Remote Sensing of Environment最新文献

{"title":"A snow properties-aware deep learning framework for penetration bias estimation of TanDEM-X DEMs over ice sheets","authors":"Alexandre Becker Campos ,&nbsp;Antoine Diez-Latteur ,&nbsp;José-Luis Bueso-Bello ,&nbsp;Matthias H. Braun ,&nbsp;Paola Rizzoli","doi":"10.1016/j.rse.2026.115243","DOIUrl":"10.1016/j.rse.2026.115243","url":null,"abstract":"<div><div>The accurate assessment of glacier volume and mass changes as well as snow depth is crucial for understanding glaciological processes and the impact of climate change. TanDEM-X, an X-band spaceborne interferometric synthetic aperture radar (InSAR) mission, offers global, high-resolution digital elevation models (DEMs) that are invaluable for these studies. However, the inherent variability in radar wave penetration into snow and ice creates challenges in accurately estimating surface elevation changes and snow depth. Variations in the acquisition geometry and snow properties can affect the estimation of the radar mean phase center, leading to penetration bias and an underestimation of the surface topographic height. In this work, we propose a novel deep learning framework for estimating the penetration bias of TanDEM-X DEMs over ice sheets, by combining the knowledge of the physical properties of snow and the InSAR system for the development of a robust regression framework. Due to the lack of extended reference data, which jeopardizes the use of fully-supervised data-driven approaches, we propose a deep learning approach based on two intrinsically connected tasks: a first unsupervised snow facies segmentation model designed to capture the overall properties of the snowpack independently of the single-pass InSAR acquisition geometries; and a subsequent downstream penetration bias regression model. To ensure that the robustness against the InSAR geometries of the first model is preserved, we propose two approaches: first, we employ a fine-tuning approach to transfer the weights of the segmentation model for a downstream regression task, leveraging the knowledge acquired by the pretext segmentation task for the regression of the penetration bias of TanDEM-X DEMs; and second, a multitask learning approach for the downstream task by jointly training both the segmentation and regression models, ensuring that the snow-related feature representations identified during the segmentation task are consistently leveraged to improve the final regression performance. We demonstrate that utilizing the first model as a pretext task improves convergence and overall performance, whereas the multitask approach enables better generalization. Experimental results over the Greenland Ice Sheet during boreal winter, using IceBridge laser altimeter measurements as reference data, demonstrate that our method estimates the penetration bias with a coefficient of determination <span><math><msup><mi>R</mi><mn>2</mn></msup></math></span> = 90% and RMSE of 0.65 m, independently of the InSAR acquisition geometry and snow properties. The work performed here is crucial for enhancing the accuracy of TanDEM-X DEMs over snow and ice-covered regions, thereby improving our understanding of glaciological processes and their climatic responses.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115243"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145993350","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":"Flood pulse monitoring in wetlands with multi-temporal Sentinel-1 interferometric coherence data: Application to the Okavango Delta (Botswana)","authors":"Louis Gaudaré ,&nbsp;Samuel Corgne ,&nbsp;Marc Jolivet ,&nbsp;Olivier Dauteuil ,&nbsp;Cécile Doubre ,&nbsp;Piotr Wolski ,&nbsp;Raphaël Grandin ,&nbsp;Marie-Pierre Doin ,&nbsp;Philippe Durand ,&nbsp;FLATSIM Working Group","doi":"10.1016/j.rse.2025.115173","DOIUrl":"10.1016/j.rse.2025.115173","url":null,"abstract":"<div><div>Flood-pulsed wetlands are characterized by significant seasonal water fluctuations, which play a critical role in the dynamics of these sensitive ecosystems. Among the growing number of existing remote sensing products, we explore the potential of interferometric (InSAR) coherence time series, derived from Sentinel-1 synthetic-aperture radar images, to characterize the hydrological dynamics of the Okavango Delta, a vast flood-pulsed wetland. Interferometric coherence reflects changes in surface conditions, making it a powerful tool for detecting flood propagation. By fitting harmonic functions, we produce parameters that quantify the seasonality of coherence time series with short isotemporal baselines (12 days). In particular, we developed a normalized seasonal index based on the ratio between the seasonal amplitude and the root-mean-square error of the fitted harmonic function, to map the seasonality of the coherence time series. A multi-annual analysis of coherence time series reveals a strong relationship between their seasonality, land cover, and flood frequency. Unsupervised clustering applied to statistical and seasonal metrics of coherence time series yields consistent classifications that map the variability of flood frequencies across wetland areas and clearly distinguish wetlands from dry zones. Similarly, thresholds applied to normalized seasonal indices delineate the year-to-year extent of flood pulses with accuracy around 79 %. We show that coherence time series in never flooded areas exhibit a pronounced seasonal pattern driven by rainfall cycle, whereas this seasonality is disrupted by flood pulses in wetlands. Building on this, we developed a change-detection approach to map the floods by identifying the date when coherence time series diverge from their seasonal pattern. The resulting flood arrival dates achieve 74–83 % accuracy compared to a reference dataset derived from optical data. Our results highlight the potential of coherence time series as a robust indicator of seasonal variations in inundation extent in flood-pulsed wetlands.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115173"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145689079","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":"Delta-X: An airborne remote sensing framework to calibrate hydrodynamic and ecogeomorphic processes responsible for land building in coastal deltas","authors":"Marc Simard ,&nbsp;Cathleen E. Jones ,&nbsp;Robert R. Twilley ,&nbsp;Edward Castañeda-Moya ,&nbsp;Sergio Fagherazzi ,&nbsp;Cédric G. Fichot ,&nbsp;Michael P. Lamb ,&nbsp;Paola Passalacqua ,&nbsp;Tamlin M. Pavelsky ,&nbsp;David R. Thompson ,&nbsp;Saoussen Belhadj-aissa ,&nbsp;Pradipta Biswas ,&nbsp;Alexandra Christensen ,&nbsp;Luca Cortese ,&nbsp;Michael Denbina ,&nbsp;Carmine Donatelli ,&nbsp;Sarah Flores ,&nbsp;Andy Fontenot ,&nbsp;Joshua P. Harringmeyer ,&nbsp;Daniel Jensen ,&nbsp;Yang Zheng","doi":"10.1016/j.rse.2025.115201","DOIUrl":"10.1016/j.rse.2025.115201","url":null,"abstract":"<div><div>Coastal river deltas are highly dynamic regions with hydrological processes that vary on hourly, daily, and seasonal timescales. Soil formation in deltas relies on the balance between mineral sediment deposition, erosion, and organic matter production, which are intricately controlled by vegetation and hydrodynamic conditions. The spatial complexity and rapid variations in flow, particularly due to tides, present a major challenge to spaceborne remote sensing achieving the required spatial resolution and temporal sampling. Here, we present an airborne remote sensing and in situ framework that measures parameters that are critical to calibrate and validate hydrodynamic, sediment transport, morphodynamic, and ecogeomorphic models. We discuss the measurements and models within the context of the NASA Earth Venture-Suborbital Delta-X mission, which implemented the framework in two deltaic regions of the Mississippi River Delta with contrasting hydrological regimes, namely the Atchafalaya (i.e., active, river-dominated) and Terrebonne (inactive, river-abandoned) basins that are undergoing land gain and land loss, respectively. The Delta-X framework uses two airborne radar instruments to monitor hydrodynamic processes, measuring water surface level and slope within channels, and tide-induced water level change within wetlands. In addition, an airborne imaging spectrometer provides estimates of suspended sediment concentrations in open water as well as vegetation type and aboveground biomass. We also discuss how the data are used to calibrate and validate the models that estimate sediment deposition and organic soil production, which build land to offset subsidence and sea level rise.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115201"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145836950","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":"Long-term forest structure trends in the peninsular Spain from lidar-optical sensors synergies","authors":"M. Tanase ,&nbsp;J.P. Martini ,&nbsp;P. Miranda ,&nbsp;D. Garcia ,&nbsp;V. Wilke ,&nbsp;S. Miguel ,&nbsp;C. Mihai ,&nbsp;J. Diez ,&nbsp;S. Natal ,&nbsp;D. San Martin ,&nbsp;P. Ruiz-Benito","doi":"10.1016/j.rse.2025.115196","DOIUrl":"10.1016/j.rse.2025.115196","url":null,"abstract":"<div><div>Information on forest structure is needed for many management aspects, from carbon stock evaluation to fire hazard prediction. Such information is increasingly available from remote sensing data, including light detection and ranging (lidar) sensors. However, continuous forest monitoring is hindered by the limited temporal availability of lidar acquisitions. This study focused on integrating temporally consistent optical acquisitions with temporally sparse lidar acquisitions to provide long term information on forest structural characteristics across the peninsular Spain. We evaluated different modeling approaches including machine learning and advanced deep learning models to ascertain their limitations for long-term forest monitoring. Subsequently, annual estimates of forest structural attributes were generated from 1985 to 2024. Across all forests, height (H), canopy cover (FCC), and above ground biomass (AGB) increased by 34 %, 29 % and respectively 17.5 % from the first (1985–1989) to the last lustrum (2019–2024). By biome, the increase in H, FCC and AGB was larger over the Mediterranean (35 %, 33 %, and 18 % respectively) when compared to the Atlantic (33 %, 10 %, and 15 % respectively) forests. For the model training year, deep learning models improved estimation accuracy by 27 ± 2.6 % (mean value across regions and variables) when compared to machine learning models. However, when applied to data from other years (temporal inference), model precision was similar except for height in the Mediterranean were deep learning models improved RMSE estimates by 7 %. Overall, the deep learning models presented significant computational drawbacks when applied to large geographic extents and extensive temporal ranges, substantially increasing both training and prediction times without providing a clear improvement in accuracy compared to machine learning across years. Therefore, the long-term forest database was generated using machine learning models. This database provides a spatially explicit understanding of forest structural changes over nearly four decades, offering a valuable resource for forest management, ecological assessments, and climate-related policy-making.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115196"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145752945","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":"S2Coast-2023: The first global 10-meter resolution coastline dataset derived from enhanced Sentinel-2 composite imagery using Google Earth Engine","authors":"Yuanqiang Duan ,&nbsp;Arturo Sanchez-Azofeifa ,&nbsp;Chunpeng Chen ,&nbsp;Bo Tian ,&nbsp;Xing Li ,&nbsp;Dhritiraj Sengupta ,&nbsp;Yunxuan Zhou","doi":"10.1016/j.rse.2025.115186","DOIUrl":"10.1016/j.rse.2025.115186","url":null,"abstract":"<div><div>Coastlines serve as dynamic interfaces between terrestrial and marine ecosystems. While advances in satellite remote sensing have promoted coastline monitoring, no comprehensive global coastline dataset derived from Sentinel-2 imagery has been produced, despite its 10-m resolution and frequent revisit capabilities. To address this, we propose S2Coast, a knowledge-based framework based on the Google Earth Engine (GEE) platform, designed to automatically detect the unified High Water Line (HWL) from annually composited Sentinel-2 imagery, termed HWL_Sentinel-2. This method integrates multi-temporal observational information, spectral characteristics, and spatial features to delineate the stable extent of high seawater submergence captured in cloud-free satellite images over a year. The boundaries in the resultant “Land-Water” binarization image represent HWL_Sentinel-2, derived through integrated threshold segmentation for three decision layers. Subsequently, raster-to-vector conversion and optimization steps were performed. Following the sequential execution of 12,275 sub-tasks, the resultant S2Coast-2023 dataset includes approximately 2.17 million kilometers of coastline for 2023, covering all continents and most islands larger than 100 m², excluding Antarctica and remote polar islands. Global validation based on 1146 samples demonstrates that the developed tool, S2Coast, exhibits robust stability and universality, with average 88 % of sampled coastline segments falling within a 10-m buffer when comparing repeatedly generated coastlines from consecutive years (2021 to 2023). For positional accuracy, using 532 coastline samples with very high resolution image-based OpenStreetMap coastlines as reference data reveal an average positional deviation of −1.10 m (95 % CI: −2.06 to −0.15 m) and an average root mean square error (RMSE) of 17.40 m (95 % CI: 16.23 to 18.65 m). As the first global coastline dataset with 10-m resolution and a unified coastline indicator, it will serve as a crucial foundational resource for future coastal research.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115186"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145697052","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 transformer based multi-task deep learning model for urban livability evaluation by fusing remote sensing and textual geospatial data","authors":"Wen Zhou ,&nbsp;Claudio Persello ,&nbsp;Dongping Ming ,&nbsp;Shaowen Wang ,&nbsp;Alfred Stein","doi":"10.1016/j.rse.2026.115232","DOIUrl":"10.1016/j.rse.2026.115232","url":null,"abstract":"<div><div>Livable cities enhance urban economic development, improve physical and mental health, foster well-being, and foster urban sustainability. Evaluating urban livability is therefore important for policymakers to develop urban planning and development strategies aimed at improving livability. Mainstream methods of evaluating urban livability assign different weights to diverse indicators extracted from survey data, statistical data, and geospatial data. To relieve such time-consuming and labor-intensive data collection, this study proposes a transformer-based multi-task multimodal regression (TMTMR) model for the simultaneous evaluation of urban livability focusing on five domain-specific scores. Pretrained state-of-the-art computer vision and natural language processing models serve as backbones to extract features from high spatial resolution remote sensing (RS) images, digital surface models (DSM), night light remote sensing (NLRS) images and point of interest (POI) data. An attention mechanism helps the TMTMR model to assign varying significance levels to features from different modalities, thus capturing both intrinsic information and interrelationships among modalities for livability evaluation. Focusing on 13 Dutch areas, our research demonstrates that the TMTMR model efficiently evaluates urban livability with correlation coefficients ranging from 0.605 to 0.779, and root mean square error values between 0.070 and 0.112 in four unseen test areas. Furthermore, we analyze the synergy between different modalities. We found that modalities of urban livability can be effectively evaluated by aligning, in a descending order, contributions from RS images, NLRS images, DSM, and POI data. We demonstrated that the proposed TMTMR model is capable of effectively evaluating urban livability directly from multimodal geospatial data.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115232"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938772","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 synergistic super-ellipsoidal particle shape and ice cloud optical thickness retrieval method based on satellite polarimetric observations","authors":"Yizhen Meng ,&nbsp;Lei Bi ,&nbsp;Wei Han","doi":"10.1016/j.rse.2025.115172","DOIUrl":"10.1016/j.rse.2025.115172","url":null,"abstract":"<div><div>Ice clouds, composed of irregular ice crystals, play a critical role in the Earth's radiative balance and climate regulation. Satellite polarimetric observations, such as those from the POLarization and Directionality of Earth Reflectance-3 (POLDER-3), exhibit high sensitivity to particle characteristics, making them valuable for deriving ice cloud microphysical properties. Conventional ice cloud remote sensing methods typically rely on single-particle models, which assume a prior particle shape across entire regions, thereby neglecting the inherent spatial heterogeneity. Under this context, the super-ellipsoidal particle model was developed, enabling continuous variation in surface morphology through three parameters (i.e., aspect ratio, roundness, and surface roughness), thus facilitating the retrieval of particle shape variations. To comprehensively consider the spatial heterogeneity of ice crystals and assess the effectiveness of the super-ellipsoidal multi-particle model, a synergistic retrieval of particle shape parameters and ice cloud optical thickness (IOT) was conducted across six tropical cyclone (TC) and cloud cases. The retrieval framework was built upon vector radiative transfer simulations derived from the adding-doubling model, linking POLDER-3 observations with the super-ellipsoidal particle models and IOT. The retrieved particle shapes and IOT were validated by comparing re-simulated radiance with satellite observations. The findings indicate an order-of-magnitude enhancement over single-particle models in retrieval performance, with root mean square errors (RMSEs) for normalized radiance decreasing from [0.0371, 0.1063] to [0.0023, 0.0042], and for polarized radiance reducing from [0.0036, 0.0061] to [0.0008, 0.0018]. The proposed novel method offers substantial improvements in retrieving IOT, contributing valuable insights for advancing ice cloud remote sensing techniques.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115172"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145752888","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 review of forward modelling and retrieval approaches for forest soil moisture and vegetation optical depth using L-band radiometry","authors":"Andreas Colliander ,&nbsp;Mike Schwank ,&nbsp;Yiwen Zhou ,&nbsp;Mehmet Kurum ,&nbsp;Cristina Vittucci ,&nbsp;Leung Tsang ,&nbsp;Alex Roy ,&nbsp;Aaron Berg","doi":"10.1016/j.rse.2025.115158","DOIUrl":"10.1016/j.rse.2025.115158","url":null,"abstract":"<div><div>Forests are a critical component of the Earth system, accounting for approximately one-third of global photosynthetic activity and carbon storage. They also provide essential habitats for countless species and vital resources for human activities. Low-frequency (L-band; 1–2 GHz) microwave radiometry enables the measurement of forest soil moisture (SM) and L-band vegetation optical depth (L-VOD), offering valuable insights into processes such as tree growth, water infiltration, soil fertility, fuel moisture, carbon stocks, wildfire vulnerability, and biodiversity dynamics. These measurements also support the study of carbon and water fluxes, tree responses to hydrological stress (e.g., drought), and fuel moisture estimation. However, existing algorithms for retrieving SM and L-VOD were primarily developed for low-biomass vegetation types (e.g., grasslands and croplands), differing structurally from forests. This motivates the present review to evaluate the current retrieval approaches, their performance assessment methods, and available validation resources. The review found that systematic uncertainties persist in forest retrievals, despite the demonstrated sensitivity of L-band brightness temperature (TB) to forest SM and L-VOD. Moreover, the focus on non-forest ecosystems has led to a lack of suitable ground truth and reference data for validating forest SM and L-VOD products, and current validation techniques remain underdeveloped. To fully harness the potential of L-band radiometry in forest monitoring, new retrieval algorithms that account for the unique structural and compositional characteristics of forests are required. Additionally, validation efforts must be enhanced both quantitatively and qualitatively—particularly for L-VOD—to improve confidence in these remote sensing products.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115158"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145759818","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":"NRT-GSF: A novel near-real-time ground-satellite fusion algorithm to retrieve daily green area index at field scale","authors":"Wenjuan Li ,&nbsp;Marie Weiss ,&nbsp;Samuel Buis ,&nbsp;Aleixandre Verger ,&nbsp;Sylvain Jay ,&nbsp;Zihan Ren ,&nbsp;Wenbin Wu ,&nbsp;Jingyi Jiang ,&nbsp;Alexis Comar ,&nbsp;Benoit De Solan","doi":"10.1016/j.rse.2025.115160","DOIUrl":"10.1016/j.rse.2025.115160","url":null,"abstract":"<div><div>Near-real-time (NRT) daily crop monitoring at the field scale is crucial for precision agriculture, yet remains challenging due to limitations in the spatial or temporal resolution of existing remote sensing methods. While Sentinel-2 provides adequate spatial resolution for field-level applications, its temporal resolution is insufficient for capturing rapid crop dynamics, especially in cloudy regions. Existing spatiotemporal fusion techniques require multiple clear-sky images and lack true NRT capability, while ground-based sensors offer continuous monitoring but with limited spatial coverage. To address these limitations, this study develops the Near-Real-Time Ground-Satellite Fusion (NRT-GSF) algorithm, a novel approach based on a Bayesian dynamic linear model and Kalman filtering. The algorithm uniquely integrates Sentinel-2 imagery with continuous measurements from Internet of Things for Agriculture (IoTA) systems to generate daily 10-m Green Area Index (GAI) products. Its recursive framework supports both forward prediction in NRT mode following satellite overpasses and backward updating to refine historical profiles. Implemented over French wheat fields using 34 IoTA systems and Sentinel-2 time series from 2019, the algorithm effectively enhanced spatiotemporal completeness and accuracy (<em>R</em> = 0.75–0.98, <em>RMSE</em> = 0.1–0.49). A comprehensive leave-one-out Sentinel-2 evaluation demonstrated its superiority over the current Consistent Adjustment of the Climatology to Actual Observations (CACAO) algorithm. Ground validation using handheld RGB cameras further confirmed the accuracy of the GAI products from the new algorithm (RMSE = 0.5). The NRT-GSF framework offers a robust and operationally solution for daily, high-resolution crop GAI mapping in NRT mode, and it can be extended to other traits or applications in the near-real-time context.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"333 ","pages":"Article 115160"},"PeriodicalIF":11.4,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145598723","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":"Integrating prior information for improving 3D model-driven GAI estimation with application to wheat crops","authors":"Mingxia Dong ,&nbsp;Shouyang Liu ,&nbsp;Marie Weiss ,&nbsp;Aojie Yin ,&nbsp;Chen Zhu ,&nbsp;Benoit de Solan ,&nbsp;Wei Guo ,&nbsp;Fernandes Richard ,&nbsp;Wenjuan Li ,&nbsp;Xia Yao ,&nbsp;James Burridge ,&nbsp;Zhen Chen ,&nbsp;Yanfeng Ding ,&nbsp;Frédéric Baret","doi":"10.1016/j.rse.2025.115161","DOIUrl":"10.1016/j.rse.2025.115161","url":null,"abstract":"<div><div>Green Area Index (GAI) is a key crop trait obtained through remote sensing with wide applications in agriculture. Although 3D model-driven approaches to retrieve GAI from multispectral reflectance observations are appealing, they are constrained by limitations in the realism of simulated datasets used for training. This study comprehensively explored how to integrate prior information—such as soil background, leaf optical properties, and canopy structure—into radiative transfer models to improve GAI retrieval. A suite of models (MARMIT-2 for soil reflectance, PROSPECT for leaf optical properties, ADEL-Wheat for canopy structure, and LESS for radiative transfer) was employed to generate five simulation datasets incorporating different combinations of prior information. Support Vector Regression (SVR) models were independently trained on these simulated datasets and validated against an extensive data set made of 310 samples of GAI ground measurements and the corresponding SuperDove satellite data. Our results show that stage-specific GAI retrieval integrating detailed prior information on soil and leaf properties (R² = 0.93, RMSE = 0.47) notably outperforms standard model inversion approaches (R² = 0.82, RMSE = 0.73). The improved realism of the training dataset stems from three key strategies was discussed in detail including: (1) employing models that integrates physical and biological knowledge; (2) narrowing the training space; and (3) minimizing distribution shifts. While this study focused on GAI estimation for wheat crops using SuperDove observations, the findings can be extended to other crops, vegetation variables, and satellite systems.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"333 ","pages":"Article 115161"},"PeriodicalIF":11.4,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145613834","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}