Remote Sensing of Environment最新文献

{"title":"Sentinel-1 imagery for wide-scale quantitative landslide vulnerability assessment of buildings","authors":"Francesco Poggi ,&nbsp;Francesco Caleca ,&nbsp;Olga Nardini ,&nbsp;Francesco Barbadori ,&nbsp;Matteo Del Soldato ,&nbsp;Claudio De Luca ,&nbsp;Francesco Casu ,&nbsp;Manuela Bonano ,&nbsp;Riccardo Lanari ,&nbsp;Veronica Tofani ,&nbsp;Federico Raspini","doi":"10.1016/j.rse.2025.115199","DOIUrl":"10.1016/j.rse.2025.115199","url":null,"abstract":"<div><div>The occurrence of landslides can potentially cause considerable economic impact on a global scale, resulting in damage to exposed structures and infrastructures, including buildings. In order to determine the most efficacious risk mitigation strategies, the scientific community is engaged in the analyses aimed at assessing the expected consequences of landslide activation/reactivation. This approach involves the implementation of quantitative risk assessment procedures, based on the definition of the landslide susceptibility and intensity, the identification of exposed elements, and the vulnerability assessment, which represents the most challenging parameter. The present work proposes an approach to the quantitative vulnerability assessment for buildings exposed to slow-kinematic landslides via empirical fragility and vulnerability curves, which express the probabilistic relationship between the damage severity to exposed buildings and the intensity of the landslide. A comprehensive database was compiled, collating information on landslide-induced damage to over four thousand buildings in the Northern Apennines of Central Italy. The landslide intensity is evaluated by exploiting the worldwide freely accessible Sentinel-1 SAR images, which have been processed by using the Parallel-Small BAseline (P-SBAS) – Differential SAR Interferometry (DInSAR) processing chain. The Institute for Electromagnetic Sensing of the Environment (IREA) of the National Research Council (CNR) of Italy is responsible for the processing of the Sentinel-1 SAR images, within the framework of an agreement with the Italian Ministry of Environment and Energy Security (MASE) for the detection and analysis of ground displacement over the entire Italian territory. The ultimate objective of the present study is the exploitation of the derived vulnerability curve into the quantitative risk assessment procedure for a comprehensive evaluation of buildings in the Northern Apennines.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115199"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785692","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":"Quantification of phytoplankton primary production from space: A revisit based on high-frequency observations with the aid of Himawari-8/AHI","authors":"Zhaoxin Li ,&nbsp;Wei Yang ,&nbsp;Huangrong Chen ,&nbsp;Chong Shi ,&nbsp;Husi Letu ,&nbsp;Fang Shen","doi":"10.1016/j.rse.2026.115238","DOIUrl":"10.1016/j.rse.2026.115238","url":null,"abstract":"<div><div>Synoptic quantification of phytoplankton depth-integrated primary production (IPP) has advanced significantly over recent decades by leveraging satellite observations and sophisticated IPP models. However, monthly mean upstream products from polar-orbiting satellites, <em>e.g.</em>, the Moderate Resolution Imaging Spectroradiometer (MODIS), are commonly used to generate IPP products, raising a concern about whether neglecting diurnal or daily IPP variabilities may compromise the accuracy of monthly- and annual-scale quantifications. Here, we aim to investigate this concern by comparing IPP quantified using high-frequency data at multiple timescales. A theoretical time-resolved model (TPM) was utilized for IPP modeling, driven by either diurnal photosynthetically available radiation (PAR) from the Advanced Himawari Imager (AHI) onboard Himawari-8 (H8) or daily PAR from MODIS. Preliminary evaluation against <em>in situ</em> measurements corroborated the superiority of AHI-based daily IPP estimation over MODIS, attributed to the robustness of AHI PAR data across varying sky conditions. Satellite IPP products were generated in the full-disk area of H8 between 2016 and 2019 under “daily-to-monthly-to-annual” (DtA) and “monthly-to-annual” (MtA) scenarios for comparison, using other requisite daily and gap-free biogeochemical products. Our analysis unveiled moderate spatiotemporal discrepancies between DtA-based IPP products from AHI and MODIS, confirming an overestimation in MODIS-derived monthly (&lt; 8%) and annual total IPP (∼5%). In contrast, under the MtA scenario, MODIS substantially overestimated monthly (∼14–30%) and annual total IPP (∼20%) and gave biased temporal trends (∼1.3–1.6 times higher) compared to DtA-based IPP estimates of AHI. The discrepancies between IPP products were largely subject to the cloud-induced variabilities in daily PAR products and ocean color data coverage. By upscaling our results to the global ocean, it is anticipated that the annual total IPP previously estimated from MODIS with TPM-like models is overestimated by at least 19%. This study emphasizes the necessity of modeling IPP at finer timescales using high-frequency observations and provides insights for improving IPP quantification with the aid of geostationary satellites.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115238"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145962692","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":"AnyTime-CD: Self-supervised change detection in arbitrary-length dense time series of high-resolution remote sensing images","authors":"Yang Qu ,&nbsp;JiaYi Li ,&nbsp;Xin Huang","doi":"10.1016/j.rse.2026.115239","DOIUrl":"10.1016/j.rse.2026.115239","url":null,"abstract":"<div><div>With the continuous enhancement of the observation capabilities of remote sensing satellites, dense multi-temporal high-resolution remote sensing images have become an important data source for monitoring surface changes. However, multi-temporal change detection (MTCD) remains challenging due to inconsistent time intervals, spatial misalignments, and a scarcity of labeled data, all of which hinder the generalization and practical deployment of deep learning models in large-scale applications. To address the above challenges, this paper proposed a multi-task self-supervised change detection framework (i.e., AnyTime-CD), which supports the input of time series of any length and can jointly model temporal-spectral-spatial features without manual annotation. This framework consists of three sub-tasks: temporal dynamic modeling, spectral semantic consistency learning, and spatial feature alignment, which respectively address the issues of irregular time interval, spectral perturbation, and spatial offset, thereby collaboratively optimize network performance. We evaluated AnyTime-CD on two globally distributed multi-temporal high-resolution datasets. The experimental results showed that, under an unsupervised setting, this method achieved performance close to or even surpassing that of the supervised methods. Compared with the best-performing self-supervised method, it achieved relative gains of over 13% (spatial) and 8% (temporal) in F1 scores (using a one-frame tolerance strategy), respectively. After sample fine-tuning, it further achieved F1 scores of 86.40% (spatial) and 79.50% (temporal), exceeding the state-of-the-art supervised method by 4.44% and 9.09%, respectively. Furthermore, AnyTime-CD showed good adaptability to different pre-training configurations. It is worth noting that it exhibited strong robustness under medium and low cloud cover conditions and could even utilize cloud perturbations as data augmentation to further enhance performance. In conclusion, AnyTime-CD offers a flexible and label-free solution for MTCD tasks, suitable for complex, dynamic, and interference-prone remote sensing scenarios, and is expected to promote the application of self-supervised methods in surface monitoring.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115239"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145962691","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":"Mapping wide-area land subsidence from groundwater use in the North China plain by machine learning-based InSAR adjustment","authors":"Mi Jiang ,&nbsp;Zhou Wu ,&nbsp;Xudong Wang ,&nbsp;Lin Bai ,&nbsp;Zhiwei Li ,&nbsp;Zhong Lu","doi":"10.1016/j.rse.2025.115226","DOIUrl":"10.1016/j.rse.2025.115226","url":null,"abstract":"<div><div>Since 2014, a series of China's water resource management policies have been implemented to mitigate groundwater-induced land subsidence in the North China Plain (NCP). While previous studies have demonstrated the benefits of Synthetic Aperture Radar interferometry (InSAR) in providing policy-relevant insights into the spatio-temporal dynamics of subsidence and groundwater recovery, most have focused on localized regions, leaving the long-term impact of these measures across the entire NCP insufficiently evaluated. A key challenge is the variation of long-wavelength errors in each SAR frame, which results in inconsistencies in the subsidence velocity field over large areas. To address this issue, this paper proposes a machine learning-based adjustment approach for routinely wide-area subsidence mapping and then fully evaluating land subsidence and associated groundwater depletion in the NCP from the end of 2014 to 2022. The novelty of this method lies in the adaptive selection of the optimal model for each SAR frame, which minimizes the varying long-wavelength errors, rather than relying on a unified model for all SAR frames as commonly used in state-of-the-art approaches. Additionally, we mitigated the difference of InSAR measurement in the overlap regions between consecutive tracks caused by the varying incidence angles by incorporating GNSS data and a plate motion model. Using synthetic and real Sentinel-1 data, we validated the performance of the proposed method against prevalent approaches through an independent GNSS validation dataset, demonstrating accuracy improvements from 3.8-17.5 mm/yr to 2.0 mm/yr. The results indicated that approximately 56,882 km² of the NCP area experienced land subsidence greater than 20 mm/yr. The central alluvial and coastal plains were the primary areas of subsidence, with a maximum cumulative subsidence of up to 2 m. The average subsidence velocity peaked in 2018 at 38.5 mm/yr. Subsidence has been alleviated after 2021. Our results revealed the lower bound of groundwater loss from the confined aquifer in the NCP, totaling 24.9 billion m³ between the end of 2014 and 2022. Of this total, 20.2 billion m³ (81 %) was lost from October 2014 to the end of 2020, with the loss decreasing to 4.7 billion m³ (19 %) during the period from January 2021 to December 2022. This study provides new evidence for China's groundwater management practices in addressing land subsidence in the NCP.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115226"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145902873","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 radiometrically and spatially consistent super-resolution framework for Sentinel-2","authors":"Cesar Aybar,&nbsp;Julio Contreras,&nbsp;Simon Donike,&nbsp;Enrique Portalés-Julià,&nbsp;Gonzalo Mateo-García,&nbsp;Luis Gómez-Chova","doi":"10.1016/j.rse.2025.115222","DOIUrl":"10.1016/j.rse.2025.115222","url":null,"abstract":"<div><div>Deep learning-based super-resolution (SR) models offer a promising approach to enhancing the effective spatial resolution of optical satellite images. However, existing SR implementations have shown that, while these models can reconstruct fine-scale details, they often introduce undesirable artifacts, such as nonexistent local structures, reflectance distortions, and geometric misalignment. To mitigate these issues, fully synthetic data approaches have been explored for training, as they provide complete control over the degradation process and allow precise supervision and ground-truth availability. However, challenges in domain transfer have limited their effectiveness when applied to real satellite images. In this work, we propose <em>SEN2SR</em>, a new deep learning framework trained to super-resolve Sentinel-2 images while preserving spectral and spatial alignment consistency. Our approach harmonizes synthetic training data to match the spectral and spatial characteristics of Sentinel-2, ensuring realistic and artifact-free enhancements. SEN2SR generates 2.5-meter resolution images for Sentinel-2, upsampling the 10-meter RGB and NIR bands and the 20-meter Red Edge and SWIR bands. To ensure that SR models focus exclusively on enhancing spatial resolution, we introduce a low-frequency hard constraint layer at the final stage of SR networks that always enforces spectral consistency by preserving the original low-frequency content. We evaluate a range of deep learning architectures, including Convolutional Neural Networks, Mamba, and Swin Transformers, within a comprehensive assessment framework that integrates Explainable AI (xAI) techniques. Quantitatively, our framework achieves superior PSNR while maintaining near-zero reflectance deviation and spatial misalignment, outperforming state-of-the-art SR frameworks. Moreover, we demonstrate maintained radiometric fidelity in downstream tasks that demand high-fidelity spectral information and reveal a significant correlation between model performance and pixel-level model activation. Qualitative results show that SR networks effectively handle diverse land cover scenarios without introducing spurious high-frequency details in out-of-distribution cases. Overall, this research underscores the potential of SR techniques in Earth observation, paving the way for more precise monitoring of the Earth’s surface. Models, code, and examples are publicly available at <span><span>https://github.com/ESAOpenSR/SEN2SR</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115222"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145903476","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":"Temporal attention multi-resolution fusion of satellite image time-series, applied to Landsat-8/9 and Sentinel-2: all bands, any time, at best spatial resolution","authors":"Julien Michel,&nbsp;Jordi Inglada","doi":"10.1016/j.rse.2025.115159","DOIUrl":"10.1016/j.rse.2025.115159","url":null,"abstract":"<div><div>This paper introduces a general formulation for the fusion of Satellite Image Time Series (SITS) of variable length from several sensors at different spatial resolutions and acquisition times over the same geographical area. In this formulation, all the spectral bands from all the input sensors are predicted at the best input spatial resolution, and at any observed or non-observed acquisition time requested. To address this general problem, an advanced Masked Auto-Encoder training strategy is proposed, utilising two new loss functions: a Linear-Regression Learned Perceptual Image Similarity term to favor high spatial frequency details, and a mask-contrastive term to ignore clouds and other non-informative areas in the input data. This strategy is applied to the training of Temporal Attention Multi-Resolution Fusion of Satellite Image Time-Series (TAMRF-SITS), a novel Deep Learning architecture designed to implement the proposed general formulation. Experiments with joint Landsat-8/9 and Sentinel-2 time-series were conducted on four different tasks from the literature and demonstrate that a single pre-trained TAMRF is on par with or better than existing ad-hoc methods. For instance, TAMRF provides a gain of 0.01 surface reflectance Root Mean Square Error on a Spatio-Temporal Fusion task when compared to competing algorithms, while showing the highest spatial frequency content. Moreover, the proposed method relaxes unrealistic assumptions routinely found in the literature, including: same or similar spectral bands in different sensors, same-day acquisitions, and scale-invariance of the relationship between high and low resolution images. To the best of our knowledge, our method is the first to achieve this range of capabilities with a single model, without making any of these assumptions. The complete source code for training and experiments is available here: <span><span>https://github.com/Evoland-Land-Monitoring-Evolution/tamrfsits</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115159"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731843","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":"Began+: Leveraging bi-temporal SAR-optical data fusion to reconstruct clear-sky satellite imagery under large cloud cover","authors":"Yu Xia ,&nbsp;Wei He ,&nbsp;Liangpei Zhang ,&nbsp;Hongyan Zhang","doi":"10.1016/j.rse.2025.115171","DOIUrl":"10.1016/j.rse.2025.115171","url":null,"abstract":"<div><div>In recent years, optical remote sensing imagery has played an increasingly vital role in Earth observation, but cloud contamination exists as an inevitable degradation. Combining synthetic aperture radar (SAR) and optical data with machine learning offers a promising solution for reconstructing clear-sky satellite imagery. Nevertheless, several challenges persist, including insufficient attention to large cloud cover, difficulties in restoring temporal changes, and limited practicality of deep models. To address these issues, this paper introduces a novel deep learning-based cloud removal framework, termed Began+, which integrates bi-temporal SAR-optical data to deal with cloudy images with high cover ratios. The Began+ framework comprises two primary components: a deep network and a flexible post-processing step, combining the strengths of data-driven models for restoring change information and traditional gap-filling algorithms for mitigating radiance discrepancies. First, a bi-output enhanced generative adversarial network, abbreviated as Began, is designed for image synthesis, featuring an enhanced channel-wise fusion block (ECFB) and a multi-scale depth-wise convolution residual block (MDRB). By applying the dual-tasking optimization and co-learning strategy, the Began model identifies potential change areas from bi-temporal SAR and pre-temporal optical inputs, guiding the synthesis of target optical images. Second, a range of cloud masking and gap-filling techniques can be optionally employed to effectively reduce radiometric discrepancies between the synthesized images and the cloudy data, ultimately yielding high-quality, clear-sky imagery. To meet the big data requirements of deep learning, we constructed two globally distributed cloud removal datasets, named BiS1L8-CR and BiS1S2-CR. Supported by these datasets, extensive experiments demonstrated that the Began+ framework effectively captures bi-temporal change features, reconstructing precise surface information in both Landsat-8 and Sentinel-2 satellite images under large cloud cover. Compared to the latest solutions and algorithms, our proposed Began+ framework exhibits significant advantages from both qualitative and quantitative perspectives in both simulated and real experiments. Furthermore, without strict constraints on input timing, the Began+ framework enables accurate reconstruction of large-scale dual-sensor imagery under high-ratio cloud cover, effectively restoring changing surfaces and improving the quality of unsupervised vegetation extraction.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115171"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145732131","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":"Integrating classifier transfer and sample transfer strategies for in-season crop mapping based on sample weighting technique","authors":"Yunze Zang ,&nbsp;Junxiong Zhou ,&nbsp;Xuehong Chen ,&nbsp;Tianyu Liu ,&nbsp;Miaogen Shen ,&nbsp;Wei Yang ,&nbsp;Xiufang Zhu ,&nbsp;Fei Zhang ,&nbsp;Jin Chen","doi":"10.1016/j.rse.2025.115208","DOIUrl":"10.1016/j.rse.2025.115208","url":null,"abstract":"<div><div>Timely crop mapping is crucial for field management, policy formulation, phenological monitoring, and yield forecasting. However, acquiring sufficient labeled samples in the current year presents a formidable challenge for in-season mapping. Previously proposed solutions mainly include classifier transfer and sample transfer strategies. The classifier transfer strategy trains classifiers with historical samples associated with historical-year features and then transfers the trained historical sample classifiers (HSC) to classify remote sensing data in the current year; the sample transfer strategy generates trusted samples associated with current-year remote sensing features by predicting labels of the current-year sample based on some prior knowledge (e.g., crop rotation pattern) and then trains trusted sample classifiers (TSC) for current-year classification. However, the performance of the classifier-transfer strategy may degrade when there is large interannual feature variation, while the performance of the sample-transfer strategy depends on the reliability of the generated trusted samples. This study proposes a novel approach that integrates the above two strategies for in-season mapping through a sample weighting technique. Firstly, two sample sets, trusted samples and classified samples associated with current-year features, are generated by crop rotation prediction and HSC, respectively. Subsequently, based on an independent assumption between the rotational prediction errors and the current-year remote sensing features, the optimal weights of these two sample sets are derived based on the Bayesian principle. Finally, an optimal weighted sample classifier (OWSC) is trained using the weighted samples for in-season classification. To illustrate the robustness of the proposed OWSC, we compared it with different methods combined with various classification models across four regions with different interannual feature variation and crop rotation stability. Results demonstrated that OWSC maintained its advantages across various regions and different available lengths of historical crop-type sequences. Owing to its independence from specific classifiers, the proposed sample weighting method can be seamlessly applied to any classification model and thus continues to benefit from advances in classification algorithms. Additionally, sensitivity experiments regarding the uncertainty in trusted samples and historical crop-type sequences showed that OWSC performed stably across different scenarios. Therefore, OWSC provides a promising solution for in-season crop mapping without current-year samples.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115208"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145836274","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":"Leaf fall witnessed by night-time light: A first attempt to detect urban leaf fall dates using satellite nighttime light data","authors":"Liming Wang ,&nbsp;Yang Hu ,&nbsp;Xiaoyue Tan ,&nbsp;Zixuan Pei ,&nbsp;Xiaolin Zhu ,&nbsp;Jin Chen","doi":"10.1016/j.rse.2025.115211","DOIUrl":"10.1016/j.rse.2025.115211","url":null,"abstract":"<div><div>Autumn leaf fall is a critical phenological event in temperate deciduous forests, with important ecological and socioeconomic implications. Traditional estimates based on daytime vegetation indices primarily capture foliage color changes rather than the actual timing of leaf fall, and are often affected by mixed-pixel effects that reduce the accuracy. This study proposes a novel workflow for detecting the autumn leaf fall date (LFD) using nighttime light (NTL) data in urban areas, validated through in-situ observations from phenological cameras and city-scale assessments. Results showed that NTL-derived LFDs closely matched in-situ observations across three cities (New York City, Boston, and Beijing), with an RMSE of around 5 days and a bias of 0.77 days. In Beijing, both interannual (2012–2024) and spatial variations (2024) in LFD were delayed by higher preseason temperature and precipitation but advanced by greater strong-wind frequency, consistent with known autumn phenological controls and supporting the reliability of the NTL-based approach. These results demonstrate that NTL data can provide more accurate and interpretable LFD estimates than traditional daytime remote sensing, enabling detailed city-scale mapping. The use of NTL-derived LFD dynamics facilitates a more comprehensive understanding of their spatiotemporal patterns in urban environments and their linkages to climate change and human activities.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115211"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145881019","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":"Development of an interference mitigation chlorophyll index for mitigating soil and canopy dependence to improve vegetation chlorophyll content monitoring","authors":"Tao Sun ,&nbsp;Zijun Tang ,&nbsp;Youzhen Xiang ,&nbsp;Junsheng Lu ,&nbsp;Yaohui Cai ,&nbsp;Wangyang Li ,&nbsp;Ruiqi Du ,&nbsp;Xianghui Lu ,&nbsp;Shouyang Liu ,&nbsp;Tianjie Zhao ,&nbsp;Zhijun Li ,&nbsp;Fucang Zhang","doi":"10.1016/j.rse.2025.115202","DOIUrl":"10.1016/j.rse.2025.115202","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Chlorophyll is a fundamental component of photosynthesis and a key indicator in quantitative vegetation remote sensing. However, accurate retrieval of leaf chlorophyll content from top-of-canopy (TOC) reflectance is often hindered by soil background and canopy structural effects. To address these challenges, we developed the Interference-Mitigation Chlorophyll Index (IMCI), derived from the spectral properties of leaves and soils together with canopy radiative transfer processes, to enhance robustness under complex soil and canopy conditions.&lt;/div&gt;&lt;div&gt;The development of IMCI consists of three steps. First, the soil single-scattering component (&lt;span&gt;&lt;math&gt;&lt;msubsup&gt;&lt;mi&gt;ρ&lt;/mi&gt;&lt;mi&gt;s&lt;/mi&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;/msubsup&gt;&lt;/math&gt;&lt;/span&gt;) is removed from TOC reflectance using two spectral features: (a) vegetation reflectance remains equivalent at 598 and 694 nm regardless of leaf color, while soil reflectance differs; and (b) soil reflectance exhibits an approximately linear dependence on wavelength within 590–750 nm. These features enable accurate estimation and correction of &lt;span&gt;&lt;math&gt;&lt;msubsup&gt;&lt;mi&gt;ρ&lt;/mi&gt;&lt;mi&gt;s&lt;/mi&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;/msubsup&gt;&lt;/math&gt;&lt;/span&gt;, yielding soil-adjusted TOC spectra (&lt;span&gt;&lt;math&gt;&lt;mover&gt;&lt;msub&gt;&lt;mi&gt;ρ&lt;/mi&gt;&lt;mi&gt;c&lt;/mi&gt;&lt;/msub&gt;&lt;mo&gt;∼&lt;/mo&gt;&lt;/mover&gt;&lt;/math&gt;&lt;/span&gt;). Second, a proxy for the vegetation single-scattering component (&lt;span&gt;&lt;math&gt;&lt;msubsup&gt;&lt;mi&gt;ρ&lt;/mi&gt;&lt;mi&gt;l&lt;/mi&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;/msubsup&gt;&lt;/math&gt;&lt;/span&gt;) is constructed from soil-adjusted red-edge difference–ratio reflectance values (&lt;span&gt;&lt;math&gt;&lt;mo&gt;∆&lt;/mo&gt;&lt;mover&gt;&lt;msub&gt;&lt;mi&gt;ρ&lt;/mi&gt;&lt;mi&gt;c&lt;/mi&gt;&lt;/msub&gt;&lt;mo&gt;∼&lt;/mo&gt;&lt;/mover&gt;&lt;mfenced&gt;&lt;msub&gt;&lt;mi&gt;λ&lt;/mi&gt;&lt;mi&gt;j&lt;/mi&gt;&lt;/msub&gt;&lt;/mfenced&gt;&lt;mo&gt;/&lt;/mo&gt;&lt;mo&gt;∆&lt;/mo&gt;&lt;mover&gt;&lt;msub&gt;&lt;mi&gt;ρ&lt;/mi&gt;&lt;mi&gt;c&lt;/mi&gt;&lt;/msub&gt;&lt;mo&gt;∼&lt;/mo&gt;&lt;/mover&gt;&lt;mfenced&gt;&lt;msub&gt;&lt;mi&gt;λ&lt;/mi&gt;&lt;mi&gt;i&lt;/mi&gt;&lt;/msub&gt;&lt;/mfenced&gt;&lt;/math&gt;&lt;/span&gt;). Exploiting the linearity of the red-edge, we demonstrated that the extrapolated intersection of &lt;span&gt;&lt;math&gt;&lt;mo&gt;∆&lt;/mo&gt;&lt;mover&gt;&lt;msub&gt;&lt;mi&gt;ρ&lt;/mi&gt;&lt;mi&gt;c&lt;/mi&gt;&lt;/msub&gt;&lt;mo&gt;∼&lt;/mo&gt;&lt;/mover&gt;&lt;/math&gt;&lt;/span&gt; and &lt;span&gt;&lt;math&gt;&lt;msubsup&gt;&lt;mi&gt;ρ&lt;/mi&gt;&lt;mi&gt;l&lt;/mi&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;/msubsup&gt;&lt;/math&gt;&lt;/span&gt; in the red-edge region converges to a reflectance value approaching 0 and further derived that &lt;span&gt;&lt;math&gt;&lt;mo&gt;∆&lt;/mo&gt;&lt;mover&gt;&lt;msub&gt;&lt;mi&gt;ρ&lt;/mi&gt;&lt;mi&gt;c&lt;/mi&gt;&lt;/msub&gt;&lt;mo&gt;∼&lt;/mo&gt;&lt;/mover&gt;&lt;mfenced&gt;&lt;msub&gt;&lt;mi&gt;λ&lt;/mi&gt;&lt;mi&gt;j&lt;/mi&gt;&lt;/msub&gt;&lt;/mfenced&gt;&lt;mo&gt;/&lt;/mo&gt;&lt;mo&gt;∆&lt;/mo&gt;&lt;mover&gt;&lt;msub&gt;&lt;mi&gt;ρ&lt;/mi&gt;&lt;mi&gt;c&lt;/mi&gt;&lt;/msub&gt;&lt;mo&gt;∼&lt;/mo&gt;&lt;/mover&gt;&lt;mfenced&gt;&lt;msub&gt;&lt;mi&gt;λ&lt;/mi&gt;&lt;mi&gt;i&lt;/mi&gt;&lt;/msub&gt;&lt;/mfenced&gt;&lt;/math&gt;&lt;/span&gt; can serve as a direct approximation of &lt;span&gt;&lt;math&gt;&lt;mo&gt;∆&lt;/mo&gt;&lt;msubsup&gt;&lt;mi&gt;ρ&lt;/mi&gt;&lt;mi&gt;l&lt;/mi&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;/msubsup&gt;&lt;mfenced&gt;&lt;msub&gt;&lt;mi&gt;λ&lt;/mi&gt;&lt;mi&gt;j&lt;/mi&gt;&lt;/msub&gt;&lt;/mfenced&gt;&lt;mo&gt;/&lt;/mo&gt;&lt;mo&gt;∆&lt;/mo&gt;&lt;msubsup&gt;&lt;mi&gt;ρ&lt;/mi&gt;&lt;mi&gt;l&lt;/mi&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;/msubsup&gt;&lt;mfenced&gt;&lt;msub&gt;&lt;mi&gt;λ&lt;/mi&gt;&lt;mi&gt;i&lt;/mi&gt;&lt;/msub&gt;&lt;/mfenced&gt;&lt;/math&gt;&lt;/span&gt;, which was adopted as the basic form of IMCI. Furthermore, under the chlorophyll-induced red-edge shift, we established a quantitative relationship between IMCI and red-edge displacement, confirming its sensitivity to chlorophyll content. Theoretical derivations and assumpt","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115202"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785689","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}