Pub Date : 2026-03-01Epub Date: 2025-12-26DOI: 10.1016/j.rse.2025.115219
Yue Xu , Frédéric Frappart , Guoqiang Tang , Guoqing Zhang , Peirong Lin , Liguang Jiang , Simon Papalexiou , Fangfang Yao , Xiaoran Han , Jun Xia
The water surface elevation (WSE) of rivers serves as fundamental data for various hydrological research and applications. The recently launched Surface Water and Ocean Topography (SWOT) satellite offers a revolutionary altimetry approach by providing wide-swath elevation mapping using a SAR Interferometer (InSAR) operating at Ka-band. While SWOT provides unprecedented spatio-temporal coverage of WSE, it has not been systematically compared with reference water stage databases. Currently, due to difficulties in accessing recent and globally homogenous gauge station records, established WSE derived from radar altimetry (RA) missions is the most suitable dataset to perform global validation of WSE. This study presents the first global-scale intercomparison of the two altimetry systems, the wide-swath InSAR technique used for the first time by SWOT and the classical along-track RA using the SAR technique, and identifies several representative factors influencing their consistency. SWOT WSE are compared with virtual stations derived from Sentinel-3 and Sentinel-6 missions, across five different node quality categories (“good”, “suspect”, “degraded”, “bad” and a combined “all” group without “bad” data). The analysis further examines the potential influences from river width, river ice, backscattering coefficients (sigma0), and dark water fraction in modulating data consistency. The root mean square error (and correlation coefficient) between WSE from SWOT and RA in “good” and “suspect” data are 0.80 m (0.85) and 1.62 m (0.78), respectively, while those for “degraded” and “bad” data rise significantly to 8.80 m (0.60) and 16.91 m (0.50). The combined “all” category yields an overall RMSE (CC) of 5.15 m (0.65). For rivers wider than 160 m, SWOT measurements with “good” and “suspect” quality demonstrate notably improved consistency with RA compared to narrower rivers. Under frozen conditions, the reduced consistency between SWOT and RA is most evident in the “degraded” and “bad” quality data, with average reductions in CC of 0.17 and 0.21, respectively. In addition, radar backscatter strongly impacts the quality of SWOT-based WSE, as both extremely low values (dark water) and very high values (specular ringing) can lead to unrealistic estimates. Overall, this study offers important insights into the global performance of SWOT-based WSE estimation and informs the future refinement and application of SWOT data in hydrological research.
{"title":"A global intercomparison of SWOT and traditional nadir radar altimetry for monitoring river water surface elevation","authors":"Yue Xu , Frédéric Frappart , Guoqiang Tang , Guoqing Zhang , Peirong Lin , Liguang Jiang , Simon Papalexiou , Fangfang Yao , Xiaoran Han , Jun Xia","doi":"10.1016/j.rse.2025.115219","DOIUrl":"10.1016/j.rse.2025.115219","url":null,"abstract":"<div><div>The water surface elevation (WSE) of rivers serves as fundamental data for various hydrological research and applications. The recently launched Surface Water and Ocean Topography (SWOT) satellite offers a revolutionary altimetry approach by providing wide-swath elevation mapping using a SAR Interferometer (InSAR) operating at Ka-band. While SWOT provides unprecedented spatio-temporal coverage of WSE, it has not been systematically compared with reference water stage databases. Currently, due to difficulties in accessing recent and globally homogenous gauge station records, established WSE derived from radar altimetry (RA) missions is the most suitable dataset to perform global validation of WSE. This study presents the first global-scale intercomparison of the two altimetry systems, the wide-swath InSAR technique used for the first time by SWOT and the classical along-track RA using the SAR technique, and identifies several representative factors influencing their consistency. SWOT WSE are compared with virtual stations derived from Sentinel-3 and Sentinel-6 missions, across five different node quality categories (“good”, “suspect”, “degraded”, “bad” and a combined “all” group without “bad” data). The analysis further examines the potential influences from river width, river ice, backscattering coefficients (sigma0), and dark water fraction in modulating data consistency. The root mean square error (and correlation coefficient) between WSE from SWOT and RA in “good” and “suspect” data are 0.80 m (0.85) and 1.62 m (0.78), respectively, while those for “degraded” and “bad” data rise significantly to 8.80 m (0.60) and 16.91 m (0.50). The combined “all” category yields an overall <em>RMSE</em> (<em>CC</em>) of 5.15 m (0.65). For rivers wider than 160 m, SWOT measurements with “good” and “suspect” quality demonstrate notably improved consistency with RA compared to narrower rivers. Under frozen conditions, the reduced consistency between SWOT and RA is most evident in the “degraded” and “bad” quality data, with average reductions in <em>CC</em> of 0.17 and 0.21, respectively. In addition, radar backscatter strongly impacts the quality of SWOT-based WSE, as both extremely low values (dark water) and very high values (specular ringing) can lead to unrealistic estimates. Overall, this study offers important insights into the global performance of SWOT-based WSE estimation and informs the future refinement and application of SWOT data in hydrological research.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115219"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145836211","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}
Pub Date : 2026-03-01Epub Date: 2025-12-10DOI: 10.1016/j.rse.2025.115189
Jiaming Wu , Yaxin Wang , Liang Hong , Bin Sun , Zhenping He , Zejiang Li , Zhijie Ma
Precise estimation of shrub above-ground biomass (AGB) in arid regions is crucial for carbon cycle research and ecosystem assessment. Unmanned aerial vehicle (UAV) -borne light detection and ranging (LiDAR) has become a key tool for quantifying three-dimensional vegetation structure and estimating AGB. However, the short stature of arid zone vegetation, combined with sparse and low-quality point clouds acquired by UAV, limits high-accuracy shrub AGB estimation. To address this issue, this study selected Caragana korshinskii, a typical psammophytic shrub in Ordos City, as the research object. By integrating UAV-based multispectral and LiDAR data, a biomass estimation method based on a novel Shrub Structure Index (SSI) was proposed. The SSI workflow reconstructs the three-dimensional shrub structure under sparse point cloud conditions and improves AGB estimation accuracy. This workflow comprises Object-based image analysis (OBIA) classification for individual shrub extraction, Delaunay linear up-sampling, voxel-based partitioning, and dynamic stratification by height percentiles. Experimental results demonstrate that: (1) The individual shrub extraction method utilizing the large-scale mean shift (LSMS) segmentation algorithm and support vector machine (SVM) classification achieved a total quadrat segmentation accuracy of over 90.61 %, an overall classification accuracy of 91.51 % (Kappa = 0.86). (2) In SSI construction, the height-percentile stratification thickness, point-cloud sampling, and voxel edge length together set Caragana korshinskii stratification accuracy and density scale; the 5 % height percentile interval, a sampling size of 100 points, and 0.04 m voxel edge length proved optimal. (3) Comparative experiments showed that the three-dimensional feature integrated SSI significantly outperformed single-feature, two-feature, traditional allometric equation, and random forest (RF) models, with the SSI-based model achieving R2, RMSE, MAE, and rRMSE of 0.90, 529.01 g, 432.58 g, and 26.54 %, respectively. These results indicate that SSI more effectively captures shrub spatial structure and improves AGB prediction under sparse UAV-LiDAR conditions.
{"title":"A novel UAV lidar-derived shrub structural index for estimating above-ground biomass","authors":"Jiaming Wu , Yaxin Wang , Liang Hong , Bin Sun , Zhenping He , Zejiang Li , Zhijie Ma","doi":"10.1016/j.rse.2025.115189","DOIUrl":"10.1016/j.rse.2025.115189","url":null,"abstract":"<div><div>Precise estimation of shrub above-ground biomass (AGB) in arid regions is crucial for carbon cycle research and ecosystem assessment. Unmanned aerial vehicle (UAV) -borne light detection and ranging (LiDAR) has become a key tool for quantifying three-dimensional vegetation structure and estimating AGB. However, the short stature of arid zone vegetation, combined with sparse and low-quality point clouds acquired by UAV, limits high-accuracy shrub AGB estimation. To address this issue, this study selected <em>Caragana korshinskii</em>, a typical psammophytic shrub in Ordos City, as the research object. By integrating UAV-based multispectral and LiDAR data, a biomass estimation method based on a novel Shrub Structure Index (SSI) was proposed. The SSI workflow reconstructs the three-dimensional shrub structure under sparse point cloud conditions and improves AGB estimation accuracy. This workflow comprises Object-based image analysis (OBIA) classification for individual shrub extraction, Delaunay linear up-sampling, voxel-based partitioning, and dynamic stratification by height percentiles. Experimental results demonstrate that: (1) The individual shrub extraction method utilizing the large-scale mean shift (LSMS) segmentation algorithm and support vector machine (SVM) classification achieved a total quadrat segmentation accuracy of over 90.61 %, an overall classification accuracy of 91.51 % (Kappa = 0.86). (2) In SSI construction, the height-percentile stratification thickness, point-cloud sampling, and voxel edge length together set <em>Caragana korshinskii</em> stratification accuracy and density scale; the 5 % height percentile interval, a sampling size of 100 points, and 0.04 m voxel edge length proved optimal. (3) Comparative experiments showed that the three-dimensional feature integrated SSI significantly outperformed single-feature, two-feature, traditional allometric equation, and random forest (RF) models, with the SSI-based model achieving R<sup>2</sup>, RMSE, MAE, and rRMSE of 0.90, 529.01 g, 432.58 g, and 26.54 %, respectively. These results indicate that SSI more effectively captures shrub spatial structure and improves AGB prediction under sparse UAV-LiDAR conditions.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115189"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729070","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}
Pub Date : 2026-03-01Epub Date: 2026-01-02DOI: 10.1016/j.rse.2025.115227
Yuan Qi , Bo Huang , Min Zhao , Xiaolu Jiang , Wenfei Mao
High spatiotemporal resolution land surface temperature (LST) is essential for climate-impact studies, particularly for urban thermal environment analyses and vegetation phenology tracking. However, current satellite sensors exhibit inherent hardware trade-offs: Sentinel-2 (S2) provides high-resolution (10 m/5 day) optical observations without thermal capability, whereas Landsat-8/9 (L8/9), equipped with both optical and thermal sensors, suffers from coarser resolution (30 m/16 day). This configuration fails to meet the demand for fine spatial and dense temporal surface thermal monitoring. To address this gap, we propose a novel fusion framework for high spatiotemporal resolution LST generation (HiSTR-LST). It first applies a deep-learning-based spatiotemporal fusion to densify reflectance data, then performs a spatial-spectral fusion to generate high-resolution LST. By synergizing L8/9 and S2 data, our approach reliably produces 10 m spatial-resolution LST across three overpass scenarios (joint, L8/9-only, S2-only), thus achieving an effective ∼3-day temporal resolution. Cross-validations between upscaled LST predictions and native L8/9 LST demonstrate HiSTR-LST's robust performance across eight study areas worldwide (mean R = 0.90, RMSE = 1.17 K). More significantly, ground-truth validation—previously unaddressed—confirms its satisfactory accuracy (mean R = 0.97, RMSE = 3.45 K). The combined validation shows that HiSTR-LST outperforms the state-of-the-art by 13 % in R and reduces RMSE by 9 %. Finally, we illustrate two applications—small-area vegetation-phenology tracking and fine-scale urban thermal-pattern delineation—which collectively showcase HiSTR-LST's capability to resolve subtle surface thermal variations. Our study bridges a critical gap in generating high spatiotemporal resolution LST from satellite imagery, a capability crucial for investigating the nuanced effects of global warming.
{"title":"Bridging the thermal gap: Generating 10 m, 3-day land surface temperature via Landsat–Sentinel-2 fusion","authors":"Yuan Qi , Bo Huang , Min Zhao , Xiaolu Jiang , Wenfei Mao","doi":"10.1016/j.rse.2025.115227","DOIUrl":"10.1016/j.rse.2025.115227","url":null,"abstract":"<div><div>High spatiotemporal resolution land surface temperature (LST) is essential for climate-impact studies, particularly for urban thermal environment analyses and vegetation phenology tracking. However, current satellite sensors exhibit inherent hardware trade-offs: Sentinel-2 (S2) provides high-resolution (10 m/5 day) optical observations without thermal capability, whereas Landsat-8/9 (L8/9), equipped with both optical and thermal sensors, suffers from coarser resolution (30 m/16 day). This configuration fails to meet the demand for fine spatial and dense temporal surface thermal monitoring. To address this gap, we propose a novel fusion framework for high spatiotemporal resolution LST generation (<span><math><mi>f</mi></math></span>HiSTR-LST). It first applies a deep-learning-based spatiotemporal fusion to densify reflectance data, then performs a spatial-spectral fusion to generate high-resolution LST. By synergizing L8/9 and S2 data, our approach reliably produces 10 m spatial-resolution LST across three overpass scenarios (joint, L8/9-only, S2-only), thus achieving an effective ∼3-day temporal resolution. Cross-validations between upscaled LST predictions and native L8/9 LST demonstrate <span><math><mi>f</mi></math></span>HiSTR-LST's robust performance across eight study areas worldwide (mean <em>R</em> = 0.90, RMSE = 1.17 K). More significantly, ground-truth validation—previously unaddressed—confirms its satisfactory accuracy (mean <em>R</em> = 0.97, RMSE = 3.45 K). The combined validation shows that <span><math><mi>f</mi></math></span>HiSTR-LST outperforms the state-of-the-art by 13 % in R and reduces RMSE by 9 %. Finally, we illustrate two applications—small-area vegetation-phenology tracking and fine-scale urban thermal-pattern delineation—which collectively showcase <span><math><mi>f</mi></math></span>HiSTR-LST's capability to resolve subtle surface thermal variations. Our study bridges a critical gap in generating high spatiotemporal resolution LST from satellite imagery, a capability crucial for investigating the nuanced effects of global warming.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115227"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145881023","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}
Pub Date : 2026-03-01Epub Date: 2025-12-18DOI: 10.1016/j.rse.2025.115190
Li-Qin Cao , Hang Zhao , Du Wang , Yan-Fei Zhong , Fa-Wang Ye
The Thermal Infrared Spectrometer (TIS) onboard Sustainable Development Science Satellite-1 (SDGSAT-1) features three unique channels with a broader spectral range compared to previous thermal infrared multi-channel sensors. The split-window (SW) and temperature-and-emissivity separation (TES) algorithms are suitable for land surface temperature (LST) retrieval from TIS data. However, both SW and TES require auxiliary information, and the temporal and spatial inconsistency of auxiliary information can lead to errors in LST retrieval. We propose a wide-band atmospheric correction TES algorithm, which can retrieve LST without any auxiliary atmospheric and land surface parameter input. By leveraging the stability of wide-band imaging, atmospheric transmittance and upward radiation are modeled, thereby reducing the number of unknowns in the radiative transfer equation. Additionally, a transmittance ratio refinement module is incorporated, which iteratively refines the transmittance. Experiments conducted on simulated datasets demonstrate that this method achieves an RMSE of 1.32 K, remaining stable at 1.39 K with estimated transmittance, indicating strong robustness to variations in water vapor content. Cross-validation results for the Wuhan region show a bias of −1.79 K and an RMSE of 2.28 K when compared to MODIS temperature products, suggesting that the retrieved LST captures more detailed information. Furthermore, a comparison with the general split-window (GSW) algorithm and MODTRAN-TES was conducted, selecting 108 validation points at Heihe, SURFRAD, ICOS, TERN, and BSRN stations for ground validation, yielding root mean square errors (RMSE) of 2.07 K, 1.55 K, 1.84 K, 1.72 K, and 2.14 K respectively, with an RMSE of 1.95 K across all validation sites. These results represent improvements of 0.25 K and 0.55 K over GSW and MODTRAN-TES, respectively, confirming the high accuracy of the proposed method.
{"title":"A novel land surface temperature retrieval method using channel correlation for atmospheric parameter modeling from SDGSAT-1 data","authors":"Li-Qin Cao , Hang Zhao , Du Wang , Yan-Fei Zhong , Fa-Wang Ye","doi":"10.1016/j.rse.2025.115190","DOIUrl":"10.1016/j.rse.2025.115190","url":null,"abstract":"<div><div>The Thermal Infrared Spectrometer (TIS) onboard Sustainable Development Science Satellite-1 (SDGSAT-1) features three unique channels with a broader spectral range compared to previous thermal infrared multi-channel sensors. The split-window (SW) and temperature-and-emissivity separation (TES) algorithms are suitable for land surface temperature (LST) retrieval from TIS data. However, both SW and TES require auxiliary information, and the temporal and spatial inconsistency of auxiliary information can lead to errors in LST retrieval. We propose a wide-band atmospheric correction TES algorithm, which can retrieve LST without any auxiliary atmospheric and land surface parameter input. By leveraging the stability of wide-band imaging, atmospheric transmittance and upward radiation are modeled, thereby reducing the number of unknowns in the radiative transfer equation. Additionally, a transmittance ratio refinement module is incorporated, which iteratively refines the transmittance. Experiments conducted on simulated datasets demonstrate that this method achieves an RMSE of 1.32 K, remaining stable at 1.39 K with estimated transmittance, indicating strong robustness to variations in water vapor content. Cross-validation results for the Wuhan region show a bias of −1.79 K and an RMSE of 2.28 K when compared to MODIS temperature products, suggesting that the retrieved LST captures more detailed information. Furthermore, a comparison with the general split-window (GSW) algorithm and MODTRAN-TES was conducted, selecting 108 validation points at Heihe, SURFRAD, ICOS, TERN, and BSRN stations for ground validation, yielding root mean square errors (RMSE) of 2.07 K, 1.55 K, 1.84 K, 1.72 K, and 2.14 K respectively, with an RMSE of 1.95 K across all validation sites. These results represent improvements of 0.25 K and 0.55 K over GSW and MODTRAN-TES, respectively, confirming the high accuracy of the proposed method.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115190"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785690","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}
Pub Date : 2026-03-01Epub Date: 2025-12-08DOI: 10.1016/j.rse.2025.115169
Jiheng Hu , Rui Li , Peng Zhang , Yu Wang , Shengli Wu , Husi Letu , Fuzhong Weng
Accurate accounting of microwave land surface emissivity (MLSE) facilitates applications associated with monitoring the ecohydrological dynamics in global terrestrial ecosystems, quantifying cross-sphere carbon and water exchanges, and meeting the critical accuracy required for assimilation in the global precipitation retrieval algorithm. However, collaborative applications of emissivity retrieved from individual sensors are severely hampered by discrepancies in retrieval techniques, instrumental configurations, calibration errors, and broken temporal periods. To mitigate this gap, we present an innovative framework to retrieve harmonized emissivity from observations of five passive microwave sensors, namely, GPM-CO/GMI, Fengyun-3B, −3C and −3D/MWRI, and GCOM-W1/AMSR2. Six geostationary visible and infrared imagers onboard three geostationary platforms, i.e. GOES-16/ABI, Himawari-8, −9/AHI, and MSG-1, −2, −3/SEVIRI, were collocated to jointly provide clear-sky masks covering the globe. The simultaneous conical overpass (SCO) recalibration technique was applied to scale all emissivity subsets retrieved from different sensors to be aligned with the GMI retrievals across various land types. Quantitative analyses reveal exceptionally strong consistency among emissivities across different subsets (Pearson R ≈ 0.95, RMSD <0.011, and mean bias within ±0.005). Our estimates at 10.65 GHz show strong agreement with in-situ radiometer measurements over two grass and crop fields, with errors generally within ±0.01 at vertical polarization and a systematic underestimation of approximately −0.02 at horizontal polarization. Globally, we evaluate the recalibrated emissivities against four reference datasets derived using various techniques, which includes three single-sensor retrievals from GMI and AMSR-E, as well as a climatology emissivity atlas generated using the Tool to Estimate Land Surface Emissivity at Microwaves and Millimeter waves (TELSEM). The results demonstrate strong consistencies at both vertical (R = 0.8–0.9, RMSD <0.015 or ∼ 1.5 %) and horizontal (R = 0.9–0.95, RMSD <0.02 or ∼ 2 %) polarizations on a monthly scale. The observed discrepancies are primarily attributed to differences in instrumental configurations, calibration accuracy, and retrieval methodologies. The harmonized retrieval algorithm and the sophisticated cross sensor calibrations facilitate its implementation as a self-consistent emissivity data for various applications associated with terrestrial ecohydrological dynamics, surface hydrological properties estimation, as well as the physical-based precipitation retrieval algorithms over land.
{"title":"Global retrieval of harmonized microwave land surface emissivity leveraging multi-sensor measurements from GMI, AMSR2 and MWRIs","authors":"Jiheng Hu , Rui Li , Peng Zhang , Yu Wang , Shengli Wu , Husi Letu , Fuzhong Weng","doi":"10.1016/j.rse.2025.115169","DOIUrl":"10.1016/j.rse.2025.115169","url":null,"abstract":"<div><div>Accurate accounting of microwave land surface emissivity (MLSE) facilitates applications associated with monitoring the ecohydrological dynamics in global terrestrial ecosystems, quantifying cross-sphere carbon and water exchanges, and meeting the critical accuracy required for assimilation in the global precipitation retrieval algorithm. However, collaborative applications of emissivity retrieved from individual sensors are severely hampered by discrepancies in retrieval techniques, instrumental configurations, calibration errors, and broken temporal periods. To mitigate this gap, we present an innovative framework to retrieve harmonized emissivity from observations of five passive microwave sensors, namely, GPM-CO/GMI, Fengyun-3B, −3C and −3D/MWRI, and GCOM-W1/AMSR2. Six geostationary visible and infrared imagers onboard three geostationary platforms, i.e. GOES-16/ABI, Himawari-8, −9/AHI, and MSG-1, −2, −3/SEVIRI, were collocated to jointly provide clear-sky masks covering the globe. The simultaneous conical overpass (SCO) recalibration technique was applied to scale all emissivity subsets retrieved from different sensors to be aligned with the GMI retrievals across various land types. Quantitative analyses reveal exceptionally strong consistency among emissivities across different subsets (Pearson R ≈ 0.95, RMSD <0.011, and mean bias within ±0.005). Our estimates at 10.65 GHz show strong agreement with in-situ radiometer measurements over two grass and crop fields, with errors generally within ±0.01 at vertical polarization and a systematic underestimation of approximately −0.02 at horizontal polarization. Globally, we evaluate the recalibrated emissivities against four reference datasets derived using various techniques, which includes three single-sensor retrievals from GMI and AMSR-E, as well as a climatology emissivity atlas generated using the Tool to Estimate Land Surface Emissivity at Microwaves and Millimeter waves (TELSEM). The results demonstrate strong consistencies at both vertical (<em>R</em> = 0.8–0.9, RMSD <0.015 or ∼ 1.5 %) and horizontal (<em>R</em> = 0.9–0.95, RMSD <0.02 or ∼ 2 %) polarizations on a monthly scale. The observed discrepancies are primarily attributed to differences in instrumental configurations, calibration accuracy, and retrieval methodologies. The harmonized retrieval algorithm and the sophisticated cross sensor calibrations facilitate its implementation as a self-consistent emissivity data for various applications associated with terrestrial ecohydrological dynamics, surface hydrological properties estimation, as well as the physical-based precipitation retrieval algorithms over land.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115169"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145703961","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}
Pub Date : 2026-03-01Epub Date: 2026-01-19DOI: 10.1016/j.rse.2026.115244
Jasper Feyen , Verginia Wortel , Kim Calders , John Armston , Frieke Vancoillie
Mangroves are critical coastal ecosystems known for their carbon storage capacity, biodiversity, and role in shoreline stabilization. In Suriname, mangroves develop within a dynamic coastal setting shaped by migrating mudbanks and high sedimentation rates. This study examines how 30 structural metrics derived from the Global Ecosystem Dynamics Investigation (GEDI) vary across gradients of mangrove stand age and seaward distance. Forest stand age and yearly coastline positions were derived from Landsat time series data, enabling the integration of temporal and spatial drivers to uncover patterns of mangrove succession and structural development. Nonlinear growth models, more specifically the Chapman–Richards function, captured early growth and stabilization phases, while Generalized Additive Models (GAMs) provided flexibility to represent more complex structural changes observed in mature and decaying stands. Results show that structural metrics related to forest growth, such as canopy height and aboveground biomass density, increase rapidly during early successional stages but plateau beyond approximately 12 years or 2 km from the coastline. Complexity-oriented metrics, such as Foliage Height Diversity (FHD) and the Waveform Structural Complexity Index (WSCI), continue to evolve, reflecting increased vertical stratification in mature stands. By combining GEDI spaceborne LiDAR with Landsat-derived chronosequences, this study demonstrates how remote sensing can be used to monitor mangrove successional trajectories and structural complexity, including in inaccessible coastal regions. Our findings extend traditional mangrove successional models by quantifying how both temporal (age) and spatial (seaward distance) gradients jointly determine mangrove structure across the Surinamese coastline.
{"title":"Characterizing mangrove forest succession in Suriname using GEDI waveform metrics","authors":"Jasper Feyen , Verginia Wortel , Kim Calders , John Armston , Frieke Vancoillie","doi":"10.1016/j.rse.2026.115244","DOIUrl":"10.1016/j.rse.2026.115244","url":null,"abstract":"<div><div>Mangroves are critical coastal ecosystems known for their carbon storage capacity, biodiversity, and role in shoreline stabilization. In Suriname, mangroves develop within a dynamic coastal setting shaped by migrating mudbanks and high sedimentation rates. This study examines how 30 structural metrics derived from the Global Ecosystem Dynamics Investigation (GEDI) vary across gradients of mangrove stand age and seaward distance. Forest stand age and yearly coastline positions were derived from Landsat time series data, enabling the integration of temporal and spatial drivers to uncover patterns of mangrove succession and structural development. Nonlinear growth models, more specifically the Chapman–Richards function, captured early growth and stabilization phases, while Generalized Additive Models (GAMs) provided flexibility to represent more complex structural changes observed in mature and decaying stands. Results show that structural metrics related to forest growth, such as canopy height and aboveground biomass density, increase rapidly during early successional stages but plateau beyond approximately 12 years or 2 km from the coastline. Complexity-oriented metrics, such as Foliage Height Diversity (FHD) and the Waveform Structural Complexity Index (WSCI), continue to evolve, reflecting increased vertical stratification in mature stands. By combining GEDI spaceborne LiDAR with Landsat-derived chronosequences, this study demonstrates how remote sensing can be used to monitor mangrove successional trajectories and structural complexity, including in inaccessible coastal regions. Our findings extend traditional mangrove successional models by quantifying how both temporal (age) and spatial (seaward distance) gradients jointly determine mangrove structure across the Surinamese coastline.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115244"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146001413","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}
Pub Date : 2026-03-01Epub Date: 2025-12-18DOI: 10.1016/j.rse.2025.115204
Jianbo Qi , Siying He , Xun Zhao , Su Ye , Tianjia Chu , Zhexiu Yu , Simei Lin , Huaguo Huang
<div><div>As a key biophysical parameter describing forest vegetation structure, Leaf Area Index (LAI) is an essential and widely used indicator for evaluating forest ecosystem function and health. LAI retrieval from remote sensing observations primarily relies on canopy radiative transfer models (RTMs) that quantitatively characterize the complex relationship between canopy parameters and reflectance. However, most physical models currently used for LAI retrieval are one-dimensional (1D) RTMs, which typically assume the canopy to be horizontally homogeneous and thus fail to capture the inherent heterogeneity within the canopy. Although three-dimensional (3D) RTMs can better characterize the structural complexity of forest canopies, their high computational demand and the difficulty of parameterization often limit their application to large-scale remote sensing retrievals. In this study, a novel 3D Look-Up Table (3D-LUT) approach was developed for retrieving forest LAI from Landsat by accounting for the heterogeneity within forests through the integration of LiDAR-based scene reconstructions to parameterize the RTM. Instead of using idealized homogeneous layers or simple geometric objects, our approach used airborne LiDAR data to reconstruct realistic and structurally representative 3D forest scenes for typical forest types, including Deciduous Broadleaf Forest (DBF), Deciduous Needleleaf Forest (DNF), Evergreen Broadleaf Forest (EBF), and Evergreen Needleleaf Forest (ENF). Based on these reconstructed forest scenes, type-specific LAI look-up tables (LUTs) were built by coupling the 3D RTM Large-scalE remote Sensing data and image Simulation (LESS) with an analytical model PATH_RT, an accurate and efficient RTM based on 3D path-length distribution and spectral invariant theory, enabling accurate LAI retrieval from Landsat imagery. This method was compared against field observations collected from 16 National Ecological Observatory Network (NEON) sites and 8 Integrated Carbon Observation System (ICOS) sites, which comprise a representative sample of different forest types. Additionally, intercomparison was conducted using the High-resolution Global LAnd Surface Satellite (Hi-GLASS) LAI product, Simplified Level-2 Prototype Processor (SL2P) algorithm and the MODIS LAI product. Validation against in situ data demonstrated that the proposed algorithm can achieve high-accuracy retrieval of LAI across four forest types, with RMSE ranging from 0.93 to 1.20 m<sup>2</sup>/m<sup>2</sup> and MAE from 0.73 to 1.00 m<sup>2</sup>/m<sup>2</sup>. The intercomparison results revealed that retrieval algorithms based on the PROSAIL model, such as SL2P, tend to underestimate forest LAI. In contrast, the proposed algorithm shows strong overall agreement with the Hi-GLASS LAI product and MODIS LAI product, which are derived from a deep learning framework and a 3D RTM, respectively, supporting its reliability for regional-scale forest LAI retrieval. By generating the s
{"title":"Retrieving forest LAI from Landsat via 3D look-up table generated by realistic LiDAR scenes","authors":"Jianbo Qi , Siying He , Xun Zhao , Su Ye , Tianjia Chu , Zhexiu Yu , Simei Lin , Huaguo Huang","doi":"10.1016/j.rse.2025.115204","DOIUrl":"10.1016/j.rse.2025.115204","url":null,"abstract":"<div><div>As a key biophysical parameter describing forest vegetation structure, Leaf Area Index (LAI) is an essential and widely used indicator for evaluating forest ecosystem function and health. LAI retrieval from remote sensing observations primarily relies on canopy radiative transfer models (RTMs) that quantitatively characterize the complex relationship between canopy parameters and reflectance. However, most physical models currently used for LAI retrieval are one-dimensional (1D) RTMs, which typically assume the canopy to be horizontally homogeneous and thus fail to capture the inherent heterogeneity within the canopy. Although three-dimensional (3D) RTMs can better characterize the structural complexity of forest canopies, their high computational demand and the difficulty of parameterization often limit their application to large-scale remote sensing retrievals. In this study, a novel 3D Look-Up Table (3D-LUT) approach was developed for retrieving forest LAI from Landsat by accounting for the heterogeneity within forests through the integration of LiDAR-based scene reconstructions to parameterize the RTM. Instead of using idealized homogeneous layers or simple geometric objects, our approach used airborne LiDAR data to reconstruct realistic and structurally representative 3D forest scenes for typical forest types, including Deciduous Broadleaf Forest (DBF), Deciduous Needleleaf Forest (DNF), Evergreen Broadleaf Forest (EBF), and Evergreen Needleleaf Forest (ENF). Based on these reconstructed forest scenes, type-specific LAI look-up tables (LUTs) were built by coupling the 3D RTM Large-scalE remote Sensing data and image Simulation (LESS) with an analytical model PATH_RT, an accurate and efficient RTM based on 3D path-length distribution and spectral invariant theory, enabling accurate LAI retrieval from Landsat imagery. This method was compared against field observations collected from 16 National Ecological Observatory Network (NEON) sites and 8 Integrated Carbon Observation System (ICOS) sites, which comprise a representative sample of different forest types. Additionally, intercomparison was conducted using the High-resolution Global LAnd Surface Satellite (Hi-GLASS) LAI product, Simplified Level-2 Prototype Processor (SL2P) algorithm and the MODIS LAI product. Validation against in situ data demonstrated that the proposed algorithm can achieve high-accuracy retrieval of LAI across four forest types, with RMSE ranging from 0.93 to 1.20 m<sup>2</sup>/m<sup>2</sup> and MAE from 0.73 to 1.00 m<sup>2</sup>/m<sup>2</sup>. The intercomparison results revealed that retrieval algorithms based on the PROSAIL model, such as SL2P, tend to underestimate forest LAI. In contrast, the proposed algorithm shows strong overall agreement with the Hi-GLASS LAI product and MODIS LAI product, which are derived from a deep learning framework and a 3D RTM, respectively, supporting its reliability for regional-scale forest LAI retrieval. By generating the s","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115204"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145771666","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}
Pub Date : 2026-03-01Epub Date: 2026-01-08DOI: 10.1016/j.rse.2025.115213
João M.B. Carreiras , Thomas Higginbottom , John L. Godlee , Sam Harrison , Lorena Benitez , Penelope J. Mograbi , Aurora Levesley , Karina Melgaço , David Milodowski , Georgia Pickavance , Geoff Wells , Edmar Almeida de Oliveira , Luzmila Arroyo , Sam Bowers , Roel J.W. Brienen , Domingos Cardoso , António Alberto Jorge Farias Castro , Ezequiel Chavez , Ítalo A.C. Coutinho , Tomás F. Domingues , Casey M. Ryan
<div><div>Accurate characterization of the role of the dry tropics in the global carbon cycle requires precise estimation of woody biomass changes due to ecological and anthropogenic change, including deforestation, forest degradation, regrowth, mortality and enhanced tree growth due to climate change. L-band Synthetic Aperture Radar (SAR) backscatter observations offer a reliable option to consistently map these processes as they are (i) available globally since 2007 (JAXA ALOS-1, ALOS-2 and ALOS-4), and (ii) sensitive to woody structure, such as aboveground biomass density (<span><math><mi>AGBD</mi></math></span>) up to ∼100 t ha<sup>−1</sup>. However, we lack multi-site empirical understanding of the scattering processes that determine the relationship between L-band SAR and woody vegetation structure in the dry tropics, and how this is mediated by soil properties.</div><div>This study used observations from ground plots in Africa (<em>n</em> = 171), Australia (<em>n</em> = 6), and South America (<em>n</em> = 44) to understand the impact of vegetation structure and soil properties on spatially and temporally coincident fully-polarimetric L-band SAR data. Fully-polarimetric L-band SAR single-look complex data were converted to scattering mechanisms/parameters using van Zyl, Cloude-Pottier, and Freeman-Durden polarimetric decompositions to elucidate the physical mechanisms involved. Multivariate SAR-vegetation-soil relationships were analysed using a theory-informed structural equation modelling approach. The strongest positive effects on volume scattering come from stem density (stems ha<sup>−1</sup>) and mean stem biomass of trees, and soil water and sand content (standardized regression coefficients of 0.3, 0.1, 0.2 and 0.1, respectively). The only significant effect on surface scattering is from stem density (0.1). Significant effects on double bounce scattering are from stem density (0.3) and soil sand content (−0.2). Since <span><math><mi>AGBD</mi></math></span> is the product of stem density and mean stem biomass, this modelling framework points to a stronger effect from the number of trees rather than their size/biomass. Therefore, <span><math><mi>AGBD</mi></math></span> maps relying solely on radar intensity may not reflect significant changes when <span><math><mi>AGBD</mi></math></span> is increasing due to the growth of existing stems. Additionally, such maps might overestimate changes in <span><math><mi>AGBD</mi></math></span> when driven by the recruitment of new stems or loss of existing stems. Full-polarimetric observations allow the decomposition of the radar signal into volume scattering, surface scattering, and double bounce, enabling the inversion of structural equation models to retrieve both stem density and mean stem biomass. This provides a more comprehensive description of forest structure compared to retrieving only <span><math><mi>AGBD</mi></math></span>. As this approach depends on full-polarimetric data, its effective
要准确描述干燥热带地区在全球碳循环中的作用，就需要精确估计由于生态和人为变化造成的木质生物量变化，包括森林砍伐、森林退化、再生、死亡和气候变化导致的树木生长增强。l波段合成孔径雷达（SAR）后向散射观测提供了一种可靠的选择，可以一致地绘制这些过程，因为它们(i)自2007年以来在全球范围内可用（JAXA ALOS-1， ALOS-2和ALOS-4），并且（ii）对木质结构敏感，例如地上生物量密度（AGBD）高达~ 100 t ha -1。然而，我们缺乏对l波段SAR与干旱热带木本植被结构之间关系的散射过程的多站点经验理解，以及土壤性质如何介导这种关系。本研究利用非洲（n = 171）、澳大利亚（n = 6）和南美洲（n = 44）的地面样地观测资料，了解植被结构和土壤性质对时空重合全极化l波段SAR数据的影响。利用van Zyl、cloud - pottier和Freeman-Durden极化分解方法，将全极化l波段SAR单目复杂数据转换为散射机制/参数，以阐明所涉及的物理机制。利用结构方程建模方法分析了多变量sar -植被-土壤关系。对体积散射的正向影响最大的是树木的茎密度（茎ha−1）和平均茎生物量，以及土壤含水量和含沙量（标准化回归系数分别为0.3、0.1、0.2和0.1）。唯一对表面散射有显著影响的是茎密度（0.1）。茎密度（0.3）和土壤含沙量（−0.2）对双弹跳散射有显著影响。由于AGBD是茎密度和平均茎生物量的产物，该模型框架指出，树木数量的影响比它们的大小/生物量更强。因此，单纯依靠雷达强度的AGBD地图可能无法反映出由于现有系统的生长而增加的AGBD的显著变化。此外，这样的图谱可能会高估AGBD的变化，因为它是由新茎的吸收或现有茎的丧失所驱动的。全极化观测允许将雷达信号分解为体散射、表面散射和双反弹，从而实现结构方程模型的反演，从而获得茎密度和平均茎生物量。与仅检索AGBD相比，这提供了更全面的森林结构描述。由于这种方法依赖于全极化数据，其有效性与这种观测的可用性密切相关。我们的研究结果强调了ALOS-4、PALSAR-3、BIOMASS和ROSE-L等近期和即将开展的任务的价值，并强调了优先获取四极SAR数据以支持未来大规模植被结构属性检索的必要性。
{"title":"Determinants of L-band backscatter in dry tropical ecosystems: Implications for biomass mapping","authors":"João M.B. Carreiras , Thomas Higginbottom , John L. Godlee , Sam Harrison , Lorena Benitez , Penelope J. Mograbi , Aurora Levesley , Karina Melgaço , David Milodowski , Georgia Pickavance , Geoff Wells , Edmar Almeida de Oliveira , Luzmila Arroyo , Sam Bowers , Roel J.W. Brienen , Domingos Cardoso , António Alberto Jorge Farias Castro , Ezequiel Chavez , Ítalo A.C. Coutinho , Tomás F. Domingues , Casey M. Ryan","doi":"10.1016/j.rse.2025.115213","DOIUrl":"10.1016/j.rse.2025.115213","url":null,"abstract":"<div><div>Accurate characterization of the role of the dry tropics in the global carbon cycle requires precise estimation of woody biomass changes due to ecological and anthropogenic change, including deforestation, forest degradation, regrowth, mortality and enhanced tree growth due to climate change. L-band Synthetic Aperture Radar (SAR) backscatter observations offer a reliable option to consistently map these processes as they are (i) available globally since 2007 (JAXA ALOS-1, ALOS-2 and ALOS-4), and (ii) sensitive to woody structure, such as aboveground biomass density (<span><math><mi>AGBD</mi></math></span>) up to ∼100 t ha<sup>−1</sup>. However, we lack multi-site empirical understanding of the scattering processes that determine the relationship between L-band SAR and woody vegetation structure in the dry tropics, and how this is mediated by soil properties.</div><div>This study used observations from ground plots in Africa (<em>n</em> = 171), Australia (<em>n</em> = 6), and South America (<em>n</em> = 44) to understand the impact of vegetation structure and soil properties on spatially and temporally coincident fully-polarimetric L-band SAR data. Fully-polarimetric L-band SAR single-look complex data were converted to scattering mechanisms/parameters using van Zyl, Cloude-Pottier, and Freeman-Durden polarimetric decompositions to elucidate the physical mechanisms involved. Multivariate SAR-vegetation-soil relationships were analysed using a theory-informed structural equation modelling approach. The strongest positive effects on volume scattering come from stem density (stems ha<sup>−1</sup>) and mean stem biomass of trees, and soil water and sand content (standardized regression coefficients of 0.3, 0.1, 0.2 and 0.1, respectively). The only significant effect on surface scattering is from stem density (0.1). Significant effects on double bounce scattering are from stem density (0.3) and soil sand content (−0.2). Since <span><math><mi>AGBD</mi></math></span> is the product of stem density and mean stem biomass, this modelling framework points to a stronger effect from the number of trees rather than their size/biomass. Therefore, <span><math><mi>AGBD</mi></math></span> maps relying solely on radar intensity may not reflect significant changes when <span><math><mi>AGBD</mi></math></span> is increasing due to the growth of existing stems. Additionally, such maps might overestimate changes in <span><math><mi>AGBD</mi></math></span> when driven by the recruitment of new stems or loss of existing stems. Full-polarimetric observations allow the decomposition of the radar signal into volume scattering, surface scattering, and double bounce, enabling the inversion of structural equation models to retrieve both stem density and mean stem biomass. This provides a more comprehensive description of forest structure compared to retrieving only <span><math><mi>AGBD</mi></math></span>. As this approach depends on full-polarimetric data, its effective","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115213"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145939014","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}
Pub Date : 2026-03-01Epub Date: 2025-12-23DOI: 10.1016/j.rse.2025.115209
Xinye Li , Siqi Zhang , Peng Chen , Zhanhua Zhang , Delu Pan
Ocean lidar technology, an emerging active remote sensing method, excels at revealing the vertical structure of subsurface ocean layers, addressing challenges in carbon flux, phytoplankton analysis, and biogeochemical monitoring. Current lidar inversion methods, however, rely on empirical formulations for homogeneous waters and overlook photon multiple scattering, which introduces significant uncertainties, especially in complex coastal ecosystems. To overcome this, we present an iterative hybrid multiple scattering-corrected retrieval method based on 117,456 vertical profiles (2017–2024) in the South China Sea. The model combines the optimization of backscatter–attenuation ratios, lidar ratios, and semianalytical simulations of multiple scattering effects integrated with XGBoost machine learning to relate lidar-derived optical properties (Kd, bbp) to biogeochemical parameters (Chl, POC). Compared with the satellite ocean color products, the retrieval results derived from airborne and shipborne lidar observations show strong agreement: Kd (R = 0.76, RMSD = 0.01 m−1, MAPD = 6.58 %), bbp (R = 0.80, RMSD = 0.00 m−1, MAPD = 28.93 %), Chl (R = 0.61, RMSD = 0.29 μg/L, MAPD = 32.82 %), and POC (R = 0.88, RMSD = 20.55 μg/L, MAPD = 18.14 %). These results bridge active and passive remote sensing. This study also reveals the dynamic three-dimensional characteristics of the subsurface phytoplankton layer in the South China Sea, revealing spatial and temporal heterogeneity influenced by environment factors. The nearshore subsurface phytoplankton layer shows diurnal variations in thickness and intensity driven by tidal processes: it thickens and ascends during the day and thins and descends at night. Larger tidal amplitudes are linked to shallower layers and higher chlorophyll-a concentrations. These findings demonstrate the potential of lidar technology for large-scale, long-term monitoring of subsurface ocean profiles, offering an important complement to in situ and passive satellite remote sensing data.
{"title":"IHMSC: A novel iterative hybrid multiple scattering-corrected retrieval method for enhancing accuracy in ocean lidar profiling inversions","authors":"Xinye Li , Siqi Zhang , Peng Chen , Zhanhua Zhang , Delu Pan","doi":"10.1016/j.rse.2025.115209","DOIUrl":"10.1016/j.rse.2025.115209","url":null,"abstract":"<div><div>Ocean lidar technology, an emerging active remote sensing method, excels at revealing the vertical structure of subsurface ocean layers, addressing challenges in carbon flux, phytoplankton analysis, and biogeochemical monitoring. Current lidar inversion methods, however, rely on empirical formulations for homogeneous waters and overlook photon multiple scattering, which introduces significant uncertainties, especially in complex coastal ecosystems. To overcome this, we present an iterative hybrid multiple scattering-corrected retrieval method based on 117,456 vertical profiles (2017–2024) in the South China Sea. The model combines the optimization of backscatter–attenuation ratios, lidar ratios, and semianalytical simulations of multiple scattering effects integrated with XGBoost machine learning to relate lidar-derived optical properties (<em>K</em><sub>d</sub><em>, bbp</em>) to biogeochemical parameters (<em>Chl</em>, <em>POC</em>). Compared with the satellite ocean color products, the retrieval results derived from airborne and shipborne lidar observations show strong agreement: <em>K</em><sub>d</sub> (<em>R =</em> 0.76<em>, RMSD =</em> 0.01 m<sup>−1</sup><em>, MAPD =</em> 6.58 %)<em>, bbp</em> (<em>R =</em> 0.80<em>, RMSD =</em> 0.00 m<sup>−1</sup><em>, MAPD =</em> 28.93 %)<em>, Chl</em> (<em>R =</em> 0.61<em>, RMSD =</em> 0.29 μg/L<em>, MAPD =</em> 32.82 %)<em>, and POC</em> (<em>R =</em> 0.88<em>, RMSD =</em> 20.55 μg/L<em>, MAPD =</em> 18.14 %). These results bridge active and passive remote sensing. This study also reveals the dynamic three-dimensional characteristics of the subsurface phytoplankton layer in the South China Sea, revealing spatial and temporal heterogeneity influenced by environment factors. The nearshore subsurface phytoplankton layer shows diurnal variations in thickness and intensity driven by tidal processes: it thickens and ascends during the day and thins and descends at night. Larger tidal amplitudes are linked to shallower layers and higher chlorophyll-a concentrations. These findings demonstrate the potential of lidar technology for large-scale, long-term monitoring of subsurface ocean profiles, offering an important complement to in situ and passive satellite remote sensing data.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115209"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823767","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}
Pub Date : 2026-03-01Epub Date: 2026-01-13DOI: 10.1016/j.rse.2026.115242
Xiang Wang , Zheng Fu , Philippe Ciais , Josep Peñuelas , Jingfeng Xiao , Xing Li , Xiangzhong Luo , Chi Chen , Haoyu Xia , Tao Zhou , Paul C. Stoy , Julia K. Green , Fangyue Zhang
Climate change has significantly impacted tropical water use efficiency (WUE), defined as the ratio of gross primary productivity (GPP) to evapotranspiration (ET). However, the spatiotemporal dynamics and controlling factors of WUE in these regions—particularly the effects of extreme El Niño events—remain unclear. Using multiple satellite-derived GPP and ET datasets with large-scale observations, here we quantified WUE trends from 2001 to 2020 and assessed the impact of the 2015/16 El Niño drought on WUE in the tropics. Our analysis revealed a significant upward trend in tropical WUE, increasing at a rate of 0.007 ± 0.001 g C kg−1 H2O yr−1 (mean ± standard deviation), with the largest increase observed in tropical Asia (0.01 ± 0.001 g C kg−1 H2O yr−1). Spatially, three independent remote sensing-driven datasets consistently showed a significant WUE increase in 32%–54% of tropical regions, while only 1%–3% experienced a significant decline. Furthermore, tropical ecosystems exhibited a substantial increase in GPP (5.47 ± 0.60 g C m−2 yr−1), with the highest growth rate in tropical Asia (11.45 ± 0.37 g C m−2 yr−1), whereas ET showed minor changes. This suggests that WUE changes in tropical ecosystems are primarily driven by increases of GPP rather than ET. Further analysis identified leaf area as the dominant factor influencing WUE, GPP, and ET trends across the tropics. We also found that the extreme drought during the 2015/16 El Niño event resulted in a net decrease in WUE (−0.03 ± 0.01 g C kg−1 H2O), which transitioned to a net increase (0.04 ± 0.01 g C kg−1 H2O) by 2016/17. Compared to satellite-driven results, most land surface models captured the direction of tropical WUE trends but simulated a slower rate of change, with substantial variation in predicted trend intensities among models. This study advances our understanding of tropical ecosystem WUE dynamics and provides critical insights for predicting future WUE changes under ongoing climate change, informing strategies for carbon sequestration and water resource management in vulnerable tropical regions.
气候变化显著影响了热带水分利用效率（WUE），即总初级生产力（GPP）与蒸散（ET）的比值。然而，这些地区WUE的时空动态和控制因素，特别是极端El Niño事件的影响尚不清楚。利用多个卫星衍生的GPP和ET大尺度观测数据集，我们量化了2001 - 2020年的WUE趋势，并评估了2015/16年El Niño干旱对热带地区WUE的影响。我们的分析显示，热带地区的用水效率呈显著上升趋势，增长率为0.007±0.001 g C kg−1 H2O /年（平均±标准差），其中亚洲热带地区的增幅最大（0.01±0.001 g C kg−1 H2O /年）。在空间上，三个独立的遥感驱动数据集一致显示，32%-54%的热带地区WUE显著增加，而只有1%-3%的热带地区WUE显著下降。此外，热带生态系统GPP显著增加（5.47±0.60 g C m−2 yr−1），其中亚洲热带地区增幅最大（11.45±0.37 g C m−2 yr−1），而ET变化较小。这表明热带生态系统的WUE变化主要是由GPP的增加而不是ET的增加驱动的。进一步的分析发现，叶面积是影响热带地区WUE、GPP和ET趋势的主要因素。我们还发现，2015/16年El Niño事件期间的极端干旱导致WUE的净减少（- 0.03±0.01 g C kg - 1 H2O），到2016/17年转变为净增加（0.04±0.01 g C kg - 1 H2O）。与卫星驱动的结果相比，大多数陆地表面模式捕获了热带WUE趋势的方向，但模拟的变化率较慢，模式之间预测的趋势强度存在很大差异。该研究促进了我们对热带生态系统水分利用效率动态的理解，并为预测持续气候变化下未来水分利用效率的变化提供了重要见解，为热带脆弱地区的碳封存和水资源管理策略提供了信息。
{"title":"Multi-satellite derived data reveals spatiotemporal dynamics of carbon-water coupling and its drivers in tropical ecosystems","authors":"Xiang Wang , Zheng Fu , Philippe Ciais , Josep Peñuelas , Jingfeng Xiao , Xing Li , Xiangzhong Luo , Chi Chen , Haoyu Xia , Tao Zhou , Paul C. Stoy , Julia K. Green , Fangyue Zhang","doi":"10.1016/j.rse.2026.115242","DOIUrl":"10.1016/j.rse.2026.115242","url":null,"abstract":"<div><div>Climate change has significantly impacted tropical water use efficiency (WUE), defined as the ratio of gross primary productivity (GPP) to evapotranspiration (ET). However, the spatiotemporal dynamics and controlling factors of WUE in these regions—particularly the effects of extreme El Niño events—remain unclear. Using multiple satellite-derived GPP and ET datasets with large-scale observations, here we quantified WUE trends from 2001 to 2020 and assessed the impact of the 2015/16 El Niño drought on WUE in the tropics. Our analysis revealed a significant upward trend in tropical WUE, increasing at a rate of 0.007 ± 0.001 g C kg<sup>−1</sup> H<sub>2</sub>O yr<sup>−1</sup> (mean ± standard deviation), with the largest increase observed in tropical Asia (0.01 ± 0.001 g C kg<sup>−1</sup> H<sub>2</sub>O yr<sup>−1</sup>). Spatially, three independent remote sensing-driven datasets consistently showed a significant WUE increase in 32%–54% of tropical regions, while only 1%–3% experienced a significant decline. Furthermore, tropical ecosystems exhibited a substantial increase in GPP (5.47 ± 0.60 g C m<sup>−2</sup> yr<sup>−1</sup>), with the highest growth rate in tropical Asia (11.45 ± 0.37 g C m<sup>−2</sup> yr<sup>−1</sup>), whereas ET showed minor changes. This suggests that WUE changes in tropical ecosystems are primarily driven by increases of GPP rather than ET. Further analysis identified leaf area as the dominant factor influencing WUE, GPP, and ET trends across the tropics. We also found that the extreme drought during the 2015/16 El Niño event resulted in a net decrease in WUE (−0.03 ± 0.01 g C kg<sup>−1</sup> H<sub>2</sub>O), which transitioned to a net increase (0.04 ± 0.01 g C kg<sup>−1</sup> H<sub>2</sub>O) by 2016/17. Compared to satellite-driven results, most land surface models captured the direction of tropical WUE trends but simulated a slower rate of change, with substantial variation in predicted trend intensities among models. This study advances our understanding of tropical ecosystem WUE dynamics and provides critical insights for predicting future WUE changes under ongoing climate change, informing strategies for carbon sequestration and water resource management in vulnerable tropical regions.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115242"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145962391","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}
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