Remote Sensing of Environment最新文献

{"title":"Platespect: A new model for leaf fluorescence spectra","authors":"Yujie Wang ,&nbsp;Xiangzhong Luo ,&nbsp;Christian Frankenberg","doi":"10.1016/j.rse.2025.114990","DOIUrl":"10.1016/j.rse.2025.114990","url":null,"abstract":"<div><div>Models to simulate solar-induced chlorophyll fluorescence (SIF) are widely used to interpret fluorescence observations across scales. However, leaf fluorescence spectra models often mix-use the plate model and Kubelka-Munk (KM) model, which differ in their assumptions in the internal scattering within a leaf and brings in uncertainty in explaining SIF observations. Additionally, fluorescence photons are not conserved in spectral models due to their use of a sigmoid function to adjust the fluorescence emission spectrum dependent on excitation wavelength. To resolve these problems in SIF simulation, we present a new spectral model, Platespect. It is based on the plate model that can compute backward and forward leaf fluorescence spectra and also rescales the raw fluorescence emission spectrum to conserve fluorescence photons. We theoretically compared the fluorescence simulations from Platespect and Fluspect, which adopts the commonly used KM model, at the leaf and canopy scales; we also evaluated them with leaf-level backward fluorescence observations. At the leaf level, although Platespect predicted fluorescence magnitudes similar to those of Fluspect, it showed substantial differences in the backward and forward fluorescence spectra. Accounting for scattering among leaf plates in Fluspect helps reduce the difference. Platespect predicted a higher far-red fluorescence due to the rescaled fluorescence spectra emitted by longer wavelength light. When fitted against leaf-level observations, Platespect performed slightly better in the red fluorescence region, but all models showed a systematically biased fluorescence spectrum. Assessed at the canopy level, Platespect-based simulations predicted a higher SIF and higher sensitivity to leaf chlorophyll content. Our results highlight the necessity of better representing the scattering among plates, improving the raw fluorescence emission spectrum, and conserving emitted fluorescence photons to improve the simulation of SIF across scales.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"331 ","pages":"Article 114990"},"PeriodicalIF":11.4,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027424","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":"The NASA VIIRS burned area product, global validation, and intercomparison with the NASA MODIS burned area product","authors":"Louis Giglio ,&nbsp;Luigi Boschetti ,&nbsp;David P. Roy ,&nbsp;Joanne V. Hall ,&nbsp;Maria Zubkova ,&nbsp;Michael Humber ,&nbsp;Haiyan Huang ,&nbsp;Vladyslav Oles","doi":"10.1016/j.rse.2025.115006","DOIUrl":"10.1016/j.rse.2025.115006","url":null,"abstract":"<div><div>The VIIRS was designed in recognition of the need for long-term observations in support of global-change science and to provide MODIS observation continuity. The NASA MODIS burned area (BA) product (MCD64A1) has been generated systematically for nearly 25 years and maps globally the approximate day of burning at 500-m resolution. We describe the follow-on NASA Collection 2 VIIRS BA product (VNP64A1) that became available in October 2024 and provides global BA mapping from 2012 into the next decade. The product is derived using an adaptation of the Collection 6.1 MCD64A1 BA mapping algorithm and using the same sinusoidal grid and 500-m reporting resolution. Annual and monthly intercomparisons of the VPN64A1 and MCD64A1 products at global and continental/regional scales indicate high product BA reporting consistency. Approximately 2.6 % of the land surface was mapped as burned annually; VNP64A1 mapped slightly more than MCD64A1 but with 0-day median burn-date reporting differences. A global (Stage-3) product validation, using 561 interpreted Landsat image pairs, was undertaken and the VNP64A1 product had estimated 36.7 % commission and 72.4 % omission errors, compared to MCD64A1 34.2 % commission and 71.5 % omission errors. Product accuracies were also characterized using metrics derived from the weighted regression (to incorporate sample inclusion probabilities) between 5-km BA proportions in the products and the Landsat data. The omission and commission errors observed at 30-m were largely compensated at 5-km, with coefficient of determination (0.70), slope (0.83), and intercept (−0.0006) terms for the VNP64A1 product nearly identical to those of MCD64A1. With respect to global temporal reporting accuracy, 38 % of VNP64A1 burned grid cells had burn dates that matched the date of a MODIS active fire, versus 46 % for MCD64A1, and 71 % of VNP64A1 burned grid cells had burn dates within two days (72 % for MCD64A1). The research presented in this paper indicates that the VNP64A1 product is suitable for providing continuity of the MODIS BA record.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"331 ","pages":"Article 115006"},"PeriodicalIF":11.4,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027426","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":"Predicting fire-induced individual tree mortality at the landscape level using fire intensity and airborne laser scanning data","authors":"Aaron M. Sparks ,&nbsp;Ryan Armstrong ,&nbsp;Alistair M.S. Smith ,&nbsp;Steve Scharosch ,&nbsp;Mark V. Corrao ,&nbsp;Thomas Montzka","doi":"10.1016/j.rse.2025.115007","DOIUrl":"10.1016/j.rse.2025.115007","url":null,"abstract":"<div><div>The prediction of fire-induced tree mortality is important for evaluating potential timber volume losses, replanting costs, and for assessing how mortality will impact long-term yield and carbon dynamics. However, current methods do not provide spatially explicit predictions, limiting the use of this data for proactive forest management, such as thinning and reducing fuel load to reduce tree mortality. In this study we assess whether the incorporation of individual tree inventory data derived from airborne laser scanning and modeled and observed fire intensity data can provide accurate spatially explicit tree mortality predictions. Specifically, tree-level mortality was predicted for over 1.9 million trees within six wildfires in mixed coniferous forest in northwestern Montana, USA, and validated using high resolution imagery for each segmented tree crown. Random forest classification models utilizing observed fire intensity metrics derived from VIIRS observations were the most accurate (overall accuracy: 77.2 %), followed by random forest classification models utilizing modeled fire intensity (64.5–66.1 %) and those that utilized existing logistic regression relationships (55.7–59.0 %). The random forest tree mortality models also produced lower RMSE (6.3–10.2 %) and bias (1.7–3.7 %) compared to the logistic regression approach (RMSE: 38.4 %, bias: −29.6 %) when mortality accuracy was assessed across tree size class. The predictor variable importance quantification showed that fire intensity metrics were more important than species and structural variables in the mortality classification. Ultimately, this study contributes to the remote sensing of fire effects and fire science fields by developing a remote sensing-based methodology for predicting spatially explicit individual tree mortality across large spatial extents.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"331 ","pages":"Article 115007"},"PeriodicalIF":11.4,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145020475","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":"Estimation of moisture content of sandy beaches from X-band synthetic aperture radar","authors":"Julie Paprocki ,&nbsp;Nina Stark ,&nbsp;Hans C. Graber ,&nbsp;Heidi Wadman ,&nbsp;Jesse E. McNinch","doi":"10.1016/j.rse.2025.115005","DOIUrl":"10.1016/j.rse.2025.115005","url":null,"abstract":"<div><div>Moisture content is a critical parameter for estimating the strength of partially saturated sand for engineering challenges such as beach trafficability. A framework for estimating the volumetric moisture content of sandy beaches using satellite-based <em>HH</em>-polarized X-band synthetic aperture radar imagery is presented and used to test the applicability of three theoretical scattering models: Oh, Dubois, and the Integral Equation Model (IEM). The developed framework relies on the measured backscatter coefficient, soil surface root-mean square (RMS) height, and the geometric characteristics of the image. Models for estimating the RMS height were developed based on field measurements for two distinct sites composed of predominately quartz sand and approximately uniform beach slopes: Duck, North Carolina and Cannon Beach in Yakutat, Alaska. The models were developed and tested for incidence angles of 23.3°-54.2° using data obtained from the Cosmo-SkyMED and TerraSAR-X satellites. Four sets of RMS height models were tested: Oh with moisture contents greater than 0 %, Oh with moisture contents greater than a very dry threshold, Dubois, and the IEM. Unique RMS height models, specific to a moisture content model, were developed for incidence angles ranging from a single incidence angle to a range of consecutive incidence angles. Applying the RMS height models, the root mean square error (RMSE) of moisture content was 0.7–6.9 %. Images with incidence angles of 30°- 46° and 40°-50° resulted in the best estimates of moisture content when compared to other ranges of incidence angles for the models tested. Deviations generally represented underestimates of the moisture content (0.1 %–1.3 %), with greater underestimates observed for the IEM. Spatial estimates of moisture content resulted in two distinct zones, one with low moisture contents and a second with slightly elevated moisture contents for all models except the IEM. Challenges associated with differences in scattering mechanisms, a lack of data with high moisture content, and sensitivity of models to small changes in RMS height are discussed.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"331 ","pages":"Article 115005"},"PeriodicalIF":11.4,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145020034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping canopy phenolics in European mixed temperate forests using air- and space-borne imaging spectroscopy","authors":"Rui Xie ,&nbsp;Roshanak Darvishzadeh ,&nbsp;Andrew K. Skidmore ,&nbsp;Freek van der Meer ,&nbsp;Alejandra Torres-Rodriguez ,&nbsp;Marco Heurich","doi":"10.1016/j.rse.2025.115020","DOIUrl":"10.1016/j.rse.2025.115020","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Phenolics are a rarely quantified plant biochemical trait that plays a vital role in plant physiology and ecosystem functioning, contributing to plant's chemical defence and influencing nutrient cycling and soil microbial compositions. Spatially continuous information on foliar phenolics is essential for assessing plant health and ecosystem functional diversity. However, previous efforts to predict and map phenolics have been confined to aircraft-based hyperspectral data in limited biomes. The potential of next-generation imaging spectroscopy, whether airborne- or spaceborne-based, for mapping phenolics remains underexplored, particularly in structurally complex and heterogeneous ecosystems such as European mixed temperate forests. Furthermore, much is still unknown about the consistency and uncertainties of predicting forest canopy phenolics across different acquisition levels (airborne vs. spaceborne), limiting our ability to generalise and upscale local trait estimates to broader spatial extents. In this study, we sampled sunlit top-of-canopy leaves from three dominant tree species across mixed temperate forests in southeast Germany. Leveraging next-generation airborne (AVIRIS-NG) and spaceborne (PRISMA) imaging spectroscopy (400–2400 nm), we modelled two ecologically important phenolics (total phenol and tannin) expressed in three forms (foliar mass-based, foliar area-based, and canopy-based). The predictive accuracy of two data-driven approaches, partial least squares regression (PLSR) and Gaussian processes regression (GPR), was compared to assess performance across different spatial scales. Our results demonstrate that phenolics in sunlit canopy leaves can be accurately estimated from both airborne and spaceborne data, with foliar area-based phenolics showing the strongest relationship with spectral reflectance (total phenol: &lt;em&gt;R&lt;/em&gt;&lt;sup&gt;2&lt;/sup&gt; = 0.64–0.69, NRMSE = 13.28%–15.65%; tannin: &lt;em&gt;R&lt;/em&gt;&lt;sup&gt;2&lt;/sup&gt; = 0.49–0.65, NRMSE = 15.86%–21.29%). We observed several similar patterns in model coefficients across airborne and satellite levels, with informative wavelengths aligning with known phenolic features. While the model accuracy declined slightly when scaling from canopy to landscape scale, phenolic maps derived from AVIRIS (aggregated to 30 m) and PRISMA showed good spatial agreement and linearity (GPR: &lt;em&gt;r&lt;/em&gt; = 0.68, slope = 0.86; PLSR: &lt;em&gt;r&lt;/em&gt; = 0.57, slope = 0.49). These maps successfully captured inter- and intra-species phenolic variability across the test site with low prediction uncertainty. Our findings provide valuable insights into mapping canopy traits across different observational scales, demonstrating how next-generation imaging spectroscopy can characterize the spatial and temporal dynamics of plant phenolics. This research paves the way for improved global monitoring of ecosystem functioning, as well as the pattern of phenolics across forested landscapes and trees' potential ‘chemical’ defences aga","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"331 ","pages":"Article 115020"},"PeriodicalIF":11.4,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145020033","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":"Improving 2D hydraulic modelling in floodplain areas with ICESat-2 data: A case study in the Upstream Yellow River","authors":"Monica Coppo Frías ,&nbsp;Suxia Liu ,&nbsp;Xingguo Mo ,&nbsp;Daniel Druce ,&nbsp;Dai Yamazaki ,&nbsp;Aske Folkmann Musaeus ,&nbsp;Karina Nielsen ,&nbsp;Peter Bauer-Gottwein","doi":"10.1016/j.rse.2025.115008","DOIUrl":"10.1016/j.rse.2025.115008","url":null,"abstract":"<div><div>Reliable flood inundation modelling in complex river systems that are poorly instrumented is often limited by inaccuracies in open source DEMs, particularly near river channels and vegetated regions. This study proposes a methodology to correct and enhance resolution of satellite based DEMs in floodplain areas with ICESat-2 land elevation, Sentinel-2 MSI imagery, and a simple artificial neural network (ANN). FabDEM (30-m) is selected as the base DEM, and the ANN is trained to correct elevation errors at 10-m resolution using spectral bands from Sentinel-2 and ICESat-2 ATL03 elevation. The corrected ANN DEM reduces the mean squared error by 7 cm on average and up to 38 cm in the areas closer to the main river channel. MIKE 21 is used to simulate 2D flood extent maps for four different events, that consider in-situ discharge values at high, medium and low flow, comparing modelled flood extent with observed surface water extent (SWE) maps derived from Sentinel-2 at the selected dates. To ensure that improvements are attributed to DEM corrections rather than hydraulic parametrization, simulations are performed with different uniform values of the Gauckler-Strickler coefficient <span><math><msub><mi>K</mi><mi>s</mi></msub></math></span>, which are kept consistent across FabDEM and ANN DEM based scenarios. The critical success index (CSI), F1- score and bias are calculated for simulations with FabDEM and ANN DEM. Across all events, the ANN DEM improves flood simulation accuracy, increasing the Critical Success Index (CSI) and F1 score by up to 19 % and 13 %, respectively, and reducing bias by up to 25 %. This workflow demonstrates a scalable and efficient approach to improve hydraulic model inputs in data-scarce floodplain environments, offering valuable insights for flood risk assessment and water resource management in remote regions.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"331 ","pages":"Article 115008"},"PeriodicalIF":11.4,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145009525","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":"Precise mapping of relative surface elevation using spaceborne GNSS-R phase altimetry with crossover adjustment: A case study of Lake Ladoga","authors":"Yang Wang ,&nbsp;J. Toby Minear ,&nbsp;Alexa Putnam","doi":"10.1016/j.rse.2025.114993","DOIUrl":"10.1016/j.rse.2025.114993","url":null,"abstract":"<div><div>This paper investigates a crossover adjustment method for spaceborne GNSS Reflectometry (GNSS-R) phase altimetry and demonstrates its capability for precise surface elevation mapping through a case study of Lake Ladoga, which is the largest lake in Europe and exhibits unmodeled gravitational effects on its water surface. GNSS-R phase altimetry measures relative surface height with a constant but unknown offset and may also include errors in the retrieved surface gradients due to factors such as the residual atmospheric propagation error after model correction. The crossover adjustment method estimates the offsets between multiple GNSS-R tracks and each track’s surface gradient errors by formulating and solving a constrained least-squares problem to minimize differences at intersections. Additionally, this method evaluates the accuracy of GNSS-R altimetry retrievals by comparing the consistency of each track with others, enabling the identification of measurement outliers with respect to stable geophysical features. In the case study, we utilize 871 sets of Spire grazing-angle GNSS-R data collected from 2020 to 2023, containing signals coherently reflected off Lake Ladoga, to map the unmodeled gravitational effects on the lake’s water surface. The mapping results are validated using 16 ICESat-2 altimetry datasets, demonstrating a high accuracy with a root-mean-square (RMS) difference of about 3 cm.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"331 ","pages":"Article 114993"},"PeriodicalIF":11.4,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145003472","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":"Detecting the layer height of smoke and dust aerosols over land and ocean using ultraviolet dual-wavelength measurements","authors":"Pei Li ,&nbsp;Yong Xue ,&nbsp;Davide Dionisi ,&nbsp;Botao He ,&nbsp;Peng Wang ,&nbsp;Huihui Li","doi":"10.1016/j.rse.2025.115001","DOIUrl":"10.1016/j.rse.2025.115001","url":null,"abstract":"<div><div>Vertical distribution of atmospheric aerosols has a significant impact on climate change, air quality, cloud-aerosol interactions, atmospheric remote sensing, and global transport. However, current aerosol layer height (ALH) retrieval algorithms, based on passive radiometry, are largely affected by surface reflectance, limiting their applicability to surface with low reflectance, for example, ocean and dark-target land surfaces. The Aerosol Single-scattering albedo and layer Height Estimation (ASHE) algorithm can be well adapted to both land and ocean, but it is strongly influenced by aerosol optical depth (AOD) uncertainty and requires estimation of expected aerosol-free radiation. In order to reduce the effects of AOD uncertainty and to simplify the method, a UV band difference quotient coefficient (UVD) method is developed in this study. First, in the retrieval process, UV aerosol index (UVAI), AOD, and Ångström exponent (AE) are incorporated to perform cloud screening and aerosol type classification (distinguishing between smoke and dust). Next, utilizing the high sensitivity of Rayleigh scattering and the wavelength dependence of aerosol absorption, the logarithmic difference of UV dual-band normalized radiance <span><math><mi>ln</mi><mfenced><msub><mi>r</mi><mn>340</mn></msub></mfenced><mo>−</mo><mi>ln</mi><mfenced><msub><mi>r</mi><mn>378</mn></msub></mfenced></math></span> is used to capture and amplify the impact of ALH on radiance differences. By incorporating the normalization effect of the two-band difference <span><math><msub><mi>r</mi><mn>340</mn></msub><mo>−</mo><msub><mi>r</mi><mn>378</mn></msub></math></span>, the effects from AOD uncertainty are partially offset, resulting in a monotonic, stable, and sensitive response of the UVD to the ALH. Finally, lookup tables (LUTs) for smoke and dust aerosols are built to retrieve ALH. The UVD method was validated using CALIOP observations from a series of well-documented North American biomass burning events and Atlantic dust storms. The validation results indicate that the RMSE ranges from 0.67 to 1.42 km (mean: 0.99 km), while the bias varies between −0.62 and 0.57 km, with a correlation coefficient of 0.71. Additionally, the error distribution of UVD-CALIOP is concentrated within the range of −1 km to 1 km, with 75 % of the data points having an error within 1.13 km.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"331 ","pages":"Article 115001"},"PeriodicalIF":11.4,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145003468","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":"Corrigendum to Toward a near-lossless image compression strategy for the NASA/USGS Landsat Next mission Remote Sensing of Environment Volume 329, 1 November 2025, 114929","authors":"Rehman S. Eon ,&nbsp;Craig De Groot ,&nbsp;Jeffrey A. Pedelty ,&nbsp;Aaron Gerace ,&nbsp;Matthew Montanaro ,&nbsp;Richard K. Covington ,&nbsp;Amy S. DeLisa ,&nbsp;Wen-Ting Hsieh ,&nbsp;Joy M. Henegar-leon ,&nbsp;Douglas J. Daniels ,&nbsp;Christopher Engebretson ,&nbsp;Christopher J. Crawford ,&nbsp;Thomas R.H. Holmes ,&nbsp;Philip Dabney ,&nbsp;Bruce D. Cook","doi":"10.1016/j.rse.2025.115000","DOIUrl":"10.1016/j.rse.2025.115000","url":null,"abstract":"","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"331 ","pages":"Article 115000"},"PeriodicalIF":11.4,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145003467","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":"Study on automated detection methods of shallow surface soil water content based on GPR signal level","authors":"Yunfeng Fang,&nbsp;Tianqing Hei,&nbsp;Zheng Tong,&nbsp;Tao Ma","doi":"10.1016/j.rse.2025.115003","DOIUrl":"10.1016/j.rse.2025.115003","url":null,"abstract":"<div><div>The GPR-based soil moisture detection method achieves an effective balance between spatial scale coverage and detection accuracy; however, the automation level and efficiency still need improvement. This study adopts refined gradient modeling, optimizing delay, envelope amplitude area, and centroid frequency as key indicators for soil moisture prediction. Random forest feature importance analysis indicates that the selected three indicators can effectively characterize soil moisture variation at different scales. Single-factor and three-factor soil moisture prediction models were constructed, and comparisons reveal that the three-factor model significantly outperforms the single-factor model in both prediction accuracy and stability. Bayesian regression was used to assess model and data uncertainty, and the results indicate that the model exhibits low uncertainty within the existing three-factor knowledge range. To achieve automated soil moisture detection, this study proposes an error recursive optimization framework, overcoming the bottlenecks in GPR-based soil moisture automation, and significantly improving detection accuracy and efficiency.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"331 ","pages":"Article 115003"},"PeriodicalIF":11.4,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144996052","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}