Pub Date : 2025-10-17DOI: 10.1016/j.rse.2025.115083
Jianyu Zheng , Hongbin Yu , Yaping Zhou , Yingxi Shi , Zhibo Zhang , Claudia Di Biagio , Paola Formenti , Alexander Smirnov
Airborne mineral dust significantly influences Earth's climate through perturbing Earth's radiation budget, modulating cloud formation and microphysical properties, and fertilizing the biosphere. Recent field campaigns have revealed substantially more coarse-mode dust particles in the atmosphere than previously recognized, yet current satellite retrievals and climate models inadequately represent these particles. This study presents a novel retrieval algorithm for dust aerosol optical depth at 10 μm (AOD10μm) and effective diameter (Deff) using Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared (TIR) observations over global land and ocean. Building upon the previous synergistic approach for MODIS and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), we improve the retrieval from CALIOP-track-limited coverage to full-swath MODIS observations at 10-km resolution over both ocean and land surfaces. The retrieval improvements include: (1) application of climatological CALIOP dust vertical profiles (2007–2017) to constrain dust vertical distribution for off-CALIOP-track pixels; (2) the improved optimization method to effectively handle non-monotonic cost functions arising from temperature inversions within the Saharan Air Layer; and (3) extension to land surfaces through incorporation of MODIS-retrieved surface emissivity and ERA5 reanalysis data. Validation against coarse-mode AOD from global AERONET (N = 4703) and MAN (N = 1673) observations yields R = 0.82 and 0.85 for AOD10μm, with retrieval uncertainty characterized as ε = 15 % × AOD + 0.04. The retrieved Deff demonstrates excellent agreement with in-situ measurements collected from 24 field campaigns around the globe (R = 0.84, MBE = 0.23 μm, RMSE = 0.73 μm), capturing the particle size variation from near-source regions (Deff = 7–8 μm) to long-range transport (Deff = 3–5 μm). Case studies of dust events over the Namibian coast and trans-Atlantic corridor demonstrate the retrieval's capability to resolve episodic dust properties and size-dependent deposition during transport. This improved retrieval addresses the critical observational gap for coarse and super-coarse dust particles (D > 5 μm), providing essential constraints for dust life cycle studies and climate model evaluation.
{"title":"A novel retrieval of global dust optical depth and effective diameter based on MODIS thermal infrared observations","authors":"Jianyu Zheng , Hongbin Yu , Yaping Zhou , Yingxi Shi , Zhibo Zhang , Claudia Di Biagio , Paola Formenti , Alexander Smirnov","doi":"10.1016/j.rse.2025.115083","DOIUrl":"10.1016/j.rse.2025.115083","url":null,"abstract":"<div><div>Airborne mineral dust significantly influences Earth's climate through perturbing Earth's radiation budget, modulating cloud formation and microphysical properties, and fertilizing the biosphere. Recent field campaigns have revealed substantially more coarse-mode dust particles in the atmosphere than previously recognized, yet current satellite retrievals and climate models inadequately represent these particles. This study presents a novel retrieval algorithm for dust aerosol optical depth at 10 μm (AOD<sub>10μm</sub>) and effective diameter (Deff) using Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared (TIR) observations over global land and ocean. Building upon the previous synergistic approach for MODIS and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), we improve the retrieval from CALIOP-track-limited coverage to full-swath MODIS observations at 10-km resolution over both ocean and land surfaces. The retrieval improvements include: (1) application of climatological CALIOP dust vertical profiles (2007–2017) to constrain dust vertical distribution for off-CALIOP-track pixels; (2) the improved optimization method to effectively handle non-monotonic cost functions arising from temperature inversions within the Saharan Air Layer; and (3) extension to land surfaces through incorporation of MODIS-retrieved surface emissivity and ERA5 reanalysis data. Validation against coarse-mode AOD from global AERONET (<em>N</em> = 4703) and MAN (<em>N</em> = 1673) observations yields <em>R</em> = 0.82 and 0.85 for AOD<sub>10μm</sub>, with retrieval uncertainty characterized as ε = 15 % × AOD + 0.04. The retrieved Deff demonstrates excellent agreement with in-situ measurements collected from 24 field campaigns around the globe (<em>R</em> = 0.84, MBE = 0.23 μm, RMSE = 0.73 μm), capturing the particle size variation from near-source regions (Deff = 7–8 μm) to long-range transport (Deff = 3–5 μm). Case studies of dust events over the Namibian coast and trans-Atlantic corridor demonstrate the retrieval's capability to resolve episodic dust properties and size-dependent deposition during transport. This improved retrieval addresses the critical observational gap for coarse and super-coarse dust particles (D > 5 μm), providing essential constraints for dust life cycle studies and climate model evaluation.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"332 ","pages":"Article 115083"},"PeriodicalIF":11.4,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145306303","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}
Pub Date : 2025-10-17DOI: 10.1016/j.rse.2025.115084
Qiang Zhou , Christopher S.R. Neigh , Junchang Ju , Margaret Wooten , Zhe Zhu , Tomoaki Miura , Petya K.E. Campbell , Madhu K. Sridhar , Bradley W. Baker , Rodrigo V. Leite
<div><div>NASA's Harmonized Landsat and Sentinel-2 (HLS) project recently started to produce in forward production a total of nine Vegetation Index (VI) products from the HLS version 2.0 Landsat 8–9 30 m (L30) and Sentinel-2 30 m (S30) surface reflectance data. The HLS version 2.0 dataset provides revisit observations every 1.6 days globally and every 2.2 days in the tropics (the least frequently covered latitudes), when data from four satellites (Landsat 8–9 and Sentinel-2 A/B) are available. HLS-derived VIs can provide a valuable resource for studying vegetation dynamics, including crop growth, forest loss, and disturbance severity and recovery among others. To characterize the suitability of these VIs for scientific applications, we assessed the between-sensor uncertainties for the nine HLS VI products and 12 additional ones, using VIs derived from HLS V2.0 (L30 and S30) surface reflectance for the years 2021 and 2022. A random sample of over 136 million cloud-free observations from 545 same-day L30 and S30 image pairs were selected to represent different landscapes globally in subarctic, temperate, and tropical climates. First, we evaluated between-sensor consistency for each VI derived from L30 and S30 and found high consistency (R<sup>2</sup> > 0.94) for most VIs, except for chlorophyll vegetation index (CVI, R<sup>2</sup> = 0.5). Second, we quantified the impact of potential factors on VI uncertainties using the mean absolute difference (MAD) between L30 and S30. Large view azimuth angle differences (VAD) between observation pairs (> ∼ 125°) increased MAD by ≤0.01 in most VIs. The impact on the Root Mean Square Error Interquartile Range (RMSEIQR) for these VIs varied from a decrease of 0.029 to an increase of 0.017. High solar zenith angle (SZ) (> ∼ 60°), prevalent during winter, also increased MAD by <0.07 and RMSEIQR by <0.2 for most VIs. Furthermore, one of the largest discrepancies was found in the area of terrain shadow, with a relative difference of over 20 %. The findings showed the importance of continuing HLS algorithm refinement. Finally, we analyzed VI uncertainties across VI values and for the qualitative aerosol optical depth characterization at three levels. Using VIs derived from low-level aerosols as a baseline, we assessed the impact of aerosol levels. VIs derived from moderate-level aerosol conditions closely aligned with the baseline. However, high aerosol levels introduced evident discrepancies, highlighting increased uncertainty in VIs under these conditions. Notably, even for low-level aerosol observations, uncertainties increased at VI tail values. For robust application of HLS V2.0 VIs in scientific studies, we recommend VI value ranges associated with low uncertainty. Additionally, we reported standard deviations of discrepancies, stratified by aerosol level and VI value, enabling users to account for uncertainties in their analyses, especially for VIs derived from high aerosol levels or beyond recom
{"title":"Global uncertainty assessment of vegetation indices from NASA's Harmonized Landsat and Sentinel-2 Project","authors":"Qiang Zhou , Christopher S.R. Neigh , Junchang Ju , Margaret Wooten , Zhe Zhu , Tomoaki Miura , Petya K.E. Campbell , Madhu K. Sridhar , Bradley W. Baker , Rodrigo V. Leite","doi":"10.1016/j.rse.2025.115084","DOIUrl":"10.1016/j.rse.2025.115084","url":null,"abstract":"<div><div>NASA's Harmonized Landsat and Sentinel-2 (HLS) project recently started to produce in forward production a total of nine Vegetation Index (VI) products from the HLS version 2.0 Landsat 8–9 30 m (L30) and Sentinel-2 30 m (S30) surface reflectance data. The HLS version 2.0 dataset provides revisit observations every 1.6 days globally and every 2.2 days in the tropics (the least frequently covered latitudes), when data from four satellites (Landsat 8–9 and Sentinel-2 A/B) are available. HLS-derived VIs can provide a valuable resource for studying vegetation dynamics, including crop growth, forest loss, and disturbance severity and recovery among others. To characterize the suitability of these VIs for scientific applications, we assessed the between-sensor uncertainties for the nine HLS VI products and 12 additional ones, using VIs derived from HLS V2.0 (L30 and S30) surface reflectance for the years 2021 and 2022. A random sample of over 136 million cloud-free observations from 545 same-day L30 and S30 image pairs were selected to represent different landscapes globally in subarctic, temperate, and tropical climates. First, we evaluated between-sensor consistency for each VI derived from L30 and S30 and found high consistency (R<sup>2</sup> > 0.94) for most VIs, except for chlorophyll vegetation index (CVI, R<sup>2</sup> = 0.5). Second, we quantified the impact of potential factors on VI uncertainties using the mean absolute difference (MAD) between L30 and S30. Large view azimuth angle differences (VAD) between observation pairs (> ∼ 125°) increased MAD by ≤0.01 in most VIs. The impact on the Root Mean Square Error Interquartile Range (RMSEIQR) for these VIs varied from a decrease of 0.029 to an increase of 0.017. High solar zenith angle (SZ) (> ∼ 60°), prevalent during winter, also increased MAD by <0.07 and RMSEIQR by <0.2 for most VIs. Furthermore, one of the largest discrepancies was found in the area of terrain shadow, with a relative difference of over 20 %. The findings showed the importance of continuing HLS algorithm refinement. Finally, we analyzed VI uncertainties across VI values and for the qualitative aerosol optical depth characterization at three levels. Using VIs derived from low-level aerosols as a baseline, we assessed the impact of aerosol levels. VIs derived from moderate-level aerosol conditions closely aligned with the baseline. However, high aerosol levels introduced evident discrepancies, highlighting increased uncertainty in VIs under these conditions. Notably, even for low-level aerosol observations, uncertainties increased at VI tail values. For robust application of HLS V2.0 VIs in scientific studies, we recommend VI value ranges associated with low uncertainty. Additionally, we reported standard deviations of discrepancies, stratified by aerosol level and VI value, enabling users to account for uncertainties in their analyses, especially for VIs derived from high aerosol levels or beyond recom","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"332 ","pages":"Article 115084"},"PeriodicalIF":11.4,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145306306","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}
Pub Date : 2025-10-16DOI: 10.1016/j.rse.2025.115087
David P. Roy , Michael A. Wulder , Noel Gorelick , Matthew Hansen , Sean Healey , Patrick Hostert , Justin Huntington , Volker C. Radeloff , Ted Scambos , Crystal Schaaf , Curtis E. Woodcock , Zhe Zhu
For over fifty years, the Landsat satellite series has provided continuous and comprehensive data for monitoring changes on the Earth's terrestrial surface. Eight successive missions, carrying progressively more sophisticated sensors, with improved radiometric, geometric, and spatial characteristics, have provided an unbroken series of optical and thermal imagery, unparalleled globally. With limited lifetimes for each Landsat satellite, planning of each mission typically overlaps to ensure continuity. Commencing in 2021, planning of a Landsat-9 successor gathered user needs from across the Earth Observation (EO) community, resulting in the Landsat Next (LNext) mission design of three sun-synchronous satellites to acquire reflective and thermal wavelength observations with two to three times the temporal, spatial, and spectral resolution of previous missions. Proposed 2026 U.S. budgets have significantly reduced NASA Earth Science funding. Alternate architectures are now being investigated for Landsat Next that would only meet Landsat-9 design requirements. While this would provide observation continuity, this implies a revised Landsat Next program launched in the early 2030s with nearly 30 year old capabilities, that may acquire data with lower radiometric quality than the current on-orbit Landsat-8 and 9 missions, and that will not support the new capabilities advocated for by the EO user community. This correspondence serves to raise community awareness that the decision is pending, and outlines the observation requirements originally envisioned for LNext and how they were derived to provide context for evaluating the restructured and descoped capability now being considered.
50多年来，陆地卫星系列为监测地球表面的变化提供了连续和全面的数据。8个连续的任务，携带了越来越复杂的传感器，改进了辐射测量、几何和空间特性，提供了一系列连续的光学和热图像，这在全球是无与伦比的。由于每颗地球资源卫星的使用寿命有限，每次任务的规划通常是重叠的，以确保连续性。从2021年开始，Landsat-9继任者的规划收集了来自整个地球观测（EO）社区的用户需求，导致了Landsat Next （LNext）任务设计，该任务由三颗太阳同步卫星组成，以两到三倍于以前任务的时间、空间和光谱分辨率获取反射和热波长观测。拟议的2026年美国预算大大减少了美国宇航局地球科学基金。Landsat Next的替代架构目前正在研究中，它只能满足Landsat-9的设计要求。虽然这将提供观测的连续性，但这意味着在本世纪30年代初启动的修订后的Landsat Next计划将具有近30年的旧能力，可能会获得比当前在轨Landsat-8和9任务更低的辐射质量数据，并且将不支持EO用户社区所倡导的新能力。这种通信有助于提高社区对决策悬而未决的认识，并概述了最初为LNext设想的观察需求，以及如何推导出这些需求，以便为评估现在正在考虑的重组和分析的能力提供背景。
{"title":"The next Landsat: Mission turning point?","authors":"David P. Roy , Michael A. Wulder , Noel Gorelick , Matthew Hansen , Sean Healey , Patrick Hostert , Justin Huntington , Volker C. Radeloff , Ted Scambos , Crystal Schaaf , Curtis E. Woodcock , Zhe Zhu","doi":"10.1016/j.rse.2025.115087","DOIUrl":"10.1016/j.rse.2025.115087","url":null,"abstract":"<div><div>For over fifty years, the Landsat satellite series has provided continuous and comprehensive data for monitoring changes on the Earth's terrestrial surface. Eight successive missions, carrying progressively more sophisticated sensors, with improved radiometric, geometric, and spatial characteristics, have provided an unbroken series of optical and thermal imagery, unparalleled globally. With limited lifetimes for each Landsat satellite, planning of each mission typically overlaps to ensure continuity. Commencing in 2021, planning of a Landsat-9 successor gathered user needs from across the Earth Observation (EO) community, resulting in the Landsat Next (LNext) mission design of three sun-synchronous satellites to acquire reflective and thermal wavelength observations with two to three times the temporal, spatial, and spectral resolution of previous missions. Proposed 2026 U.S. budgets have significantly reduced NASA Earth Science funding. Alternate architectures are now being investigated for Landsat Next that would only meet Landsat-9 design requirements. While this would provide observation continuity, this implies a revised Landsat Next program launched in the early 2030s with nearly 30 year old capabilities, that may acquire data with lower radiometric quality than the current on-orbit Landsat-8 and 9 missions, and that will not support the new capabilities advocated for by the EO user community. This correspondence serves to raise community awareness that the decision is pending, and outlines the observation requirements originally envisioned for LNext and how they were derived to provide context for evaluating the restructured and descoped capability now being considered.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"332 ","pages":"Article 115087"},"PeriodicalIF":11.4,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145306307","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}
Pub Date : 2025-10-16DOI: 10.1016/j.rse.2025.115070
Haixu He , Jining Yan , Lirong Liu , Xu Long , Runyu Fan , Zhongchang Sun
Urban renewal has been elevated to a national strategy in China, leading to rapid development and transformation of street blocks. However, monitoring construction events at high temporal resolution remains challenging due to the limitations of existing methods, which often struggle with noise interference and lack continuous monitoring capabilities. To address this, we propose Semantic Similarity Contrast-based Street Block Monitoring (SSC-SB), a method that leverages Sentinel-2 time series imagery for automated, high-frequency detection of street block development and renewal. By extracting deep semantic features with a pretrained encoder, SSC-SB analyzes similarity curves to identify development and demolition construction events. Applied to the Middle Yangtze River Basin (MYRB) urban agglomeration shows that SSC-SB achieves 90.4% spatial domain accuracy, with construction start and end date detection accuracies of 68.8% and 54.9%, respectively. Results indicate an increasing emphasis on urban renewal, as demolished street blocks outnumbered new developments for the first time in 2023, with Hunan Province leading in renewal efforts, where renewal blocks accounted for 41.5% of all changed street blocks, reflecting a balanced focus on expansion and infrastructure renewal. Transfer experiments in Xi’an further demonstrate that SSC-SB retains up to 80% of the performance of a locally trained model when applied across regions without fine-tuning, indicating a decent level of generalizability. By providing fine-grained, continuous monitoring, SSC-SB presents a scalable solution for tracking urban transformation.
{"title":"Monthly monitoring of urban development and renewal at the block-level in China using Sentinel-2 time series","authors":"Haixu He , Jining Yan , Lirong Liu , Xu Long , Runyu Fan , Zhongchang Sun","doi":"10.1016/j.rse.2025.115070","DOIUrl":"10.1016/j.rse.2025.115070","url":null,"abstract":"<div><div>Urban renewal has been elevated to a national strategy in China, leading to rapid development and transformation of street blocks. However, monitoring construction events at high temporal resolution remains challenging due to the limitations of existing methods, which often struggle with noise interference and lack continuous monitoring capabilities. To address this, we propose Semantic Similarity Contrast-based Street Block Monitoring (SSC-SB), a method that leverages Sentinel-2 time series imagery for automated, high-frequency detection of street block development and renewal. By extracting deep semantic features with a pretrained encoder, SSC-SB analyzes similarity curves to identify development and demolition construction events. Applied to the Middle Yangtze River Basin (MYRB) urban agglomeration shows that SSC-SB achieves 90.4% spatial domain accuracy, with construction start and end date detection accuracies of 68.8% and 54.9%, respectively. Results indicate an increasing emphasis on urban renewal, as demolished street blocks outnumbered new developments for the first time in 2023, with Hunan Province leading in renewal efforts, where renewal blocks accounted for 41.5% of all changed street blocks, reflecting a balanced focus on expansion and infrastructure renewal. Transfer experiments in Xi’an further demonstrate that SSC-SB retains up to 80% of the performance of a locally trained model when applied across regions without fine-tuning, indicating a decent level of generalizability. By providing fine-grained, continuous monitoring, SSC-SB presents a scalable solution for tracking urban transformation.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"332 ","pages":"Article 115070"},"PeriodicalIF":11.4,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145295217","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}
Pub Date : 2025-10-15DOI: 10.1016/j.rse.2025.115068
Feiyue Mao , Weiwei Xu , Zengxin Pan , Lin Zang , Ge Han , Linxin Dai , Xiuqing Hu , Weibiao Chen , Wei Gong
Satellite lidar plays a unique role in observing the global vertical distribution of aerosols and clouds. CALIPSO (Apr 2006–Aug 2023) pioneered such observations, and China's Aerosol and Carbon Detection Lidar (ACDL) on board the DQ-1 satellite (Apr 2022-) continues this mission. Consequently, it is crucial to develop aerosol and cloud products of ACDL. Particularly, detecting the vertical and horizontal extent of aerosol and cloud layers is one of the most challenging tasks. In this study, we developed an ACDL layer detection algorithm based on the Two-Dimensional Multiscale Hypothesis Testing (2D-MHT) methodology. Notably, we proposed an approach for the uncertainty estimation in lidar return signals from the background atmosphere, enabling successful layer detection for ACDL. The results demonstrate that our algorithm not only accurately identifies layers within ACDL measurements, but also provides the probability that a specific signal bin belongs to a layer. This probability enables users to customize layer definitions, a feature not available in other lidar products that typically rely on threshold-based methods. Furthermore, the ACDL layer products offer higher horizontal resolution and detect 53.0 % more layers globally compared to the CALIPSO V4.51 merged layer product in June 2022. These findings underscore the significant potential of our algorithm and ACDL layer products for advancing atmospheric and climate research.
{"title":"Aerosol-cloud layer detection algorithm of the DQ-1/ACDL","authors":"Feiyue Mao , Weiwei Xu , Zengxin Pan , Lin Zang , Ge Han , Linxin Dai , Xiuqing Hu , Weibiao Chen , Wei Gong","doi":"10.1016/j.rse.2025.115068","DOIUrl":"10.1016/j.rse.2025.115068","url":null,"abstract":"<div><div>Satellite lidar plays a unique role in observing the global vertical distribution of aerosols and clouds. CALIPSO (Apr 2006–Aug 2023) pioneered such observations, and China's Aerosol and Carbon Detection Lidar (ACDL) on board the DQ-1 satellite (Apr 2022-) continues this mission. Consequently, it is crucial to develop aerosol and cloud products of ACDL. Particularly, detecting the vertical and horizontal extent of aerosol and cloud layers is one of the most challenging tasks. In this study, we developed an ACDL layer detection algorithm based on the Two-Dimensional Multiscale Hypothesis Testing (2D-MHT) methodology. Notably, we proposed an approach for the uncertainty estimation in lidar return signals from the background atmosphere, enabling successful layer detection for ACDL. The results demonstrate that our algorithm not only accurately identifies layers within ACDL measurements, but also provides the probability that a specific signal bin belongs to a layer. This probability enables users to customize layer definitions, a feature not available in other lidar products that typically rely on threshold-based methods. Furthermore, the ACDL layer products offer higher horizontal resolution and detect 53.0 % more layers globally compared to the CALIPSO V4.51 merged layer product in June 2022. These findings underscore the significant potential of our algorithm and ACDL layer products for advancing atmospheric and climate research.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"332 ","pages":"Article 115068"},"PeriodicalIF":11.4,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145288904","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}
Pub Date : 2025-10-15DOI: 10.1016/j.rse.2025.115060
Jiangyuan Zeng , Di Long , Yongqiang Zhang , Dongryeol Ryu , Jean-Pierre Wigneron , Qi Huang
In light of the rapid advancements in hydrological science research facilitated by cutting-edge remote sensing technologies, such as synthetic aperture radar (SAR), hyperspectral imaging, and Light Detection and Ranging (LiDAR), we have curated a special issue in Remote Sensing of Environment entitled “Emerging remote sensing techniques for hydrological applications”, spanning from October 2022 to April 2024. This special issue comprises 31 publications that highlight methodologies leveraging multi-sensor satellite platforms, unmanned aerial vehicles (UAVs), and advanced physical models and machine learning approaches to improve the monitoring and modeling of key hydrological flux and state variables. These remote sensing retrievals (e.g., river discharge and soil moisture) have been applied to various operational hydrological applications such as real-time flood monitoring and drought risk assessment. To provide a systematic overview, we categorize these publications based upon hydrological themes and the number of publications, covering topics such as water body, soil moisture, river discharge, water level, drought, water storage, and other related areas. Finally, we provide an outlook that envisages how the emerging trends (e.g., multi-sensor integration and machine learning-driven approaches) identified from the published studies will evolve and shape future research directions in hydrological remote sensing.
{"title":"Emerging remote sensing techniques for hydrological applications","authors":"Jiangyuan Zeng , Di Long , Yongqiang Zhang , Dongryeol Ryu , Jean-Pierre Wigneron , Qi Huang","doi":"10.1016/j.rse.2025.115060","DOIUrl":"10.1016/j.rse.2025.115060","url":null,"abstract":"<div><div>In light of the rapid advancements in hydrological science research facilitated by cutting-edge remote sensing technologies, such as synthetic aperture radar (SAR), hyperspectral imaging, and Light Detection and Ranging (LiDAR), we have curated a special issue in <em>Remote Sensing of Environment</em> entitled “Emerging remote sensing techniques for hydrological applications”, spanning from October 2022 to April 2024. This special issue comprises 31 publications that highlight methodologies leveraging multi-sensor satellite platforms, unmanned aerial vehicles (UAVs), and advanced physical models and machine learning approaches to improve the monitoring and modeling of key hydrological flux and state variables. These remote sensing retrievals (e.g., river discharge and soil moisture) have been applied to various operational hydrological applications such as real-time flood monitoring and drought risk assessment. To provide a systematic overview, we categorize these publications based upon hydrological themes and the number of publications, covering topics such as water body, soil moisture, river discharge, water level, drought, water storage, and other related areas. Finally, we provide an outlook that envisages how the emerging trends (e.g., multi-sensor integration and machine learning-driven approaches) identified from the published studies will evolve and shape future research directions in hydrological remote sensing.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"332 ","pages":"Article 115060"},"PeriodicalIF":11.4,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145288903","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}
Pub Date : 2025-10-13DOI: 10.1016/j.rse.2025.115067
Yuhao Wu , Bin Li , Jun Li , Yonglou Liang , Naiqiang Zhang , Anlai Sun
Accurate cloud detection is essential for the quantitative applications of satellite imager observations, but nighttime cloud detection has challenges due to limited spectral bands, for example, physical methods using only infrared (IR) bands without using spatial textures as input for cloud detection often result in high uncertainties, especially in some situations such as cryosphere surface. Although numerous segmentation-style deep learning cloud detection algorithms have proposed in previous studies, they are inadequate for nighttime due to the difficulty in acquiring two-dimensional truth data for training and validation. To overcome these challenges, the Transformer based Nighttime Cloud Detection (TNCD) framework, which integrates spatial features and utilizes an advanced Transformer architecture with relative position encoding, layer scaling, and channel attention mechanisms, is proposed and investigated for nighttime cloud detection. The model was trained on labels derived from CALIOP data, utilizing a dataset comprising nearly one hundred million segments from MODIS. Independent validation indicates that TNCD achieves robust and consistent performance across various scenarios, with an overall accuracy (OA) of 93.26 % and over 90 % in cryosphere regions. The proposed algorithm avoids the pattern noise appeared in the traditional physical methodology due to the utilization of auxiliary data at coarser resolutions, it also mitigates the negative impact of stripes in IR images for cloud detection. Moreover, TNCD shows high transferable practicability across sensors, with over 90 % OA for MERSI. More importantly, our research underscores the importance of water vapor absorption bands for nighttime cloud detection over the cryosphere. TNCD's high accuracy and robustness provide unique methodology that could be used operationally for nighttime cloud detection.
{"title":"Enhancing nighttime cloud detection for moderate resolution imagers using a transformer based deep learning network","authors":"Yuhao Wu , Bin Li , Jun Li , Yonglou Liang , Naiqiang Zhang , Anlai Sun","doi":"10.1016/j.rse.2025.115067","DOIUrl":"10.1016/j.rse.2025.115067","url":null,"abstract":"<div><div>Accurate cloud detection is essential for the quantitative applications of satellite imager observations, but nighttime cloud detection has challenges due to limited spectral bands, for example, physical methods using only infrared (IR) bands without using spatial textures as input for cloud detection often result in high uncertainties, especially in some situations such as cryosphere surface. Although numerous segmentation-style deep learning cloud detection algorithms have proposed in previous studies, they are inadequate for nighttime due to the difficulty in acquiring two-dimensional truth data for training and validation. To overcome these challenges, the Transformer based Nighttime Cloud Detection (TNCD) framework, which integrates spatial features and utilizes an advanced Transformer architecture with relative position encoding, layer scaling, and channel attention mechanisms, is proposed and investigated for nighttime cloud detection. The model was trained on labels derived from CALIOP data, utilizing a dataset comprising nearly one hundred million segments from MODIS. Independent validation indicates that TNCD achieves robust and consistent performance across various scenarios, with an overall accuracy (OA) of 93.26 % and over 90 % in cryosphere regions. The proposed algorithm avoids the pattern noise appeared in the traditional physical methodology due to the utilization of auxiliary data at coarser resolutions, it also mitigates the negative impact of stripes in IR images for cloud detection. Moreover, TNCD shows high transferable practicability across sensors, with over 90 % OA for MERSI. More importantly, our research underscores the importance of water vapor absorption bands for nighttime cloud detection over the cryosphere. TNCD's high accuracy and robustness provide unique methodology that could be used operationally for nighttime cloud detection.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"332 ","pages":"Article 115067"},"PeriodicalIF":11.4,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145283136","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}
Pub Date : 2025-10-11DOI: 10.1016/j.rse.2025.115065
Dalei Han , Jing Liu , Shan Xu , Tiangang Yin , Siya Liu , Runfei Zhang , Peiqi Yang
The fraction of absorbed photosynthetically active radiation (fAPAR) of vegetation canopies is a crucial variable for understanding the ecosystem carbon cycle and assessing vegetation responses to climate change. Light absorption of the vegetation canopy is mainly determined by canopy structure and leaf optical properties. Traditional remote sensing methods typically estimate fAPAR from reflectance signals using radiative transfer models or empirical relationships with vegetation indices (VIs) and fAPAR. However, reflectance-based estimates often show moderate accuracy due to the complex relationship between reflected and absorbed fluxes. Airborne LiDAR provides direct information on canopy structural attributes relevant to radiation interception, such as fractional vegetation cover (fCover), which has been used to estimate fAPAR. However, the shortcomings of LiDAR in capturing the role of leaf optical properties introduce some uncertainty in fAPAR estimation. Combining reflectance with LiDAR data offers a promising pathway for improving fAPAR estimation. In this study, we adapted a physically-based model () to integrate reflectance and LiDAR observations for fAPAR estimation. This model is grounded in spectral invariant theory and represents fAPAR as a function of visible and near-infrared reflectance and a LiDAR-derived canopy structural parameter. The model was evaluated against both VI- and LiDAR-based methods using NEON field datasets and synthetic datasets generated by the one-dimensional SCOPE and three-dimensional LESS radiative transfer models. Across these datasets, the combination of LiDAR and reflectance through the model consistently outperformed VI- and LiDAR-based approaches, with respective maximum improvements in R2 of 0.47 and 0.09. Sensitivity analyses on the simulated datasets further indicated that exhibited higher robustness to variations in chlorophyll content and leaf area index (LAI) than other conventional methods. The proposed model effectively integrates reflectance and LiDAR data through a physically-based scheme, offering improved accuracy and robustness for large-scale fAPAR estimation and ecosystem monitoring.
{"title":"Estimation of canopy fAPAR using optical reflectance and airborne LiDAR data","authors":"Dalei Han , Jing Liu , Shan Xu , Tiangang Yin , Siya Liu , Runfei Zhang , Peiqi Yang","doi":"10.1016/j.rse.2025.115065","DOIUrl":"10.1016/j.rse.2025.115065","url":null,"abstract":"<div><div>The fraction of absorbed photosynthetically active radiation (fAPAR) of vegetation canopies is a crucial variable for understanding the ecosystem carbon cycle and assessing vegetation responses to climate change. Light absorption of the vegetation canopy is mainly determined by canopy structure and leaf optical properties. Traditional remote sensing methods typically estimate fAPAR from reflectance signals using radiative transfer models or empirical relationships with vegetation indices (VIs) and fAPAR. However, reflectance-based estimates often show moderate accuracy due to the complex relationship between reflected and absorbed fluxes. Airborne LiDAR provides direct information on canopy structural attributes relevant to radiation interception, such as fractional vegetation cover (fCover), which has been used to estimate fAPAR. However, the shortcomings of LiDAR in capturing the role of leaf optical properties introduce some uncertainty in fAPAR estimation. Combining reflectance with LiDAR data offers a promising pathway for improving fAPAR estimation. In this study, we adapted a physically-based model (<span><math><msub><mtext>fAPAR</mtext><mi>RL</mi></msub></math></span>) to integrate reflectance and LiDAR observations for fAPAR estimation. This model is grounded in spectral invariant theory and represents fAPAR as a function of visible and near-infrared reflectance and a LiDAR-derived canopy structural parameter. The model was evaluated against both VI- and LiDAR-based methods using NEON field datasets and synthetic datasets generated by the one-dimensional SCOPE and three-dimensional LESS radiative transfer models. Across these datasets, the combination of LiDAR and reflectance through the <span><math><msub><mtext>fAPAR</mtext><mi>RL</mi></msub></math></span> model consistently outperformed VI- and LiDAR-based approaches, with respective maximum improvements in R<sup>2</sup> of 0.47 and 0.09. Sensitivity analyses on the simulated datasets further indicated that <span><math><msub><mtext>fAPAR</mtext><mi>RL</mi></msub></math></span> exhibited higher robustness to variations in chlorophyll content and leaf area index (LAI) than other conventional methods. The proposed <span><math><msub><mtext>fAPAR</mtext><mi>RL</mi></msub></math></span> model effectively integrates reflectance and LiDAR data through a physically-based scheme, offering improved accuracy and robustness for large-scale fAPAR estimation and ecosystem monitoring.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"332 ","pages":"Article 115065"},"PeriodicalIF":11.4,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145261001","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}
Pub Date : 2025-10-11DOI: 10.1016/j.rse.2025.115069
Connor W. Stephens , Anthony R. Ives , Volker C. Radeloff
{"title":"Corrigendum to “Substantial increases in burned area in circumboreal forests from 1983 to 2020 captured by the AVHRR record and a new autoregressive burned area detection algorithm” [Remote Sensing of Environment 325(2025) 114789]","authors":"Connor W. Stephens , Anthony R. Ives , Volker C. Radeloff","doi":"10.1016/j.rse.2025.115069","DOIUrl":"10.1016/j.rse.2025.115069","url":null,"abstract":"","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"332 ","pages":"Article 115069"},"PeriodicalIF":11.4,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145283352","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}
An in-house-developed millimeter-wave humidity sounder onboard EOS-07 (EOS-07 MHS), launched in February 2023, operates at six frequencies around the 183.3 GHz water vapor absorption band. This study presents a preliminary performance assessment of EOS-07 MHS, including brightness temperature validation, humidity profile retrieval methodology and its validation.
Under clear-sky conditions, the biases in brightness temperature measured by EOS-07 MHS, relative to RTTOV simulations were within ±1 K, except for channels 1 and 6. Similarly, intercomparisons with ATMS observations showed biases within ±1 K and a standard deviation of 2–3 K.
A random forest-based method was employed to retrieve specific humidity profiles from EOS-07 MHS observations demonstrated agreement with ERA5 reanalysis and radiosonde observations. Compared with radiosonde data, the mean bias and standard deviation of retrieved specific humidity were approximately 0.78 g/kg and 2.3 g/kg, respectively. The mean percentage bias was within ±20 % below the 800 hPa pressure level, and ranged between ±20 % and ± 40 % above the 800 hPa pressure level. Relative to ERA5, the mean bias and root-mean-square deviation (RMSD) were under 30 % and 50 %, respectively. The estimated total precipitable water vapor showed a mean bias of 1.7–3.1 mm and a standard deviation of 5.2–5.7 mm compared to ERA5. Additionally, the EOS-07 MHS data were assimilated into the WRF model, resulting in improved atmospheric analyses and forecasts. A month-long cyclic assimilation experiment demonstrated consistent enhancements in moisture representation across the lower and middle atmosphere.
{"title":"Assessment of EOS-07 MHS satellite observations and retrieval of specific humidity profiles using a random forest-based algorithm","authors":"Manoj Kumar Mishra, Rishi Kumar Gangwar, Munn Vinayak Shukla, Prashant Kumar, Pradeep Kumar Thapliyal","doi":"10.1016/j.rse.2025.115066","DOIUrl":"10.1016/j.rse.2025.115066","url":null,"abstract":"<div><div>An in-house-developed millimeter-wave humidity sounder onboard EOS-07 (EOS-07 MHS), launched in February 2023, operates at six frequencies around the 183.3 GHz water vapor absorption band. This study presents a preliminary performance assessment of EOS-07 MHS, including brightness temperature validation, humidity profile retrieval methodology and its validation.</div><div>Under clear-sky conditions, the biases in brightness temperature measured by EOS-07 MHS, relative to RTTOV simulations were within ±1 K, except for channels 1 and 6. Similarly, intercomparisons with ATMS observations showed biases within ±1 K and a standard deviation of 2–3 K.</div><div>A random forest-based method was employed to retrieve specific humidity profiles from EOS-07 MHS observations demonstrated agreement with ERA5 reanalysis and radiosonde observations. Compared with radiosonde data, the mean bias and standard deviation of retrieved specific humidity were approximately 0.78 g/kg and 2.3 g/kg, respectively. The mean percentage bias was within ±20 % below the 800 hPa pressure level, and ranged between ±20 % and ± 40 % above the 800 hPa pressure level. Relative to ERA5, the mean bias and root-mean-square deviation (RMSD) were under 30 % and 50 %, respectively. The estimated total precipitable water vapor showed a mean bias of 1.7–3.1 mm and a standard deviation of 5.2–5.7 mm compared to ERA5. Additionally, the EOS-07 MHS data were assimilated into the WRF model, resulting in improved atmospheric analyses and forecasts. A month-long cyclic assimilation experiment demonstrated consistent enhancements in moisture representation across the lower and middle atmosphere.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"332 ","pages":"Article 115066"},"PeriodicalIF":11.4,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145261002","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}
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