra made up of different random fractions of nonphotosynthetic vegetation (NPV), green vegetation (GV), and soil. Simulated spectra were used as-is but we also tested the impact of atmospheric conditions/surface reflectance retrieval by using them to calculate top-of-atmosphere radiance then using the current EMIT surface reflectance retrieval algorithm to estimate apparent surface reflectance. We tested approaches to unmixing these simulated spectra using multiple strategies for dealing with spectrum brightness, within-class spectral variability, and library selection. We also incorporated a Monte Carlo approach to stabilize fractional cover retrievals and quantify uncertainty. The best spectral unmixing approaches produced mean absolute error &lt; 0.10 for NPV and soil and &lt; 0.06 for GV with uncertainties <span><math><mo>≤</mo><mo>±</mo></math></span> 0.02 for all classes. We named this innovative approach EndMember Combination Monte Carlo, E(MC)², unmixing and found that our fractional cover retrievals are insensitive to atmospheric residuals in the surface reflectance data.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"324 ","pages":"Article 114746"},"PeriodicalIF":11.1,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143826407","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":"Downscaling the full-spectrum solar-induced fluorescence emission signal of a mixed crop canopy to the photosystem level using the hybrid approach","authors":"Julie Krämer ,&nbsp;Bastian Siegmann ,&nbsp;Antony Oswaldo Castro ,&nbsp;Onno Muller ,&nbsp;Ralf Pude ,&nbsp;Thomas Döring ,&nbsp;Uwe Rascher","doi":"10.1016/j.rse.2025.114739","DOIUrl":"10.1016/j.rse.2025.114739","url":null,"abstract":"<div><div>Remote sensing of hyperspectral vegetation reflectance and solar-induced chlorophyll fluorescence (SIF) is essential for evaluating crop functionality and photosynthetic performance. While primarily applied in monocultures, these tools show promise in diverse cropping systems, enhancing ecological intensification. Plant-plant interactions in such systems can influence key physiological processes, such as photosynthesis, making SIF a valuable tool for evaluating how crop diversity affects photosynthetic function and productivity. However, detecting SIF in diverse stands remains challenging due to uncertainties in light re-absorption and scattering. To address these challenges, we propose a hybrid model inversion framework that combines canopy observations with physical modeling to derive leaf biochemical, canopy structural variables, and SIF spectra at leaf and photosystem levels. This approach employs a machine learning retrieval algorithm (MLRA), trained on synthetic spectra from radiative transfer model (RTM) simulations, to quantify re-absorption and scattering effects. Using the SpecFit retrieval algorithm, the temporal evolution of full-spectrum SIF at the canopy level can be derived. To downscale SIF to the photosystem level and retrieve its quantum yield, we corrected the canopy SIF spectrum for re-absorption and scattering effects calculated from TOC reflectance. Spectral measurements were gathered from field experiments conducted over three years, covering various growth stages of cereal and legume monocrops and their mixture. Our method accurately predicts important leaf biochemical and canopy structural variables, such as leaf area (LAI, R² = 0.75) and leaf chlorophyll content (LCC, R² = 0.91), and shows a general high retrieval performance for light absorption (fAPAR_Chl, R² = 0.99 for the internal model validation). We confirmed the reliability of our method in modeling re-absorption and scattering processes by comparing canopy SIF downscaled to the leaf level with independent leaf-level SIF measurements. While the results show a good prediction accuracy in terms of fluorescence magnitude at the leaf level, we did not find a strong agreement of corresponding leaf and canopy measurements at the single plot level.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"324 ","pages":"Article 114739"},"PeriodicalIF":11.1,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143826339","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":"Rapid changes in terrestrial carbon dioxide uptake captured in near-real time from a geostationary satellite: The ALIVE framework","authors":"Danielle Losos ,&nbsp;Sadegh Ranjbar ,&nbsp;Sophie Hoffman ,&nbsp;Ryan Abernathey ,&nbsp;Ankur R. Desai ,&nbsp;Jason Otkin ,&nbsp;Helin Zhang ,&nbsp;Youngryel Ryu ,&nbsp;Paul C. Stoy","doi":"10.1016/j.rse.2025.114759","DOIUrl":"10.1016/j.rse.2025.114759","url":null,"abstract":"<div><div>The terrestrial carbon cycle responds to human activity, ecosystem dynamics, and weather and climate variability including extreme events. Satellite remote sensing has transformed our ability to estimate ecosystem carbon dioxide uptake, the gross primary productivity (GPP), with increasing accuracy and spatial resolution. Many aspects of terrestrial carbon cycling happen quickly on sub-daily or daily scales. These dynamics may not be captured at the temporal scales of typical remote sensing products from polar orbiting satellites – often multiple days or longer. Imagers onboard geostationary satellites measure the Earth system at “hypertemporal” time scales of minutes or less and often have the spectral capabilities to estimate GPP and other surface-atmosphere fluxes using established approaches. Here, we use observations and data products from the Advanced Baseline Imager (ABI) on the Geostationary Environmental Operational Satellite – R Series (GOES-R) to create ALIVE_GPP (<u>A</u>dvanced Baseline Imager <u>L</u>ive <u>I</u>maging of <u>V</u>egetated <u>E</u>cosystems), a GPP product that provides open data on the native five-minute basis of GOES-R CONUS scenes with latency under one day. Our machine learning model, trained on GPP estimates from 111 eddy covariance flux towers with 276 site-years of data spanning tropical to boreal ecosystems, captures up to 70 % of the observed variability when 20 % of tower sites are withheld, with R² values of 0.78 (0.82) when aggregating to daily (weekly) periods. We compared ALIVE_GPP predictions against eight-day MODIS MOD17A2 GPP estimates and daily GPP estimates from the Breathing Earth System Simulator v2 (BESSv2) and demonstrate how ALIVE_GPP simulates the impacts of phenological transitions, flash drought, and hurricanes. Advancements to geostationary satellite imagery, machine learning, and cloud computing make it possible to estimate carbon flux in near real-time and provide new ways to understand the ever-changing carbon cycle and the processes that define it.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"324 ","pages":"Article 114759"},"PeriodicalIF":11.1,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143826340","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":"Practical global sampling methods for estimating area and map accuracy of land cover and change","authors":"Alexandra Tyukavina ,&nbsp;Stephen V. Stehman ,&nbsp;Amy H. Pickens ,&nbsp;Peter Potapov ,&nbsp;Matthew C. Hansen","doi":"10.1016/j.rse.2025.114714","DOIUrl":"10.1016/j.rse.2025.114714","url":null,"abstract":"<div><div>Recent advancements in data storage and computing, particularly cloud-based processing, enable mapping global land cover and change relatively quickly and easily. Multiple versions of a map could be produced within a matter of days with various adjustments of selected parameters of machine learning models. Sample-based validation is then required to establish correspondence between these map prototypes and the real world, thus turning them from algorithm data outputs into sources of information with quantified errors. Implementing global probability sampling of geographic data for the purposes of area estimation and map accuracy assessment presents multiple challenges, primarily linked to the way these geographic data are stored (coordinate systems and projections) and the objectives of the specific project. Here we summarize various approaches to global sampling aimed at assessing accuracy of global land cover and change maps and producing unbiased estimators of area along with the standard errors associated with these estimates for the target land cover classes. We provide a unified set of estimators that accommodate a variety of sampling designs by explicitly accounting for the area of each sample unit, as well as code and technical details necessary to implement the presented methods. While we do not compare relative precision of the presented sampling design options, our aim is to help practitioners select an appropriate sampling design and estimators for their specific data format and project objectives, and to facilitate the correct implementation and increased reproducibility of global sampling methods within the land cover mapping community.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"324 ","pages":"Article 114714"},"PeriodicalIF":11.1,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821395","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 Multiple Persistent Peaks (MPP) retracker to improve global inland water level monitoring from satellite radar altimetry","authors":"Linghong Ke ,&nbsp;Xin Ding ,&nbsp;Xinyuan Deng ,&nbsp;Jingjing Zhou ,&nbsp;Ruizhe Wang ,&nbsp;Chunqiao Song","doi":"10.1016/j.rse.2025.114744","DOIUrl":"10.1016/j.rse.2025.114744","url":null,"abstract":"<div><div>Satellite altimetry has become an indispensable technique for large-scale monitoring of inland water levels, which is fundamental to understanding the hydrologic dynamics of surface water and informed water resource management in the scenario of changing climate. Yet the capacity of inland altimetry can be further improved regarding the accuracy and the spatial-temporal coverage of water level estimates. This study presents a new Multiple Persistent Peaks (MPP) retracker to improve the retrieval of inland water levels from satellite radar altimetry. Focusing on the long-term Jason-2/3 observations, we demonstrate how the MPP retracker works in the case of complex and multipeaked waveforms commonly encountered in inland radar altimetry. The MPP retracker determines surface elevations of the target water body by grouping and multi-objective analysis of multiple along-track height profiles corresponding to persistent peaks in the aligned averaged waveform. We evaluated the results globally at 41 gauge stations and 6 crossovers with various climate, terrain, and hydrologic settings. The root-mean-square-error (RMSE) of the altimetry-derived water levels with in situ measurements is between 0.19 m and 0.78 m (median value of 0.38 m) and within the range of 0.25–0.49 m at crossovers. The validations show that the new automated method outperforms classical retrackers in terms of accuracy and robustness and is applicable to both pulse-limited and SAR altimetry. Our approach successfully retrieved good quality water level time series at river reaches with highly heterogeneous landscapes where most retrackers exhibit errors of several meters. Based on the MPP retracker, we provided an efficient tool for sophisticated outliers filtering and correction in the final time series, which is desirable for expert evaluation and improving the quality of altimetry-derived water level products. The new automated retracker and the tool we provided is a critical step toward improved monitoring of inland water levels from satellite altimetry on a global and long-term scale.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"324 ","pages":"Article 114744"},"PeriodicalIF":11.1,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821398","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":"Measuring marine hydrodynamics from space using planet satellite imagery","authors":"James B. Tlhomole ,&nbsp;Yousef Alosairi ,&nbsp;Graham O. Hughes ,&nbsp;Matthew D. Piggott","doi":"10.1016/j.rse.2025.114741","DOIUrl":"10.1016/j.rse.2025.114741","url":null,"abstract":"<div><div>The inference of coastal ocean dynamics from consecutive remote sensing images plays a central role in a diverse range of domains such as marine conservation, spatial planning, as well as flood risk. We present a methodology for systematically identifying spatially overlapping image pairs from the PlanetScope archive, with order minute scale time lags and the potential for velocity field inference using classical algorithms. This ability is demonstrated through the novel estimation of submesoscale eddies from PlanetScope image pairs in a range of contexts, providing a key novelty in this paper. These include sea ice floes in the Siberian Sea of Okhotsk, a cyanobacterial bloom in the Baltic Sea, and suspended sediment in the Port of Al-Fao located in the Arabian/Persian Gulf. Additionally, comparison of the latter with coinciding velocity fields from a Delft3D Flexible Mesh (FM) numerical model simulation shows good quantitative agreement in regions with high suspended sediment concentration. We successfully develop a workflow pipeline for identifying and processing image pairs from these opportunistic overlaps, unlocking a new large-scale source of coastal ocean surface velocity data to be used alongside modelling frameworks.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"324 ","pages":"Article 114741"},"PeriodicalIF":11.1,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143813936","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":"Rainfall-caused water film on canopy surface biases remotely-sensed vegetation greenness","authors":"Si Gao ,&nbsp;Kai Yan ,&nbsp;Guangjian Yan ,&nbsp;Miina Rautiainen ,&nbsp;Yuri Knyazikhin ,&nbsp;Ranga B. Myneni","doi":"10.1016/j.rse.2025.114747","DOIUrl":"10.1016/j.rse.2025.114747","url":null,"abstract":"<div><div>Remotely-sensed vegetation greenness exhibits obvious differences before and after the short-term heavy rainfall event. This short communication reports that residual water film on the canopy caused by precipitation directly affects the spectral signal of vegetation, which in turn biases observed vegetation greenness. We combined ground measurements, unmanned aerial vehicle (UAV) measurements, three-dimensional radiative transfer model (RTM) simulations, and satellite observations to assess this phenomenon and investigate the impact of water film on vegetation indices (VIs) across different scales and vegetation types. Our findings demonstrate that precipitation exerts a quick and significant influence on the spectral characteristics of canopy components. The presence of water can lead to reflectance attenuation in soil and vegetation components across the entire visible and infrared spectrum, particularly in the near-infrared (NIR) band, with reductions of up to 0.12. The reduction in VI measures after precipitation can be explained by an imbalance in the magnitude of visible and NIR reflectance attenuation caused by the canopy water film. Taking the difference vegetation index (DVI) as an example, the decrease in NIR reflectance is more pronounced than that of red reflectance in the presence of water, resulting in DVI decreasing after a rainfall event. Satellite observations indicated that NDVI and EVI could decrease even exceeding 0.3 in some pixels after short-term rainfall. This work reveals the water film as a possible factor contributing to the bias between true and observed vegetation greenness. The spatial distribution of rainfall seasonality exhibits variation across different regions worldwide, and mitigating the impact of water film on vegetation greenness is essential for enhancing the precision and reliability of global vegetation dynamics monitoring.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"324 ","pages":"Article 114747"},"PeriodicalIF":11.1,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817290","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}