Remote Sensing of Environment最新文献

{"title":"Large-scale diurnal responses of solar-induced chlorophyll fluorescence (SIF) to varying heat and water stresses: Implications for monitoring SIF variations from satellite observations","authors":"Dayang Zhao ,&nbsp;Zhaoying Zhang ,&nbsp;Yongguang Zhang","doi":"10.1016/j.rse.2025.114748","DOIUrl":"10.1016/j.rse.2025.114748","url":null,"abstract":"<div><div>Satellite observations of solar-induced chlorophyll fluorescence (SIF) offer a promising approach for monitoring plant heat and water stresses across spatial scales. Most studies have focused on seasonal responses of satellite-observed SIF and its physiological component, fluorescence efficiency (<span><math><msub><mi>Φ</mi><mi>f</mi></msub></math></span>), to heat and water stresses. However, their diurnal responses remain poorly understood. Besides, polar-orbiting satellites typically use a daily correction factor to upscale fixed-time SIF observations into daily averages (<span><math><msub><mi>SIF</mi><mtext>daily</mtext></msub></math></span>). Given the diurnal SIF variations, the reliability of this approach under stress conditions is uncertain. In this study, we used the ratio of SIF to near-infrared radiance of vegetation (NIRvR) as a a linear approximation of <span><math><msub><mi>Φ</mi><mi>f</mi></msub></math></span>. Then, we applied the eXtreme Gradient Boosting (XGBoost) algorithm to model hourly observations of summer (June–August) SIF and <span><math><msub><mi>Φ</mi><mi>f</mi></msub></math></span> from Orbiting Carbon Observatory-3 (OCO-3) data. Our modeling was constrained to mainland China from 2019 to 2022. We calculated anomalies in SIF and <span><math><msub><mi>Φ</mi><mi>f</mi></msub></math></span> at different times of the day and conducted linear regressions on them with daily air temperature or soil moisture anomalies. Our results showed that the responses of SIF and <span><math><msub><mi>Φ</mi><mi>f</mi></msub></math></span> to stresses varied significantly with time of day and vegetation type. On days experiencing high water and heat stresses, morning and afternoon SIF exhibited weaker declines than midday. In contrast, morning <span><math><msub><mi>Φ</mi><mi>f</mi></msub></math></span> generally exhibited stronger increases than midday, whereas afternoon <span><math><msub><mi>Φ</mi><mi>f</mi></msub></math></span> showed weaker increases. Such diurnal differences in SIF and <span><math><msub><mi>Φ</mi><mi>f</mi></msub></math></span> responses were more pronounced in forests than in grasslands and intensified with rising water and heat stress levels. Additionally, we found that morning polar-orbiting satellite SIF observations tended to overestimate <span><math><msub><mi>SIF</mi><mtext>daily</mtext></msub></math></span> changes, whereas midday observations tended to underestimate them. These biases also intensified with rising stress levels. Our findings emphasize the importance of diurnal satellite SIF observations in deepening our understanding of plant responses to water and heat stress, as well as in improving the monitoring of plant stresses.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"324 ","pages":"Article 114748"},"PeriodicalIF":11.1,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817289","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":"PARAN: A novel physics-assisted reconstruction adversarial network using geostationary satellite data to reconstruct hourly sea surface temperatures","authors":"Sihun Jung ,&nbsp;Jungho Im ,&nbsp;Daehyeon Han","doi":"10.1016/j.rse.2025.114749","DOIUrl":"10.1016/j.rse.2025.114749","url":null,"abstract":"<div><div>Diurnal variability in sea surface temperature (SST) significantly influences ocean–atmosphere thermal interactions. Conventional numerical methods for reconstructing hourly SST are limited by high computational demands and difficulties in accommodating real-time atmospheric and oceanic conditions. Therefore, this study introduces the Physics-Assisted Reconstruction Adversarial Network (PARAN), a novel deep-learning framework that reconstructs high-resolution (2 km), hourly SST using geostationary satellite data. By integrating numerical model knowledge and leveraging generative adversarial network architectures, PARAN achieves enhanced reconstruction performance. We applied the PARAN framework to the Northwest Pacific region, generating a continuous, hourly subskin SST layer from January 2019 to December 2021. The network was validated against high-resolution satellite data and in situ buoy observations, demonstrating substantial accuracy improvements over existing high-resolution numerical models (GLO12v4 and HYCOM). We obtained higher correlation coefficients (0.994 and 0.982), negligible biases (0.007 and −0.165), and lower root mean square errors (0.435 and 0.766) than those of existing models when comparing PARAN with drifting and mooring buoys, respectively. In particular, PARAN effectively captured detailed spatiotemporal SST variations, including diurnal warming, and showed robustness under both clear and cloudy conditions.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"323 ","pages":"Article 114749"},"PeriodicalIF":11.1,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143806298","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":"High spatial-resolution satellite mapping of suspended particulate matter in global coastal waters using particle composition-adaptive algorithms","authors":"Wenxiu Teng ,&nbsp;Qian Yu ,&nbsp;Dariusz Stramski ,&nbsp;Rick A. Reynolds ,&nbsp;Jonathan D. Woodruff ,&nbsp;Brian Yellen","doi":"10.1016/j.rse.2025.114745","DOIUrl":"10.1016/j.rse.2025.114745","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Delivery of suspended particles, referred hereafter also to as suspended sediment, to coastal zones plays a first order control on the development and maintenance of muddy geomorphic features like river deltas, mudflats, and tidal wetlands. While sediment delivery from rivers is relatively straightforward to monitor and has been well studied, suspended sediment derived from erosion of coastal bluffs and resuspension of shallow subtidal sediments remains poorly constrained. Estimates of the concentration of suspended particulate matter (SPM) provide one of the best remotely sensed metrics for suspended sediment supply to the coast. Spaceborne ocean color sensors with coarse spatial resolution (∼1 km pixel size at nadir) are generally inadequate to resolve smaller-scale sediment dynamics in coastal waters and additionally there is a limitation associated with adjacency effect of 1-km land pixels on near-shore water pixels. In contrast, satellites dedicated primarily to land observations with a smaller pixel size (∼30 m) provide more adequate spatial resolution for observations of coastal waters. This paper presents a particle composition adaptive algorithm for retrieving SPM from ocean remote-sensing reflectance, &lt;em&gt;R&lt;/em&gt;&lt;sub&gt;rs&lt;/sub&gt;(λ), in coastal waters which is applicable to most land observation satellites. For the algorithm development, we compiled more than 800 paired in situ spectral reflectance and SPM measurements from 12 marine sites worldwide, representing a wide range of suspended particle concentration and composition. We first classify the satellite image data into three water types: organic-rich, mineral-rich, or extremely mineral-rich based on the POC/SPM ratio that is derived from &lt;em&gt;R&lt;/em&gt;&lt;sub&gt;rs&lt;/sub&gt;(λ). The ratio of particulate organic carbon (POC) to SPM serves as a particle composition metric. Then, SPM is estimated from &lt;em&gt;R&lt;/em&gt;&lt;sub&gt;rs&lt;/sub&gt;(λ) using a particle composition-specific algorithm which employs the reflectance at red band for organic-rich waters and near-infrared (NIR) for mineral-rich waters. We compared the performance of this algorithm with eight previously published SPM algorithms, including empirical, semi-analytical, and machine learning approaches. Results show that our algorithm produces reliable SPM estimation with coefficient of determination (&lt;em&gt;R&lt;/em&gt;&lt;sup&gt;2&lt;/sup&gt;), root mean square error (RMSE in log space), and median absolute percent error (MAPE) of 0.91, 0.20, and 30.5 %, respectively. To examine the capability of our algorithm to study the long-term variability in coastal SPM at high spatial resolution, we implemented the algorithm to the 40-year Landsat data archive in Google Earth Engine (GEE). The Landsat mapping results of SPM were validated using both the satellite-in situ matchups of SPM data as well as in situ water turbidity measurements. Finally, we demonstrate a few scenarios of fine-scale SPM patterns as well as seasonal and long-term variability across different marine ","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"323 ","pages":""},"PeriodicalIF":11.1,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143784970","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":"One-step retrieval of water-quality parameters from satellite top-of-atmosphere measurements","authors":"Hanyang Qiao ,&nbsp;Zhongping Lee ,&nbsp;Daosheng Wang ,&nbsp;Zhihuang Zheng ,&nbsp;Xiaomin Ye ,&nbsp;Changyong Dou","doi":"10.1016/j.rse.2025.114709","DOIUrl":"10.1016/j.rse.2025.114709","url":null,"abstract":"<div><div>Traditionally, deriving water-quality parameters from satellite ocean color measurements involves multiple complex steps, including in-orbit radiometric calibration, atmospheric correction, and water property retrieval, all of which require substantial resources and effort. Given the abundance of water-quality related products provided by established ocean color satellite missions, we propose a scheme (DN2WP) to estimate water-quality parameters directly from the digital number (DN) data obtained at satellite altitude. Using Secchi Disk Depth (Z_SD) as an example and the Z_SD data from Sentinel-3 as a reference, HiSea-II DN data were converted to Z_SD directly by DN2WP. In the training phase, the coefficient of determination (R²) for Z_SD reached 0.95, with a mean absolute percentage difference (MAPD) of ∼10 %. When applied to new datasets, the R² for Z_SD exceeded 0.8, and the MAPD remained within 20 %. The DN2WP scheme was further tested using the multispectral sensors of the CZI on board the HY-1C satellite and MII on the SDGSAT-1 satellite, respectively, and a high level of performance was maintained by achieving an R² of 0.83 (0.84) with MAPD as 19.8 % (15.8 %) for CZI (SDGSAT-1) on independent data. These results are significantly better than the consistency measure of Z_SD between such satellites and Sentinel-3A/3B obtained individually through the traditional multi-step approach. It indicates that DN2WP is an efficient approach to get water-quality parameters from top-of-atmosphere DN data measured by a satellite sensor, which also results in much better consistency across different satellite missions.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"323 ","pages":""},"PeriodicalIF":11.1,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143785208","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":"Mapping tree species fractions in temperate mixed forests using Sentinel-2 time series and synthetically mixed training data","authors":"David Klehr ,&nbsp;Johannes Stoffels ,&nbsp;Andreas Hill ,&nbsp;Vu-Dong Pham ,&nbsp;Sebastian van der Linden ,&nbsp;David Frantz","doi":"10.1016/j.rse.2025.114740","DOIUrl":"10.1016/j.rse.2025.114740","url":null,"abstract":"<div><div>Monitoring and mapping of forest stands, including tree species composition can support forest protection and management. Sentinel-2 imagery provide a viable data source due to their high spectral, temporal, and spatial resolution. However, especially in temperate mixed forests challenges with tree species classification persist, mainly due to the high mixing ratio of tree species, which cannot be fully resolved even with the 10 m resolution of Sentinel-2 data. Additional challenges are associated with the commonly low number of reference data for rare tree species, resulting in low classification accuracy for these species. This study proposes an approach to map sub-pixel tree species fractions in mixed temperate forests by combining dense annual multi-spectral Sentinel-2 time series to target differences between species in phenological relevant periods with synthetically mixed training data. This allows for a limited number of pure training samples per tree species, which serves as basis for randomized linear mixing to compute a synthetic spectral library. An artificial neural network is trained for regression for tree species fractions per pixel. To enhance model robustness and stabilize predictions, we implemented this library generation and artificial neural network regression as an ensemble approach. We effectively mapped tree species fractions for the federal state of Rhineland-Palatinate, Germany, with Mean Absolute Errors of 2.76% to 16.05% and <em>R</em>^<em>2</em> values up to 0.92 – when validated against forest planning data. We show that the data augmentation through synthetic mixing allows for a sample size as small as 30 pure pixels per class, to sufficiently distinguish nine tree species and one ‘other species’ class, hence substantially increasing the operational potential for deployment when reference data for rare species are limited – while simultaneously generating accurate and information-rich tree species distributions over large areas of mixed forest.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"323 ","pages":""},"PeriodicalIF":11.1,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143784969","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":"New 30-m resolution dataset reveals declining soil erosion with regional increases across Chinese mainland (1990–2022)","authors":"Jining Yan ,&nbsp;Sheng Wang ,&nbsp;Jiaxin Feng ,&nbsp;Haixu He ,&nbsp;Lizhe Wang ,&nbsp;Ziyong Sun ,&nbsp;Chunmiao Zheng","doi":"10.1016/j.rse.2025.114681","DOIUrl":"10.1016/j.rse.2025.114681","url":null,"abstract":"<div><div>Hydraulic erosion-induced soil loss critically undermines soil fertility, leading to vegetation degradation, reduced crop yields, and increased flood and pollution risks, thereby threatening food security, ecological integrity, and economic stability. Existing soil erosion estimations using the Revised Universal Soil Loss Equation (RUSLE) model are constrained by the absence of high-resolution (30 <span><math><mi>m</mi></math></span>) nationwide rainfall data, significant spatial and temporal inconsistencies among multi-source data, and limited regional adaptability of empirical RUSLE parameters. To address these challenges, we developed the first 30-<span><math><mi>m</mi></math></span> resolution Hydraulic Soil Erosion Dataset (CSWED) for Chinese mainland, spanning from 1990 to 2022. This was achieved by implementing a Random Forest (RF) interpolation method to generate 30-<span><math><mi>m</mi></math></span> annual rainfall data and designing a multi-equation combined vegetation cover management factor tailored to China’s diverse soil characteristics. The mean absolute error (MAE) compared to the China Soil and Water Conservation Bulletin was 9.48%, and the CSWED illustrates good consistency with collected runoff plot observation data, significantly surpassing the accuracy of existing publicly available datasets. Our analysis reveals a predominant influence of slope length, steepness, and support practice factors on soil erosion, coupled with a synergistic effect of vegetation cover and topography. Notably, soil erosion in Chinese mainland has exhibited an overall decreasing trend from 1990 to 2022, with significant reductions in light, moderate, and severe erosion categories by 31.70%, 22.24%, and 44.74%, respectively. Also, the sensitivity of soil erosion in 2022 was dominated by strong sensitivity and moderate sensitivity, accounting for 74.149 % and 14.072 %. Then, the decrease in cropland area creates a decrease in soil erosion of 123.54 <span><math><msup><mrow><mi>t/hm</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> from 1990 to 2022, while for every additional hectare of forested area, soil erosion was reduced by 58.67 <span><math><mi>t</mi></math></span>. Special attention should be given to the substantial expansion of soil erosion brought on by extreme rainfall in the Middle and Lower Yangtze River (MLYR) and Southwest China (SWC), where mean soil erosion was 11.71 <span><math><mrow><mi>t</mi><mo>/</mo><mrow><mo>(</mo><msup><mrow><mi>hm</mi></mrow><mrow><mn>2</mn></mrow></msup><mspace></mspace><mi>a</mi><mo>)</mo></mrow></mrow></math></span> and 15.79 <span><math><mrow><mi>t</mi><mo>/</mo><mrow><mo>(</mo><msup><mrow><mi>hm</mi></mrow><mrow><mn>2</mn></mrow></msup><mspace></mspace><mi>a</mi><mo>)</mo></mrow></mrow></math></span>, respectively, accounting for 60% of the national total.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"323 ","pages":"Article 114681"},"PeriodicalIF":11.1,"publicationDate":"2025-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783550","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":"Towards resolution enhancement of P-band brightness temperature data using passive-passive downscaling with L-band radiometer data","authors":"Luisa F. White-Murillo ,&nbsp;Jeffrey P. Walker ,&nbsp;Nan Ye ,&nbsp;James Hills ,&nbsp;Xiaoling Wu ,&nbsp;Lixiaozhou Zhou ,&nbsp;Ziwei Xiong ,&nbsp;Liujun Zhu ,&nbsp;Brian Ng ,&nbsp;Mahta Moghaddam ,&nbsp;Simon Yueh","doi":"10.1016/j.rse.2025.114737","DOIUrl":"10.1016/j.rse.2025.114737","url":null,"abstract":"<div><div>Current space-borne L-band radiometers have been routinely providing global soil moisture maps every 2 to 3 days for more than 15 years. However, they can only estimate soil moisture in the top 5 cm of the soil, which limits its usefulness in applications that need deep layer soil moisture. Fortuitously, recent work has shown that there is a potential to retrieve root zone soil moisture (RZSM) when combining P-band radiometer data with L-band radiometer data at the same spatial resolution. Nevertheless, there are antenna size limitations that currently restrict L-band radiometer data to a resolution of 40 km. Accordingly, if the same antenna is used for a joint P-band (750 MHz) and L-band (1.4GHz) satellite mission, the footprint size at P-band will be double that attained at L-band, limiting its usefulness in applications and making joint interpretation difficult. It is therefore essential to downscale the P-band radiometer measurements to at least the same resolution as the L-band measurements. Consequently, this paper has explored three alternative passive-passive P-band downscaling algorithms using the spatial information that exists in the L-band radiometer data, to achieve P-band brightness temperature (Tb) information at the same resolution as the L-band passive measurements. Analysis was conducted using data from three airborne campaigns in south-eastern Australia, with resolutions ranging from 36 km to 200 m under a range of moisture conditions and spatial characteristics. The three alternate downscaling algorithms used in this paper, designated as Algorithm A, B, and C, were not only compared to determine which generated the best performance, but were also analysed according to different land cover types and seasons. The results demonstrated that the Smoothing Filter-based Intensity Modulation (SFIM) method, referred to as Algorithm A, outperformed the others, with a median root mean square error (RMSE) compared to the original observations of 3 K when downscaling to half of the original spatial resolution, increasing to around 8 K when downscaling to 36 times finer than the original resolution.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"323 ","pages":"Article 114737"},"PeriodicalIF":11.1,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143768819","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":"Advancing hourly gross primary productivity mapping over East Asia using Himawari-8 AHI and artificial intelligence: Unveiling the impact of aerosol-induced radiation dynamics","authors":"Sejeong Bae ,&nbsp;Bokyung Son ,&nbsp;Taejun Sung ,&nbsp;Yoojin Kang ,&nbsp;Jungho Im","doi":"10.1016/j.rse.2025.114735","DOIUrl":"10.1016/j.rse.2025.114735","url":null,"abstract":"<div><div>Advancements in geostationary satellites allow monitoring of terrestrial photosynthesis at sub-daily scales, offering unprecedented opportunities for understanding vegetation productivity. However, at short temporal scales, photosynthesis is highly influenced by illumination conditions, particularly diffuse radiation (D_dif). Existing empirical models often overlook D_dif's impact, leading to uncertainties in hourly gross primary productivity (GPP) mapping. We determined that incorporating D_dif effects into GPP modeling improves clear-sky GPP mapping using Himawari-8. We employed a light gradient boosting machine (LGBM) considering D_dif and compared it with other empirical models: parametric, regression, and machine learning. The LGBM outperformed others, achieving an R² of 0.8146 and root mean square error of 2.848 μmol CO₂/m²/s against ground observations from 2020 to 2021. To investigate input variable contributions in LGBM predictions, we performed SHapely Additive exPlanation (SHAP) analysis. Results confirmed that aerosol optical depth (AOD) had a greater impact during morning and evening when D_dif influence increased due to solar path length. Hourly GPP maps over East Asia from 2020 to 2021 using the LGBM demonstrated that diurnal patterns differ by landcover, with variations observed in latitudinal profiles. This underscores the need to examine GPP spatial distribution at high frequency. We confirmed that the spatial distribution of AOD SHAP values varied over time, highlighting temporal dynamics of aerosol effects. Our findings demonstrate the necessity of GPP mapping using geostationary satellites and suggest various impact studies can use our proposed framework. This approach provides a valuable tool for understanding vegetation's rapid response to atmospheric aerosols, contributing to more accurate ecosystem flux modeling.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"323 ","pages":"Article 114735"},"PeriodicalIF":11.1,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143768818","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":"Net primary production in the Labrador Sea between 2014 and 2022 derived from ocean colour remote sensing based on ecological regimes","authors":"E. Devred ,&nbsp;S. Clay ,&nbsp;M. Ringuette ,&nbsp;T. Perry ,&nbsp;M. Amirian ,&nbsp;A. Irwin ,&nbsp;Z. Finkel","doi":"10.1016/j.rse.2025.114713","DOIUrl":"10.1016/j.rse.2025.114713","url":null,"abstract":"<div><div>Phytoplankton play a major role in carbon export and storage in the ocean interior through remineralization of particulate carbon into dissolved inorganic carbon (DIC), and represent the “gain” side of the biological carbon pump. Shifts in phytoplankton community structure and species succession impact primary production, quality of food for zooplankton consumers and the fate of organic matter in marine ecosystems.</div><div>In the Labrador Sea (LS), a sub-arctic environment, the emergence of large blooms of <em>Phaeocystis</em> spp. in spring at the expenses of diatoms may disrupt phytoplankton species succession with drastic consequences on the carbon cycle and the functioning of the marine ecosystem as these small flagellates aggregate in colonies of up to several millimeters, embedded in gelatinous matrices, that modify elemental stoichiometry, sinking rates and transfer of energy to higher trophic levels. In this study, we develop an ecological approach to estimate primary production due to <em>Phaeocystis</em> sp. in the LS from satellite remote sensing data. We used a regionally-tuned primary production model to assign phytoplankton photosynthesis efficiency as a function of oceanographic regimes defined by phytoplankton community structure and biomass, and sea-surface temperature. We found that four oceanographic regimes corresponded to broad phytoplankton taxonomic assemblages and environmental factors in the LS: the diatom-dominated Shelf, the low chlorophyll-a Basin, the mesotrophic Basin regimes and a last oceanographic regime within the Basin, where the flagellated prymnesiophyte <em>Phaeocystis</em> spp. likely dominated the assemblage. Annual primary production in the Labrador Sea varied between 200 and 300 Tg of carbon between 2014 and 2022 in agreement with previous studies. While <em>Phaeocystis</em> spp. contributed about 10 % of the annual production, two unusual blooms that occurred in 2015 and 2022 contributed about 14 and 20 % of total production, respectively. During these two events <em>Phaeocystis</em> sp. contributed 40 % and 60 % to the May production and extended over more than half the Labrador Sea. Spring blooms dominated by <em>Phaeocystis</em> may occur more frequently due to climate change and have the potential to impact the fate of carbon and alter the functioning of the LS ecosystem.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"323 ","pages":"Article 114713"},"PeriodicalIF":11.1,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759902","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":"Impact of data density and endmember definitions on long-term trends in ground cover fractions across European grasslands","authors":"Katarzyna Ewa Lewińska ,&nbsp;Akpona Okujeni ,&nbsp;Katja Kowalski ,&nbsp;Fabian Lehmann ,&nbsp;Volker C. Radeloff ,&nbsp;Ulf Leser ,&nbsp;Patrick Hostert","doi":"10.1016/j.rse.2025.114736","DOIUrl":"10.1016/j.rse.2025.114736","url":null,"abstract":"<div><div>Long-term monitoring of grasslands is pivotal for ensuring continuity of many environmental services and for supporting food security and environmental modeling. Remote sensing provides an irreplaceable source of information for studying changes in grasslands. Specifically, Spectral Mixture Analysis (SMA) allows for quantification of physically meaningful ground cover fractions of grassland ecosystems (i.e., green vegetation, non-photosynthetic vegetation, and soil), which is crucial for our understanding of change processes and their drivers. However, although popular due to straightforward implementation and low computational cost, ‘classical’ SMA relies on a single endmember definition for each targeted ground cover component, thus offering limited suitability and generalization capability for heterogeneous landscapes. Furthermore, the impact of irregular data density on SMA-based long-term trends in grassland ground cover has also not yet been critically addressed.</div><div>We conducted a systematic assessment of i) the impact of data density on long-term trends in ground cover fractions in grasslands; and ii) the effect of endmember definition used in ‘classical’ SMA on pixel- and map-level trends of grassland ground cover fractions. We performed our study for 13 sites across European grasslands and derived the trends based on the Cumulative Endmember Fractions calculated from monthly composites. We compared three different data density scenarios, i.e., 1984–2021 Landsat data record as is, 1984–2021 Landsat data record with the monthly probability of data after 2014 adjusted to the pre-2014 levels, and the combined 1984–2021 Landsat and 2015–2021 Sentinel-2 datasets. For each site we ran SMA using a selection of site-specific and generalized endmembers, and compared the pixel- and map-level trends. Our results indicated no significant impact of varying data density on the long-term trends from Cumulative Endmember Fractions in European grasslands. Conversely, the use of different endmember definitions led in some regions to significantly different pixel- and map-level long-term trends raising questions about the suitability of the ‘classical’ SMA for complex landscapes and large territories. Therefore, we caution against using the ‘classical’ SMA for remote-sensing-based applications across broader scales or in heterogenous landscapes, particularly for trend analyses, as the results may lead to erroneous conclusions.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"323 ","pages":"Article 114736"},"PeriodicalIF":11.1,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759903","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}