Remote Sensing of Environment最新文献

{"title":"Statistical retrieval of volcanic activity in long time series orbital data: Implications for forecasting future activity","authors":"Michael S. Ramsey ,&nbsp;Claudia Corradino ,&nbsp;James O. Thompson ,&nbsp;Tyler N. Leggett","doi":"10.1016/j.rse.2023.113704","DOIUrl":"https://doi.org/10.1016/j.rse.2023.113704","url":null,"abstract":"<div><p>Several high spatial resolution thermal infrared (TIR) missions are planned for the coming decade and their data will be crucial to constrain volcanic activity patterns throughout pre- and post-eruption phases. Foundational to these patterns is the subtle (1−2 K) thermal behavior, which is easily overlooked using lower spatial resolution data. In preparation for these new data, we conducted the first study using the entire twenty-two-year archive of higher spatial, lower temporal resolution TIR data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor. This archive presents a unique opportunity to quantify low-magnitude temperature anomalies and small plumes over long time periods. We developed a new statistical algorithm to automatically detect the full range of thermal activity and applied it to &gt;5000 ASTER scenes of five volcanoes with well-documented eruptions. Unique to this algorithm is its ability to use both day and night data, account for clouds, quantify accurate background temperatures, and dynamically scale depending on the anomaly size. Results improve upon those from the more commonly used lower spatial resolution data, despite the less frequent temporal coverage of ASTER, and show that high spatial resolution TIR data are equally as effective. Significantly, the smaller, subtle thermal detections served as precursory signals in ∼81% of eruptions, and the algorithm's results create a framework for classifying future eruptive styles.</p></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"295 ","pages":"Article 113704"},"PeriodicalIF":13.5,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"3207840","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 the saturation effect of vegetation indices in forests using 3D radiative transfer simulations and satellite observations","authors":"Si Gao ,&nbsp;Run Zhong ,&nbsp;Kai Yan ,&nbsp;Xuanlong Ma ,&nbsp;Xinkun Chen ,&nbsp;Jiabin Pu ,&nbsp;Sicong Gao ,&nbsp;Jianbo Qi ,&nbsp;Gaofei Yin ,&nbsp;Ranga B. Myneni","doi":"10.1016/j.rse.2023.113665","DOIUrl":"https://doi.org/10.1016/j.rse.2023.113665","url":null,"abstract":"<div><p>Vegetation indices (VIs) have been used extensively for qualitative and quantitative remote sensing monitoring of vegetation vigor and growth dynamics. However, the saturation phenomenon of VIs (i.e., insignificant change at moderate to high vegetation densities) poses a known limitation to their ability to characterize surface vegetation over the dense canopy. Although the mechanisms underlying saturation are relatively straightforward and several VIs have been proposed to mitigate the saturation effect, the assessment of the saturation effect of VIs remains insufficient. Notably, no unified metric has been proposed to quantify the VI saturation phenomenon, limiting VI selection in practical applications. In this study, we proposed two indicators to describe the saturation phenomenon and utilized a well-validated three-dimensional (3D) canopy radiative transfer (RT) model large-scale remote sensing data and image simulation framework (LESS) to simulate the bidirectional reflectance factor (BRF) of six forests scenes and assessed the variations in VIs in relation to leaf area index (LAI) values over different backgrounds, sun-sensor geometries, and spatial distribution types. The saturation characteristics of 36 VIs were evaluated in combination with simulation results and satellite observations from multiple sensors. The ranking of VI saturation from simulated and satellite results revealed a good agreement. Our results indicated that the simple ratio vegetation index (SR) performed best with the highest saturation point and can well characterize the surface vegetation condition until LAI reaches 4. Besides, we found that the saturation effect of VIs was influenced by soil brightness, sun-sensor geometry, and canopy structure. SR, modified simple ratio (MSR) and normalized green red difference index (NGRDI) were the most susceptible to these disturbing factors, although they had higher resistance to saturation. Modified triangular vegetation index 1 (MTVI1), modified non-linear vegetation index (MNLI), triangular greenness index (TGI), and triangular vegetation index (TriVI) performed well overall, combining the ability to resist saturation and disturbance factors. Appropriate application of VIs can help better understand vegetation responses to climate change and accurately assess ecosystem status. Our results contribute to the understanding of the VI saturation effect and provide a combined model and satellite data experimental workflow in appropriate VI selection to accurately characterize vegetation.</p></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"295 ","pages":"Article 113665"},"PeriodicalIF":13.5,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"3342105","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":"Multi-sensor imaging of winter buried lakes in the Greenland Ice Sheet","authors":"Lei Zheng ,&nbsp;Lanjing Li ,&nbsp;Zhuoqi Chen ,&nbsp;Yong He ,&nbsp;Linshan Mo ,&nbsp;Dairong Chen ,&nbsp;Qihan Hu ,&nbsp;Liangwei Wang ,&nbsp;Qi Liang ,&nbsp;Xiao Cheng","doi":"10.1016/j.rse.2023.113688","DOIUrl":"https://doi.org/10.1016/j.rse.2023.113688","url":null,"abstract":"<div><p>Recent studies have highlighted that meltwater in supraglacial lakes (SLs) can be buried during frozen season in the Greenland Ice Sheet (GrIS). Meltwater in buried lakes (BLs) can even persist through the winter, disturbing the englacial thermal regime and providing an important buffer against GrIS's contribution to sea-level rise. However, little is known about the inter-annual BL dynamics in the GrIS, and there is no quantitative statistic about the overall buried percentage. Here, we conduct a satellite-based study to automatically map the winter BLs over the GrIS during 2017–2022 using multi-source optical and synthetic aperture radar (SAR) images on the Google Earth Engine (GEE) platform. To eliminate the interferences from other weak microwave reflecting surfaces, summer SLs are first extracted from Landsat 8 and Sentinel-2 images to determine the potential BL searching areas on winter Sentinel-1 images. A self-adaptive thresholding algorithm is proposed to extract BLs within the dilated summer SLs using histogram-based morphological edge detectors. BLs extracted by the proposed method and visual interpretation show a substantial agreement with a precision of 0.82 and a Kappa coefficient of 0.70. On average, a total buried lake area of 182.27 km² was observed each winter during the period 2017–2022. BLs were mainly distributed in the Center-West, South-West and North-East Basins, with the majority occurring at elevations between 800 and 1700 m. In 2019–2020, a sudden extension of BLs was observed over the GrIS, especially in the North-East Basin where abnormally high temperatures and surface runoff were recorded. In 2021–2022, a widespread distribution of BLs in the South-West Basin was observed after abnormal snowfall. Overall, about 13% of the GrIS summer SLs can persist through winter, suggesting the potential for meltwater hydrofracture in winter over large areas.</p></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"295 ","pages":"Article 113688"},"PeriodicalIF":13.5,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"3341132","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":"Unraveling contributions of Greenland's seasonal and transient crustal deformation during the past two decades","authors":"Wenhao Li ,&nbsp;Jintao Lei ,&nbsp;C.K. Shum ,&nbsp;Fei Li ,&nbsp;Shengkai Zhang ,&nbsp;Chanfang Shu ,&nbsp;Wei Chen","doi":"10.1016/j.rse.2023.113701","DOIUrl":"https://doi.org/10.1016/j.rse.2023.113701","url":null,"abstract":"<div><p>Contemporary research on Greenland surface mass balance (SMB) is largely focused on the characteristics of decadal or longer trends, periodic oscillations, and acceleration. However, the specific components of the SMB such as snowfall (SF), rainfall (RF) and runoff (RU), and their corresponding temporal and spatial variability remain poorly understood. Here, we explore the respective contributions of SF, RF, and RU to the seasonal and transient crustal deformations of Greenland during the past two decades using GPS network and satellite gravimetry (GRACE) datasets, and regional climate model output. Our study unraveled that the largest annual vertical displacement caused by precipitations is in southeastern Greenland, reaching 7.27 mm. The largest surface displacement caused by RU is in western Greenland, reaching 19.82 mm. Ice mass gain/loss in Greenland shows a clear correlation between latitude and temperature, with greater variations in the south compared to the north. The transient deformation signals in Greenland mainly manifested in terms of abrupt subsidence in 2010, followed by uplift in 2014. The 2014 uplift can mainly be attributable to the combined effect of SF, RF, and RU. The largest transient signal occurs in the southeast subregions, with peak-to-peak amplitude exceeding 10 mm. Transient crustal deformation is mainly caused by precipitation in southeastern Greenland, while the contribution of RU dominates most of the time and in most subregions. We find that even though RF is increasing due to an increasingly warmer climate, its effect on SMB is still negligible, when compared with SF and RU. In some subregions and some periods, SF could become the primary contributor to transient SMB variations in Greenland.</p></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"295 ","pages":"Article 113701"},"PeriodicalIF":13.5,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"3080687","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":"MODIS observations reveal decrease in lake suspended particulate matter across China over the past two decades","authors":"Zhigang Cao ,&nbsp;Chuanmin Hu ,&nbsp;Ronghua Ma ,&nbsp;Hongtao Duan ,&nbsp;Miao Liu ,&nbsp;Steven Loiselle ,&nbsp;Kaishan Song ,&nbsp;Ming Shen ,&nbsp;Dong Liu ,&nbsp;Kun Xue","doi":"10.1016/j.rse.2023.113724","DOIUrl":"https://doi.org/10.1016/j.rse.2023.113724","url":null,"abstract":"<div><p>Variations in the concentrations and distribution of suspended particulate matter (SPM) of lakes can be used to show the responses of lake environment to climate and landscape change. However, the shifts and trends of SPM and potential drivers have not been well investigated across large spatial and temporal dimensions. This study developed a robust machine learning model to generate SPM time series in 269 lakes across China larger than 30 km² from 2002 to 2021 using MODIS/Aqua imagery. The support vector regression model showed satisfactory performance on SPM retrievals over four orders of magnitude (0.1–1000 mg L⁻¹) (mean absolute percentage error = 26%). The model performance was shown to be insensitive to changes in environmental and observing conditions (e.g., aerosol type and thickness, viewing geometry), based on a radiative transfer simulation model. The long-term MODIS record showed a spatial pattern of lower SPM in the western lakes compared to the shallow lakes of east China. Importantly, the SPM showed a significant decrease in the 21st century (average rate of change of −0.2 mg L⁻¹/decade). The interannual variations in SPM were aggregated into five categories, ranging from lakes with continuous changing patterns to those with reversed changing patterns. The driving factors behind the changing patterns vary between different climate zones and ecoregions. A warmer and wetter climate was associated with decreasing SPM in western lakes, while the decrease in wind speed and reduced possibility of soil erosion were the primary drivers of progressively lower SPM in the eastern shallow lakes. These results not only show a comprehensive picture of the SPM dynamics of lakes in China but also provide new insights into the complex mechanisms that drive SPM spatiotemporal dynamics.</p></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"295 ","pages":"Article 113724"},"PeriodicalIF":13.5,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"1564156","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 novel Greenness and Water Content Composite Index (GWCCI) for soybean mapping from single remotely sensed multispectral images","authors":"Hui Chen ,&nbsp;Huapeng Li ,&nbsp;Zhao Liu ,&nbsp;Ce Zhang ,&nbsp;Shuqing Zhang ,&nbsp;Peter M. Atkinson","doi":"10.1016/j.rse.2023.113679","DOIUrl":"https://doi.org/10.1016/j.rse.2023.113679","url":null,"abstract":"<div><p>As a critical source of food and one of the most economically significant crops in the world, soybean plays an important role in achieving food security. Large area accurate mapping of soybean has long been a vital, but challenging issue in remote sensing, relying heavily on large-volume and representative training samples, whose collection is time-consuming and inefficient, especially for large areas (e.g., national scale). Thus, methods are needed that can map soybean automatically and accurately from single-date remotely sensed imagery. In this research, a novel Greenness and Water Content Composite Index (GWCCI) was proposed to map soybean from just a single Sentinel-2 multispectral image in an end-to-end manner without employing training samples. By capitalizing on the product of the NDVI (related to greenness) and the short-wave infrared (SWIR) band (related to canopy water content), the GWCCI provides the required information with which to discriminate between soybean and other land cover types. The effectiveness of the proposed GWCCI was investigated in seven typical soybean planting regions within four major soybean-producing countries across the world (i.e., China, the United States, Brazil and Argentina), with diverse climates, cropping systems and agricultural landscapes. In the experiments, an optimal threshold of 0.17 was estimated and adopted by the GWCCI in the first study site (S1) in 2021, and then generalised to the other study sites over multiple years for soybean mapping. The GWCCI method achieved a consistently higher accuracy in 2021 compared to two conventional comparative classifiers (support vector machine (SVM) and random forest (RF)), with an average overall accuracy (OA) of 88.30% and a Kappa coefficient (<em>k</em>) of 0.77; significantly greater than those of RF (OA: 80.92%, <em>k</em>: 0.62) and SVM (OA: 80.29%, <em>k</em>: 0.60). Furthermore, the OA of the extended years was highly consistent with that of 2021 for study sites S2 to S7, demonstrating the great generalisation capability and robustness of the proposed approach over multiple years. The proposed GWCCI method is straightforward, reliable and robust, and represents an important step forward for mapping soybean, one of the most significant crops grown globally.</p></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"295 ","pages":"Article 113679"},"PeriodicalIF":13.5,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"1564262","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":"Calibration of the SNPP and NOAA 20 VIIRS sensors for continuity of the MODIS climate data records","authors":"Alexei Lyapustin ,&nbsp;Yujie Wang ,&nbsp;Myungje Choi ,&nbsp;Xiaoxiong Xiong ,&nbsp;Amit Angal ,&nbsp;Aisheng Wu ,&nbsp;David R. Doelling ,&nbsp;Rajendra Bhatt ,&nbsp;Sujung Go ,&nbsp;Sergey Korkin ,&nbsp;Bryan Franz ,&nbsp;Gerhardt Meister ,&nbsp;Andrew M. Sayer ,&nbsp;Miguel Roman ,&nbsp;Robert E. Holz ,&nbsp;Kerry Meyer ,&nbsp;James Gleason ,&nbsp;Robert Levy","doi":"10.1016/j.rse.2023.113717","DOIUrl":"https://doi.org/10.1016/j.rse.2023.113717","url":null,"abstract":"<div><p>Accurate long-term sensor calibration and periodic re-processing to ensure consistency and continuity of atmospheric, land and ocean geophysical retrievals from space within the mission period and across different missions is a major requirement of climate data records. In this work, we applied the Multi-Angle Implementation of Atmospheric Correction (MAIAC)-based vicarious calibration technique over Libya-4 desert site to perform calibration analysis of Visible Infrared Imaging Radiometer Suite (VIIRS) on Suomi National Polar-orbiting Partnership (SNPP) and NOAA-20 satellites. For both VIIRS sensors we characterized residual linear calibration trends and cross-calibrated both sensors to MODerate resolution Imaging Spectroradiometer (MODIS) Aqua regarded as a calibration standard. The relative spectral response (RSR) differences were accounted for using the German Aerospace Center (DLR) Earth Sensing Imaging Spectrometer (DESIS) hyperspectral surface reflectance data. Our results agree with independent vicarious calibration results of both the MODIS/VIIRS Characterization Support Team as well as the CERES Imager and Geostationary Calibration Group within estimated uncertainty of 1–2%. Analysis of MAIAC geophysical products with the new calibration shows a high level of agreement of MAIAC aerosol, surface reflectance and NDVI records between MODIS and VIIRS. Excluding high aerosol optical depth (AOD), all three sensors agree in AOD with <em>mean difference</em> (<em>MD)</em> less than 0.01 and <em>residual mean squared difference rmsd</em> ∼ 0.04. Spectral geometrically normalized surface reflectance agrees within <em>rmsd</em> of 0.003–0.005 in the visible and 0.01–0.012 at longer wavelengths. The residual surface reflectance differences are fully explained by differences in spectral filter functions. Finally, difference in NDVI is characterized by <em>rmsd</em> ∼ 0.02 and <em>MD</em> less than 0.003 for NDVI based on VIIRS imagery bands I1/I2 and less than 0.01 for NDVI based on VIIRS radiometric bands M5/M7. In practical sense, these numbers indicate consistency and continuity in MAIAC records ensuring the smooth transition from MODIS to VIIRS.</p></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"295 ","pages":"Article 113717"},"PeriodicalIF":13.5,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"2276190","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 hyperspectral inversion framework for estimating absorbing inherent optical properties and biogeochemical parameters in inland and coastal waters","authors":"Ryan E. O'Shea ,&nbsp;Nima Pahlevan ,&nbsp;Brandon Smith ,&nbsp;Emmanuel Boss ,&nbsp;Daniela Gurlin ,&nbsp;Krista Alikas ,&nbsp;Kersti Kangro ,&nbsp;Raphael M. Kudela ,&nbsp;Diana Vaičiūtė","doi":"10.1016/j.rse.2023.113706","DOIUrl":"https://doi.org/10.1016/j.rse.2023.113706","url":null,"abstract":"<div><p>The simultaneous remote estimation of biogeochemical parameters (BPs) and inherent optical properties (IOPs) from hyperspectral satellite imagery of globally distributed optically distinct inland and coastal waters is a complex, unsolved, non-unique inverse problem. To tackle this problem, we leverage a machine-learning model termed Mixture Density Networks (MDNs). MDNs outperform operational algorithms by calculating the covariance between the simultaneously estimated products. We train the MDNs on a large (<em>N</em> = 8237) dataset of co-aligned, <em>in situ</em> measured, hyperspectral remote sensing reflectance (R_rs), BPs, and absorbing IOPs from globally representative optically distinct inland and coastal waters. The estimated IOPs include absorption due to phytoplankton (a_ph), chromophoric dissolved organic matter (a_cdom), and non-algal particles (a_nap). The estimated BPs include chlorophyll-<em>a</em>, total suspended solids, and phycocyanin (PC). MDNs dramatically reduce uncertainty in the retrievals, relative to operational algorithms, when using a 50/50 dataset split, where the MDNs are trained on a randomly selected half of the <em>in situ</em> dataset and validated on the other half. Our model is shown to have higher, or equivalent, generalization performance than the calculated operational algorithms available for all BPs and IOPs (except PC) <em>via</em> a leave-one-out cross-validation assessment. The MDNs are sensitive to uncertainties in the hyperspectral satellite R_rs, resulting from instrument noise and atmospheric correction; there is a difference of ∼37.4–62.8% (using median symmetric accuracy) between the MDNs' estimates derived from co-located satellite-derived R_rs and <em>in situ</em> R_rs. Of the IOPs, a_cdom and a_nap are less sensitive to uncertainties in hyperspectral satellite imagery relative to a_ph, with remote estimates of a_ph exhibiting incorrect spectral shape and magnitude relative to <em>in situ</em> measured IOPs. Despite the uncertainties in satellite derived R_rs, the spatial distributions of BPs and IOPs in MDN-derived product maps of Lake Erie and the Curonian Lagoon, based on imagery taken with the Hyperspectral Imager for the Coastal Ocean (HICO) and PRecursore IperSpettrale della Missione Applicativa (PRISMA), are confirmed <em>via</em> co-aligned <em>in situ</em> measurements and agree with the literature's understanding of these well-studied regions. The consistency and accuracy of the model on HICO and PRISMA imagery, despite radiometric uncertainties, demonstrate its applicability to future hyperspectral missions, such as the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, where the simultaneous estimation model will serve as a key part of phytoplankton community composition analysis.</p></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"295 ","pages":"Article 113706"},"PeriodicalIF":13.5,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"2276191","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 novel framework for combining polarimetric Sentinel-1 InSAR time series in subsidence monitoring - A case study of Sydney","authors":"Alex Hay-Man Ng ,&nbsp;Ziyue Liu ,&nbsp;Zheyuan Du ,&nbsp;Hengwei Huang ,&nbsp;Hua Wang ,&nbsp;Linlin Ge","doi":"10.1016/j.rse.2023.113694","DOIUrl":"https://doi.org/10.1016/j.rse.2023.113694","url":null,"abstract":"<div><p>The rapid growth of the city of Sydney, Australia over the last decades, has led to significant development of residential and transportation infrastructure. Land subsidence associated with the urban development can lead to serious issues which should be thoroughly understood and carefully managed. To address this challenge, an enhanced polarisation time-series InSAR (Pol-TS-InSAR) processing framework was developed, using the dual polarisation (DP) Sentinel-1 data to integrate information from different polarimetric channels with different weighting during the TS-InSAR deformation analysis. Ninety DP Sentinel-1 images acquired between 2019 and 2022 are analysed using Pol-TS-InSAR to map the land subsidence in Sydney, with the assistance of the GPS measurements. Improvement of measurement points density from Pol-TS-InSAR is observed compared to the single polarimetric TS-InSAR counterpart for all land use types (ranging between 68% and 208%). The comparison between the Pol-TS-InSAR measurements and GPS measurements shows an absolute mean difference and RMS difference of 0.75 mm/yr and 0.95 mm/yr, respectively, in vertical direction. The results of the ground subsidence analysis revealed that the main subsidence factors in Sydney are related to groundwater extraction, mining activities, underground tunnel construction and landfill. The latter two factors were less well-known prior to this study. In additional to these factors, land subsidence related to high-rise building construction has also been observed, even though the impact seems to be less significant than other factors.</p></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"295 ","pages":"Article 113694"},"PeriodicalIF":13.5,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"2438247","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":"Comparison between deep learning architectures for the 1 km, 10/15-min estimation of downward shortwave radiation from AHI and ABI","authors":"Ruohan Li ,&nbsp;Dongdong Wang ,&nbsp;Shunlin Liang","doi":"10.1016/j.rse.2023.113697","DOIUrl":"https://doi.org/10.1016/j.rse.2023.113697","url":null,"abstract":"<div><p>The retrieval of downward shortwave radiation (DSR) with high spatiotemporal resolution and short latency is critical. It is the fundamental driving force of surface energy, carbon, and hydrological circulations, and a key energy source for photovoltaic electricity. However, existing methods face significant challenges owing to cloud heterogeneity and their reliance on other satellite-derived products, which hinder the retrieval of accurate and timely DSR with high spatiotemporal resolution. In addition to the spectral features used in traditional approaches, deep learning (DL) can incorporate the spatial and temporal features of satellite data. This study developed and compared three DL methods, namely the <span><math><mtext>DenseNet</mtext></math></span>, the bidirectional gated recurrent unit without surface albedo as inputs (<span><math><msub><mtext>BiGRU</mtext><mi>nor</mi></msub></math></span>), and the convolutional neural network with gated recurrent unit without surface albedo as inputs (<span><math><msub><mtext>CNNGRU</mtext><mi>nor</mi></msub></math></span>). These methods were used to estimate DSR at 1 km and 10/15 min resolutions directly from top-of-atmosphere reflectance over the Advanced Himawari Imager (AHI) onboard Himawari-8 and the Advanced Baseline Imager (ABI) onboard GOES-16 coverage, achieving high accuracies. The instantaneous root mean square error (RMSE) and relative RMSE for the three models were 68.4 (16.1%), 69.4 (16.3%), and 67.1 (15.7%) <span><math><mi>W</mi><mo>/</mo><msup><mi>m</mi><mn>2</mn></msup></math></span>, respectively, which are lower than the baseline machine learning method, the multilayer perceptron model (MLP), with RMSE at 76.8 <span><math><mi>W</mi><mo>/</mo><msup><mi>m</mi><mn>2</mn></msup></math></span> (18.0%). Hourly accuracies for the three DL methods were 58.6 (14.1%), 57.8 (14.0%), and 57.3 (13.8%) <span><math><mi>W</mi><mo>/</mo><msup><mi>m</mi><mn>2</mn></msup></math></span>, which are within the DSR RMSEs that we estimated for existing datasets of the Earth's Radiant Energy System (CERES) (88.8 <span><math><mi>W</mi><mo>/</mo><msup><mi>m</mi><mn>2</mn></msup></math></span>, 21.4%) and GeoNEX (77.8 <span><math><mi>W</mi><mo>/</mo><msup><mi>m</mi><mn>2</mn></msup></math></span>, 18.8%). The study illustrates that DL models that incorporate temporal information can eliminate the need for surface albedo as an input, which is crucial for timely monitoring and nowcasting of DSR. Incorporating spatial information can enhance retrieval accuracy in overcast conditions, and incorporating infrared bands can further improve the accuracy of DSR estimation.</p></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"295 ","pages":"Article 113697"},"PeriodicalIF":13.5,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"3272045","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}