Remote Sensing of Environment最新文献

{"title":"Ground surface displacement measurement from SAR imagery using deep learning","authors":"Jinwoo Kim, Hyung-Sup Jung, Zhong Lu","doi":"10.1016/j.rse.2024.114577","DOIUrl":"https://doi.org/10.1016/j.rse.2024.114577","url":null,"abstract":"Offset tracking using synthetic aperture radar (SAR) amplitude imagery is a valuable technique for detecting large ground displacements. However, the traditional offset tracking methods with the SAR datasets are computationally intensive and require significant time for processing. We have developed a novel cross-connection Siamese ResNet (CC-ResSiamNet). The model leverages multi-kernel offset tracking for preprocessing, followed by deep learning architectures that incorporate U-Net, cross-connections, and residual and attention blocks to predict pixel offsets between two SAR amplitude images. It is trained and tested on 200 K pairs of reference and secondary SAR amplitude images, alongside corresponding target offset data from Alaska's glaciers. The comparative analysis with multiple deep learning models confirmed that our designed model is highly generalizable, achieving rapid convergence, minimal overfitting, and high prediction accuracy. Through multi-scenario inference with glacier movements, earthquakes, and volcanic eruptions worldwide, the model demonstrates strong performance, closely matching the accuracy of traditional methods while offering significantly faster processing times through parallel computing. The model's rapid displacement mapping capability shows particular promise for improving disaster response and near real-time surface monitoring. While the approach encounters challenges in accurately capturing small-scale displacements, it opens new possibilities for SAR-based surface displacement prediction using machine learning. This research highlights the advantages of combining deep learning with SAR imagery for advancing geophysical analysis, with future applications anticipated as more commercial and scientific SAR missions launch globally.","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"111 1","pages":""},"PeriodicalIF":13.5,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142867408","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":"Coupled hydrologic-electromagnetic framework to model permafrost active layer organic soil dielectric properties","authors":"Kazem Bakian-Dogaheh, Yuhuan Zhao, John S. Kimball, Mahta Moghaddam","doi":"10.1016/j.rse.2024.114560","DOIUrl":"https://doi.org/10.1016/j.rse.2024.114560","url":null,"abstract":"Arctic permafrost soils contain a vast reservoir of soil organic carbon (SOC) vulnerable to increasing mobilization and decomposition from polar warming and permafrost thaw. How these SOC stocks are responding to global warming is uncertain, partly due to a lack of information on the distribution and status of SOC over vast Arctic landscapes. Soil moisture and organic matter vary substantially over the short vertical distance of the permafrost active layer. The hydrological properties of this seasonally thawed soil layer provide insights for understanding the dielectric behavior of water inside the soil matrix, which is key for developing more effective physics-based radar remote sensing retrieval algorithms for large-scale mapping of SOC. This study provides a coupled hydrologic-electromagnetic framework to model the frequency-dependent dielectric behavior of active layer organic soil. For the first time, we present joint measurement and modeling of the water matric potential, dielectric permittivity, and basic physical properties of 66 soil samples collected across the Alaskan Arctic tundra. The matric potential measurement allows for estimating the soil water retention curve, which helps determine the relaxation time through the Eyring equation. The estimated relaxation time of water molecules in soil is then used in the Debye model to predict the water dielectric behavior in soil. A multi-phase dielectric mixing model is applied to incorporate the contribution of various soil components. The resulting organic soil dielectric model accepts saturation water fraction, organic matter content, mineral texture, temperature, and microwave frequency as inputs to calculate the effective soil dielectric characteristic. The developed dielectric model was validated against lab-measured dielectric data for all soil samples and exhibited robust accuracy. We further validated the dielectric model against field-measured dielectric profiles acquired from five sites on the Alaskan North Slope. Model behavior was also compared against other existing dielectric models, and an in-depth discussion on their validity and limitations in permafrost soils is given. The resulting organic soil dielectric model was then integrated with a multi-layer electromagnetic scattering forward model to simulate radar backscatter under a range of soil profile conditions and model parameters. The results indicate that low frequency (P-, L-band) polarimetric synthetic aperture radars (SARs) have the potential to map water and carbon characteristics in permafrost active layer soils using physics-based radar retrieval algorithms.","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"31 1","pages":""},"PeriodicalIF":13.5,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142857725","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":"Joint mapping of melt pond bathymetry and water volume on sea ice using optical remote sensing images and physical reflectance models","authors":"Chuan Xiong, Xudong Li","doi":"10.1016/j.rse.2024.114571","DOIUrl":"https://doi.org/10.1016/j.rse.2024.114571","url":null,"abstract":"Melt ponds are a common phenomenon on the surface of Arctic sea ice during the summer, and their low albedo strongly influences the energy balance of the Arctic sea ice. Estimating Melt Pond Fraction (MPF) and Melt Pond Depth (MPD) using optical remote sensing is crucial for a better understanding of rapid climate change in the Arctic region. However, current retrieval algorithms for monitoring Arctic melt ponds using optical imagery often fail to estimate MPD. In this study, a radiative transfer model for melt ponds is establish to describe the relationship between melt pond reflectance and its physical properties. Using Sentinel-2 observation data, we propose a novel algorithm for the simultaneous retrieval of MPF and MPD, thereby enabling the estimation of Melt Pond Volume (MPV). This method does not depend on prior assumptions regarding the spectral reflectance of sea ice and melt ponds, and it accounts for the spatiotemporal variability in their reflectance. Compared with other high-resolution MPF and MPD products, the results of this study demonstrate comparable spatial distributions. The root mean square error (RMSE) of the retrieved MPF is less than 10 %, and the RMSE for MPD is approximately 24.51 cm. The analysis of melt pond evolution along the MOSAiC track shows the rapid expansion of melt ponds and their significant spatial variability. Ultimately, using Google Earth Engine (GEE) and machine learning, a dataset of MPF, MPD, and MPV for the Arctic from 2013 to 2023 is generated from 57,842 Landsat-8 images. Correlation analysis shows that MPF, MPD, and MPV all have a positive correlation with downward surface radiation. The approach outlined in this study is entirely based on remote sensing imagery, demonstrating significant potential for large scale application. This offers new opportunities for estimating the volume of water stored in Arctic summer melt ponds.","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"19 1","pages":""},"PeriodicalIF":13.5,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142857729","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":"Quantitative characterization of global nighttime light: A method for measuring energy intensity based on radiant flux and SNPP-VIIRS data","authors":"Haihang Zeng, Mingming Jia, Xiangyu Ning, Zhaohui Xue, Rong Zhang, Chuanpeng Zhao, Yangyang Yan, Zongming Wang","doi":"10.1016/j.rse.2024.114576","DOIUrl":"https://doi.org/10.1016/j.rse.2024.114576","url":null,"abstract":"Nighttime light (NTL) remote sensing has become an important tool to study human activities and their impact on the environment. However, accurately and quantitatively measuring NTL has remained a challenge. In this study, we propose using radiant flux as a more precise measure of NTL energy intensity, which takes into account both radiance and image pixel area. To achieve this, we develop a conversion model from radiance to radiant flux based on the SNPP-VIIRS dataset and calculate the global radiant flux map for 2022. A validation of the model was conducted in 50 representative cities worldwide, confirming its rationality and accuracy. The use of radiant flux provides a more intuitive reflection of NTL energy intensity and eliminates system error caused by variations in pixel areas. This research emphasizes the importance of using a quantitative measurement for NTL and highlights the potential for further evaluation of socio-economic parameters and ecological impacts using NTL radiant flux.","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"87 1","pages":""},"PeriodicalIF":13.5,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142849542","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 flexible framework for built-up height mapping using ICESat-2 photons and multisource satellite observations","authors":"Xiayu Tang, Guojiang Yu, Xuecao Li, Hannes Taubenböck, Guohua Hu, Yuyu Zhou, Cong Peng, Donglie Liu, Jianxi Huang, Xiaoping Liu, Peng Gong","doi":"10.1016/j.rse.2024.114572","DOIUrl":"https://doi.org/10.1016/j.rse.2024.114572","url":null,"abstract":"Built-up heights serve as a nexus in understanding the complex relationship between urban forms and socioeconomic activities. With the advent of remote sensing technology, built-up height mapping from satellite observations has become available over the past years. However, the absence of high-precision sample data poses a significant limitation to built-up height mapping at large (regional or global) scales, particularly in developing regions. To address this issue, we proposed a flexible mapping framework to derive precise building height samples using the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) data for built-up height estimation. First, we calculated building heights from ICESat-2 photons using advanced algorithms such as Random Sample Consensus (RANSAC) linear fitting and cloth simulation filtering. Then, we constructed large-scale built-up height samples by aggregating the height information into grid cells with optimal size. Finally, aided by these grids with height information from ICEsat-2 and other satellite observations from Sentinel data as well as the digital surface model (DSM), we mapped built-up heights in two mega-cities (i.e., New York and Shenzhen) using the random forest regression model. Our results demonstrate building height estimation using ICESat-2 data generally exhibits in relation to other studies high accuracy, showing great potential to support large-scale built-up height mapping using satellite observations. We found the optimal grid size for built-up height mapping is around 300 m, after a comprehensive sensitivity analysis regarding the building fraction within the grid and its size. Overall, the mapped built-up heights are reliable, with relatively low mean absolute errors (MAE) of 2.69 m in New York and 3.87 m in Shenzhen, similar to or better than previous studies. By leveraging high-precision elevation data provided by the ICESat-2 data, our proposed approach can effectively collect samples in regions with limited information on building heights, showing great potential for large-scale built-up height monitoring and supporting future urban studies.","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"64 1","pages":""},"PeriodicalIF":13.5,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142857723","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":"Unveiling multimodal consolidation process of the newly reclaimed HKIA 3rd runway from satellite SAR interferometry, ICA analytics and Terzaghi consolidation theory","authors":"Zhuo Jiang, Guoqiang Shi, Songbo Wu, Xiaoli Ding, Chaoying Zhao, Man Sing Wong, Zhong Lu","doi":"10.1016/j.rse.2024.114561","DOIUrl":"https://doi.org/10.1016/j.rse.2024.114561","url":null,"abstract":"The three-runway system expansion project of the Hong Kong International Airport (HKIA) began with the land reclamation to the north of its original runway. To facilitate quick stabilization, the Deep Cement Mixing (DCM) in this project was featured as the novel reclamation method firstly applied in Hong Kong. Understanding ground deformation and underground consolidation is crucial for subsequent soil improvement, civil construction, and future planning at the new platform. Synthetic Aperture Radar Interferometry (InSAR) is used to investigate the spatiotemporal characteristics of land deformation following the completion of the third runway pavement. A combined strategy of persistent scatterer (PS) and distributed scatterer (DS) interferometry was implemented to address low radar coherence at the site. The new reclamation is subject to varying degrees of land subsidence, with a maximum monitored sinking rate to be ∼150 mm/year during September 2021 and October 2023. Whereas the 3rd runway was urgently transformed to operation, spatial details of consolidation status of this new land were not yet evaluated. We applied the Independent Component Analysis (ICA) to identify the underlying sources leading to the measured deformation from InSAR. Three distinct sources have been unveiled, including an exponential decay signal (a quick compaction subsidence of surficial materials), a linear signal (a continuous subsiding from marine deposits) and a periodic signal (thermal effects correlated with buildings and bridges). Notably, the linear deformation component is mainly located in areas with prefabricated vertical drains (PVD), which is strongly correlating with the current monitored subsidence pattern. We incorporated the Terzaghi consolidation theory to further characterize InSAR displacement and estimate the subsidence decay property, consolidation time, ultimate primary settlement and consolidation degree at the 3rd runway, with unprecedented spatial details. Our results indicate the DCM method achieves geological stability more rapidly than the PVD method, with a time advantage of approximately 0.08–1.39 years. Meanwhile, DCM can effectively control the primary settlement to 29 % - 83 % of the PVD method. This research benefits our understanding of the consolidation process at the 3rd runway and offer reliable and detailed data of underground properties. This facilitates more accurate planning of follow-up reinforcement measures at specific locations if needed, which also serves as a valuable reference for future reclamation practices in Hong Kong, particularly using the DCM method.","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"87 1","pages":""},"PeriodicalIF":13.5,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142833054","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":"Automatic SAR-based rapeseed mapping in all terrain and weather conditions using dual-aspect Sentinel-1 time series","authors":"Shuai Xu, Xiaolin Zhu, Ruyin Cao, Jin Chen, Xiaoli Ding","doi":"10.1016/j.rse.2024.114567","DOIUrl":"https://doi.org/10.1016/j.rse.2024.114567","url":null,"abstract":"Timely and reliable rapeseed mapping is crucial for vegetable oil supply and bioenergy industry. Synthetic Aperture Radar (SAR) remote sensing is able to track rapeseed phenology and map rapeseed fields in cloudy regions. However, SAR-based rapeseed mapping is challenging in mountainous areas due to the highly fragmented farming land and terrain-induced distortions on SAR signals. To address this challenge, this study proposed a novel SAR-based automatic rapeseed mapping (SARM) method for all terrain and weather conditions. SARM first composites high-quality dual-aspect Sentinel-1 time series by combining ascending and descending orbits and smoothing temporal noises. Second, SARM embeds a novel terrain-adjustment modeling to mitigate confounding terrain effects on the SAR intensity of sloped pixels. Third, SARM quantifies unique shape and intensity features of SAR signals during the leaf-flower-pod period to estimate the probability of rapeseed cultivation with the aid of automatically extracted local high-confidence rapeseed pixels. SARM was tested at three sites with varying topographic conditions, rapeseed phenology and cultivation systems. Results demonstrate that SARM achieved accurate rapeseed mapping with the overall accuracy 0.9 or higher, and F1 score 0.85 or higher at all three sites. Compared with the existing rapeseed mapping methods, SARM excelled in mapping fragmented rapeseed fields in both flat and sloped terrains. SARM utilizes unique and universal SAR time-series features of rapeseed growth without relying on any prior knowledge or pre-collected training samples, making it flexible and robust for cross-regional rapeseed mapping, especially for cloudy and mountainous regions where optical data is often contaminated by clouds during rapeseed growing stages.","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"28 1","pages":""},"PeriodicalIF":13.5,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825601","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":"How high are we? Large-scale building height estimation at 10 m using Sentinel-1 SAR and Sentinel-2 MSI time series","authors":"Ritu Yadav, Andrea Nascetti, Yifang Ban","doi":"10.1016/j.rse.2024.114556","DOIUrl":"https://doi.org/10.1016/j.rse.2024.114556","url":null,"abstract":"Accurate building height estimation is essential to support urbanization monitoring, environmental impact analysis and sustainable urban planning. However, conducting large-scale building height estimation remains a significant challenge. While deep learning (DL) has proven effective for large-scale mapping tasks, there is a lack of advanced DL models specifically tailored for height estimation, particularly when using open-source Earth observation data. In this study, we propose T-SwinUNet, an advanced DL model for large-scale building height estimation leveraging Sentinel-1 SAR and Sentinel-2 multispectral time series. T-SwinUNet model contains a feature extractor with local/global feature comprehension capabilities, a temporal attention module to learn the correlation between constant and variable features of building objects over time and an efficient multitask decoder to predict building height at 10 m spatial resolution. The model is trained and evaluated on data from the Netherlands, Switzerland, Estonia, and Germany, and its generalizability is evaluated on an out-of-distribution (OOD) test set from ten additional cities from other European countries. Our study incorporates extensive model evaluations, ablation experiments, and comparisons with established models. T-SwinUNet predicts building height with a Root Mean Square Error (RMSE) of 1.89 m, outperforming state-of-the-art models at 10 m spatial resolution. Its strong generalization to the OOD test set (RMSE of 3.2 m) underscores its potential for low-cost building height estimation across Europe, with future scalability to other regions. Furthermore, the assessment at 100 m resolution reveals that T-SwinUNet (0.29 m RMSE, 0.75 <span><span style=\"\"></span><span data-mathml='<math xmlns=\"http://www.w3.org/1998/Math/MathML\"><msup is=\"true\"><mrow is=\"true\"><mi is=\"true\">R</mi></mrow><mrow is=\"true\"><mn is=\"true\">2</mn></mrow></msup></math>' role=\"presentation\" style=\"font-size: 90%; display: inline-block; position: relative;\" tabindex=\"0\"><svg aria-hidden=\"true\" focusable=\"false\" height=\"2.432ex\" role=\"img\" style=\"vertical-align: -0.235ex;\" viewbox=\"0 -945.9 1213.4 1047.3\" width=\"2.818ex\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g fill=\"currentColor\" stroke=\"currentColor\" stroke-width=\"0\" transform=\"matrix(1 0 0 -1 0 0)\"><g is=\"true\"><g is=\"true\"><g is=\"true\"><use xlink:href=\"#MJMATHI-52\"></use></g></g><g is=\"true\" transform=\"translate(759,410)\"><g is=\"true\"><use transform=\"scale(0.707)\" xlink:href=\"#MJMAIN-32\"></use></g></g></g></g></svg><span role=\"presentation\"><math xmlns=\"http://www.w3.org/1998/Math/MathML\"><msup is=\"true\"><mrow is=\"true\"><mi is=\"true\">R</mi></mrow><mrow is=\"true\"><mn is=\"true\">2</mn></mrow></msup></math></span></span><script type=\"math/mml\"><math><msup is=\"true\"><mrow is=\"true\"><mi is=\"true\">R</mi></mrow><mrow is=\"true\"><mn is=\"true\">2</mn></mrow></msup></math></script></span>) also outperfo","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"10 1","pages":""},"PeriodicalIF":13.5,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825602","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 radiative transfer model for characterizing photometric and polarimetric properties of leaf reflection: Combination of PROSPECT and a polarized reflection function","authors":"Xiao Li, Zhongqiu Sun, Shan Lu, Kenji Omasa","doi":"10.1016/j.rse.2024.114559","DOIUrl":"https://doi.org/10.1016/j.rse.2024.114559","url":null,"abstract":"Light photometric and polarimetric characteristics are crucial for describing the optical properties of leaf reflections, which play an essential role in investigating biochemical and surface structural trait inversion and radiative balance between vegetation and atmospheric system. Although several physical models are available, research on a comprehensive model that accounts for both photometric and polarimetric characteristics and incorporates biochemical and surface structural traits is still inadequate. In this study, we introduced PROPOLAR, a leaf model that considered leaf reflection in terms of polarized and unpolarized components and linked leaf reflection to leaf traits. PROPOLAR employed PROSPECT to simulate non-polarized component associated with biochemical traits, while used a three-parameter function (linear coefficient, refractive index factor, and roughness of leaf surface) to simulate the polarized component. The model was validated using a dataset (composed of both photometric and polarimetric measurements) collected from 533 samples of 13 plant species under various illumination-viewing geometries. The results showed that PROPOLAR outperformed PROSPECT and PROSPECULAR (a leaf model charactering BRF) in simulating light intensity (R² = 0.98), and effectively simulated bidirectional polarization reflectance factor (BPRF) and degree of linear polarization (Dolp) across a wide spectral range (450–2300 nm) and species, with R² = 0.92, and 0.80, respectively. Furthermore, PROPOLAR enhanced the accuracy of PROSPECT and showed comparable accuracy with PROSPECULAR in the inversion of biochemical traits from the multi-angular polarization measurements, including chlorophyll (R² = 0.89, RMSE = 12.83 μg/cm²), equivalent water thickness (R² = 0.90, RMSE = 0.0032 g/cm²), and leaf mass per area (R² = 0.38, RMSE = 0.0031 g/cm²), due to the incorporation of polarization reflection and a linear coefficient during calibration. Notably, PROPOLAR can invert roughness and showed reasonable consistency with measured roughness (R² = 0.61). These results demonstrated the effectiveness of PROPOLAR in simulating both photometric and polarimetric properties of leaf reflection, as well as its potential for biochemical and surface structural trait inversion. PROPOLAR may advance remote sensing applications in vegetation management by integrating photometric and polarimetric properties.","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"14 1","pages":""},"PeriodicalIF":13.5,"publicationDate":"2024-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142820925","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":"Predicting drought vulnerability with leaf reflectance spectra in Amazonian trees","authors":"Maquelle N. Garcia, Lucas B.S. Tameirão, Juliana Schietti, Izabela Aleixo, Tomas F. Domingues, K. Fred Huemmrich, Petya K.E. Campell, Loren P. Albert","doi":"10.1016/j.rse.2024.114562","DOIUrl":"https://doi.org/10.1016/j.rse.2024.114562","url":null,"abstract":"Hydraulic traits mediate trade-offs between growth and mortality in plants yet characterizing these traits at the community level remains challenging, particularly in the Amazon, where they vary widely across species and environments. While previous studies have used reflectance-based estimates, hydraulic traits, which arise from wood and/or whole-plant anatomy and physiology, have not been comprehensively explored.For the first time, we comprehensively investigated the use of leaf reflectance to predict hydraulic traits alongside leaf functional traits in tropical evergreen and deciduous trees. For 196 Amazonian trees, we measured water potential, leaf mass per area (LMA), leaf reflectance, hydraulic conductivity curves (e.g., P50), and wood density (WD). We examined the relationships between leaf reflectance and traits using partial least square regression (PLSR).Our findings indicate that leaf reflectance accurately predicts variation in LMA (R² = 0.8), and reasonably estimates xylem water potential (R² = 0.51) and WD (R² = 0.52). However, P50 predictions were much less reliable (R² = 0.27), with water absorption bands greatly influencing the PLSR model. Leaf phenological strategy had little impact on the results.These findings suggest that reflectance-based remote sensing could monitor water status and forest carbon dynamics through water potential and wood density, respectively. However, our case study applying the PLSR approach to hyperspectral canopy spectra to predict wood density revealed challenges to upscaling. Despite these limitations, remote sensing of forest hydraulic traits at scale could enhance our understanding of drought vulnerability and carbon dynamics in Amazonian forests, with significant implications for conservation.","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"200 1","pages":""},"PeriodicalIF":13.5,"publicationDate":"2024-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142820924","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}