Remote Sensing of Environment最新文献

{"title":"Machine learning forecast of surface solar irradiance from meteo satellite data","authors":"Alessandro Sebastianelli ,&nbsp;Federico Serva ,&nbsp;Andrea Ceschini ,&nbsp;Quentin Paletta ,&nbsp;Massimo Panella ,&nbsp;Bertrand Le Saux","doi":"10.1016/j.rse.2024.114431","DOIUrl":"10.1016/j.rse.2024.114431","url":null,"abstract":"<div><div>In order to facilitate the shift towards sustainable practices and to support the transition to renewable energy, there is a requirement for faster and more accurate predictions of solar irradiance. Surface solar energy predictions are essential for the establishment of solar farms and the enhancement of energy grid management. This paper presents a novel approach to forecast surface solar irradiance up to 24 h in advance, utilizing various machine and deep learning architectures. Our proposed Machine Learning (ML) models include both point-based (1D) and grid-based (3D) solutions, offering a comprehensive exploration of different methodologies. Our forecasts leverage two days of input data to predict the next day of solar exposure at country scale. To assess the models’ performance, extensive testing is conducted across three distinct geographical areas of interest: Austria (where models were trained and validated), Switzerland and Italy (where we tested our models under a transfer learning regime), and sensitivity to the season is also discussed. The study incorporates comparisons with established benchmarks, including state-of-the-art numerical weather predictions, as well as fundamental predictors such as climatology and persistence. Our findings reveal that the ML-based methods clearly outperform traditional forecasting techniques, demonstrating high accuracy and reliability in predicting surface solar irradiance. This research not only contributes to the advancement of solar energy forecasting but also highlights the effectiveness of machine learning and deep learning models in being competitive to conventional methods for short-term solar irradiance predictions.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114431"},"PeriodicalIF":11.1,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142320296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping urban construction sites in China through geospatial data fusion: Methods and applications","authors":"Chaoqun Zhang ,&nbsp;Ziyue Chen ,&nbsp;Lei Luo ,&nbsp;Qiqi Zhu ,&nbsp;Yuheng Fu ,&nbsp;Bingbo Gao ,&nbsp;Jianqiang Hu ,&nbsp;Liurun Cheng ,&nbsp;Qiancheng Lv ,&nbsp;Jing Yang ,&nbsp;Manchun Li ,&nbsp;Lei Zhou ,&nbsp;Qiao Wang","doi":"10.1016/j.rse.2024.114441","DOIUrl":"10.1016/j.rse.2024.114441","url":null,"abstract":"<div><div>The rapid increase in Urban Construction Sites (UCSs) due to urbanization has become a global trend. UCSs are crucial for timely tracking of urban expansion and renewal progress, understanding settlement environments and human activities, and achieving Sustainable Development Goals (SDGs) 3 and 11. However, distinguishing UCSs from other land covers remains challenging, whether using spatial texture and spectral features or time-series characteristics. There is an urgent need for a universally applicable UCS mapping method at the national scale, a gap that current research has yet to fill. In this study, we proposed a method combining geospatial data with remote sensing data for national UCS mapping under medium spatial resolution. Additionally, we combine the UCS mapping results with SDGSAT-1 GLI data to evaluate the utilization status of new construction areas, thereby supporting SDG 11.3. The results showed that, for six representative cities, the F1-Score and Matthews Correlation Coefficients (MCC) for exposed UCS mapping results ranged from 98.83 % to 99.49 % and from 0.64 to 0.77, respectively. Variable importance detected in the Random Forest (RF) model highlighted that the key to identifying UCSs lay in geospatial information describing UCS spatial distribution, including distance to roads, city boundaries, and dust-proof nets. The assessment of the utilization status for new construction areas highlights the differences in the utilization status with which cities at various stages of development utilize these new areas. We then compared the ability of UCS distribution with existing impervious surface products in reflecting the dynamics of urban construction. The results showed that UCS spatial distribution could reflect urban construction patterns more timely and accurately, providing key insights for urban planners. Overall, this study provides a universal methodology that can be referenced for mapping land covers that have low separability in spectral and textural features in complex urban environments. The proposed method offers a cost-effective and reliable way to map nationwide UCS distribution, providing clear and timely spatial information for urban planning and achieving SDGs.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114441"},"PeriodicalIF":11.1,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142320297","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":"Global aerosol retrieval over land from Landsat imagery integrating Transformer and Google Earth Engine","authors":"Jing Wei ,&nbsp;Zhihui Wang ,&nbsp;Zhanqing Li ,&nbsp;Zhengqiang Li ,&nbsp;Shulin Pang ,&nbsp;Xinyuan Xi ,&nbsp;Maureen Cribb ,&nbsp;Lin Sun","doi":"10.1016/j.rse.2024.114404","DOIUrl":"10.1016/j.rse.2024.114404","url":null,"abstract":"<div><div>Landsat imagery offers remarkable potential for various applications, including land monitoring and environmental assessment, thanks to its high spatial resolution and over 50 years of data records. However, the presence of atmospheric aerosols greatly hinders the precision of land classification and the quantitative retrieval of surface parameters. There is a pressing need for reliable and accurate global aerosol optical depth (AOD) data derived from Landsat imagery, particularly for atmospheric correction purposes and various other applications. To address this issue, we introduce an innovative framework for retrieving AOD from Landsat imagery over land, which leverages the deep-learning Transformer model (named AeroTrans-Landsat) and operates on the Google Earth Engine (GEE) cloud platform. We gather Landsat 8 and 9 images starting from their launch dates (February 2013 and September 2021, respectively) until the end of 2022, which are used to construct a robust aerosol retrieval model. The global AOD retrievals are then rigorously validated across ∼560 monitoring stations on land using diverse spatiotemporally independent methods. Leveraging information from multiple spectral channels, which contributes to 80 % according to the SHapley Additive exPlanation (SHAP) method, our retrieved AODs from 2013 to 2022 generally agree well with surface observations, with a sample-based cross-validation correlation coefficient of 0.905 and a root-mean-square error of 0.083. Around 86 % and 55 % of our AOD retrievals meet the criteria of Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue expected errors [±(0.05 + 20 %)] and the Global Climate Observation System {[max(0.03, 10 %)]}, respectively. Additionally, our model is not as sensitive to fluctuations in both surface and atmospheric conditions, enabling the generation of spatially continuous AOD distributions with exceptionally fine-scale information over dark to bright surfaces. This capability extends to areas characterized by high pollution levels originating from both anthropogenic and natural sources.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114404"},"PeriodicalIF":11.1,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142312118","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":"Deep learning solver unites SDGSAT-1 observations and Navier–Stokes theory for oceanic vortex streets","authors":"He Gao ,&nbsp;Baoxiang Huang ,&nbsp;Ge Chen ,&nbsp;Linghui Xia ,&nbsp;Milena Radenkovic","doi":"10.1016/j.rse.2024.114425","DOIUrl":"10.1016/j.rse.2024.114425","url":null,"abstract":"<div><div>The world’s first scientific satellite for sustainable development goals (SDGSAT-1) provides valuable data about offshore small-scale ocean phenomena, including the Kármán vortex street phenomenon. Although the simulation of the oceanic vortex street phenomenon is crucial for understanding not only the mechanisms of vortex formation in fluid dynamics but also their impact on the surrounding environment, the traditional simulation relies on the strong theoretical hypothesis of Navier–Stokes equations. Here, we propose a self-supervised neural network with high generalization ability to implement Navier–Stokes equations, simulating realistic oceanic vortex streets. Specifically, the physical informed convolutional neural network is first employed to determine the corresponding pressure and velocity fields, achieving accurate simulation of oceanic vortex streets with lower computational cost; Then, the observational islands in SDGSAT-1 imagery are embedded as obstacles, meanwhile, the marine background field including wind and terrain is synchronously incorporated to achieve more realistic simulation results compared with traditional methods; Finally, the morphological parameters of oceanic vortex streets are calculated and associated analysis are carried out to deepen our understanding of small scale vortex street phenomena. In addition, the experimental results demonstrated our proposed method can obtain promising time efficiency. With this partial differential equation deep learning solver framework combining observation and theory, there will be potential to expedite the cognitive process of oceanic phenomena.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114425"},"PeriodicalIF":11.1,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142315474","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":"The EnMAP spaceborne imaging spectroscopy mission: Initial scientific results two years after launch","authors":"Sabine Chabrillat ,&nbsp;Saskia Foerster ,&nbsp;Karl Segl ,&nbsp;Alison Beamish ,&nbsp;Maximilian Brell ,&nbsp;Saeid Asadzadeh ,&nbsp;Robert Milewski ,&nbsp;Kathrin J. Ward ,&nbsp;Arlena Brosinsky ,&nbsp;Katrin Koch ,&nbsp;Daniel Scheffler ,&nbsp;Stephane Guillaso ,&nbsp;Alexander Kokhanovsky ,&nbsp;Sigrid Roessner ,&nbsp;Luis Guanter ,&nbsp;Hermann Kaufmann ,&nbsp;Nicole Pinnel ,&nbsp;Emiliano Carmona ,&nbsp;Tobias Storch ,&nbsp;Tobias Hank ,&nbsp;Sebastian Fischer","doi":"10.1016/j.rse.2024.114379","DOIUrl":"10.1016/j.rse.2024.114379","url":null,"abstract":"<div><div>Imaging spectroscopy has been a recognized and established remote sensing technology since the 1980s, mainly using airborne and field-based platforms to identify and quantify key bio- and geo-chemical surface and atmospheric compounds, based on characteristic spectral reflectance features in the visible-near infrared (VNIR) and short-wave infrared (SWIR). Spaceborne missions, a leap in technology, were sparse, starting with the CHRIS/PROBA and EO1/Hyperion missions in the early 2000s, and providing spectroscopy data with limited spectral coverage and/or low data quality in the SWIR. Since 2019, several countries and agencies have successfully launched a number of spaceborne imaging spectroscopy systems into orbit or deployed them on the International Space Station (ISS) such as DESIS, PRISMA, HISUI, GF-5, EnMAP and EMIT. Among these recent missions, the German Environmental Mapping and Analysis Program (EnMAP) stands for its long-term development, sophisticated design with on-board calibration, high data quality requirements, and extensive accompanying science program. EnMAP was launched in April 2022 and, following a successful commissioning phase, started its operational activities in November 2022. The EnMAP mission encompasses global coverage from 80° N to 80° S through on-demand data acquisitions. Data are free and open access with 30 m spatial resolution, a high spectral resolution with a spectral sampling distance of 6.5 nm and 10 nm in the VNIR and SWIR regions respectively, and a high signal-to-noise ratio. In this paper, we aim to present the mission's current status, coverage, science capabilities and performance two years after launch. We show the potential of EnMAP for space-based imaging spectroscopy to operate in various environments, including high and low light levels, dense forests, Antarctic glaciers, and arid agricultural areas. EnMAP enables various applications in fields such as agriculture and forestry, soil compositional, raw materials, and methane mapping, as well as water quality assessment, and snow and ice properties. The results show that EnMAP's performance exceeds the mission requirements, and highlights the significant potential for contribution to scientific exploitation in various geo- and biochemical sciences. EnMAP is also expected to serve as a key tool for the development and testing of data processing algorithms for upcoming global operational missions.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114379"},"PeriodicalIF":11.1,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142369401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Retrieval of high-resolution melting-season albedo and its implications for the Karakoram Anomaly","authors":"Fuming Xie ,&nbsp;Shiyin Liu ,&nbsp;Yu Zhu ,&nbsp;Xinyi Qing ,&nbsp;Shucheng Tan ,&nbsp;Yongpeng Gao ,&nbsp;Miaomiao Qi ,&nbsp;Ying Yi ,&nbsp;Hui Ye ,&nbsp;Muhammad Mannan Afzal ,&nbsp;Xianhe Zhang ,&nbsp;Jun Zhou","doi":"10.1016/j.rse.2024.114438","DOIUrl":"10.1016/j.rse.2024.114438","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Glacial responses to climate change exhibit considerable heterogeneity. Although global glaciers are generally thinning and retreat, glaciers in the Karakoram region are distinct in their surging or advancing, exhibiting nearly zero or positive mass balance—a phenomenon known as the Karakoram Anomaly. This anomaly has sparked significant scientific interest, prompting extensive research into glacier anomalies. However, the dynamics of the Karakoram anomaly, particularly its evolution and persistence, remain insufficiently explored. In this study, we employed Landsat reflectance data and Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A3 albedo products to developed high-resolution albedo retrieval models using two machine learning (ML) regressions––random forest regression (RFR) and back-propagation neural network regression (BPNNR). The optimal BPNNR model (Pearson correlation coefficient [&lt;em&gt;r&lt;/em&gt;] = 0.77–0.97, unbiased root mean squared error [&lt;em&gt;ubRMSE&lt;/em&gt;] = 0.056–0.077, &lt;em&gt;RMSE&lt;/em&gt; = 0.055–0.168, &lt;em&gt;Bias =&lt;/em&gt; −0.149 ∼ −0.001) was implemented on the Google Earth Engine cloud-based platform to estimate summer albedo at a 30-m resolution for the Karakoram region from 1990 to 2021. Validation against in-situ albedo measurements on three glaciers (Batura, Mulungutti and Yala Glacier) demonstrated that the model achieved an average &lt;em&gt;ubRMSE&lt;/em&gt; of 0.069 (&lt;em&gt;p&lt;/em&gt; &lt; 0.001), with &lt;em&gt;RMSE&lt;/em&gt; and &lt;em&gt;ubRMSE&lt;/em&gt; improvements of 0.027 compared to MODIS albedo products. The high-resolution data was then used to identify firn/snow extents using a 0.37 threshold, facilitating the extraction of long-term firn-line altitudes (FLA) to indicate the glacier dynamics. Our findings revealed that a consistent decline in summer albedo across the Karakoram over the past three decades, signifying a darkening of glacier surfaces that increased solar radiation absorption and intensified melting. The reduction in albedo showed spatial heterogeneity, with slower reductions in the western and central Karakoram (−0.0005–0.0005 yr&lt;sup&gt;−1&lt;/sup&gt;) compared to the eastern Karakoram (−0.006 ∼ −0.01 yr&lt;sup&gt;−1&lt;/sup&gt;). Notably, surge- or advance-type glaciers, avalanche-fed glaciers and debris-covered glaciers exhibited slower albedo reduction rates, which decreased further with increasing glacier size. Additionally, albedo reduction accelerated with altitude, peaking near the equilibrium-line altitude. Fluctuations in the albedo-derived FLAs suggest a transition in the dynamics of Karakoram glaciers from anomalous behavior to retreat. Most glaciers exhibited anomalous behavior from 1995 to 2010, peaking in 2003, but they have shown signs of retreat since the 2010s, marking the end of the Karakoram anomaly. These insights deepen our understanding of the Karakoram anomaly and provide a theoretical basis for assessing the effect of glacier anomaly to retreat dynamics on the water resources and adaptation strategies for the Indus and Tarim Ri","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114438"},"PeriodicalIF":11.1,"publicationDate":"2024-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142312119","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":"Monitoring river discharge from space: An optimization approach with uncertainty quantification for small ungauged rivers","authors":"Daniel Scherer,&nbsp;Christian Schwatke,&nbsp;Denise Dettmering,&nbsp;Florian Seitz","doi":"10.1016/j.rse.2024.114434","DOIUrl":"10.1016/j.rse.2024.114434","url":null,"abstract":"<div><p>The number of in-situ stations measuring river discharge, one of the Essential Climate Variables (ECV), is declining steadily, and numerous basins have never been gauged. With the aim of improving data availability worldwide, we propose an easily applicable and transferable approach to estimate reach-scale discharge solely using remote sensing data that is suitable for filling gaps in the in-situ network. We combine 20 years of satellite altimetry observations with high-resolution satellite imagery via a hypsometric function to observe large portions of the reach-scale bathymetry. The high-resolution satellite images, which are classified using deep learning image segmentation, allow for detecting small rivers (narrower than 100<!--> <!-->m) and can capture small width variations. The unobserved part of the bathymetry is estimated using an empirical width-to-depth function. Combined with precise satellite-derived slope measurements, river discharge is calculated at multiple consecutive cross-sections within the reach. The unknown roughness coefficient is optimized by minimizing the discharge differences between the cross-sections. The approach requires minimal input and approximate boundary conditions based on expert knowledge but is not dependent on calibration. We provide realistic uncertainties, which are crucial for data assimilation, by accounting for errors and uncertainties in the different input quantities. The approach is applied globally to 27 river sections with a median normalized root mean square error of 12% and a Nash–Sutcliffe model efficiency of 0.560. On average, the 90% uncertainty range includes 91% of the in-situ measurements.</p></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114434"},"PeriodicalIF":11.1,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0034425724004607/pdfft?md5=19b087fa43931e98c5d3f8d80a8857cc&pid=1-s2.0-S0034425724004607-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142272976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Void filling of digital elevation models based on terrain feature-guided diffusion model","authors":"Ji Zhao ,&nbsp;Yingying Yuan ,&nbsp;Yuting Dong ,&nbsp;Yaozu Li ,&nbsp;Changliang Shao ,&nbsp;Haixia Yang","doi":"10.1016/j.rse.2024.114432","DOIUrl":"10.1016/j.rse.2024.114432","url":null,"abstract":"<div><p>Digital Elevation Models (DEMs) are pivotal in scientific research and engineering because they provide essential topographic and geomorphological information. Voids in DEM data result in the loss of terrain information, significantly impacting its broad applicability. Although spatial interpolation methods are frequently employed to address these voids, they suffer from accuracy degradation and struggle to reconstruct intricate terrain features. Generative Adversarial Network (GAN)-based approaches have emerged as promising solutions to enhance elevation accuracy and facilitate the reconstruction of partial terrain features. Nonetheless, GAN-based methods exhibit limitations with specific void shapes, and their performance is susceptible to artifacts and elevation jumps around the void boundaries. To address shortcomings mentioned above, we propose a terrain feature-guided diffusion model (TFDM) to fill the DEM data voids. The training and inference processes of the diffusion model were constrained by terrain feature lines to ensure the stability of the generated DEM surface. The TFDM is distinguished by its ability to generate seamless DEM surfaces and maintain stable terrain contours in response to varying terrain conditions. Experiments were conducted to validate the applicability of TFDM using different DEMs, including Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Models (ASTER GDEMv3) and the TanDEM-X global DEM. The proposed TFDM algorithm and comparison methods such as DDPM, GAN, and Kriging were applied to a full test set of 271 DEM images covering different terrain environments. The mean absolute error (MAE) and root mean square error (RMSE) of the DEM restored by TFDM were 28.91 ± 9.45 m and 38.16 ± 13.00 m, respectively, while the MAE and RMSE of the comparison algorithms were no less than 60.87 ± 26.24 m and 82.80 ± 36.51 m or even higher, validating the effectiveness of the TFDM algorithm in filling DEM voids. Profile analysis in partial details indicates that the TFDM outperforms alternative methods in reconstructing terrain features, as confirmed through visual inspection and quantitative comparison. TFDM exhibits versatility when applied to DEM data with diverse resolutions and produced using various measurement techniques.</p></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114432"},"PeriodicalIF":11.1,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142272975","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":"Automated grounding line delineation using deep learning and phase gradient-based approaches on COSMO-SkyMed DInSAR data","authors":"Natalya Ross ,&nbsp;Pietro Milillo ,&nbsp;Luigi Dini","doi":"10.1016/j.rse.2024.114429","DOIUrl":"10.1016/j.rse.2024.114429","url":null,"abstract":"<div><p>The grounding line marks the transition between a glacier's floating and grounded parts and serves as a crucial parameter for monitoring sea level changes and assessing glacier retreat. The Differential Interferometric Synthetic Aperture Radar (DInSAR) technique for grounding line mapping currently requires the involvement of human experts, which becomes challenging with the continuously growing volume of grounding line data available for every Antarctic glacier. While a deep learning approach has been recently proposed for mapping grounding lines over C-band Sentinel-1 DInSAR data, its effectiveness has not been assessed over X-Band COSMO-SkyMed DInSAR data. Similarly, the applicability of an analytical algorithm developed for X-band TerraSAR-X DInSAR data has not been evaluated over a large diverse dataset. Here we apply both techniques to map grounding lines over a large X-band COSMO-SkyMed DInSAR dataset from 2020 to 2022, covering Stancomb-Wills, Veststraumen, Jutulstraumen, Moscow University, and Rennick Antarctic glaciers. We determine strengths and limitations of each algorithm, compare their performance with manual mapping and provide recommendations for choosing appropriate data processing methods for effective grounding line mapping. We also note that since 1996, Moscow University glacier's main trunk was retreating at a rate of 340 ± 80 m/year, while the other four glaciers experienced no retreat. Considering the grounding zone widths, which represent the difference between the high and low tide grounding line positions during a tidal cycle, we detect a grounding zone of 9.7 km over Veststraumen Glacier, which is almost six times larger than the average grounding zone of the other four glaciers.</p></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114429"},"PeriodicalIF":11.1,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142272973","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 new constant scattering angle solar geometry definition for normalization of GOES-R ABI reflectance times series to support land surface phenology studies","authors":"Shuai Gao ,&nbsp;Xiaoyang Zhang ,&nbsp;Hankui K. Zhang ,&nbsp;Yu Shen ,&nbsp;David P. Roy ,&nbsp;Weile Wang ,&nbsp;Crystal Schaaf","doi":"10.1016/j.rse.2024.114407","DOIUrl":"10.1016/j.rse.2024.114407","url":null,"abstract":"<div><p>The Advanced Baseline Imager (ABI) sensors on the Geostationary Operational Environment Satellite-R series (GOES-R) broaden the application of global vegetation monitoring due to their higher temporal (5–15 min) and appropriate spatial (0.5–1 km) resolution compared to previous geostationary and current polar-orbiting sensing systems. Notably, ABI Land Surface Phenology (LSP) quantification may be improved due to the greater availability of cloud-free observations as compared to those from legacy GOES satellite generations and from polar-orbiting sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS). Geostationary satellites sense a location with a fixed view geometry but changing solar geometry and consequently capture pronounced temporal reflectance variations over anisotropic surfaces. These reflectance variations can be reduced by application of a Bidirectional Reflectance Distribution Function (BRDF) model to adjust or predict the reflectance for a new solar geometry and a fixed view geometry. Empirical and semi-empirical BRDF models perform less effectively when used to predict reflectance acquired at angles not found in the observations used to parameterize the model, or acquired under hot-spot sensing conditions when the solar and viewing directions coincide. Consequently, using a fixed solar geometry or even the geometry at local solar noon may introduce errors due to diurnal and seasonal variations in the position of the sun and the incidence of hot-spot sensing conditions. In this paper, a new solar geometry definition based on a Constant Scattering Angle (CSA) criterion is presented that, as we demonstrate, reduces the impacts of solar geometry changes on reflectance and derived vegetation indices used for LSP quantification. The CSA criterion is used with the Ross-Thick-Li-Sparse (RTLS) BRDF model applied to North America ABI surface reflectance data acquired by GOES-16 (1 January 2018 to 31 December 2020) and GOES-17 (1 January 2019 to 31 December 2020) to normalize solar geometry BRDF effects and generate 3-day two-band Enhanced Vegetation Index (EVI2) time series. Compared to the local solar noon geometry, the CSA criterion is shown to reduce solar geometry reflectance and EVI2 time series artifacts. Further, comparison with contemporaneous VIIRS NBAR (Nadir BRDF-Adjusted Reflectance) EVI2 time series is also presented to illustrate the efficacy of the CSA criterion. Finally, the CSA-adjusted EVI2 time series are shown to produce LSP results that agree well with PhenoCam-based observations, with no obvious systematic bias in onsets of vegetation maturity, senescence, and dormancy dates compared to about 10-day bias found with local solar noon adjusted EVI2 time series.</p></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114407"},"PeriodicalIF":11.1,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142272977","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}