Remote Sensing of Environment最新文献

{"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}

{"title":"SIFFI: Bayesian solar-induced fluorescence retrieval algorithm for remote sensing of vegetation","authors":"Antti Kukkurainen, Antti Lipponen, Ville Kolehmainen, Antti Arola, Sergio Cogliati, Neus Sabater","doi":"10.1016/j.rse.2024.114558","DOIUrl":"https://doi.org/10.1016/j.rse.2024.114558","url":null,"abstract":"Remote sensing of solar-induced vegetation chlorophyll fluorescence (SIF) has a rich history of more than 50 years of research covering active and passive techniques from leaf, canopy, and satellite scale. Current satellite-derived SIF products primarily focus on the far-red spectral range, with variations in techniques dependent on sensor capabilities. However, these retrieval methods often rely on parametric spectral models and are constrained to narrow absorption regions. In this paper, we introduce a novel Bayesian retrieval technique, referred to as SIFFI (Siffi Is For Fluorescence Inference), designed for the flexible and robust estimation of spectrally resolved fluorescence spectra. SIFFI utilizes spectral representations for both fluorescence and surface reflectance, enabling its application to distinct spectral ranges, e.g., red, far-red, and full spectral range. Also, its applicability extends to top-of-canopy (TOC) and top-of-atmosphere (TOA) measurements, with the latter being possible when auxiliary information about the atmospheric state is available. For the assessment of SIFFI, we performed an extensive proof-of-concept simulation exercise involving diverse scenarios that integrated measured leaf-level fluorescence and reflectance signals, propagated them to the TOC and TOA levels, and perturbed the resultant signal with instrument Gaussian noise to simulate realistic conditions. Additionally, we extend our assessment exercise to TOC measurements acquired by a fluorescence box (FloX) instrument during two diurnal cycles on sunlit and cloudy conditions. In all the TOC cases, simulations- and measured-based scenarios, we compared our SIF estimates with the results from two well-established methods: the improved Fraunhofer line discrimination method (iFLD) and the Spectral Fitting (SpecFit) method covering the full fluorescence spectra. Notably, our results highlight the versatility and accuracy of SIFFI in estimating spectrally resolved fluorescence, achieving Mean Absolute Error (MAE) values of 0.07 (0.09) <span><span style=\"\"></span><span data-mathml='<math xmlns=\"http://www.w3.org/1998/Math/MathML\"><mrow is=\"true\"><mo is=\"true\">[</mo><mi mathvariant=\"normal\" is=\"true\">mW</mi><mo is=\"true\">/</mo><mrow is=\"true\"><mo is=\"true\">(</mo><msup is=\"true\"><mrow is=\"true\"><mi mathvariant=\"normal\" is=\"true\">m</mi></mrow><mrow is=\"true\"><mn is=\"true\">2</mn></mrow></msup><mspace width=\"1em\" is=\"true\" /><mi mathvariant=\"normal\" is=\"true\">sr</mi><mspace width=\"1em\" is=\"true\" /><mi mathvariant=\"normal\" is=\"true\">nm</mi><mo is=\"true\">)</mo></mrow><mo is=\"true\">]</mo></mrow></math>' role=\"presentation\" style=\"font-size: 90%; display: inline-block; position: relative;\" tabindex=\"0\"><svg aria-hidden=\"true\" focusable=\"false\" height=\"3.24ex\" role=\"img\"","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"31 1","pages":""},"PeriodicalIF":13.5,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142816040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Retrieval of global surface soil and vegetation temperatures based on multisource data fusion","authors":"Xiangyang Liu, Zhao-Liang Li, Si-Bo Duan, Pei Leng, Menglin Si","doi":"10.1016/j.rse.2024.114564","DOIUrl":"https://doi.org/10.1016/j.rse.2024.114564","url":null,"abstract":"Soil and vegetation temperatures are crucial for various fields, including ecology, agriculture, and climate change. However, there remains a lack of entirely observation-based global datasets for these two component temperatures. To fill this gap, this study developed a multisource data Fusion-based global surface Soil and Vegetation Temperature retrieval method (FuSVeT). This novel method not only utilizes temporal and spatial information from MODIS data by adopting a temperature cycle model to capture temporal variation and using adjacent pixels to consider spatial differences and increase the number of equations solved, but also leverages ERA5-Land data to reduce unknown parameters, effectively compensating for the limitations of satellite observations. Its performances were comprehensively evaluated with simulated data, high-resolution satellite products, and in situ measurements, demonstrating competitive accuracy with root mean square errors below 2 K and Biases of under 1 K in most cases. Compared to previous retrieval method that relies solely on satellite-based temporal and spatial information, FuSVeT present enhanced accuracy, more complete spatial coverage, and improved computational efficiency, making it more applicable for global soil and vegetation temperature mapping. Using this method, we generated global 0.05° monthly mean soil and vegetation temperatures for January and July 2020. These data can capture more pronounced temperature heterogeneities within biomes than existing soil temperature products, indicating its superiority for global analyses. Importantly, FuSVeT can also be applied to satellite observations with higher spatiotemporal resolution, holding significant potential for providing accurate, long-term, global maps of surface soil and vegetation temperatures.","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"17 1","pages":""},"PeriodicalIF":13.5,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142816466","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":"Evaluating rainfall and graupel retrieval performance of the NASA TROPICS pathfinder through the NOAA MiRS system","authors":"John Xun Yang, Yong-Keun Lee, Shuyan Liu, Christopher Grassotti, Quanhua Liu, William Blackwell, Robert Vincent Leslie, Tom Greenwald, Ralf Bennartz, Scott Braun","doi":"10.1016/j.rse.2024.114570","DOIUrl":"https://doi.org/10.1016/j.rse.2024.114570","url":null,"abstract":"The NASA TROPICS mission encompasses a constellation of CubeSats equipped with microwave radiometers, dedicated to investigating tropical meteorology and storm systems. In a departure from traditional microwave sounders, the TROPICS Microwave Sounder (TMS) employs new frequencies at F-band near 118 GHz and features an additional G-band channel at 205 GHz. We have expanded the capabilities of the Microwave Integrated Retrieval System (MiRS), a state-of-the-art one-dimensional variational (1DVAR) algorithm, for the retrieval of geophysical variables with the TROPICS Pathfinder early-phase data. Here we focus on assessing the retrieved precipitation in terms of rainfall and graupel. TROPICS captures well the spatial distribution and temporal evolution of Hurricane Ida and Super Typhoon Mindulle. TROPICS depicted the eyewall replacement cycle of Mindulle as it weakened and reintensified. The global precipitation distribution and dynamics are well represented by TROPICS. We compare TROPICS with other precipitation datasets, including Global Precipitation Mission (GPM) GPM Microwave Imager (GMI) and Dual-Frequency Precipitation Radar (DPR) products. For example, when compared with GMI, MiRS TROPICS instantaneous precipitation yields a correlation coefficient of 0.5 and an RMSE of 2.0 mm/h. For graupel, MiRS TROPICS retrievals show a correlation of 0.52 and an RMSE of 0.53 kg/m². The retrieval performance is comparable to other sensors such as the Advanced Technology Microwave Sounder (ATMS), while the higher number of channels of ATMS, including its low-frequency channels serve to better constrain retrievals. TMS observes at higher spectral frequencies than the coincident ATMS channels, showing higher sensitivity to rainfall and graupel. The TMS high-frequency channels and lower orbit allow for greater resolution of precipitation features, while lower-frequency ATMS channels excel at resolving hurricane warm-core structures. The results underscore the value of the TROPICS mission for precipitation measurement and demonstrate the successful integration of TROPICS processing capability within the MiRS retrieval algorithm framework.","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"29 1","pages":""},"PeriodicalIF":13.5,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142816038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Retrieval of 1 km surface soil moisture from Sentinel-1 over bare soil and grassland on the Qinghai-Tibetan Plateau","authors":"Zanpin Xing, Lin Zhao, Lei Fan, Gabrielle De Lannoy, Xiaojing Bai, Xiangzhuo Liu, Jian Peng, Frédéric Frappart, Kun Yang, Xin Li, Zhilan Zhou, Xiaojun Li, Jiangyuan Zeng, Defu Zou, Erji Du, Chong Wang, Lingxiao Wang, Zhibin Li, Jean-Pierre Wigneron","doi":"10.1016/j.rse.2024.114563","DOIUrl":"https://doi.org/10.1016/j.rse.2024.114563","url":null,"abstract":"Most existing soil moisture (SM) products from earth observations and land surface models over the Qinghai-Tibetan Plateau (QTP) have coarse resolutions or are mostly generated with high spatial resolutions based on downscaling methods. The former could hinder the applications in hydrological and ecological analyses at the regional scale and the performance of the latter could be limited by the intricate relationship between SM and downscaling factors in regions with complex topography. To address this issue, this paper aims to retrieve a 1 km SM product from 2017 to 2021 using Sentinel-1 Synthetic Aperture Radar (SAR) observations based on a semi-empirical method specific to the QTP region (SM_S-1) as different from the previous downscaled SM products. The main interest in our retrievals is that the semi-empirical modeling approach allows exploring the relationships between microwave backscatters and the soil and vegetation parameters spatially based on well-defined mathematics. The SM_S-1 retrievals were evaluated against the observations from five <em>in-situ</em> networks over the QTP and against six other existing downscaled 1 km SM products. The temporal evaluation against <em>in-situ</em> measurements showed that SM_S-1 retrievals performed better than most 1 km SM products obtained from Machine Learning methods (median <em>R</em> = 0.57, ubRMSD = 0.064 m³/m^3, RMSD = −0.107 m³/m³ and bias = −0.042 m³/m³) except for SM_Sg. Furthermore, the SM_S-1 retrievals presented reasonable spatial patterns that are consistent with the spatial distribution of the grassland-type map. Our Sentinel-1 SAR-based method can therefore potentially serve as a foundation for the advance of active microwave remote sensing SM algorithm to retrieve spatially high-resolution SM.","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"14 1","pages":""},"PeriodicalIF":13.5,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142809979","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":"Entity-based image analysis: A new strategy to map rural settlements from Landsat images","authors":"Yan Wang, Xiaolin Zhu, Tao Wei, Fei Xu, Trecia Kay-Ann Williams, Helin Zhang","doi":"10.1016/j.rse.2024.114549","DOIUrl":"https://doi.org/10.1016/j.rse.2024.114549","url":null,"abstract":"Accurate and timely mapping of rural settlements using medium-resolution satellite imagery, such as Landsat data, is crucial for evaluating rural infrastructure, estimating ecological service values, assessing the quality of life for rural populations, and promoting sustainable rural development. Current mapping techniques, including pixel-based and object-based classifications, primarily focus on identifying artificial surfaces, often failing to capture the complete spatial footprint of rural settlements. These settlements consist of diverse land cover elements, such as houses, roads, agricultural buildings, ponds, parks, and woodlands, which together form entities with distinct local characteristics. To address this limitation, we introduce a novel classification strategy: Entity-Based Image Analysis (EBIA). Inspired by cognitive principles of human visual perception, EBIA groups related land cover elements and differentiates settlements from their background. The key innovation of EBIA lies in its ability to incorporate semantic features within rural settlements, transforming pixel-level land cover classification results (Phase 1) into entity-level settlement mapping results (Phase 2). Our results demonstrate that EBIA effectively maps the comprehensive footprint of rural settlement entities, achieving F1 scores ranging from 0.79 to 0.88 across five globally selected experimental areas. Furthermore, EBIA can be utilized to monitor changes in rural settlements using long-term Landsat imagery. As a new classification strategy, EBIA holds potential for mapping other geographic entities.","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"1 1","pages":""},"PeriodicalIF":13.5,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142809720","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}