Remote Sensing of Environment最新文献

{"title":"Mapping of sea ice in 1975 and 1976 using the NIMBUS-6 Scanning Microwave Spectrometer (SCAMS)","authors":"Wiebke Margitta Kolbe , Rasmus T. Tonboe , Julienne Stroeve","doi":"10.1016/j.rse.2025.114815","DOIUrl":"10.1016/j.rse.2025.114815","url":null,"abstract":"<div><div>The Scanning Microwave Spectrometer (SCAMS) onboard the NIMBUS-6 satellite operated between 15 June 1975 and 1 June 1976. Its primary mission objective was to map tropospheric temperature profiles for improving weather predictions, measuring Brightness Temperature(s) (<span><math><mrow><mi>T</mi><mi>B</mi></mrow></math></span>s) at five different frequencies (22.235, 31.65, 52.85, 53.85 and 55.45 GHz). However, the top-of-the-atmosphere emission measured at the 22.235 and the 31.65 GHz radiometer channels on the satellite are dominated by surface emission over polar open water and sea ice and can therefore be used for mapping sea ice concentration (SIC) on large scale (<span><math><mo>∼</mo></math></span>100 km).</div><div>Here we present a SIC and ice type data set, which is based on the <span><math><mrow><mi>T</mi><mi>B</mi></mrow></math></span> observations of the two lowest frequencies of SCAMS (center frequencies at 22.235 & 31.65 GHz). While the SCAMS channels do not completely align with the usual frequencies for sea ice retrievals (19, 22 and 37 GHz) in modern processing methods, it is still possible to apply modern techniques to reduce noise in the data. The SIC dataset provides important insights into the sea ice concentration, extent and type of the mid 1970s, where other satellite datasets e.g. the NIMBUS-5 ESMR have gaps and irregular coverage. The SCAMS data has been processed following modern methods, including a regional noise reduction over open water using a simple radiative transfer model, land-spillover corrections and estimation of uncertainties, as well as dynamical tie-points to calibrate the algorithm. The data set has been resampled into daily files with EUMETSAT’s OSI-SAF and ESA CCI compatible daily grids and land masks, for easier comparison with other data sets, such as the modern multi-frequency period starting with NIMBUS-7 SMMR in October 1978 to present and the 1972–1977 period covered by the NIMBUS-5 ESMR with some interruptions. The SCAMS <span><math><mrow><mi>T</mi><mi>B</mi></mrow></math></span>s were processed with a hybrid SIC algorithm, combining a one and a two-channel algorithm over open water and ice respectively.</div><div>We find that the SIC calculated by the two-channel algorithm has more noise over water and low SIC areas than the single-channel algorithm. However, the two-channel algorithm does not systematically underestimate SIC in regions covered by Multi Year Ice (MYI) as the single channel algorithm does. A classification of sea ice types for First Year Ice (FYI) and MYI in the Northern Hemisphere (NH) proved successful, while it was also possible to identify two surface types A and B for the Southern Hemisphere (SH) sea ice, with different radiometric signatures.</div><div>A comparison of monthly mean sea ice extent (SIE) with the NIMBUS-5 ESMR showed good alignment in the both hemispheres, where the SCAMS SIE is larger by 386 676 km<span><math><msup><mrow></mrow><mrow><mn>2</mn","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114815"},"PeriodicalIF":11.1,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144223054","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":"Multi-grained estimation of nighttime light dynamics during the COVID-19 surge in Shanghai with SDGSAT-1 GIU imagery and point of interest data","authors":"Zheng Zhang ,&nbsp;Huadong Guo ,&nbsp;Dongmei Yan ,&nbsp;Zhiqiang Liu ,&nbsp;Weixiong Zhang ,&nbsp;Jun Yan ,&nbsp;Ping Tang","doi":"10.1016/j.rse.2025.114822","DOIUrl":"10.1016/j.rse.2025.114822","url":null,"abstract":"<div><div>Nighttime light (NTL) imagery remotely sensed from outer space has been suggested to be a suitable proxy to investigate socioeconomic dynamics. Since the outbreak of COVID-19, many studies have used NTL imagery to estimate the impacts of the pandemic. However, finer-grained analytics are rarely achieved limited by the spatial resolution of major NTL data sources. In November, 2021, the Sustainable Development Science Satellite-1 (SDGSAT-1) was launched and one of its payloads, Glimmer Imager for Urbanization (GIU) can provide 10m/40 m panchromatic and multispectral NTL images for public use. In this study, we estimate the fine-grained NTL dynamics before and after the COVID-19 surge in the city of Shanghai during the second quarter of 2022 using SDGSAT-1 GIU nighttime imagery. To distinguish the different behaviors among urban functional entities, categorized Point of Interest (POI) data are adopted. The estimation is conducted in three progressive levels: city-level, POI-class-level, and POI-object-level. To characterize each urban objects from multiple angles, two additional NTL indices, NTL and luminous area ratio composite index (NTL-CI) and NTL background relative activeness index (NTL-AI) are introduced and estimated. On the basis of raw NTL, NTL-CI further considers the change of luminous area and NTL-AI further considers the relative change to the average standard. Moreover, detailed visual observations at typical POI objects are conducted, for instance, the Shanghai Disney Resort, the Shanghai Tesla Gigafactory, and multiple cabin hospitals temporarily converted from large stadiums and exhibition centers. This study aims to present a comprehensive investigation of the socioeconomic influence of COVID-19 in Shanghai from the perspective of NTL changes in multiple granularities, and the utility of SDGSAT-1 GIU nighttime imagery in supporting SDG 3 (The Sustainable Development Goals 3: Good Health and Well-Being) is also demonstrated with this set of quantitative analytics.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114822"},"PeriodicalIF":11.1,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144203859","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 universal yet easy-to-use data-driven method for angular normalization of directional land surface temperatures acquired from polar orbiters across global cities","authors":"Huilin Du ,&nbsp;Wenfeng Zhan ,&nbsp;Zihan Liu ,&nbsp;Chenguang Wang ,&nbsp;Fan Huang","doi":"10.1016/j.rse.2025.114840","DOIUrl":"10.1016/j.rse.2025.114840","url":null,"abstract":"<div><div>Urban thermal anisotropy poses significant challenges for accurately retrieving land surface temperature (LST) in urban environments using wide-swath polar orbiters. Existing physical and kernel-driven models often require detailed urban structural and property information or rely on simultaneous multi-angle LST observations, limiting their applicability for normalizing directional LSTs across diverse urban settings worldwide. Here we propose a UNIversal, easy-To-usE Data-driven (UNITED) method for angular normalization of directional LSTs across global cities, integrating advanced machine learning techniques with multi-source remote sensing and reanalysis data. We applied this method to normalize directional urban LSTs from all available wide-swath polar orbiters (Aqua MODIS, Terra MODIS, Suomi-NPP VIIRS) on Google Earth Engine, leveraging their full archives of multi-angle observations (2003–2024 for MODIS and 2012–2024 for VIIRS). The method's high accuracy in normalizing these three products was rigorously validated using quasi-simultaneous, near-nadir LSTs from various satellite platforms (e.g., Landsat) across tens of millions of urban pixels worldwide under diverse spatial, temporal, and angular conditions. For example, for Aqua MODIS observations with viewing zenith angle exceeding ±55°, angular normalization reduces the root mean square error and bias relative to nadir VIIRS LSTs (used as the reference) from 5.71 °C and −4.92 °C to 2.43 °C and −0.40 °C, respectively, underscoring the effectiveness of the UNITED method in harmonizing directional urban LSTs. Our study holds significant implications for advancing urban thermal remote sensing.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114840"},"PeriodicalIF":11.1,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144203858","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":"Spatial and temporal dynamics of plant water source distribution in China","authors":"Hongjiang Chen,&nbsp;Genxu Wang,&nbsp;Juying Sun,&nbsp;Li Guo,&nbsp;Chunlin Song,&nbsp;Xiangyang Sun","doi":"10.1016/j.rse.2025.114843","DOIUrl":"10.1016/j.rse.2025.114843","url":null,"abstract":"<div><div>Plant water use strategies play a crucial role in regulating soil moisture, mediating plant-climate feedbacks, and influencing species competition and symbiotic relationships. However, the lack of long-term and large-scale studies on plant water sources has significantly limited comprehensive estimations of the spatiotemporal variations in plant water sources and their impacts on ecohydrological processes. To address this challenge, this study compiled literature data from 210 study regions across China and applied a multivariate random forest model to generate a 0.25° × 0.25° spatial resolution map of plant water source distributions in China from 2001 to 2022. The model estimated the proportions of water uptake by plants from different soil depths and groundwater, and analyzed the variations in water sources across different vegetation types, as well as the key factors influencing plant water sources. The model results aligned well with existing experimental studies, demonstrating their reliability in capturing the spatiotemporal distribution and trends of plant water sources. Nationally, plants derived on average 34.64 ± 6.45 % of their water from shallow soil (0–30 cm), 24.60 ± 3.42 % from middle soil (30–60 cm), 32.68 ± 6.74 % from deep soil (&gt;60 cm), and 8.08 ± 4.7 % from groundwater. Significant differences in plant water sources were observed between southern and semi-arid northern regions of China, with plants in the southern regions predominantly relying on shallow and middle depth soil water, while those in the northwestern regions showed a greater dependence on deep soil water and groundwater. The correlation between soil water content and precipitation with variations in plant water sources was found to be more pronounced. Specifically, shallow soil moisture content and precipitation were positively correlated with the proportion of water absorbed by plants from shallow soil, but negatively correlated with the proportion of water absorbed from deep soil. From 2001 to 2022, there was a nationwide trend of an increased proportion of plant water uptake from shallow and deep soil layers, while the proportion from middle and groundwater decreased. This study fills a critical gap in the large-scale integrated study of plant water sources in China, providing valuable data and methodological references for related scientific research. The results of this study also contribute to the understanding of the ability to enhance vegetation adaptation to environmental changes, provide important driving data for ecohydrological model simulations and important data to support a more accurate assessment of ecosystem adaptation and water resource changes in the context of current extreme climate change.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114843"},"PeriodicalIF":11.1,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144203857","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":"Global retrieval of canopy chlorophyll content from Sentinel-3 OLCI TOA data using a two-step upscaling method integrating physical and machine learning models","authors":"Dong Li ,&nbsp;Holly Croft ,&nbsp;Gregory Duveiller ,&nbsp;Adam P. Schreiner-McGraw ,&nbsp;Anirudh Belwalkar ,&nbsp;Tao Cheng ,&nbsp;Yan Zhu ,&nbsp;Weixing Cao ,&nbsp;Kang Yu","doi":"10.1016/j.rse.2025.114845","DOIUrl":"10.1016/j.rse.2025.114845","url":null,"abstract":"<div><div>Canopy chlorophyll content per ground area (CCC, g·m⁻²) is tightly related to vegetation photosynthesis and is a promising indicator of photosynthetic capacity. However, a global operational CCC product is not yet available. To fill this gap, we developed a two-step upscaling method to estimate global CCC from Sentinel-3 OLCI top-of-atmosphere (TOA) reflectance. In the first step, a physically-based PROSAIL-D inversion model produced accurate CCC maps from over 20,000 high-spatial resolution (1 m) airborne hyperspectral images collected across 50 sites within the National Ecological Observatory Network (NEON) between 2019 and 2021. The validation against ground CCC measurements showed an R² of 0.89 and an RMSE of 0.30 g·m⁻². In the second step, these high-resolution CCC maps were resampled or upscaled to a spatial resolution of 300 m, and combined with Sentinel-3 OLCI TOA reflectance images to train random forest (RF) models. The RF model demonstrated robust performance with leave-one-site-out cross-validation, yielding an R² of 0.92 and RMSE of 0.14 g·m⁻². The two-step method also showed minimal sensitivity to angular effects and land cover variations, underscoring its robustness. In comparison, the traditional direct inversion method (the one-step method) led to underestimation of CCC by 0.16 g·m⁻² and a moderate estimation accuracy (R² = 0.65, RMSE = 0.30 g·m⁻²). We generated a long-term global OLCI CCC product using Sentinel-3 OLCI TOA reflectance data from 2016 to 2024, which can also be continuously updated using current data. This global CCC product can provide important plant physiological information, for parameterizing terrestrial biosphere models and capturing spatiotemporal photosynthetic patterns, thereby advancing research on vegetation carbon dynamics cycles at the global scale.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114845"},"PeriodicalIF":11.1,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196225","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":"Spectral properties and remote sensing of snow algal blooms in the Antarctic Peninsula","authors":"Barjeece Bashir ,&nbsp;Dong Liang ,&nbsp;Rong Cai ,&nbsp;Faisal Mumtaz ,&nbsp;Lingyi Kong ,&nbsp;Yahui Zou","doi":"10.1016/j.rse.2025.114839","DOIUrl":"10.1016/j.rse.2025.114839","url":null,"abstract":"<div><div>Snow algae, microscopic organisms thriving in snow-covered environments, significantly affect snow albedo and broader climatic processes. This study introduces the Algae Presence Index (API), a novel spectral tool using Sentinel-2 multispectral imagery to detect and classify red and green algae on King George Island, Antarctica. From 2019 to 2023, we analyzed temporal and spatial variations in algae presence during austral summers and observed corresponding reductions in surface albedo, demonstrating how algal blooms influence snowmelt. Green algae showed a stronger albedo reduction (up to 8.46 %) compared to red algae (5.33 %), emphasizing their greater role in accelerating snowmelt. The API outperformed traditional indices, such as the red/green band ratio and Red-Green Normalized Difference. It eliminated spectral overlap and accurately distinguished algae types from algae-free snow. These findings underscore the critical role of snow algae in climate feedback mechanisms and highlight the importance of monitoring their growth during Antarctic warming. This methodology provides a robust framework for assessing algae impacts on the cryosphere, with important implications for climate models and conservation.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114839"},"PeriodicalIF":11.1,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144190055","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":"On the generalization ability of probabilistic neural networks for hyperspectral remote sensing of absorption properties across optically complex waters","authors":"Mortimer Werther ,&nbsp;Olivier Burggraaff ,&nbsp;Daniela Gurlin ,&nbsp;Arun M. Saranathan ,&nbsp;Sundarabalan V. Balasubramanian ,&nbsp;Claudia Giardino ,&nbsp;Federica Braga ,&nbsp;Mariano Bresciani ,&nbsp;Andrea Pellegrino ,&nbsp;Monica Pinardi ,&nbsp;Stefan G.H. Simis ,&nbsp;Moritz K. Lehmann ,&nbsp;Kersti Kangro ,&nbsp;Krista Alikas ,&nbsp;Dariusz Ficek ,&nbsp;Daniel Odermatt","doi":"10.1016/j.rse.2025.114820","DOIUrl":"10.1016/j.rse.2025.114820","url":null,"abstract":"<div><div>Machine learning models have steadily improved in estimating inherent optical properties (IOPs) from remote sensing observations. Yet, their generalization ability when applied to new water bodies, beyond those they were trained on, is not well understood. We present a novel approach for assessing model generalization across various scenarios, including interpolation within <em>in situ</em> observation datasets, extrapolation beyond the training scope, and application to hyperspectral observations from the PRecursore IperSpettrale della Missione Applicativa (PRISMA) satellite involving atmospheric correction. We evaluate five probabilistic neural networks (PNNs), including novel architectures like recurrent neural networks, for their ability to estimate absorption at 443 and 675 nm from hyperspectral reflectance. The median symmetric accuracy (MdSA) worsens from <span><math><mo>≥</mo></math></span>25% in interpolation scenarios to <span><math><mo>≥</mo></math></span>50% in extrapolation scenarios, and reaches <span><math><mo>≥</mo></math></span>80% when applied to PRISMA satellite imagery. Across all scenarios, models produce uncertainty estimates exceeding 40%, often reflecting systematic underconfidence. PNNs show better calibration during extrapolation, suggesting an intrinsic awareness of retrieval constraints. To address this miscalibration, we introduce an uncertainty recalibration method that only withholds 10% of the training dataset, but improves model calibration in 86% of PRISMA evaluations with minimal accuracy trade-offs. Resulting well-calibrated uncertainty estimates enable reliable uncertainty propagation for downstream applications. IOP retrieval uncertainty is predominantly aleatoric (inherent to the observations). Therefore, increasing the number of measurements from the same distribution or selecting a different neural network architecture trained on the same dataset does not enhance model accuracy. Our findings indicate that we have reached a predictability limit in retrieving IOPs using purely data-driven approaches. We therefore advocate embedding physical principles of IOPs into model architectures, creating physics-informed neural networks capable of surpassing current limitations.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114820"},"PeriodicalIF":11.1,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196224","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 multi-parameter optimized sub-waveform retracker for monitoring river water levels using SAR altimetry","authors":"Xianwen Gao ,&nbsp;Taoyong Jin ,&nbsp;Xiaoli Deng ,&nbsp;Weiping Jiang ,&nbsp;Jiancheng Li","doi":"10.1016/j.rse.2025.114838","DOIUrl":"10.1016/j.rse.2025.114838","url":null,"abstract":"<div><div>Synthetic Aperture Radar (SAR) altimetry has been widely used for monitoring river water levels, especially over large and medium-sized rivers. However, challenges still remain in obtaining continuous and high-precision water levels over small rivers due to the altimeter's sparse along-track sampling, distorted waveforms, and river slopes. This study presents a new multi-parameter optimized sub-waveform (MulPOS) retracker, which retracks the waveforms across all cycles through a quantitatively considered integration of the spatial consistency and temporal continuity of water levels, river slopes, and the strong reflectivity of the river surface. Firstly, along-track sampling is increased by searching for off-nadir observations within half the sampling resolution from nadir water bodies to retrieve continuous river water levels. Secondly, waveform preprocessing, including interpolation and filtering is used to determine more accurate retracking points, and then all possible sub-waveform sets that correspond to river reflections are formed. The most likely sub-waveform sets are determined by their four-parameter weighting function, which considers spatial consistency, temporal continuity of water level variations, and the high reflectivity of the river water surface. Finally, slope corrections are computed using the robust Helmert variance component estimation method by combining the differences between water levels in adjacent cycles and along the track. The MulPOS has been applied to 290 virtual stations formed by Sentinel-3A/3B and Sentinel-6 MF over rivers in the United States (52 % of which are narrower than 100 m). For comparison purposes, six other retrackers have been used, including OCOG, ICE1, threshold, NPPTR, SAMOSA+, and MWaPP+. The results have been validated against the in-situ measurements from the United States Geological Survey, indicating that the water levels derived by MulPOS are superior to other retrackers with a median RMSE of 17.9 cm, a median relative RMSE of 7.2 %, a median correlation coefficient of 0.96, and an abnormal water level occurrence rate of 0.60 %, whereas the corresponding metrics for other retrackers are &gt;24.2 cm, &gt;9.8 %, &lt;0.94, and &gt; 2.36 %. Moreover, MulPOS achieves steady and high-precision water levels across most small rivers under varying river widths (e.g., RMSE for MulPOS is 20.9 cm vs. &gt;29.5 cm for other retrackers over rivers narrower than 50 m), varying angles between satellite ground tracks and rivers, and complex river morphologies. MulPOS is expected to generate a dataset with continuous, high-precision water level data for more small and medium-sized rivers, and this will expand the application of altimetry to inland water monitoring.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"327 ","pages":"Article 114838"},"PeriodicalIF":11.1,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178334","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":"Observing carbon monoxide and volatile organic compounds from Canadian wildfires in 2023 from FengYun-3E/HIRAS-II in a dawn-dusk sun-synchronous orbit","authors":"Jiancong Hua ,&nbsp;Shangyi Liu ,&nbsp;Chengli Qi ,&nbsp;Sirui Wu ,&nbsp;Lu Lee ,&nbsp;Xiuqing Hu ,&nbsp;Xiaoyi Zhao ,&nbsp;Kimberly Strong ,&nbsp;Victoria Flood ,&nbsp;Bruno Franco ,&nbsp;Lieven Clarisse ,&nbsp;Cathy Clerbaux ,&nbsp;Debra Wunch ,&nbsp;Coleen Roehl ,&nbsp;Paul Wennberg ,&nbsp;Zhao-Cheng Zeng","doi":"10.1016/j.rse.2025.114829","DOIUrl":"10.1016/j.rse.2025.114829","url":null,"abstract":"<div><div>This study presents the first attempt to observe wildfire enhancements of carbon monoxide (CO) and volatile organic compounds (VOCs) around sunrise and sunset from a hyperspectral infrared sounder in a dawn-dusk sun-synchronous orbit. The 2nd generation of High Spectral Infrared Atmospheric Sounder (HIRAS-II) on board FengYun-3E (FY-3E), the world's first civilian dawn-dusk orbit meteorological satellite, provides global observations in the thermal infrared spectral range with equatorial overpass times of 5:30 am/pm local solar time (LST). The spectral observations are used to retrieve CO, formic acid (HCOOH) and peroxyacetyl nitrate (PAN) emitted from three major Canadian wildfire events from June to August 2023. Extreme enhancements of CO, HCOOH and PAN were detected in the 2023 Canadian wildfires which are unprecedented in time and spatial scales and intensity. The HIRAS-II successfully captured the strong signals of CO, HCOOH, and PAN. The averaging kernel (AK) matrix, indicative of detection vertical sensitivity, peaks mostly in the free troposphere where extensive transport typically takes place. Comparison with the Infrared Atmospheric Sounding Interferometer (IASI) and the Cross-track Infrared Sounder (CrIS) reveals that the spatial distribution patterns of the total columns extracted from HIRAS-II are in good agreement. Validation with the CAMS model and ground-based observations from TCCON and NDACC confirms that HIRAS-II retrievals are consistent. The HCOOH-to-CO and the PAN-to-CO column enhancement ratios derived from HIRAS-II are close to those derived from IASI. This paper exhibits the capability of FY-3E/HIRAS-II in observing wildfire emissions during dawn and dusk hours, which will potentially enhance the climate-monitoring capability of low-orbit meteorological satellites.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"327 ","pages":"Article 114829"},"PeriodicalIF":11.1,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144137754","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":"Retrieval of terrain surface elevation in mountainous areas with ICESat-2/ATLAS","authors":"Yanli Zhang ,&nbsp;Pan Zhao ,&nbsp;Xin Li ,&nbsp;Bisheng Yang ,&nbsp;Jun Zhao ,&nbsp;Jiazheng Hu ,&nbsp;Qi Wei ,&nbsp;Kegong Li ,&nbsp;Mingliang He","doi":"10.1016/j.rse.2025.114823","DOIUrl":"10.1016/j.rse.2025.114823","url":null,"abstract":"<div><div>Land elevation data are indispensable for topographic mapping and geological disaster monitoring. However, the existing ICESat-2/ATL08 (V04) product has a coarse resolution (≥100 m) and is characterized by high uncertainty in mountainous areas; thus, it cannot be used to describe terrain relief characteristics accurately. In this study, a new method for extracting terrain surface elevation is proposed, which uses a local statistical denoising algorithm for mountainous areas (LSDAMA) based on the raw georeferenced photon product ICESat-2/ATL03. Coarse denoising is based on performing histogram thresholding, and refined denoising is based on local slope fitting; the process of performing coarse denoising twice and then refined denoising not only improves the removal effect for noise photons but also increases the signal photon retention rate in mountainous areas. Additionally, the estimation accuracy of the terrain surface elevation can be improved by setting rectangular dynamic windows along the fitted slope direction. Using the Babao River Basin in the Qilian Mountains as the research area, a total of 137 validation points from 777 GPS CORS in 40 quadrats and UAV LiDAR measurements were used to verify the accuracy. The results showed that the terrain surface elevations estimated by the LSDAMA are more accurate than those estimated by the ATL08 official products, especially in mountainous areas with slopes greater than 20°. The root mean square error (<em>RMSE</em>) of the LSDAMA decreased from 2.72 m for the ATL08 product to 0.60 m, and the mean deviation (<em>MBE</em>) decreased from −1.27 to 0.04 m. Additionally, the LSDAMA greatly improved the signal photon retention rate and reduced the interval between adjacent elevation points from 100 m for the ATL08 product to 2–7 m; this reduced interval can be used to describe the terrain fluctuation characteristics in detail, thus providing reliable basic data for monitoring terrain surface elevation changes in mountainous areas.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"327 ","pages":"Article 114823"},"PeriodicalIF":11.1,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144137753","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}