Remote Sensing of Environment最新文献

{"title":"An ICESat-2 photon cloud classification model coupling slope and complex canopy structure in forest areas","authors":"Yi Li,&nbsp;Haiqiang Fu,&nbsp;Jianjun Zhu","doi":"10.1016/j.rse.2025.115022","DOIUrl":"10.1016/j.rse.2025.115022","url":null,"abstract":"<div><div>The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) is widely used in forest ecosystem research. Photon cloud classification is crucial for ICESat-2's application in sub-canopy topography and forest structure parameter estimation. However, steep topography, complex canopy structure, and dense canopy cover are important error factors affecting the results of photon cloud classification in forest areas. The existing basic photon cloud classification methods classify ground and canopy photons based on the spatial distribution of photons in the elevation direction. However, in areas of steep topography, it is difficult for the existing photon cloud classification methods to distinguish ground photons from canopy photons because ground photons and some canopy photons will have the same elevation, making their characteristics unclear. In addition, the complex canopy structure and dense canopy cover cause the distribution of photons in the elevation direction to have complex multi-peak characteristics, increasing the difficulty of distinguishing ground photons from canopy photons. In this paper, we propose a novel photon cloud classification method coupling slope and complex canopy structure to account for the abovementioned error factors in photon cloud classification. The proposed model describes the spatial distribution of photons under various slopes and canopy structures based on the probability distribution function (PDF) of the photon cloud elevation. The proposed model generates PDFs with simple or complex canopy structures under every slope to characterize the photons' spatial distribution in the elevation direction. A slope lookup table is then introduced to find the most appropriate PDF based on the constructed essential criteria with physical meaning. Finally, the coupling of the proposed model is solved by the most appropriate PDF, and the slope and photon cloud classification results can be obtained. The proposed model was tested in 130 forest plots covering various topographies and canopy structures. The results show that the root-mean-square error (RMSE) of the retrieved slopes, the RMSE of the classified ground photons, and the F-value of the ground photons classified by the proposed model reach 1.95°, 1.4 m, and 0.82, respectively. These accuracy indicators illustrate that the proposed model significantly outperforms the existing basic models in the case of steep topography and complex forest structure. This study will substantially improve the accuracy of ground elevation and forest structure parameter estimation through the use of ICESat-2 data worldwide.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"331 ","pages":"Article 115022"},"PeriodicalIF":11.4,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145134167","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":"Novel unsupervised Bayesian method for Near Real-Time forest loss detection using Sentinel-1 SAR time series: Assessment over sampled deforestation events in Amazonia and the Cerrado","authors":"Marta Bottani ,&nbsp;Laurent Ferro-Famil ,&nbsp;Juan Doblas Prieto ,&nbsp;Stéphane Mermoz ,&nbsp;Alexandre Bouvet ,&nbsp;Thierry Koleck ,&nbsp;Thuy Le Toan","doi":"10.1016/j.rse.2025.115037","DOIUrl":"10.1016/j.rse.2025.115037","url":null,"abstract":"<div><div>Over the past four decades, forests have experienced major disturbances, highlighting the need for Near Real-Time (NRT) monitoring. Traditional optical-based detection is cloud-sensitive, whereas Synthetic Aperture Radar (SAR)-based frameworks enable all-weather observation. Yet, SAR monitoring has mainly focused on humid tropical forests, with reduced performance in regions showing strong seasonal backscatter variation, such as tropical savannas. Detecting small-scale forest loss also remains difficult due to the spatial resolution loss from speckle filtering. This paper presents an unsupervised SAR-based disturbance detection method with NRT capabilities, using Bayesian inference. Building on an existing methodology, the approach processes single-polarization Sentinel-1 SAR time series through Bayesian conjugate analysis. Forest disturbance is framed as a changepoint detection problem, where each new observation updates the probability of forest loss using prior information and a data model. The algorithm uses a hidden Markov chain to adapt recursively to seasonal variation and bypasses spatial filtering, preserving native data resolution and enhancing small-scale forest loss detection. Additionally, a methodology accounts for proximity to past disturbances. The method is tested on two 2020 reference datasets from the Brazilian Amazon and Cerrado savanna. The first covers small validation polygons (0.1–1 ha, excluding selective logging), totaling 2,650 ha in the Amazon and 450 ha in the Cerrado. The second includes larger clearings totaling 11,200 ha in the Amazon, and 12,700 ha in the Cerrado. A further comparison is conducted with operational NRT forest loss monitoring approaches. Results show substantial gains in detecting small-scale disturbances with reduced false alarms. In the Amazon, the method achieves an F1-score of 97.3% versus 93.1% for the current leading NRT approach. In the Cerrado, it reaches an F1-score of 97.4%, far exceeding the 33.3% of the optical-based method. For larger clearings, performance matches existing SAR approaches in the Amazon. While combined optical-SAR monitoring increases true positives, it also raises false alarm rates. In the Cerrado, the proposed method clearly outperforms optical monitoring, and in both regions it improves timeliness relative to individual operational approaches.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"331 ","pages":"Article 115037"},"PeriodicalIF":11.4,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145134166","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":"GEOV2-AVHRR: Continuous and consistent time series of global leaf area index and fraction absorbed PAR from 1981 to 2022","authors":"Aleixandre Verger ,&nbsp;Marie Weiss ,&nbsp;Frédéric Baret","doi":"10.1016/j.rse.2025.115029","DOIUrl":"10.1016/j.rse.2025.115029","url":null,"abstract":"<div><div>Long-term time series of global leaf area index (LAI) and fraction of absorbed photosynthetic active radiation (FAPAR) are required for characterizing vegetation dynamics in global change studies. The recently developed Copernicus Land Monitoring Service GEOV2-CLMS products were demonstrated to outperform other existing LAI and FAPAR products in terms of completeness, temporal smoothness, consistency across variables and accuracy. However, these GEOV2-CLMS products are derived from the SPOT/VGT and PROBA-V constellation with temporal coverage from 1999 to 2020 which limits its applicability for global change studies. We present here an adaptation of the GEOV2-CLMS algorithm to AVHRR to extend these time series and generate long-term global vegetation products from July 1981 to December 2022. The GEOV2-AVHRR algorithm was specifically designed to maximize the temporal consistency over the successive AVHRR sensors on board NOAA and MetOp-B satellites while keeping high agreement with GEOV2-CLMS products. Neural networks first transform AVHRR surface reflectance into LAI and FAPAR values at the daily time step. The daily estimates are then filtered, smoothed, gap filled and composited every 10-day.</div><div>GEOV2-AVHRR showed accuracy error between −0.2 and 0.3 LAI and ∼ −0.03 FAPAR and uncertainty &lt;1 LAI and ∼ 0.10–0.15 for woody and non-woody sites of both DIRECT2.1 and GBOV V3 datasets. GEOV2-AVHRR agreed well with GEOV2-CLMS (MODIS): 92 % (76 %) of land pixels are within ±max(20 %, 0.5) LAI and 71 % (34 %) within ±max(10 %, 0.05) FAPAR uncertainty requirements. The gap filling and temporal filters applied in GEOV2-AVHRR proved effective in improving the completeness (only 1 % of missing data) and temporal precision (smoothness) of LAI and FAPAR time series as compared to MODIS. The intra-annual consistency of GEOV2-AVHRR highly agree with GEOV2-CLMS, indicating it is mostly driven by the algorithm. On the contrary, the inter-annual consistency of LAI and FAPAR datasets appears to be very sensitive to the consistency of the input surface reflectance. GEOV2-AVHRR showed high stability as evaluated with MODIS LAI/FAPAR and improves the stability of GEOV2-CLMS. Some residual inter-annual inconsistencies from the transition to sensors are observed for GEOV2-AVHRR as well as for other long term AVHRR datasets (i.e. GIMMS, GLASS and C3S). GEOV2-AVHRR shows overall greening trends in ∼70 % (∼50 % significant at <em>p</em> &lt; 0.05) of land pixels, and the magnitude and spatial pattern of trends highly agree with those of GIMMS.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"331 ","pages":"Article 115029"},"PeriodicalIF":11.4,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145109824","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":"Longevity of internal solitary waves in the Pacific Cold Tongue: synergies with SWOT","authors":"J.C.B. da Silva ,&nbsp;J.M. Magalhaes ,&nbsp;A.M. Santos-Ferreira ,&nbsp;R. Huerre","doi":"10.1016/j.rse.2025.115038","DOIUrl":"10.1016/j.rse.2025.115038","url":null,"abstract":"<div><div>Analysis of satellite images from the SWOT mission provide unprecedent details of trains of Internal Solitary-like Waves (ISWs) in the central and eastern equatorial Pacific Ocean. A total of 226 ISWs were found between March 27 and September 10 (2024), 44 of which are directly associated with small-scale sharp fronts. Satellite synergies between the Ka-band Radar Interferometer (KaRIn) and optical imagery reveal that these waves propagate for long distances within the Pacific Cold Tongue (PCT). A selection of 21 cases shows that waves propagate on average for at least 19 h and may endure up to more than 48 h. These ISW trains may travel from one edge of the PCT, across its meridional width, to the other edge. Since ISWs transport mass and momentum and therefore convey information between the north and south equatorial fronts, we briefly discuss possible physical implications.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"331 ","pages":"Article 115038"},"PeriodicalIF":11.4,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145109826","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":"Geodetic glacier mass balance in the Karakoram (2011–2019) from TanDEM-X: An InSAR DEM differencing framework","authors":"Shiyi Li ,&nbsp;Irena Hajnsek","doi":"10.1016/j.rse.2025.115023","DOIUrl":"10.1016/j.rse.2025.115023","url":null,"abstract":"<div><div>Glaciers serve as sensitive indicators of climate change, influencing both regional water supplies and global sea-level rise. Contrasting to the global tendency towards retreat, glaciers in the Karakoram exhibits an unusual pattern of stability and modest thickening. However, the spatial variability and underlying causes of the mass balance anomalies remain insufficiently understood, primarily due to the limitations in previous measurement methods. To address this gap, we conducted a comprehensive geodetic analysis of glacier elevation changes in the central and eastern Karakoram, covering 681 glaciers of over 10,000 km² between 2011 and 2019. The elevation was measured exclusively with TanDEM-X InSAR data to reduce penetration bias and temporal ambiguities. The geodetic analysis was conducted using a three-module DEM Differencing framework. In this framework, the first module generates high-quality InSAR DEM with an iterative approach to address the challenges of mountainous terrain for InSAR processing; the second module employed an innovative voids filling method using Gaussian Process Regression for robust elevation change mapping; and the third module incorporates a non-stationary uncertainty analysis for rigorous uncertainty quantification. The results reveal a regional mean elevation change rate of <span><math><mrow><mn>0</mn><mo>.</mo><mn>0038</mn><mo>±</mo><mn>0</mn><mo>.</mo><mn>0042</mn><mspace></mspace><mi>m</mi><mspace></mspace><msup><mrow><mi>yr</mi></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></mrow></math></span> and a specific mass balance of <span><math><mrow><mn>0</mn><mo>.</mo><mn>0032</mn><mo>±</mo><mn>0</mn><mo>.</mo><mn>0052</mn><mspace></mspace><mi>m</mi><mspace></mspace><mi>w.e.</mi><mspace></mspace><msup><mrow><mi>yr</mi></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></mrow></math></span>, indicating slight overall thickening during the study period. The spatial patterns of elevation change display pronounced heterogeneity and clear differences between surge-type and non-surge glaciers, reflecting the complex interplay of dynamic, climatic, and morphological factors in the region. This study demonstrates the capability of high-resolution TanDME-X InSAR DEM for accurate geodetic mass balance analysis in challenging mountain environments. The proposed framework provides a scalable methodology for future large-scale glacier studies.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"331 ","pages":"Article 115023"},"PeriodicalIF":11.4,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145116695","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":"Tropical peatland hydrological dynamics affect the efficacy of C-band Small BAseline Subset InSAR approaches","authors":"Magdalena M. Mleczko ,&nbsp;Kitso Kusin ,&nbsp;Teuntje P. Hollaar ,&nbsp;Mark E. Harrison ,&nbsp;Nomeritae Nomeritae ,&nbsp;Darmae Nasir ,&nbsp;Marek S. Mróz ,&nbsp;F.J. Frank van Veen ,&nbsp;Muhammad A. Imron ,&nbsp;A. Jonay Jovani-Sancho ,&nbsp;Chris D. Evans ,&nbsp;Adi Jaya ,&nbsp;Karen Anderson","doi":"10.1016/j.rse.2025.115009","DOIUrl":"10.1016/j.rse.2025.115009","url":null,"abstract":"<div><div>Tropical peatlands storing ∼18–25 % of global peat volume contribute significantly to the global carbon cycle. To balance preservation and protection of tropical peatlands requires assessment of their ecohydrological conditions and continuous monitoring through seasons. This is challenging to achieve using <em>in situ</em> sampling, but there is a great promise to use C-band Sentinel-1 data for this due to its weather-independence, and particularly its increased acquisition capacity and spatial/temporal resolution compared to L- and P-band current sensors. Acknowledging that Small BAseline Subset (SBAS) Interferometry using C-Band Sentinel-1 data has been shown previously to be useful for retrieving peatland surface displacement, but also that the amplitude and phase of the SAR signal are dependent on surface hydrology; there remains a critical question about the extent to which the efficacy of SBAS approaches is themselves sensitive to surface hydrological conditions. This is a particular methodological concern in tropical peatlands due to the dynamically changing hydrological conditions arising from significant rainfall events, which can cause groundwater level (GWL) to vary from 1 m to −2 m. The research area was situated in lowland Central Kalimantan (Indonesia) using Synthetic Aperture Radar (SAR) observations from the 2017–2022 period. We used SBAS-derived ground displacements and compared to groundwater level (GWL) and peat surface elevation acquired from local networks of monitoring sites. Our work shows that the prevailing hydrological condition affects the area efficacy of the SBAS approach using C-band SAR data. When surface water significantly floods above the ground surface during the wet season, the coherence is not sustained for a long time. This is the opposite of the dry season, when coherence is preserved in longer intervals between acquisitions. Additionally, the range of correlation values between SBAS-derived displacements and <em>in-situ</em> peat surface and ground water table measurements is higher for the dry season than for the wet and whole hydrological year. We show that the SBAS approach can retrieve surface displacement for 34.4 % to 59.8 % on the peat soils of the tested area (391 to 826 km2), excluding areas of dense forests and open water, due to C-band SAR limitations, <em>i.e.</em> volume scattering mechanism and/or loss of signal coherence on water bodies. We show that appropriate hydrological conditions must be met to determine the change in water level above the ground surface. Too large fluctuations in water level may not be detected because of wavelength limitations, and outliers from the assumed linear model may be filtered out or removed due to the specific properties of this approach. These findings underpin the application of Sentinel-1C-band SAR for monitoring tropical peatlands' ecohydrological conditions.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"331 ","pages":"Article 115009"},"PeriodicalIF":11.4,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145109825","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":"Estimating the biological carbon pump from satellite-based observations using a semi-analytical algorithm in the Arctic Ocean","authors":"Tingyu Gu ,&nbsp;Min Wu ,&nbsp;Shuo He ,&nbsp;Zhaoru Zhang ,&nbsp;Musheng Lan ,&nbsp;Jianfeng He ,&nbsp;Chengfeng Le","doi":"10.1016/j.rse.2025.115032","DOIUrl":"10.1016/j.rse.2025.115032","url":null,"abstract":"<div><div>The Arctic Ocean constitutes a globally significant carbon sink, where carbon sequestration is predominantly mediated by the biological carbon pump (BCP). Quantifying BCP magnitude and efficiency persists as a key challenge in marine carbon cycle research. Net community production (NCP) serves as a critical proxy for quantifying carbon export fluxes and assessing carbon sequestration efficacy. This study introduces the Nitrate-based Semi-Analytical Model (NSAM), an innovative framework advancing Arctic NCP quantification through synergistic integration of satellite-based observations, biogeochemical model and hydrodynamic processes. By integrating seasonal nitrate budgets derived from satellite-based observations and reanalysis data using a machine learning algorithm, the NCP is computed by applying Redfield stoichiometry (C: <em>N</em> = 6.6) to convert seasonal nitrogen changes into carbon export estimates. Results demonstrate strong agreement between satellite-derived NCP and in situ measurements (RMSD = 9.52 mmol C m⁻² d⁻¹), underscoring the utility of NSAM for quantifying the Arctic BCP. Satellite-based NCP provides unprecedented pan-Arctic spatial coverage, overcoming the limitations of ship-based methods constrained to discrete cruise tracks. This advancement enables refined assessments of the Arctic Ocean's contribution to global carbon sequestration dynamics.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"331 ","pages":"Article 115032"},"PeriodicalIF":11.4,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145093600","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":"Strategic sampling for training a semantic segmentation model in operational mapping: Case studies on cropland parcel extraction","authors":"Rui Lu ,&nbsp;Ronghua Liao ,&nbsp;Ran Meng ,&nbsp;Yingchu Hu ,&nbsp;Yi Zhao ,&nbsp;Yan Guo ,&nbsp;Yingfan Zhang ,&nbsp;Zhou Shi ,&nbsp;Su Ye","doi":"10.1016/j.rse.2025.115034","DOIUrl":"10.1016/j.rse.2025.115034","url":null,"abstract":"<div><div>Semantic segmentation of remotely sensed images has become increasingly popular for a wide range of natural resource and urban application, yielding promising results. To an operational semantic segmentation mapping project, having more samples generally enables the model to better extract target features, achieving higher accuracies. However, annotating remote sensing image samples for model training is a time-consuming and labor-intensive process. Strategic sampling aims to minimize the efforts in collecting new training samples for a mapping project, which has been not well studied yet for semantic segmentation. To approach this topic, we employed a hybrid way for combining meta-analysis and case studies to investigate the best practices for strategic sampling. Three factors relating to strategic sampling will be investigated: sample size, distribution and transferring methods. We first reviewed 334 recently published papers that adopted semantic segmentation for operational mapping projects to summarize the current status of training sample design from various mapping scenarios. Subsequently, we constructed a large dataset of over 12,000 high-quality annotated image patches for cropland parcel mapping across five study sites, and evaluated various sampling strategies using a baseline segmentation model. We also proposed a novel balanced sampling method, which leveraged patch-based entropy and edge complexity to classify sample diversity. Our findings revealed that (1) both meta-analysis and the case studies suggested that ∼4 % of the total mapping patches were the optimal training sample size under random sampling, i.e., the minimum size to reach accuracy saturation; (2) compared to random sampling, the newly proposed balanced sampling was superior due to its decreasing the required sample size from ∼4 % to 2.5 % of the total patches in mapped areas; (3) sample transfer and model transfer present identical performance for relaxing the average local sample demand from 2.5 % to 0.5 % of total patches, with sample transfer being slightly more accurate than model transfer (Global Total-Classification errors: 0.298 vs 0.308). This study offers a heuristic framework for applying strategic sampling in semantic segmentation, providing valuable practical guidance for implementing deep learning in an operational scenario.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"331 ","pages":"Article 115034"},"PeriodicalIF":11.4,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145089353","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":"Optimizing DEM error mitigation in multi-temporal InSAR: A detection-and-estimation strategy based on phase gradient direction consistency","authors":"Xinyou Song ,&nbsp;Lei Zhang ,&nbsp;Zhong Lu ,&nbsp;Hongyu Liang ,&nbsp;Weijia Ren","doi":"10.1016/j.rse.2025.115028","DOIUrl":"10.1016/j.rse.2025.115028","url":null,"abstract":"<div><div>Accurate topographic phase removal in Differential InSAR (DInSAR) processing relies on Digital Elevation Models (DEMs), yet limitations in DEM accuracy and currency hinder precise surface displacement measurement. Although modern SAR satellites feature a relatively narrow orbit tube, the phases induced by DEM errors cannot be safely ignored especially in areas under rapid urbanization. Current Multi-Temporal InSAR (MT-InSAR) methods, which estimate DEM errors alongside deformation, suffer from potential biases due to inaccurate deformation models and high computational cost from per-point processing. We present here a novel detection-and-estimation strategy for efficient DEM error mitigation. Our key innovation is a phase gradient direction consistency (GDC) criterion, which provides a direct and intuitive visualization of pixels affected by DEM errors (PEEs)—a capability not previously available. This is a significant advancement as it allows targeted correction instead of exhaustive estimation. We further develop a generalizable framework for DEM error retrieval applicable to various scenarios. Validation with simulated and real-world data from urban and mountainous environments demonstrates effective separation of DEM errors from various spatiotemporal deformation signals. In addition, the proposed method achieves an order-of-magnitude improvement in processing efficiency compared to conventional approaches. By directly identifying and estimating DEM errors from wrapped phases, our approach streamlines deformation retrieval and is readily integrated into existing MT-InSAR workflows.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"331 ","pages":"Article 115028"},"PeriodicalIF":11.4,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145083829","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":"Revealing the spatial distribution of crown base height across China based on close-range Lidar data","authors":"Zekun Yang ,&nbsp;Zhiyong Qi ,&nbsp;Yuling Chen ,&nbsp;Kai Cheng ,&nbsp;Haitao Yang ,&nbsp;Mengxi Chen ,&nbsp;Jiachen Xu ,&nbsp;Yixuan Zhang ,&nbsp;Yu Ren ,&nbsp;Weiyan Liu ,&nbsp;Danyang Lin ,&nbsp;Guoran Huang ,&nbsp;Tianyu Xiang ,&nbsp;Guangcai Xu ,&nbsp;Qinghua Guo","doi":"10.1016/j.rse.2025.115030","DOIUrl":"10.1016/j.rse.2025.115030","url":null,"abstract":"<div><div>Crown base height (CBH) is essential for characterizing forest vertical structure over time for sustainable forest management and serves as a key input in fire model and growth model. At plot level, the average CBH (CBH_a) is mainly used to assess tree growth and construct biomass models while the minimum CBH (CBH_min) can indicate the fire risk and fire behaviour. However, there are currently few CBH products available at a national or global scale. Close-range light detection and ranging (Lidar) has shown great potential in collecting plot-level forest structure parameters and can be easily scaled up to national or global scale. But considering the integrity of point clouds, CBH estimation utilizing airborne Lidar data would be always overestimated compared with other close-range Lidar data such as TLS data or backpack data. This is mainly because of the significant difference in density between the upper and lower point clouds, as well as lacking considering the tree shape. By filling the point clouds with the same density and lowering the CBH condition which considers the tree shape, we proposed an improved CBH estimation method to reduce the overestimation when using airborne Lidar data. Verified by field-measured data in six plots, the proposed method improved the root-mean-square error (RMSE) by nearly 50 % compared with the original method. The mean absolute error (MAE) was 0.694 m, R² was 0.777 and the RMSE was 1.039 m for the validation trees. Facing different sensors and point densities, this method generally generates stable CBH estimation results. Then, we developed a newly tree-based framework that uses machine learning and multiple source remote sensing data for generating CBH products across China. We collected over 1117 km² close-range Lidar data and used the proposed method for estimating CBH. The CBH estimation results were converted to average value and minimum value in a 1 km × 1 km plot and served as training data to generate CBH maps across China at 1 km resolution. To our best knowledge, this is the first CBH map across China, and also the first national-scale average and minimum CBH maps around the world. The results showed that the average CBH_a and CBH_min were 6.76 m and 2.70 m with standard deviations of 1.59 m and 0.85 m. The methods and maps would provide a new dimension in monitoring changes in forest structure, assessing fire risk and constructing biomass models in future studies.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"331 ","pages":"Article 115030"},"PeriodicalIF":11.4,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145083872","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}