Remote Sensing of Environment最新文献

{"title":"A new Multivariate Drought Severity Index to identify short-term hydrological signals: case study of the Amazon River basin","authors":"Artur Lenczuk ,&nbsp;Christopher Ndehedehe ,&nbsp;Anna Klos ,&nbsp;Janusz Bogusz","doi":"10.1016/j.rse.2024.114464","DOIUrl":"10.1016/j.rse.2024.114464","url":null,"abstract":"<div><div>The Earth's climate is changing rapidly and unexpectedly, causing more frequent, longer and more severe droughts, with lasting impacts on plants, ecosystems, communities and people. Consequently, this is leading to an increased importance of monitoring the climate and water storage trends in different regions. This information on a global scale is already commonly derived using satellite-based geodetic techniques such as the Global Positioning System (GPS) and the Gravity Recovery and Climate Experiment (GRACE). The use of both techniques has significant advantages, especially in regions where changes in the hydrosphere are notable, such as the Amazon basin, where 25 GPS stations were lately classified as benchmarks for hydrogeodesy. We show that the vertical displacements obtained from GPS and GRACE have good spatio-temporal agreement with the Standardized Precipitation and Standardized Precipitation Evapotranspiration indices, abbreviated respectively as SPI and SPEI, for all these stations. Drought severity index (DSI) estimated separately from GPS-observed and GRACE-derived vertical displacements on a station-by-station basis is capable to identify dry and wet events previously reported for the Amazon basin. However, due to the weaknesses of both techniques, such as technique-related systematic errors or coarse spatial resolution, a few extreme hydrological events may not be properly captured by GPS-DSI and/or GRACE-DSI. To take full advantage of both techniques and overcome their weaknesses, we introduce a completely new methodology to combine individual GPS-DSI and GRACE-DSI indices. As a novelty, both indices are estimated using short-term changes (&lt;9 months) of monthly vertical displacements observed by GPS permanent stations and those derived by GRACE for GPS locations. Then, to capture and detect drought events that either both geodetic techniques metrics missed or incorrectly depicted, the Multivariate Drought Severity Index (MDSI) is estimated through the concept of Frank copulas. We demonstrate that the MDSI captures more hydroclimatic events reported in previous studies, which are not identified by individual series of GPS-DSI or GRACE-DSI indices, and is temporally consistent with Standardized Streamflow Index (SSI) based on the in-situ river discharge changes.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114464"},"PeriodicalIF":11.1,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142431850","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":"Innovative hybrid algorithm for simultaneous land surface temperature and emissivity retrieval: Case study with SDGSAT-1 data","authors":"Mengmeng Wang ,&nbsp;Guojin He ,&nbsp;Tian Hu ,&nbsp;Mingsi Yang ,&nbsp;Zhengjia Zhang ,&nbsp;Zhaoming Zhang ,&nbsp;Guizhou Wang ,&nbsp;Hua Li ,&nbsp;Wei Gao ,&nbsp;Xiuguo Liu","doi":"10.1016/j.rse.2024.114449","DOIUrl":"10.1016/j.rse.2024.114449","url":null,"abstract":"<div><div>The split-window (SW) and temperature-and-emissivity separation (TES) algorithms have been widely used for land surface temperature (LST) estimation from thermal infrared (TIR) observations for various missions. However, the SW algorithm requires prior estimates of land surface emissivity (LSE). The TES algorithm encompasses an atmospheric correction module, which increases the complexity and uncertainty of operational LST retrieval. To address this, we proposed a split-window-driven temperature-and-emissivity separation (SWDTES) algorithm in this study to estimate LST and LSE simultaneously without the need of atmospheric correction by combining the respective advantages of SW and TES. The inputs to the SWDTES algorithm are largely simplified, which only requires atmospheric water vapor content (AWVC) apart from the top-of-atmosphere TIR radiance. The developed SWDTES algorithm was applied to the high spatial resolution Thermal Infrared Spectrometer (TIS) data from the newly launched Sustainable Development Science Satellite-1 (SDGSAT-1) mission, and its performance was assessed using the MODIS data and ground measurements. The cross validation shows that the correlation coefficient (<em>r</em>), <em>bias</em> and root mean square error (RMSE) between MODIS-converted LSE and retrieved LSE using the SWDTES algorithm for the nighttime case is 0.904, −0.033 and 0.038 for band 1; 0.677, −0.008 and 0.014 for band 2; and 0.576, −0.000 and 0.008 for band 3, indicating a good consistency between the two LSE estimates. In addition, the evaluation using ground measurements shows that the <em>r</em>, <em>bias</em> and RMSE between the <em>in-situ</em> LST and retrieved LST using the SWDTES algorithm are 0.99, −0.67 K and 2.10 K, respectively. Compared to the OSW and TES algorithms, the SWDTES algorithm reduces the RMSE by 0.34 K and 0.90 K, respectively, indicating an improvement in LST retrieval accuracy. We conclude that the proposed SWDTES algorithm can achieve high-accuracy and high-resolution LST retrieval from the SDGSAT-1 mission, supporting fine-scale applications in energy, water, and carbon cycle modeling.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114449"},"PeriodicalIF":11.1,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142431781","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":"Tracking mangrove condition changes using dense Landsat time series","authors":"Xiucheng Yang ,&nbsp;Zhe Zhu ,&nbsp;Kevin D. Kroeger ,&nbsp;Shi Qiu ,&nbsp;Scott Covington ,&nbsp;Jeremy R. Conrad ,&nbsp;Zhiliang Zhu","doi":"10.1016/j.rse.2024.114461","DOIUrl":"10.1016/j.rse.2024.114461","url":null,"abstract":"<div><div>Mangroves in tropical and subtropical coasts are subject to episodic disturbances, notably from severe storms, leading to potential widespread vegetation mortality. The ability of vegetation to recover varies, and with disturbances becoming more frequent and severe, it is vital to track and project vegetation responses to support management and policy decisions. Prior studies have largely focused on binary mangrove mapping (i.e., presence or absence), while tracking conditions and condition change have not received sufficient attention. In this paper, we demonstrate a method based on dense time series Landsat images for continuous monitoring of mangrove conditions, where we track three kinds of post-disturbance mangrove conditions, including disturbed (disturbed, with rebound to the previous state within one growing season), recovering (undergoing natural recovery in longer than one growing season), and declining (showing long-term decline after disturbance). The method starts with disturbance detection using the DEtection and Characterization Of the tiDal wEtland change (DECODE) algorithm, an existing dense time series model designed to detect disturbances in tidal wetlands with adaptation to tidal fluctuations. This algorithm is well suited for the detection of tidal wetland disturbances but does not provide satisfactory post-disturbance monitoring results, due to the substantial variability in post-disturbance Landsat observations. To better monitor post-disturbance conditions, a new time series fitting approach, DECODER (DECODE and Recovery), is proposed for the recovery stage. Additionally, for temporal segments divided by disturbance events, we built a random forest classifier with temporal-spectral variables derived from the time series model to characterize mangrove conditions. Employing this approach in Florida's mangroves, we generated condition maps, such as dieback and recovery, with an overall accuracy of approximately 97.96 ± 0.86- [95 % confidence intervals]. Comparing post-hurricane conditions in Florida revealed that the increased frequency and severity of disturbances are challenging mangrove resilience, potentially diminishing their ability to recover and sustain ecosystem functions.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114461"},"PeriodicalIF":11.1,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142405045","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":"Monitoring Earth's atmosphere with Sentinel-5 TROPOMI and Artificial Intelligence: Quantifying volcanic SO2 emissions","authors":"Claudia Corradino ,&nbsp;Paul Jouve ,&nbsp;Alessandro La Spina ,&nbsp;Ciro Del Negro","doi":"10.1016/j.rse.2024.114463","DOIUrl":"10.1016/j.rse.2024.114463","url":null,"abstract":"<div><div>Identifying changes in volcanic unrest and tracking eruptive activity are fundamental for volcanic surveillance and monitoring. Magmatic gases, particularly sulphur dioxide (SO₂), play a crucial role in influencing eruptive styles, making the monitoring of SO₂ emissions essential. Recent advancements in satellite remote sensing technology, including higher spatial resolution and sensitivity, have enhanced our ability to detect SO₂ emissions from volcanoes worldwide. However, traditional fixed-threshold algorithms struggle to automatically distinguish volcanic SO₂ emissions from non-volcanic sources. Additionally, accurately quantifying SO₂ emissions is challenging due to their dependence on plume height, particularly when reaching high altitudes. To address these challenges, we developed an Artificial Intelligence (AI) algorithm that detects and quantifies volcanic SO₂ emissions in near real-time. Our approach utilizes a Random Forest (RF) model, a supervised Machine Learning (ML) algorithm, to identify volcanic SO₂ emissions and integrates Cloud Top Height (CTH) data to enhance the accuracy of SO₂ mass quantification during intense volcanic eruptions. This AI algorithm, fully implemented in Google Earth Engine (GEE), leverages data from the TROPOspheric Monitoring Instrument (TROPOMI) aboard the Copernicus Sentinel-5 Precursor (S5P) satellite to automatically retrieve daily volcanic SO₂ plumes and CTH. We validated the model's performance against the Radius classifier, a state-of-the-art tool, and generalized its application across various volcanoes (Etna, Villarrica, Fuego, Pacaya, and Cumbre Vieja) with differing degassing activities, SO₂ emission rates, and plume geometries. Our findings demonstrate that the proposed AI approach effectively identifies and quantifies SO₂ plumes emitted by different volcanoes, enabling the investigation of SO₂ emission time series that reflect magma dynamics.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114463"},"PeriodicalIF":11.1,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142405048","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":"Avian diversity across guilds in North America versus vegetation structure as measured by the Global Ecosystem Dynamics Investigation (GEDI)","authors":"Jin Xu ,&nbsp;Laura Farwell ,&nbsp;Volker C. Radeloff ,&nbsp;David Luther ,&nbsp;Melissa Songer ,&nbsp;William Justin Cooper ,&nbsp;Qiongyu Huang","doi":"10.1016/j.rse.2024.114446","DOIUrl":"10.1016/j.rse.2024.114446","url":null,"abstract":"<div><div>Avian diversity, a key indicator of ecosystem health, is closely related to canopy structure. Most avian diversity models are based on either optical remote sensing or airborne lidar data, but the latter is limited to small study areas. The launch of the Global Ecosystem Dynamics Investigation (GEDI) instrument in 2018 has opened new avenues for exploring the influence of vegetation structure on avian diversity. To examine how direct measurements of canopy structural characteristics explain bird diversity across North America, we analyzed 18 GEDI metrics from 2019 to 2022, along with corresponding Breeding Bird Survey (BBS) counts and AVONET morphological data, analyzing effects across broad regions and at varying spatial extents. We grouped 440 bird species into 20 ecological guilds under six guild categories and employed random forest algorithms to model avian diversity across eight spatial extents (1, 2, 3, 4, 5, 10, 20, and 39.2 km). The models predicted six diversity indices, including species richness (sRich), functional richness (fRich), evenness (fEve), dispersion (fDis), divergence (fDiv), and redundancy (fRed) across eight spatial extents. The best-predicted guilds varied for each diversity index. The most accurate models were sRich (pseudo-R² = 0.71, RMSE = 4.28) and fRed (pseudo-R² = 0.60, RMSE = 0.13) for forest specialists guilds; fRich (pseudo-R² = 0.55, RMSE = 0.18) for urban guilds; fEve (pseudo-R² = 0.28, RMSE = 0.08) for insectivore guilds; and fDiv (pseudo-R² = 0.38, RMSE = 0.12) and fDis (pseudo-R² = 0.53, RMSE = 0.87) for short distance migrants guilds. Our results highlight the critical role of canopy structure, including its horizontal and vertical distribution and variation, in predicting avian diversity, as measured by the mean number of detected modes (num_detectedmodes), the standard deviation of foliage height diversity (FHD), num_detectedmodes, canopy cover, and plant area index (PAI) across the spatial extents centered on BBS routes. Therefore, we recommend incorporating the GEDI metrics into avian diversity modeling and mapping across North America, thereby potentially enhancing bird habitat management and conservation efforts.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114446"},"PeriodicalIF":11.1,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142398391","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":"Reconstructing Tibetan Plateau lake bathymetry using ICESat-2 photon-counting laser altimetry","authors":"Xiaoran Han ,&nbsp;Guoqing Zhang ,&nbsp;Jida Wang ,&nbsp;Kuo-Hsin Tseng ,&nbsp;Jiaqi Li ,&nbsp;R. Iestyn Woolway ,&nbsp;C.K. Shum ,&nbsp;Fenglin Xu","doi":"10.1016/j.rse.2024.114458","DOIUrl":"10.1016/j.rse.2024.114458","url":null,"abstract":"<div><div>Lake bathymetry is important for quantifying and characterizing underwater morphology and its geophysical state, which is critical for hydrological and ecological studies. Due primarily to the harsh environment of the Tibetan Plateau, there is a severe lack of lake bathymetry measurements, limiting the accurate estimation of total lake volumes and their evolutions. Here, we propose a novel lake bathymetry reconstruction by combining ICESat-2/ATLAS (Advanced Topography Laser Altimetry System) data with a numerical model. An improved grid-based photon noise removal method is used to address the photon signal buried in the background noise during the local daytime. The developed model was validated for seven lakes on the Tibetan Plateau and showed good agreement between simulated and measured lake volumes, with an average absolute percentage error of 8.0 % for maximum water depth and 19.7 % for lake volume simulations. The model was then utilized to estimate the water volume of other lakes by combining it with the self-affine theory. The lake depths obtained from ICESat-2/ATLAS show good agreement (RMSE = 0.69 m; rRMSE = 10.3 %) with available in-situ measurements for lakes with depths &lt;16.5 m, demonstrating the potential of ICESat-2/ATLAS for improved reconstruction of the bathymetry of clear water inland lakes. Our study reveals for the first time, that the Tibetan Plateau has an estimated total lake water volume of 1043.69 ± 341.31 km³ for 33,477 lakes (&gt;0.01 km²) in 2022. Over 70 % (∼734.8 km³) of the lake water storage is concentrated in the Inner Plateau, with the Yellow River basin accounting for 10.9 % (∼113.9 km³), followed by the Indus River basin with 7.2 % (∼75.1 km³). Our study provides a robust method for estimating total lake volumes where in-situ measurements are scarce and can be extended to other clear water lakes, thus contributing to more accurate global assessments and towards comprehensive quantification of Earth's surface water resources distribution.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114458"},"PeriodicalIF":11.1,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142398390","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":"Unlocking the full potential of Sentinel-1 for flood detection in arid regions","authors":"Shagun Garg ,&nbsp;Antara Dasgupta ,&nbsp;Mahdi Motagh ,&nbsp;Sandro Martinis ,&nbsp;Sivasakthy Selvakumaran","doi":"10.1016/j.rse.2024.114417","DOIUrl":"10.1016/j.rse.2024.114417","url":null,"abstract":"<div><div>Climate change has intensified flooding in arid and semi-arid regions, presenting a major challenge for flood monitoring and mapping. While satellites, particularly Synthetic Aperture Radar (SAR), allow synoptically observing flood extents, accurately differentiating between sandy terrains and water for arid region flooding remains an open challenge. Current global flood mapping products exclude arid areas from their analyses due to the sand and water confusion, resulting in a critical lack of observations which impedes response and recovery in these vulnerable regions. This paper explores the full potential of Sentinel-1 SAR to improve near-real-time flood mapping in arid and semi-arid regions. By investigating the impact of various parameters such as polarization, temporal information, and interferometric coherence, the most important information sources for detecting arid floods were identified. Using three distinct arid flood events in Iran, Pakistan, and Turkmenistan, different scenarios were constructed and tested using RF to evaluate the effectiveness of each feature. Permutation feature importance analysis was additionally conducted to identify key elements that reduce computational costs and enable a faster response during emergencies. Fusing VV coherence and amplitude information in pre-flood and post-flood imagery proved to be the most suitable approach. Results also show that leveraging crucial features reduces computational time by <span><math><mo>∼</mo></math></span>35% as well as improves flood mapping accuracy by <span><math><mo>∼</mo></math></span>50%. With advancements in cloud processing capabilities, the computational challenges associated with interferometric SAR computations are no longer a barrier. The demonstrated adaptability of the proposed approach across different arid areas, offers a step forward towards improved global flood mapping.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114417"},"PeriodicalIF":11.1,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397700","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":"Improved phenology-based rice mapping algorithm by integrating optical and radar data","authors":"Zizhang Zhao ,&nbsp;Jinwei Dong ,&nbsp;Geli Zhang ,&nbsp;Jilin Yang ,&nbsp;Ruoqi Liu ,&nbsp;Bingfang Wu ,&nbsp;Xiangming Xiao","doi":"10.1016/j.rse.2024.114460","DOIUrl":"10.1016/j.rse.2024.114460","url":null,"abstract":"<div><div>Information on rice planting areas is critically important for food and water security, as well as for adapting to climate change. Mapping rice globally remains challenging due to the diverse climatic conditions and various rice cropping systems worldwide. Synthetic Aperture Radar (SAR) data, which is immune to climatic conditions, plays a vital role in rice mapping in cloudy, rainy, low-latitude regions but it suffers from commission errors in high-latitude regions. Conversely, optical data performs well in high-latitude regions due to its high observation frequency and less cloud contamination but faces significant omission errors in low-latitude regions. An effective integrated method that combines both data types is key to global rice mapping. Here, we propose a novel adaptive rice mapping framework named Rice-Sentinel that combines Sentinel-1 and Sentinel-2 data. First, we extracted key phenological phases of rice (e.g., the flooding and transplanting phase and the rapid growth phase), by analyzing the characteristic V-shaped changes in the Sentinel-1 VH curve. Second, we identified potential flooding signals in rice pixels by integrating the VH time series from Sentinel-1 with the Land Surface Water Index (LSWI) and Enhanced Vegetation Index (EVI) from Sentinel-2, utilizing the generated phenology phases. Third, the rapid growth signals of rice following its flooding phase were identified using Sentinel-2 data. Finally, rice fields were identified by integrating flooding and rapid growth signals. The resultant rice maps in six different case regions of the world (Northeast and South China, California, USA, Mekong Delta of Vietnam, Sakata City in Japan, and Mali in Africa) showed overall accuracies over 90 % and F1 scores over 0.91, outperforming the existing methods and products. This algorithm combines the strengths of both optical and SAR time series data and leverages biophysical principles to generate robust rice maps without relying on any prior ground truth samples. It is well-positioned for global applications and is expected to contribute to global rice monitoring efforts.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114460"},"PeriodicalIF":11.1,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397735","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":"Generation and evaluation of energy and water fluxes from the HOLAPS framework: Comparison with satellite-based products during extreme hot weather","authors":"Almudena García-García,&nbsp;Jian Peng","doi":"10.1016/j.rse.2024.114451","DOIUrl":"10.1016/j.rse.2024.114451","url":null,"abstract":"<div><div>Improving our understanding of the energy and water exchanges between the land surface and the lower atmosphere (i.e. land–atmosphere interactions), and how climate change may affect them, is crucial to predict changes in temperature and precipitation extremes. Observations of energy and water fluxes at the land surface are typically retrieved from the eddy covariance method, which presents limitations related to spatial and temporal gaps, and the non-closure of the energy and water balances. Meanwhile, soil moisture (SM) products derived from satellite data have been widely used at regional and global scales, but they are limited to capture only surface soil water content and variations. The combination of remote sensing (RS) data and modelling frameworks is called to be the solution to improve the spatial coverage and vertical resolution of land–atmosphere interactions data, ensuring the energy and water balance closure. Here, we explore the combination of remote sensing and meteorological data with a physical-based modelling framework, the High resOlution Land Atmosphere Parameters from Space (HOLAPS). We used HOLAPS to produce hourly consistent estimates of energy and water fluxes over Europe at 5 km resolution. HOLAPS and other satellite-based evapotranspiration and sensible heat flux products from the literature are evaluated against the water balance method and eddy covariance measurements. HOLAPS SM estimates together with other RS-modelling products are also evaluated against ground-based measurements at the surface and in the root zone. The evaluation of HOLAPS ET estimates show similar performance to the other products with Kling–Gupta efficiency (KGE) ¿ -0.41 in comparison with eddy covariance measurements from FLUXNET in all seasons but in boreal winter. The simulation of H is more uncertain than for ET with KGE values ranging from -2.5 to 0.8 along the products and stations at monthly scales. HOLAPS reaches slightly better results than the rest of ET and H products at daily scales in summer (KGE ¿ 0.3 for ET and KGE ¿ 0.0 for H) and during hot conditions (KGE ¿ 0.2 for ET and KGE ¿-0.2 for H), while the state-of-the-art products show KGE ¿ 0.1 for ET and KGE ¿ -0.41 for H in summer and KGE ¿ -0.1 for ET and KGE ¿ -0.6 for H during hot conditions. All products evaluated here yield a reasonable performance (KGE ¿-0.41 at most sites) in simulating SM at the surface and in the root zone. Our results expose the need for further investigating and improving product performances during extreme conditions. The good performance of HOLAPS together with its inherent advantages (RS data driven, high temporal and spatial resolution, spatial and temporal continuity, soil moisture at different depths and long-term consistent evapotranspiration and sensible heat flux estimates) support its use for agricultural and forest management activities as well as to study land–atmosphere interactions based on Earth Observations.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114451"},"PeriodicalIF":11.1,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397647","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":"An accuracy assessment of the surface reflectance product from the EMIT imaging spectrometer","authors":"Red Willow Coleman ,&nbsp;David R. Thompson ,&nbsp;Philip G. Brodrick ,&nbsp;Eyal Ben Dor ,&nbsp;Evan Cox ,&nbsp;Carlos Pérez García-Pando ,&nbsp;Todd Hoefen ,&nbsp;Raymond F. Kokaly ,&nbsp;John M. Meyer ,&nbsp;Francisco Ochoa ,&nbsp;Gregory S. Okin ,&nbsp;Daniela Heller Pearlshtien ,&nbsp;Gregg Swayze ,&nbsp;Robert O. Green","doi":"10.1016/j.rse.2024.114450","DOIUrl":"10.1016/j.rse.2024.114450","url":null,"abstract":"<div><div>The Earth surface Mineral dust source InvesTigation (EMIT) is an imaging spectrometer launched to the International Space Station in July 2022 to measure the mineral composition of Earth’s dust-producing regions. We present a systematic accuracy assessment of the EMIT surface reflectance product in two parts. First, we characterize the surface reflectance product’s overall performance using multiple independent vicarious calibration field experiments with hand-held and automated field spectrometers. We find that the EMIT surface reflectance product has a standard error of <span><math><mrow><mo>±</mo><mn>1</mn><mo>.</mo><mn>0</mn></mrow></math></span>% in absolute reflectance units for temporally coincident observations. Discrepancies rise to <span><math><mrow><mo>±</mo><mn>2</mn><mo>.</mo><mn>7</mn></mrow></math></span> % for spectra acquired at different dates and times of day, which we attribute mainly to changes in solar geometry. Second, we develop an error budget that explains the differences between EMIT and in-situ field spectrometer data. We find that uncertainties in spatial footprints, field spectroscopy, and the EMIT-reported measurement were sufficient to explain discrepancies in most cases. Our approach did not detect any systematic calibration or reflectance errors in the timespan considered. Together, these findings demonstrate that a space-based imaging spectrometer can acquire high-quality spectra across a wide range of observational and atmospheric conditions.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114450"},"PeriodicalIF":11.1,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142383854","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}