Science of Remote Sensing最新文献

{"title":"A novel fusion of Sentinel-1 and Sentinel-2 with climate data for crop phenology estimation using Machine Learning","authors":"Shahab Aldin Shojaeezadeh ,&nbsp;Abdelrazek Elnashar ,&nbsp;Tobias Karl David Weber","doi":"10.1016/j.srs.2025.100227","DOIUrl":"10.1016/j.srs.2025.100227","url":null,"abstract":"<div><div>Crop phenology describes the physiological development stages of crops from planting to harvest which is valuable information for decision makers to plan and adapt agricultural management strategies. In the era of big Earth observation data ubiquity, attempts have been made to accurately detect crop phenology using Remote Sensing (RS) and high resolution weather data. However, most studies have focused on large scale predictions of phenology or developed methods which are not adequate to help crop modeler communities on leveraging Sentinel-1 and Sentinal-2 data and fusing them with high resolution climate data, using a novel framework. For this, we trained a Machine Learning (ML) LightGBM model to predict 13 phenological stages for eight major crops across Germany at 20 m scale. Observed phenologies were taken from German national phenology network (German Meteorological Service; DWD) between 2017 and 2021. We proposed a thorough feature selection analysis to find the best combination of RS and climate data to detect phenological stages. At national scale, predicted phenology resulted in a reasonable precision of R² &gt; 0.43 and a low Mean Absolute Error of 6 days, averaged over all phenological stages and crops. The spatio-temporal analysis of the model predictions demonstrates its transferability across different spatial and temporal context of Germany. The results indicated that combining radar sensors with climate data yields a very promising performance for a multitude of practical applications. Moreover, these improvements are expected to be useful to generate highly valuable input for crop model calibrations and evaluations, facilitate informed agricultural decisions, and contribute to sustainable food production to address the increasing global food demand.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"11 ","pages":"Article 100227"},"PeriodicalIF":5.7,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143898532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modelling multi-layer fine fuel loads in temperate eucalypt forests using airborne LiDAR and inventory data","authors":"Trung H. Nguyen ,&nbsp;Simon Jones ,&nbsp;Karin J Reinke ,&nbsp;Mariela Soto-Berelov","doi":"10.1016/j.srs.2025.100226","DOIUrl":"10.1016/j.srs.2025.100226","url":null,"abstract":"<div><div>Wildfires are increasing in intensity and frequency due to climate change and land-use changes, posing critical threats to ecosystems, economies, and human safety. Fine fuels (&lt;6 mm, such as leaves and twigs) are known key drivers of wildfire ignition and spread, particularly in temperate forests where high flammability increases wildfire hazard. Accurately quantifying fine fuel loads (FFL) across vertical forest layers is essential for understanding and predicting wildfire behaviour, yet past studies using Airborne Laser Scanning (ALS) have been limited to canopy fuels, overlooking surface and understorey layers that play a key role in wildfire propagation. This study addresses this gap by developing an ALS-based modelling approach to estimate FFL across four vertical layers: canopy, elevated (or ladder), near-surface, and surface. The study was conducted in eucalypt-dominated forests in Victoria, southeastern Australia. We stratified ALS point clouds into distinct layers (overstorey, intermediate, shrub, and herb), computed layer-specific structural metrics, and trained Random Forest models to predict multi-layer FFL. The models performed well, with the highest accuracy for canopy FFL (R² = 0.74, relative RMSE = 49.42 %) and moderate accuracy for elevated, near-surface, and surface FFL (R² = 0.42–0.56, relative RMSE = 59.77–77.57 %). Model interpretation revealed that integrating ALS metrics from multiple forest layers maximised accuracy and highlighted the complex role of vertical forest structure in predicting FFL. Prediction maps captured horizontal and vertical FFL variations across landscapes, reflecting differences in forest structure. Furthermore, pre-fire FFL, especially in surface and canopy layers, showed statistically significant associations with wildfire-induced forest loss. This study advances multi-layer FFL estimation using ALS data, offering a more comprehensive fuel information for wildfire hazard assessment and forest management. Future research should explore the scalability of this method by integrating satellite-derived data to extend FFL mapping at broader spatial scales.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"11 ","pages":"Article 100226"},"PeriodicalIF":5.7,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A general spectral bandpass adjustment function (SBAF) for harmonizing landsat-sentinel over inland and coastal waters","authors":"Thainara M.A. Lima ,&nbsp;Vitor S. Martins ,&nbsp;Rejane S. Paulino ,&nbsp;Cassia B. Caballero ,&nbsp;Daniel A. Maciel ,&nbsp;Claudia Giardino","doi":"10.1016/j.srs.2025.100225","DOIUrl":"10.1016/j.srs.2025.100225","url":null,"abstract":"<div><div>Landsat 8/9 Operational Land Imager (OLI) and Sentinel-2 Multispectral Imager (MSI) are the most relevant medium spatial resolution data sources for aquatic applications, and integrating these spectral images into a single product constellation offers significant potential for monitoring dynamic processes over coastal and inland waters. Due to water's inherently low reflectance values, small differences in the relative spectral responses (RSR) between the two sensors can result in significant discrepancies in water reflectance retrievals. To ensure compatibility and consistency in harmonized products for aquatic studies, spectral bandpass adjustment function (SBAF) for cross-calibration between OLI and MSI sensors must be carefully derived and applied. This study provides a global analysis of 4047 match-ups of atmospherically corrected Landsat-8/9 OLI and Sentinel-2 A/B MSI L1 products over inland and coastal waters to generate a new general SBAF for aquatic studies. Atmospheric correction was performed using the 6 S V radiative transfer model, a widely validated approach for aquatic remote sensing applications. The selected images were sensed ≤30 min apart on the same day, under &lt;5 % cloud cover, ≤5° solar zenith difference across different atmospheric conditions and aquatic systems (928 coastal and inland water bodies) over the world. A robust quality-controlled protocol was developed to remove low-quality image pairs under sun/sky glint, ice/snow surface, and cirrus clouds. Following this procedure, 2,2 million quality filtered water reflectance pixels were extracted. The SBAF coefficients (i.e., slope and offset) were derived through statistical regression between Landsat-8/9 and Sentinel-2 reflectance values. Additionally, we simulated sensor band responses using a global in-situ hyperspectral water dataset and calculated the SBAF coefficients for comparison with the pixel-based results. The application of SBAF was demonstrated through comparative analyses of spectral reflectance from Landsat-8/9 and Sentinel-2 before and after the cross-calibration. Our findings underscore the effectiveness of these coefficients in reducing spectral discrepancies between Landsat-8/9 and Sentinel-2 water reflectance measurements.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"11 ","pages":"Article 100225"},"PeriodicalIF":5.7,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An enhanced RPV model to better capture hotspot signatures in vegetation canopy reflectance observed by the geostationary meteorological satellite Himawari-8","authors":"Wei Yang ,&nbsp;Zhi Qiao ,&nbsp;Wei Li ,&nbsp;Xuanlong Ma ,&nbsp;Kazuhito Ichii","doi":"10.1016/j.srs.2025.100222","DOIUrl":"10.1016/j.srs.2025.100222","url":null,"abstract":"<div><div>The hotspot effect denotes a special case of the Bidirectional Reflectance Distribution Function (BRDF) when the solar direction coincides with the sensor viewing direction, which is essential for remote estimation of canopy structure information. In contrast to polar-orbiting satellites, third-generation geostationary (GEO) meteorological satellites provide a new opportunity to investigate the hotpot effect at a modest spatial resolution (∼1 km) due to their extremely high observation frequencies. Nevertheless, modeling of the hotspot effect observed by GEO satellites is a significant challenge because their observations of Bidirectional Reflectance Factor (BRF) are usually off the principal plane. Among the existing semi-empirical BRDF models, the Rahman-Pinty-Verstraete (RPV) model has been widely applied to simulate intricate fields of canopy BRF. However, the RPV model has also been criticized for underestimating the hotspot signatures. Consequently, an enhanced version of the RPV model (i.e., ERPV) was proposed in this study to improve its capability for modeling the hotspot signatures of canopy reflectance. To verify the proposed ERPV model, a reflectance dataset of hotspot effect for different vegetation types was created using the atmospherically corrected Hiwamari-8 reflectance, and the ERPV model was applied to estimate foliage Clumping Index (CI) through constructing hotspot and dark spot within the principal plane. Validation results demonstrated that the land surface reflectance of Himwari-8 could measure the hotspot effect properly for each vegetation type. The EPRV model yielded satisfactory accuracies in capturing the hotspot signatures with Root-Mean-Square-Error (RMSE) of 0.0034 and 0.0056, and Bias of −0.0019 and −0.0028, for the red and near-infrared (NIR) bands, respectively. In contrast, the RMSE and Bias for the original RPV model and three existing kernel-driven BRDF models ranged from 0.0187 to 0.125, and from −0.0149 to −0.114, respectively. Moreover, the estimated CI based on the ERPV model (0.66) was closer to the field measurement (0.65) for a mixed forest site than the RPV-based CI estimate (0.72) and the MODIS CI product (0.705). The findings demonstrate that our ERPV model can not only improve the modeling accuracies of hotspot signatures, but also has the potential to construct reliable BRF within the principal plane for CI retrieval.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"11 ","pages":"Article 100222"},"PeriodicalIF":5.7,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Remote sensing-driven machine learning models for spatiotemporal analysis of coastal phytoplankton blooms under climate change scenarios","authors":"Siqi Wang ,&nbsp;Shuzhe Huang ,&nbsp;Yinguo Qiu ,&nbsp;Xiang Zhang ,&nbsp;Chao Wang ,&nbsp;Nengcheng Chen","doi":"10.1016/j.srs.2025.100224","DOIUrl":"10.1016/j.srs.2025.100224","url":null,"abstract":"<div><div>Coastal phytoplankton blooms pose significant environmental challenges, yet spatiotemporal analyses of bloom dynamics under ocean warming and eutrophication remain limited. To address this, we developed machine learning-based regression and classification models for predicting bloom areas and warning levels. These models incorporate remote sensing data and key environmental variables from Coupled Model Intercomparison Project Phase 6 (CMIP6) outputs under different climate change scenarios. We evaluated multiple machine learning approaches including Logistic Regression (LR), Linear Discriminant Analysis (LDA), Classification and Regression Tree (CART), Extreme Gradient Boosting (XGboost), and Light Gradient Boosting Machine (LightGBM) for their predictive capabilities. The LightGBM model, incorporating multi-season remote sensing data and key variables, achieved the highest accuracy, with R-values of 0.95 for warning level classification and 0.6 for bloom area regression. The spatial autocorrelation analysis validated the robustness of our models, demonstrating minimal cross-correlation between training and testing datasets. Furthermore, pixel-level analysis identified the East China Sea as the most bloom-prone region, with consistently higher bloom frequency and magnitude, particularly during summer. Under the historical scenario (incorporating both anthropogenic and natural forcings), we observed higher bloom frequencies and broader area variations compared to scenarios with isolated forcings. Notably, there was a trend toward more frequent yet smaller-scale blooms, with an increase in minor bloom occurrences despite a decrease in extreme events. Critical factors influencing bloom dynamics included sea surface temperature, air temperature, wind speed, sea level pressure, salinity, and nutrient concentrations. Our findings highlight satellite data's importance in understanding anthropogenic-natural factor interactions on coastal blooms, emphasizing the need for targeted nutrient management in vulnerable areas.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"11 ","pages":"Article 100224"},"PeriodicalIF":5.7,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143833815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Importance of the layered structure of leaves on the determination of their scattering and absorption properties","authors":"Corinna Konrad ,&nbsp;Markus Wagner ,&nbsp;Florian Foschum ,&nbsp;Alwin Kienle","doi":"10.1016/j.srs.2025.100223","DOIUrl":"10.1016/j.srs.2025.100223","url":null,"abstract":"<div><div>The influence of the layered structure of plant leaves on the optical properties obtained with a homogeneous theoretical model was investigated. To this aim, silicone phantoms with different optical properties modeling synthetic leaf layers were fabricated to study a two-layer model of the leaf. The optical properties were determined with spectrally resolved integrating sphere measurements using solutions of the radiative transport equation for a homogeneous medium. When the optical properties of a two-layer stack were retrieved, they showed large differences from the optical properties of the involved single layers depending, in addition, on the orientation of the two-layer stack in the measurements. We also obtained the optical properties of a <em>Taraxacum officinale</em> leaf with a homogeneous theoretical model and compared them to those retrieved from a two-layer forward model using Monte Carlo simulations. The optical properties obtained from the simulations showed similar features of the optical properties retrieved from the measurements, including the difference when changing the orientation of the leaf. Therefore, our model is able to provide an explanation for the found effects.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"11 ","pages":"Article 100223"},"PeriodicalIF":5.7,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143844294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vegetation browning as an indicator of drought impact and ecosystem resilience","authors":"Ignacio Fuentes ,&nbsp;Javier Lopatin ,&nbsp;Mauricio Galleguillos ,&nbsp;James McPhee","doi":"10.1016/j.srs.2025.100219","DOIUrl":"10.1016/j.srs.2025.100219","url":null,"abstract":"<div><div>Climate change influences climate variability, increasing the frequency and severity of droughts. These events may trigger vegetation browning, a key indicator of drought propagation and shifts in resilience. While long-term trends often measure browning, rapid vegetation declines require alternative approaches. This study examines drought-induced vegetation browning, resilience, and propagation in central Chile using Moderate Resolution Imaging Spectroradiometer (MODIS) time series of normalised difference vegetation index (NDVI), leaf area index (LAI), and gross primary productivity (GPP). The Continuous Change Detection and Classification (CCDC) algorithm identified negative vegetation changes, filtering out non-browning events to reduce uncertainties. Spatial variations in browning were analysed across latitudinal gradients, topographies, and vegetation types, while shifts in temporal autocorrelation served as a proxy for resilience. Results indicated declines in NDVI across 19% of the study area, GPP in 12%, and LAI in 8%. NDVI responded to drought within six months, with productivity losses lagging by 8.7 months. Recovery was slow, averaging 3.6 years, and only 20%–25% of the affected areas recovered. Variations in browning timing and magnitude were driven by topography, vegetation, and latitude. A decline in vegetation resilience highlights the need for strategies to enhance adaptability to climate change.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"11 ","pages":"Article 100219"},"PeriodicalIF":5.7,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143705794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nationwide conflict damage mapping with interferometric synthetic aperture radar: A study of the 2022 Russia–Ukraine conflict","authors":"Corey Scher ,&nbsp;Jamon Van Den Hoek","doi":"10.1016/j.srs.2025.100217","DOIUrl":"10.1016/j.srs.2025.100217","url":null,"abstract":"<div><div>The full-scale Russian invasion of Ukraine that began in February 2022 has killed thousands of civilians, displaced 3.7 million people, and wrought economic damage on the order of hundreds of billions of US dollars. However, the scale, timing, and geographic distribution of damage to Ukraine’s built environment has never been comprehensively assessed. In this study, we use 17,532 Sentinel-1 interferometric synthetic aperture radar coherence images within a coherent change detection (CCD) framework to identify likely damage across human settlements in Ukraine from March 2022 through October 2023. Overall, we map 264 km² of likely damage across 5.35% (n<span><math><mo>=</mo></math></span>2,288) of administrative settlement polygons. The geographic breadth and protraction of this conflict are well captured through remote monitoring. Two thirds (67.0%) of detected damage is within 10 km of the conflict’s front line demarcating territorial control and active fighting between Ukrainian and Russian forces. Damage is detected during every month of the study with one quarter (27.55%) of damage detected during the first two months of the Russian invasion and another one quarter (24.81%) of damage detected during the 2022 counteroffensives in Kharkiv and Kherson. To calibrate our detection approach and assess agreement with known locations of damage, we use data on 17,043 damage locations in 25 Ukrainian settlements mapped by the United Nations Satellite Centre (UNOSAT) and based on visual interpretation of sub-meter optical satellite imagery. Overall, we detect 59.13% of UNOSAT-mapped damage locations in areas under monitoring with false positive rates ranging from 0.81%–1.14% for testing and training partitions respectively, overcoming a major limitation of using Sentinel-1 CCD for nationwide war damage mapping across seasonal cycles. Our approach is scalable, rapid, low-cost, and can be used to prioritize specific regions for in-depth remote or field-based damage assessments. Given the proliferation of urban armed conflicts around the world, the results of this study show a promising path forward not only for nationwide, sustained damage mapping but also for informing post-conflict recovery and rebuilding with a transparent and replicable approach.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"11 ","pages":"Article 100217"},"PeriodicalIF":5.7,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-scale estimation of photosynthetic capacity in larch forests using UAV hyperspectral data: From leaf to canopy","authors":"Chunyan Wu ,&nbsp;Tingdong Yang ,&nbsp;Min Cheng ,&nbsp;Dongsheng Chen ,&nbsp;Xiaomei Sun ,&nbsp;Shougong Zhang","doi":"10.1016/j.srs.2025.100220","DOIUrl":"10.1016/j.srs.2025.100220","url":null,"abstract":"<div><div>Understanding forest photosynthetic capacity is essential for monitoring carbon dynamics under global change. UAV-based imaging spectroscopy is a powerful tool for assessing canopy leaf traits, but the extension of spectral-trait relationships to the canopy scale remains unclear. This study uses UAV-based hyperspectral imaging data to evaluate the photosynthetic characteristics of larch forests across different climate zones in China. We investigate UAV-derived imaging spectroscopy for mapping canopy-level leaf physiological traits, including chlorophyll content, leaf nitrogen, and photosynthetic capacity (Vc, max and Jmax) across three distinct climate zones. High-resolution UAV imaging spectral data and ground-based leaf trait measurements, including biochemical (chlorophyll, leaf nitrogen), morphological (leaf mass per area, LMA), and physiological traits (Vc, max and Jmax), were collected from 150 tree crowns at all sites. We developed and validated models for estimating physiological traits from canopy spectra using Partial Least Squares Regression (PLSR), focusing on the transferability of leaf-level models to the canopy scale. The results show that UAV-based canopy spectra can effectively estimate canopy-level Vc, max25 (R² = 0.56, RMSE = 9.57 μmol CO₂ m⁻² s⁻¹, nRMSE = 17.7 %) and Jmax25 (R² = 0.38, RMSE = 34.8, nRMSE = 18.6 %). Additionally, other leaf traits across all climate zones were accurately predicted, including leaf mass per area (LMA), leaf water content (LWC), chlorophyll content (Chl), nitrogen content (Narea), and phosphorus content (Parea), with R² values ranging from 0.30 to 0.44 and nRMSE between 18.8 % and 24.4 %. Significant differences in canopy trait variability were observed, with Vc, max25 and Jmax25 values driven by climate variability. The range of Vc, max25 (40.5–70.6 μmol CO₂ m⁻² s⁻¹) and Jmax25 (80.6–120.4 μmol CO₂ m⁻² s⁻¹) was wider at the ES site compared to the FS and TS sites, indicating that species differences have a greater impact on photosynthetic capacity. These models demonstrated good transferability, showing robust performance across forests in different climate zones with only slight differences in predictive accuracy. However, canopy structure significantly influenced spectral-trait relationships, particularly for Vc, max and Jmax. While canopy structure had a moderate impact on accuracy, canopy-scale models performed slightly lower than leaf-level models in some cases. This study offers new insights into UAV-based imaging spectroscopy for mapping canopy leaf physiological traits and emphasizes the need to understand different physiological mechanisms at the canopy scale when expanding spectral-trait relationships.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"11 ","pages":"Article 100220"},"PeriodicalIF":5.7,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143683363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment and improvement of GEDI canopy height estimation in tropical and temperate forests","authors":"Myung-Sik Cho ,&nbsp;David P. Roy ,&nbsp;Herve B. Kashongwe ,&nbsp;Lin Yan ,&nbsp;Meicheng Shen","doi":"10.1016/j.srs.2025.100221","DOIUrl":"10.1016/j.srs.2025.100221","url":null,"abstract":"<div><div>The Global Ecosystem Dynamics Investigation (GEDI) relative height product provides 25 m diameter footprint information that can be used to estimate canopy height but with variably reported accuracy. A methodology is presented to improve the GEDI canopy height accuracy using coincident Airborne Laser Scanner (ALS) data. The GEDI canopy height for each footprint is surface adjusted by adding the residual difference between the GEDI ground elevation estimate and coincident footprint ALS digital terrain model (DTM) value, and then calibrated using site-level additive offsets derived by Theil-Sen regression with coincident footprint ALS canopy height model (CHM) data. The approach is demonstrated at a tropical evergreen lowland forest site in the Democratic Republic of Congo (MNDP), a temperate pine and hardwood forest site in Alabama (TALL), and a temperate mix-species forest site in Maryland (SERC). The GEDI canopy height accuracy is first quantified by comparison with ALS CHM data to provide a baseline. The methodology improved the root mean squared error (RMSE) from 7.5m to 3.4m (MNDP), 5.6m to 3.5m (TALL), and 6.7m to 4.3m (SERC), and improved the relative RMSE from 33.0 % to 14.8 % (MNDP), 27.4 % to 17.3 % (TALL), and 24.9 % to 15.8 % (SERC), for GEDI beam sensitivity ≥0.9, with similar improvements demonstrated for beam sensitivity ≥0.95 and ≥ 0.98. Further accuracy improvements were demonstrated for footprints over homogenous canopies where the simulated impact of GEDI geolocation errors were small, underscoring the need for improved GEDI geolocation. Extrapolation of the methodology to regional or national scale merits further research and is discussed.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"11 ","pages":"Article 100221"},"PeriodicalIF":5.7,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143683364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}