Remote Sensing of Environment最新文献

{"title":"Nationwide operational mapping of grassland first mowing dates combining machine learning and Sentinel-2 time series","authors":"Henry Rivas ,&nbsp;Hélène Touchais ,&nbsp;Vincent Thierion ,&nbsp;Jerome Millet ,&nbsp;Laurence Curtet ,&nbsp;Mathieu Fauvel","doi":"10.1016/j.rse.2024.114476","DOIUrl":"10.1016/j.rse.2024.114476","url":null,"abstract":"<div><div>Grassland dynamics are modulated by management intensity and impact overall ecosystem functioning. In mowed grasslands, the first mowing date is a key indicator of management intensification. The aim of this work was to assess several supervised regression models for mapping grassland first mowing date at national-level using Sentinel-2 time series. Three deep-learning architectures, two conventional machine learning models and two threshold-based methods (fixed and relative) were compared. Algorithms were trained/calibrated and tested from field observations, using a spatial cross-validation approach. Our findings showed that time aware deep-learning models – Lightweight Temporal Attention Encoder (LTAE) and 1D Convolutional Neural Network (1D-CNN) – yielded higher performances compared to Multilayer Perceptron, Random Forest and Ridge Regression models. Threshold-based methods under-performed compared to all other models. Best model (LTAE) mean absolute error was within six days with a coefficient of determination of 0.52. Additionally, errors were accentuated at extreme (late/early) mowing dates, which were underrepresented in the data set. Oversampling techniques did not improve predicting extreme mowing dates. Finally, the best prediction accuracy was obtained when the number of clear dates surrounding the mowing event was greater than 2. Our outputs evidenced time aware deep-learning models’ potential for large-scale grassland first mowing event monitoring. A national-level map was produced to support bird-life monitoring or public policies for biodiversity and agro-ecological transition in France.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114476"},"PeriodicalIF":11.1,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142541764","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":"Resolution enhancement of SMOS brightness temperatures: Application to melt detection on the Antarctic and Greenland ice sheets","authors":"Pierre Zeiger ,&nbsp;Ghislain Picard ,&nbsp;Philippe Richaume ,&nbsp;Arnaud Mialon ,&nbsp;Nemesio Rodriguez-Fernandez","doi":"10.1016/j.rse.2024.114469","DOIUrl":"10.1016/j.rse.2024.114469","url":null,"abstract":"<div><div>A large part of the surface of the Greenland Ice Sheet (GrIS) and the margins of Antarctica are melting every summer, affecting their surface mass balance. Wet/dry snow status has been detected for decades using the peaks of brightness temperature at 19 GHz, and more recently at L-band (1.4 GHz) using both the SMOS and SMAP missions. SMOS owns a longer time series than SMAP with data since 2010, but the 52.5°incidence bin in the Level 3 (L3) product from Centre Aval de Traitement des Données SMOS (CATDS) that was previously used to detect melt suffers from a coarse spatial resolution. For this reason, we developed a new SMOS enhanced resolution brightness temperature (<span><math><msub><mrow><mi>T</mi></mrow><mrow><mtext>B</mtext></mrow></msub></math></span>) product building on the radiometer version of the Scatterometter Image Reconstruction (rSIR) algorithm. We also exploited the SMOS L1C observations near 40°incidence angle instead of 52.5°as the native spatial resolution of SMOS is better at low incidence. The new product is posted on a 12.5 km polar stereographic grid and covers all the GrIS and Antarctica for 2010–2024 with twice-daily morning and afternoon acquisitions. The effective spatial resolution was evaluated to <span><math><mo>∼</mo></math></span>30 km, a 30% enhancement compared to the SMOS L3TB at 40°and almost a 50% enhancement compared to the SMOS L3TB at 52.5°. Then, we applied a melt detection algorithm to both the enhanced resolution product at 40°and the L3TB product at 52.5°which is used in the literature. The spatial resolution enhancement results not only in the detection of smaller melt regions but also in a widespread increase in the annual number of melt days. This increase is larger than 30 days per year in the GrIS percolation area and on multiple Antarctic ice shelves. This is primarily due to the mix of dry and wet snow regions near the ice shelves grounding line, resulting in lower brightness temperature peaks in the SMOS L3TB product due to a large power spread. These findings highlight the dependence of melt detection in particular, and geophysical applications in general, on the spatial resolution of passive microwave observations. This study provides a new open dataset suitable to monitor melt at the surface and at depth on the two main ice-sheets.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114469"},"PeriodicalIF":11.1,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142490200","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":"Forest disturbance detection in Central Europe using transformers and Sentinel-2 time series","authors":"Christopher Schiller ,&nbsp;Jonathan Költzow ,&nbsp;Selina Schwarz ,&nbsp;Felix Schiefer ,&nbsp;Fabian Ewald Fassnacht","doi":"10.1016/j.rse.2024.114475","DOIUrl":"10.1016/j.rse.2024.114475","url":null,"abstract":"<div><div>Forests provide important ecosystem functions such as carbon sequestration and climate regulation, particularly in countries with high forest cover. Climate change-induced extreme weather events have a negative impact on many forest ecosystems. In Germany, for instance, the drought of the years 2018 until 2020 resulted in signs of damage in almost 80% of trees. This decline in forest vitality has additionally led to severe bark beetle infestations and widespread tree mortality, posing significant challenges to forest managers to obtain a complete picture of the state of their forests. Since a completely ground-based monitoring of forest condition is not feasible due to the forests' vast extent, remote sensing and particularly multispectral satellite image time series (SITS) analysis were suggested as efficient alternatives. Transformers, a state-of-the-art Deep Learning (DL) architecture, have shown promising results in the classification of multivariate SITS for other applications. Here, we use Transformers in combination with Sentinel-2 (S2) time series data to test if they can improve forest disturbance detection capabilities in comparison to conventional methods by automatically extracting relevant information from background variability throughout the whole time series. To match the large training data needs of Transformers, we use a two-step approach including pre-training and finetuning. During pre-training, we use outputs of earlier presented SITS approaches, while during finetuning, we use detailed reference data of known disturbances covering between 10 and 100% of a Sentinel-2 pixel as extracted from aerial images. We test three setups: <em>DL base</em> using ten S2 bands, <em>DL IND</em> using ten vegetation indices (VIs), and <em>DL +IND</em> utilising both as model input. F1-scores across all of our six study sites range between approx. 0.65 (DL +IND) and 0.72 (DL base) in a binary classification (undisturbed vs. disturbed) when considering both full and partial disturbances. DL base outperforms the other setups in forest disturbance detection, and detects disturbance extents as small as 40 m² within pixels of 100 m² size. Given the best performance of DL base, handcrafted vegetation indices (VIs) do not improve the model. Our model is competitive with existing approaches and slightly outperforms most earlier reported results, even though a direct comparison is challenging. Considering the option to further refine our trained model if additional reference data becomes available over time, we conclude that a combination of Transformers and Sentinel-2 time series can be developed into an effective tool for forest disturbance monitoring of Central European forests at fine spatial grain.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114475"},"PeriodicalIF":11.1,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142489279","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":"Temperature dependence of L-band vegetation optical depth over the boreal forest from 2011 to 2022","authors":"Mike Schwank ,&nbsp;Yiwen Zhou ,&nbsp;Arnaud Mialon ,&nbsp;Philippe Richaume ,&nbsp;Yann Kerr ,&nbsp;Christian Mätzler","doi":"10.1016/j.rse.2024.114470","DOIUrl":"10.1016/j.rse.2024.114470","url":null,"abstract":"&lt;div&gt;&lt;div&gt;The dependence of L-band Vegetation Optical Depth (L-VOD, &lt;span&gt;&lt;math&gt;&lt;mi&gt;τ&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt;) on Vegetation temperature &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mi&gt;V&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; is investigated for 1165 boreal forest grid cells selected for latitudes &gt; 55° and high radiometric forest fraction &lt;span&gt;&lt;math&gt;&lt;mi&gt;FFO&lt;/mi&gt;&lt;mo&gt;≥&lt;/mo&gt;&lt;mn&gt;90&lt;/mn&gt;&lt;mo&gt;%&lt;/mo&gt;&lt;/math&gt;&lt;/span&gt;. SMOS Level-3 Brightness Temperatures (BT) at ascending orbits acquired from 2011 to 2022 are used. This is a spatio-temporal extension of our previous study on &lt;span&gt;&lt;math&gt;&lt;mi&gt;τ&lt;/mi&gt;&lt;mfenced&gt;&lt;msub&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mi&gt;V&lt;/mi&gt;&lt;/msub&gt;&lt;/mfenced&gt;&lt;/math&gt;&lt;/span&gt; made over the “Sodankylä grid cell” (Finland) in 2019. It demonstrated the Electromagnetic (EM) reasons for &lt;span&gt;&lt;math&gt;&lt;mi&gt;τ&lt;/mi&gt;&lt;mfenced&gt;&lt;msub&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mi&gt;V&lt;/mi&gt;&lt;/msub&gt;&lt;/mfenced&gt;&lt;/math&gt;&lt;/span&gt; reaching maximum at 0°C and decreasing when &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mi&gt;V&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; is moving away from 0°C. The parameterisation of the \"L-VOD model\" developed in the previous study is simplified and updated to take into account the conservation of salt in sap-water during freezing. The “forward operator” based on the Two-Stream Microwave Emission Model (2S-MEM) is inverted to retrieve &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;τ&lt;/mi&gt;&lt;mi&gt;SMOS&lt;/mi&gt;&lt;/msub&gt;&lt;mfenced&gt;&lt;msub&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mi&gt;V&lt;/mi&gt;&lt;/msub&gt;&lt;/mfenced&gt;&lt;/math&gt;&lt;/span&gt; together with effective Ground permittivities &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;ε&lt;/mi&gt;&lt;mi&gt;G&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; during seasonal Warming Periods (WPs) determined from ERA-interim air temperatures. The “L-VOD model” parameters &lt;span&gt;&lt;math&gt;&lt;mfenced&gt;&lt;msub&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mi&gt;melt&lt;/mi&gt;&lt;/msub&gt;&lt;msub&gt;&lt;mi&gt;WC&lt;/mi&gt;&lt;mi&gt;wood&lt;/mi&gt;&lt;/msub&gt;&lt;/mfenced&gt;&lt;/math&gt;&lt;/span&gt; are estimated for the boreal forest grid cells by minimizing squared differences between &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;τ&lt;/mi&gt;&lt;mi&gt;SMOS&lt;/mi&gt;&lt;/msub&gt;&lt;mfenced&gt;&lt;msub&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mi&gt;V&lt;/mi&gt;&lt;/msub&gt;&lt;/mfenced&gt;&lt;/math&gt;&lt;/span&gt; and simulated &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;τ&lt;/mi&gt;&lt;mi&gt;sim&lt;/mi&gt;&lt;/msub&gt;&lt;mfenced&gt;&lt;msub&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mi&gt;V&lt;/mi&gt;&lt;/msub&gt;&lt;/mfenced&gt;&lt;/math&gt;&lt;/span&gt;. The vegetation melt-parameter &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mi&gt;melt&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; represents the “number of degrees below 0°C” at which sap-water melts, and &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;WC&lt;/mi&gt;&lt;mi&gt;wood&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; is the gravimetric wood-Water Content of branches. Reasonable values of &lt;span&gt;&lt;math&gt;&lt;mfenced&gt;&lt;msub&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mi&gt;melt&lt;/mi&gt;&lt;/msub&gt;&lt;msub&gt;&lt;mi&gt;WC&lt;/mi&gt;&lt;mi&gt;wood&lt;/mi&gt;&lt;/msub&gt;&lt;/mfenced&gt;&lt;/math&gt;&lt;/span&gt; are achieved for a majority of the boreal forest grid cells. It is found that &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mi&gt;melt&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; tends to be too high over Northern Europe, a region with longer WP durations compared to other regions of the boreal forest belt. By optimising the scattering albedo used to retrieve &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;τ&lt;/mi&gt;&lt;mi&gt;SMOS&lt;/mi&gt;&lt;/msub&gt;&lt;mfenced&gt;&lt;msub&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mi&gt;V&lt;/mi&gt;&lt;/msub&gt;&lt;/mfenced&gt;&lt;/math&gt;&lt;/span&gt;, the correlations between &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;τ&lt;/mi&gt;&lt;mi&gt;sim&lt;/mi&gt;&lt;/msub&gt;&lt;mfenced&gt;&lt;msub&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mi&gt;V&lt;/mi&gt;&lt;/msub","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114470"},"PeriodicalIF":11.1,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142487538","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":"Can satellite products monitor solar brightening in Europe?","authors":"Ruben Urraca ,&nbsp;Jörg Trentmann ,&nbsp;Uwe Pfeifroth ,&nbsp;Nadine Gobron","doi":"10.1016/j.rse.2024.114472","DOIUrl":"10.1016/j.rse.2024.114472","url":null,"abstract":"<div><div>Satellite products provide the best way to monitor the solar radiation reaching the Earth’s surface on a global scale. However, their capability to monitor solar radiation trends needs to be constantly evaluated. This depends on their temporal stability and the accurate representation of all processes driving solar radiation. This study evaluates these aspects by comparing and cross-comparing different solar radiation products (ERA5, CAMS-RAD 4.6, SARAH-3, CLARA-A3, CERES-EBAF 4.2) against in-situ measurements over Europe.</div><div>All products show a moderate positive bias over Europe but strong differences in their root mean squared deviation (RMSD) related to their different cloud transmittance models. Geostationary-based products (SARAH-3, CAMS-RAD 4.6) provide the smallest RMSD closely followed by CLARA-A3, whereas ERA5 shows a large RMSD due to random errors in cloud transmittance.</div><div>All products show an increase in surface solar radiation, or brightening, over the last 40 years over Europe, but the magnitude of the trends and their spatiotemporal variability differ between products. Despite finding temporal inhomogeneities in some products, the different trends are mostly due to different aerosol modeling approaches implemented by each product. Both SARAH-3 (+2.3 <span><math><mrow><msup><mrow><mi>W/m</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>/</mo><mi>decade</mi></mrow></math></span>, 2001–22) and CERES-EBAF 4.2 (+2.2 <span><math><mrow><msup><mrow><mi>W/m</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>/</mo><mi>decade</mi></mrow></math></span>, 2001–22) provide the most consistent trends compared to in-situ data, showing that after stabilizing in the late 2000s, brightening is particularly recovering in Western Europe. In-situ measurements show a reduction of aerosol optical depth from 2001 to 2022 that has been accentuated in the last 10 years, particularly in Western Europe. This would be consistent with the hypothesis that brightening recovery is driven by an aerosol reduction, though other analyses suggest that clouds also play a role in this recovery. More work is needed to understand the contribution of aerosols to solar radiation trends and the exact aerosol effects represented by each solar radiation product.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114472"},"PeriodicalIF":11.1,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142488943","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":"4D imaging of the volcano feeding system beneath the urban area of the Campi Flegrei caldera","authors":"Pietro Tizzani ,&nbsp;José Fernández ,&nbsp;Andrea Vitale ,&nbsp;Joaquín Escayo ,&nbsp;Andrea Barone ,&nbsp;Raffaele Castaldo ,&nbsp;Susi Pepe ,&nbsp;Vincenzo De Novellis ,&nbsp;Giuseppe Solaro ,&nbsp;Antonio Pepe ,&nbsp;Anna Tramelli ,&nbsp;Zhongbo Hu ,&nbsp;Sergey V. Samsonov ,&nbsp;Isabel Vigo ,&nbsp;Kristy F. Tiampo ,&nbsp;Antonio G. Camacho","doi":"10.1016/j.rse.2024.114480","DOIUrl":"10.1016/j.rse.2024.114480","url":null,"abstract":"<div><div>This paper describes an approach to analyze ground deformation data collected by InSAR (Interferometric Synthetic Aperture Radar) imaging the volcano feeding system (VFS) beneath a caldera. The approach is applied to the Campi Flegrei caldera in southern Italy, a densely populated area at high risk for volcanic eruption. The method is a 4D tomographic inversion that considers a combination of 3D pressure sources and dislocations (strike-slip, dip-slip and tensile) acting simultaneously. This is in contrast to traditional methods that assume a priori geometries and type for the volcanic source. Another novelty is that we carry out a time-series analysis of multifrequency InSAR displacement data. The analysis of these multiplatform and multifrequency InSAR data from 2011 to 2022 reveals an inflating source at a depth of 3–4 km that is interpreted as a pressurized magmatic intrusion. The source broadens and migrates laterally over time, with a possible new magmatic pulse arriving in 2018–2020. The model also identifies a shallow region (at 400 m depth) that may be feeding fumaroles in the area. The analysis also reveals a zone of weakness (dip-slip) that could influence the path of rising magma. This method provides a more detailed dynamic 4 - dimensional image of the VFS than previously possible and could be used to improve hazard assessments in active volcanic areas.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114480"},"PeriodicalIF":11.1,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142487104","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":"Improvements in land surface temperature and emissivity retrieval from Landsat-9 thermal infrared data","authors":"Xiaopo Zheng,&nbsp;Youying Guo,&nbsp;Zhongliang Zhou,&nbsp;Tianxing Wang","doi":"10.1016/j.rse.2024.114471","DOIUrl":"10.1016/j.rse.2024.114471","url":null,"abstract":"<div><div>Land surface temperature (LST) is the key parameter for characterizing the water and energy balance of the Earth’ surface. At present, thermal infrared (TIR) remote sensing provides the most efficient way to obtain accurate LST regionally and globally. Among existing satellites, the Landsat-9 could observe the Earth's surface via two TIR channels, making it possible to generate the global LST product with a remarkable spatial resolution of 100 m. Currently, the single channel method and split window method generally were used to recover LST from the Landsat-9 TIR measurements. However, accurate land surface emissivity (LSE) is needed in both algorithms, which is very difficult to obtain at the pixel scale. To overcome this issue, an improved LST and LSE separation method was proposed in this study. Firstly, the traditional water vapor scaling (WVS) method was refined to address the atmospheric effects in the satellite measurements. Then, the traditional temperature and emissivity separation method (TES) was adapted to the Landsat-9 observations with only two TIR channels. Finally, an iterative process was designed to retrieve the LST and LSE simultaneously. Validations using in-situ measured LST indicated that the root mean square error (RMSE) of the retrieved LST was around 2.92 K, outperforming the official Landsat-9 LST product with an RMSE of about 4.20 K. Taking ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) products as the references, the RMSE of our retrieved LST and LSE was found to be &lt; 1.55 K and &lt; 0.015, respectively. Overall, conclusions can be made that the proposed method was able to retrieve accurate LST and LSE simultaneously from the Landsat-9 TIR measurements with high spatial resolution, which may greatly facilitate the relevant applications.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114471"},"PeriodicalIF":11.1,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142487103","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":"Corrigendum to “Retrieval of high-resolution melting-season albedo and its implications for the Karakoram Anomaly” [Remote Sensing of Environment Volume 315 (2024) 114438]","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.2024.114474","DOIUrl":"10.1016/j.rse.2024.114474","url":null,"abstract":"","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114474"},"PeriodicalIF":11.1,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142451655","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":"Development of China's atmospheric environment monitoring satellite CO2 IPDA lidar retrieval algorithm based on airborne campaigns","authors":"Shuaibo Wang ,&nbsp;Chonghui Cheng ,&nbsp;Sijie Chen ,&nbsp;Jiqiao Liu ,&nbsp;Xingying Zhang ,&nbsp;Lingbing Bu ,&nbsp;Jingxin Zhang ,&nbsp;Kai Zhang ,&nbsp;Jiesong Deng ,&nbsp;Wentao Xu ,&nbsp;Weibiao Chen ,&nbsp;Dong Liu","doi":"10.1016/j.rse.2024.114473","DOIUrl":"10.1016/j.rse.2024.114473","url":null,"abstract":"<div><div>China successfully launched the Atmospheric Environment Monitoring Satellite (AEMS) equipped with an Atmospheric Carbon Dioxide Lidar (ACDL) on April 16, 2022, which is the world's first satellite based on Integrated Path Differential Absorption (IPDA) technique to detect the atmospheric CO₂ column-weighted dry-air mixing ratio (XCO₂). In order to accurately and quickly process the AEMS measurements, we proposed a systematic retrieval algorithm for the AEMS ACDL and conducted two airborne campaigns to validate its performance. The first airborne campaign was conducted in the land-sea interface region of northeast China in 2019. The CO₂ retrieval algorithm distinguished significant horizontal XCO₂ gradients over different underlying surfaces and obtained an apparent XCO₂ enhancement of 8–18 ppm between the urban and forests. The CO₂ retrievals not only demonstrated the excellent detection capability of the ACDL for carbon sources and sinks, but also proved the feasibility of the retrieval algorithm in complex terrain and variable atmospheric conditions. The second airborne experiment was conducted in 2021 in the interior desert region of China, which is an excellent flight field to explore the accuracy and precision limits of the retrieval algorithm. We validated the XCO₂ retrievals with the airborne in-situ CO₂ profiles and demonstrated that the XCO₂ accuracy and precision were 0.29 ppm and 0.63 ppm with 1.5-km averages over the desert surface, indicating the accuracy of the retrieval algorithm. The hard target elevation (HTE) retrieval validation results indicate that the IPDA lidar ranging precision is 0.69 m and 6.29 m for the ocean and land surface, respectively. In addition, further analysis combined with the space-borne IPDA lidar simulator showed high consistency in CO₂ precision between airborne measurements and simulation results in East Asia, demonstrating the robustness of the retrieval algorithm at continental scales.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114473"},"PeriodicalIF":11.1,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142452200","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":"Glacier mass change and evolution of Petrov Lake in the Ak-Shyirak massif, central Tien Shan, from 1973 to 2023 using multisource satellite data","authors":"Yingzheng Wang ,&nbsp;Donghai Zheng ,&nbsp;Yushan Zhou ,&nbsp;Yanyun Nian ,&nbsp;Shanshan Ren ,&nbsp;Weiwei Ren ,&nbsp;Zhongzheng Zhu ,&nbsp;Zhiguang Tang ,&nbsp;Xin Li","doi":"10.1016/j.rse.2024.114437","DOIUrl":"10.1016/j.rse.2024.114437","url":null,"abstract":"<div><div>Warming in the Third Pole region accelerates glacier and snow melt, leading to a rise in glacial lake numbers and sizes. However, accurately measuring their water level changes poses challenges, hindering precise volume assessments and evaluation of glacier mass balance contributions. Here, we took the Ak-Shyirak glaciers and the largest Petrov proglacial lake in the Central Tien Shan as a case study to investigate these phenomena. Specifically, firstly, we conducted mass balance assessments for the Ak-Shyirak massif for six sub-periods from 1973 to 2023 using KH-9 DEMs, SRTM DEM, and ASTER DEMs. The results indicate that glaciers were in a state of rapid melting for 1980–2000 and 2005–2012, with rates of −0.46 m w.e./a and − 0.37 m w.e./a; moderate melting during 1973–1980 and 2012–2018, with rates of −0.26 m w.e./a and − 0.28 m w.e./a, while slower melting during 2000–2005 and 2018–2023, with rates of −0.08 m w.e./a and − 0.18 m w.e./a. Subsequently, we conducted assessments of the area change of Petrov Lake for 1973–2023 using KH-9 and Landsat images. The results reveal a significant increase in the glacial lake area by 2.81 km² (150.25 %), corresponding to a rate of 0.054 km²/a over the entire study period. Furthermore, we conducted monitoring of Petrov Lake's water level from 2019 to 2023 by utilizing ICESat-2 laser altimetry and Sentinel-3 radar altimetry data. Our findings indicate that the glacial lake level shows intra-annual fluctuations and inter-annual change, with amplitudes of 0.67 ± 0.09 m and increase rate of 0.30 ± 0.05 m/a, respectively, as determined by a periodic fluctuation model. Finally, after a comprehensive analysis of ERA5-Land meteorological data, topography, glacier mass balance, lake area, and water level, we can draw the following conclusions: (1) glacier mass balance is predominantly influenced by the air temperature and snowfall; (2) changes in glacial lake area are driven by factors such as the lake basin, glacier surface elevation, and drainage event; (3) intra-annual fluctuations and inter-annual change in glacial lake levels are both primarily influenced by precipitation and glacier mass balance; (4) glacier mass balance accounts for (36.19 ± 8.47)% of the water supply contributing to changes in glacial lake volume change, while precipitation represents (63.81 ± 5.08)%. Glacier mass balance measurements reveal changing patterns in the Ak-Shyirak massif, Central Tien Shan, due to climate change. Inaugural proglacial lake level measurements provide unique insights into both intra-annual and inter-annual changes, serving as a reference for Third Pole region-wide glacial lake monitoring. Additionally, quantifying glacier meltwater contributions to lake volumes will aid future glacial lake evaluation and potential outburst flood impacts.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114437"},"PeriodicalIF":11.1,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142444363","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}