Remote Sensing of Environment最新文献

{"title":"Refinement of gross dry matter productivity (GDMP) product from Copernicus Land Monitoring Service (CLMS): An ecophysiological assessment of Mediterranean Evergreen forests","authors":"Wafa Chebbi ,&nbsp;Eva Rubio ,&nbsp;Nikos Markos ,&nbsp;Dan Yakir ,&nbsp;Francisco Antonio García-Morote ,&nbsp;Manuela Andrés-Abellán ,&nbsp;Rocío Arquero-Escañuela ,&nbsp;Marta Isabel Picazo-Córdoba ,&nbsp;Eyal Rotenberg ,&nbsp;Kalliopi Radoglou ,&nbsp;Francisco Ramón López-Serrano","doi":"10.1016/j.rse.2025.114856","DOIUrl":"10.1016/j.rse.2025.114856","url":null,"abstract":"<div><div>The impacts of climate change pose significant challenges to global forest ecosystems, particularly in Mediterranean evergreen forests dominated by Aleppo pine (<em>Pinus halepensis</em> Mill.). Since the CLMS 10-daily Gross Dry Matter Productivity (GDMP) product represents the potential productivity and, by definition, does not account for water stress, this study aims to evaluate and improve the Gross Primary Productivity (GPP) estimation based on GDMP for these forests. To achieve this, we assessed the GDMP-derived GPP (<em>RS-GPP</em>_<em>st</em>) against eddy covariance GPP data (<em>EC-GPP</em>) from six Aleppo pine forest stands across the Mediterranean basin, covering 30 site-years and spanning a climatic gradient from semi-arid to semi-humid conditions. Additionally, we analyzed the ecophysiological response of Aleppo pine to drought, focusing on environmental factors such as temperature and water stress (i.e., atmospheric based on vapor pressure deficit and edaphic based on soil water content) to refine the GPP model. Our results indicated that the <em>RS-GPP</em>_<em>st</em> underestimates <em>EC-GPP</em> during cold periods. Using ERA5-Land data, we proposed a simplified approach to remove the temperature limitation factor and incorporated a soil water content factor, which significantly enhanced model accuracy, reduced uncertainty, and improved precision. The soil water-corrected GPP model achieved a Pearson correlation of <em>r</em> = 0.85, a negligible bias, and an RMSE of 1.1 gC m⁻² d⁻¹, providing a more accurate representation of GPP across varying climatic conditions. These findings highlight the importance of identifying and integrating key limiting environmental stressors, particularly water stress, into GPP models for water-limited ecosystems. The improved model, relying solely on remote sensing data without requiring in-situ measurements, offers a robust approach for large-scale carbon cycle monitoring.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114856"},"PeriodicalIF":11.1,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144278621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel hyperspectral index for quantifying chlorophyll-a concentration in productive waters","authors":"Huaxin Yao ,&nbsp;Junsheng Li ,&nbsp;Yaming Zhou ,&nbsp;Yao Liu ,&nbsp;Dalin Jiang ,&nbsp;Shoujing Yin ,&nbsp;Xuezhu Jiang ,&nbsp;Fangfang Zhang ,&nbsp;Shenglei Wang ,&nbsp;Bing Zhang","doi":"10.1016/j.rse.2025.114847","DOIUrl":"10.1016/j.rse.2025.114847","url":null,"abstract":"<div><div>Hyperspectral remote sensing has great potential for monitoring chlorophyll-a concentration (Chla) in optically complex waters. However, various hyperspectral indices currently used for retrieving Chla in productive waters exhibit drawbacks due to interference from other water parameters, imperfect atmospheric correction, and the constraints imposed by discrete spectral bands. To address these issues, we proposed a novel spectral index called the Red-Edge reflectance Peak Width Index (REPWI) for productive waters, which is defined as the horizontal wavelength distance from the fixed wavelength of red reflectance valley (678 nm) to the intersection of the right side of the red-edge reflectance peak curve. Firstly, the bio-optical model was used to clarify the theoretical basis and relationship between REPWI and Chla. Then, <em>in situ</em> remote sensing reflectance and Chla data from 57 water bodies worldwide were used to calibrate and validate the REPWI-based Chla retrieval model. The REPWI-based model achieved high accuracy (coefficient of determination (R²) of 0.92, mean relative error of 26.5 %, and root mean square error of 13.7 mg/m³), significantly outperforming other spectral indices. Furthermore, REPWI maintained a strong correlation with Chla (R² = 0.87–0.90) when applied to major on-orbit hyperspectral satellites equivalent spectra. Finally, the REPWI-based Chla retrieval model was applied to ZY1-02D hyperspectral imagery, which obtained high accuracy and demonstrated resistance to imperfect atmospheric correction. In summary, this novel hyperspectral index, REPWI, has demonstrated a solid theoretical foundation and significant advantages in monitoring Chla in productive waters.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114847"},"PeriodicalIF":11.1,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144268940","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":"On the hotspot-Sun angular offset in urban thermal anisotropy","authors":"Wenfeng Zhan ,&nbsp;Lu Jiang ,&nbsp;Guanwen Chen ,&nbsp;Jian Hang ,&nbsp;Pan Dong ,&nbsp;Shasha Wang ,&nbsp;Long Li ,&nbsp;Huilin Du","doi":"10.1016/j.rse.2025.114864","DOIUrl":"10.1016/j.rse.2025.114864","url":null,"abstract":"<div><div>The hotspot-sun angular offset (the spherical circular angle between hotspot and sun positions, termed ΔCA) in urban thermal anisotropy (UTA) plays a pivotal role in advancing remote sensing of urban climates. However, its diurnal and monthly variations across scenarios influenced by sun position, urban morphology, and thermal inertia remain largely unknown. Here we filled this knowledge gap based on 1049 UTA scenarios incorporating 12 urban surface models using both computer simulations and in-situ measurements. Our findings reveal ΔCA varies from 0° to 50° (predominantly 10° – 30°). Surface thermal inertia, urban morphology (i.e., building-street aspect ratio), and sun position collectively drive ΔCA variation, with their relative contributions exhibiting significant diurnal and monthly variabilities. In general, ΔCA is most pronounced around noon with relatively low thermal inertia (279–739 W·s^1/2·m⁻¹·K⁻¹) and low aspect ratio values (&lt; 1.5), leading to high ΔCA values exceeding 30°. Monthly ΔCA variations shows a bimodal pattern, with peaks (ΔCA is ∼25°) in April and August and a trough (ΔCA is ∼15°) from May to July around the summer solstice when the solar zenith angle (SZA) is relatively small. Hourly ΔCA variations exhibits sinusoidal variation during daytime, also characterized by a noon trough around the summer solstice. Troughs in diurnal/annual cycles are associated with small SZA (&lt; 20°) and high sunlit roof/ground temperatures that ensure hotspot-sun adjacency. Our results indicate pronounced ΔCAs under scenarios with lower thermal inertia, lower aspect ratio, and midday periods in May to August. Our findings could facilitate designing kernel-driven models for simulating UTA.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114864"},"PeriodicalIF":11.1,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144262143","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":"Methodology and potential applications of ice/snow surface temperature over polar regions using SDGSAT-1 satellite","authors":"Chenlie Shi ,&nbsp;Ninglian Wang ,&nbsp;Yuwei Wu ,&nbsp;Quan Zhang ,&nbsp;Zhenxiang Fang","doi":"10.1016/j.rse.2025.114868","DOIUrl":"10.1016/j.rse.2025.114868","url":null,"abstract":"<div><div>High spatial resolution Ice/snow Surface Temperature (IST) data provides prominent advantages for polar research, such as identification of sea ice lead, monitoring of surface melting on ice shelves and variations of polynyas. As the first satellite dedicated to sustainable development goals, SDGSAT-1 is equipped with 30 m thermal infrared bands, making it highly promising for monitoring fine scale process in polar regions. In this study, an optimal IST retrieval algorithm for SDGSAT-1 was selected from ten widely used Split-Window Algorithms (SWAs), with emphasis on two key criteria: low sensitivity to emissivity and high absolute retrieval accuracy. Sensitivity analysis identified four SWAs (PR1984, VI1991, UL1994, and Enter2019) exhibited low sensitivity to emissivity and sensor equivalent noise, and thereby for subsequent validation. Evaluation using simulated data showed that the overall uncertainty of four SWAs was less than 0.2 K, with PR1984 exhibiting a slight cold Bias of −0.16 K compared to the other three SWAs. Validation using in-situ IST data indicated that the overall uncertainty for four SWAs was less than 1.7 K, with a Bias of approximately −1 K, and PR1984 showed larger Bias and RMSE. Intercomparisons among the four SWAs and cross-validation with MODIS IST also demonstrated that PR1984 had a cold Bias compared to the other three algorithms, while VI1991, UL1994, and Enter2019 showed similar accuracy. Considering that Enter2019 has stability and low sensitivity to surface emissivity, high IST retrieval accuracy, and is widely applied and well recognized as the official land surface temperature retrieval algorithm for the VIIRS sensor, this study recommends Enter2019 as the optimal IST retrieval algorithm for SDGSAT-1. Additionally, three representative application cases—identification of sea ice leads, polynya monitoring, and extraction of geothermal springs, demonstrated the application capacity of SDGSAT-1 thermal infrared data in refined monitoring of polar ice/snow surface.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114868"},"PeriodicalIF":11.1,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144268941","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":"Challenges in remote sensing of night lights – a research agenda for the next decade","authors":"Noam Levin","doi":"10.1016/j.rse.2025.114869","DOIUrl":"10.1016/j.rse.2025.114869","url":null,"abstract":"<div><div>In recent years new sensors and products have been developed to advance our capabilities in assessing human activities based on the remote sensing of night lights. Correctly understanding patterns in human activity and land use based on night lights, is key to gauge our advancement in reaching the United Nations Sustainable Development Goals. In this paper I focus on five challenges in remote sensing of night lights. For each of these research challenges I provide a brief review of previous work, and then demonstrate how this challenge can be tackled, using a variety of ground-based sensors (TESS-4C, LANcube, SVC-HR1024 field spectrometer and webcams), UAV imagery, and spaceborne sensors (SDGSAT-1 and VIIRS/DNB). Challenge 1: Providing a cloud mask in future night light missions; Here I demonstrate that using a thermal night time image acquired simultaneously with the night time light image (as in the case for some of the SDGSAT-1 images), a cloud mask can be created, allowing to analyze night lights over cloud-free areas. Challenge 2: Monitoring hourly changes in night lights over a single night. Here I demonstrated that using mobile measurements conducted with a LANcube photometer at different hours, it was possible to detect locations where and when night lights have decreased during the night (e.g. a large sport stadium). In addition, using a citizen science webcam, I showed that hourly changes in night lights can be discerned even from a non-calibrated camera, and that within the city of Jerusalem, where there is a large population of orthodox Jews, certain land uses had less lights during holy days. Challenge 3: Fusing night time imagery from spaceborne sensors with different overpass times and spatial resolution to better understand hourly changes in night lights; merging SDGSAT-1 images (21:30 overpass) and VIIRS/DNB images (01:30 overpass) I was able to map the turning off of street lights across rural areas in France that accelerated since the 2022 energy crisis of Europe. Challenge 4: Estimating the emissions of blue light using night light sensors; here I demonstrated that the measured portion of blue light varies between ground based and spaceborne sensors, and that it is affected by atmospheric scattering; Challenge 5: Quantifying the anisotropy of night light emissions; here I demonstrated that using panoramic images acquired from a UAV at different heights, it is possible to examine how viewing angles affect the light sources that are observed. I conclude that while the number of designated night lights sensors is still limited, with the availability of new multispectral sensors, we can advance in our understanding of the dynamics of human activities at night-time, by fusing data from different sensors, to take advantage of the unique spatial, spectral, temporal and directional capabilities of each of them.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114869"},"PeriodicalIF":11.1,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144260412","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":"Arctic and Antarctic Surface Temperatures from AVHRR thermal Infrared satellite sensors 1982–2023","authors":"Wiebke Margitta Kolbe ,&nbsp;Gorm Dybkjær ,&nbsp;Rasmus T. Tonboe ,&nbsp;Steinar Eastwood ,&nbsp;Pia Nielsen-Englyst ,&nbsp;Jacob Høyer ,&nbsp;André Toft Jensen ,&nbsp;Magnus Barfod Suhr","doi":"10.1016/j.rse.2025.114816","DOIUrl":"10.1016/j.rse.2025.114816","url":null,"abstract":"&lt;div&gt;&lt;div&gt;42-years of Arctic and Antarctic Surface Temperatures from thermal Infrared satellite radiometers (AASTI) are presented as the Copernicus Climate Change Service Ice surface temperature record v1.1 dataset (C3S IST). It covers snow, ice and ocean surfaces with mean and max–min daily temperatures poleward of 50 degrees North and South, for the period 1982–2023. The C3S IST is provided as a Level 3 (L3) dataset in a polar 0.25 ° latitude and longitude grid. It consists of two parts: (1) the C3S IST climate data record (ISTCDR v1.1), covering the period 1. January 1982 to 30. June 2019, and (2) the C3S IST Interim CDR version 1.1 (ICDR v1.1) covering 1. July 2019 to 31. December 2023. The surface temperatures (STs) are calculated from satellite thermal infrared Brightness Temperature (&lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mi&gt;B&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;) measurements from the Global Area Coverage – Advanced Very High Resolution Radiometer (GAC - AVHRR) data, creating a comprehensive data set based solely on a single sensor type. The underlying AASTI algorithm is a combination of algorithms specifically tuned for sea ice, marginal ice zone, land ice and high latitude open water. In addition, each of the algorithm coefficients are tuned specifically for each of the AVHRR instruments, using simulated Top of the atmosphere (TOA) &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mi&gt;B&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;s and ERA-Interim reanalysis surface and atmosphere data. Simulated TOA &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mi&gt;B&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;’s are computed using the community radiative transfer model, RTTOV v12.3. Spatially and temporally varying uncertainties are computed for each data-point. The C3S IST surface temperatures were validated against different in situ observation types, where comparison against radiometric temperatures from flight campaigns and ice sheet station data resulted in smaller mean differences of 0.20&lt;span&gt;&lt;math&gt;&lt;mo&gt;°&lt;/mo&gt;&lt;/math&gt;&lt;/span&gt;C and -1.84&lt;span&gt;&lt;math&gt;&lt;mo&gt;°&lt;/mo&gt;&lt;/math&gt;&lt;/span&gt;C in the Northern Hemisphere (NH) than for validations against met station air temperatures, which were usually 1–3&lt;span&gt;&lt;math&gt;&lt;mo&gt;°&lt;/mo&gt;&lt;/math&gt;&lt;/span&gt;C higher. Surface temperature climatology and trends have been computed for sea ice and ice sheets, showing large regional differences in surface temperature trends within the NH. For the entire dataset, the average trend is +1.11 &lt;span&gt;&lt;math&gt;&lt;mo&gt;°&lt;/mo&gt;&lt;/math&gt;&lt;/span&gt;C/decade for the NH sea ice, +0.16 &lt;span&gt;&lt;math&gt;&lt;mo&gt;°&lt;/mo&gt;&lt;/math&gt;&lt;/span&gt;C/decade for the Southern Hemisphere (SH) sea ice, +0.38 &lt;span&gt;&lt;math&gt;&lt;mo&gt;°&lt;/mo&gt;&lt;/math&gt;&lt;/span&gt;C/decade for the Greenland ice sheet and -0.13 &lt;span&gt;&lt;math&gt;&lt;mo&gt;°&lt;/mo&gt;&lt;/math&gt;&lt;/span&gt;C/decade for the Antarctic ice sheet. The positive trends are typically small during summer and larger during winter, e.g. in the Barents Sea, where trends exceed +0.3 &lt;span&gt;&lt;math&gt;&lt;mo&gt;°&lt;/mo&gt;&lt;/math&gt;&lt;/span&gt;C/year in winter. Negative temperature trends are observed in some regions such as the Bering Strait’s ice edge. For Antarc","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114816"},"PeriodicalIF":11.1,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144262141","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":"CELNet: A comprehensive efficient learning network for atmospheric plume identification from remotely sensed methane concentration images","authors":"Fang Chen ,&nbsp;Robert J. Parker ,&nbsp;Harjinder Sembhi ,&nbsp;Ashiq Anjum ,&nbsp;Heiko Balzter","doi":"10.1016/j.rse.2025.114828","DOIUrl":"10.1016/j.rse.2025.114828","url":null,"abstract":"<div><div>Methane is an important greenhouse gas contributing to global warming and climate change. The effective identification of atmospheric plumes in spatial images of methane concentration data retrieved from remote sensing is a critical step in quantifying emissions and ultimately helping to mitigate climate change by reducing large methane emission sources. In this paper, we propose a comprehensive efficient learning network (CELNet) for atmospheric plume detection, which is constructed with several deep neural modules and detects the shape of plumes in methane concentration images effectively. Specifically, to conduct an efficient plume identification, a generative module is constructed, which is tasked to generate feature maps for the characterisation of potential plumes in remotely sensed methane concentration data. This helps to reduce the search space in the detection implementation. Methane plumes in remotely sensed image data normally exhibit complex morphological structures with high background noise, which can interfere with the delineation of the shapes of plumes. Thus, the generative module alone cannot guarantee an accurate identification. To conduct high quality methane plume delineation, an extractor module is introduced to extract features that intrinsically characterise methane plumes in remotely sensed image data. The extracted intrinsic features are encoded using an encoder module for compact representation, which convey important information for implementing a better methane plume delineation. In particular, to enhance the capability of the generative module for generating accurate features, we structurally pair it with a discriminative module. In the training process, the discriminative module takes the generated features and the intrinsic features as inputs and improves its capability to discriminate the generated features from the intrinsic ones, whereas the generative module strives to generate accurate features that the discriminative module is unable to identify. They thus build an adversarial game which is beneficial for enhancing the feature generation capability of the generative module during the training process. The generated features along with the intrinsic features are then fed into the decoder module to produce accurate methane plume detection maps, where the intrinsic features incorporated provide additional supervision information that enables the CELNet to perform a more effective methane plume identification. We validate the proposed technique with different types of remote sensing image datasets (e.g., Landsat 5, AVIRIS-NG), and the accuracy achieved by CELNet outperforms the other comparison methods over 6%. This highlights its applicability for different sourced images with high performance, making it valuable for remote sensing community.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114828"},"PeriodicalIF":11.1,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144262142","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 ocean surface current vector field from SAR doppler shift measurements","authors":"Shengren Fan ,&nbsp;Vladimir Kudryavtsev ,&nbsp;Yury Yurovsky ,&nbsp;Biao Zhang","doi":"10.1016/j.rse.2025.114855","DOIUrl":"10.1016/j.rse.2025.114855","url":null,"abstract":"<div><div>The Doppler shift observed by single-beam synthetic aperture radar (SAR) has been widely used to retrieve the radial velocity of ocean surface currents. However, operational marine forecasting centers require full surface current vector fields for data assimilation and forecast validation. To address this need, we propose a method to reconstruct the two-dimensional surface current vector from SAR Doppler shift measurements, under the assumption that ocean mesoscale currents are quasi-geostrophic. In this approach, the known range-directed current velocity derived from SAR Doppler shift measurements is used to estimate the azimuthal component based on the geostrophic approximation. The feasibility of the proposed method is preliminarily assessed by comparing the reconstructed surface current fields with global ocean analysis and forecast products from the Copernicus Marine Environment Monitoring Service (CMEMS). The results show a bias of 0.01 m/s and a root mean square error (RMSE) of 0.1 m/s. Additionally, surface current fields in the Gulf Stream and Agulhas Current regions are reconstructed using Sentinel-1A SAR Doppler shift observations and validated against collocated drifting buoy measurements, yielding a bias of 0.05 m/s and a RMSE of 0.19 m/s. These findings suggest that the potential of the proposed method for accurately reconstructing surface current fields from SAR Doppler measurements.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114855"},"PeriodicalIF":11.1,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144254182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A TCN-Transformer Parallel model for reconstruction of a global, daily, spatially seamless FY-3B soil moisture dataset","authors":"Qunming Wang ,&nbsp;Yanling You ,&nbsp;Haoxuan Yang ,&nbsp;Ronghan Xu ,&nbsp;Hankui K. Zhang ,&nbsp;Ping Lu ,&nbsp;Xiaohua Tong","doi":"10.1016/j.rse.2025.114841","DOIUrl":"10.1016/j.rse.2025.114841","url":null,"abstract":"<div><div>Soil moisture (SM) is a critical variable in land-atmosphere interactions. As an important passive microwave remote sensing dataset, the Fengyun-3B (FY-3B) SM has been applied in a variety of scientific studies and applications. However, due to the discontinuous coverage of satellite revisit orbits, the FY-3B SM contains a large range of data gaps, which greatly limit the applicability. To solve this problem, we proposed a one-dimensional deep learning network-based time series reconstruction model called TTP (Temporal Convolutional Network (TCN)-Transformer Parallel) model, which makes full use of the TCN to capture short-term dynamic changes and the Transformer to obtain long-term dependencies, thus, extracting local and global features of the one-dimensional time series simultaneously. Based on the proposed TTP model, a global, daily, spatially seamless FY-3B SM dataset from 12 July 2011 to 19 August 2019 was generated. The performance of TTP was examined using two types of experiments: 1) in-situ data validation (the in-situ data at the same location were served as the reference); 2) original FY-3B SM validation (gaps were simulated by randomly masking the observations, with the original FY-3B SM as the reference). The TTP-based global, daily, spatially seamless dataset presents great consistency with the in-situ data, with an average root mean square error (RMSE) of 0.0900 m³/m³. Additionally, the reconstructed FY-3B SM based on TTP is consistently more accurate than four benchmarks (i.e., the self-supervised learning for interpolation (SSLI), the multivariate time series imputation method (MITST), the ModernTCN, and the harmonic analysis of time series (HANTS)). Moreover, the reconstructed FY-3B SM demonstrates reliable accuracy across various missing data simulated with different proportions and continuous lengths. The TTP can provide methodological support for large-scale remote sensing data reconstruction, and the generated dataset can provide data support for research in fields such as soil science and hydrology. The dataset is publicly available at <span><span>https://doi.org/10.6084/m9.figshare.27957429.v45</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114841"},"PeriodicalIF":11.1,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144241367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A framework for mapping conservation agricultural fields using optical and radar time series imagery","authors":"Yue Zhou ,&nbsp;Manon S. Ferdinand ,&nbsp;Jelle van Wesemael ,&nbsp;Klara Dvorakova ,&nbsp;Philippe V. Baret ,&nbsp;Kristof Van Oost ,&nbsp;Bas van Wesemael","doi":"10.1016/j.rse.2025.114858","DOIUrl":"10.1016/j.rse.2025.114858","url":null,"abstract":"<div><div>The importance of conservation agriculture (CA) is undeniable, both for improving soil health and offering a viable path towards achieving carbon neutrality. However, to date, survey statistics on the extent of conservation agriculture were based on farmer declarations or field inspections. This is a major impediment to the promotion or monitoring of conservation agriculture. Here, we collected the management practices of a total of 247 fields under conservation agriculture in the Walloon region of Belgium in 2020–2021, with the aim of developing a classification model for the prediction of conservation agriculture by combining remotely sensed data with census data. We identified seven variables in the model, linked to each of the three main principles of conservation agriculture (crop diversification, maximum soil cover and minimum mechanical soil disturbance). The number of different annual crops and cereals in the rotation was obtained from the agricultural census. For the extent of soil cover, the Google Earth Engine (GEE) platform was used to obtain a time series of optical remote sensing images (2015–2020, Sentinel-2, Landsat-7, Landsat-8) and precipitation data. We then analyzed the variation of spectral indices such as the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Tillage Index (NDTI) and constructed indicators to distinguish between bare soil and cover crop. For minimum mechanical soil disturbance, in addition to the above data, radar data (Sentinel-1) were also obtained from the GEE platform to establish a tillage practice model. Subsequently, the Random Forest (RF) classification method was used to construct a classification model distinguishing fields under conservation from those under conventional practices. The results of a ten-fold cross-validation showed a good overall accuracy of 92 %. The model was utilized to classify the farming systems in all croplands of the Hesbaye region of Belgium. The results show that 15.5 % (2875 fields) out of 18,516 cropland fields can be classified as conservation agriculture. These fields tend to adopt non-inversion tillage and have diverse crop rotations.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114858"},"PeriodicalIF":11.1,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144241368","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}