Remote Sensing of Environment最新文献

{"title":"Influence of data and methods on high-resolution imagery-based tree species recognition considering phenology: The case of temperate forests","authors":"Xinlian Liang ,&nbsp;Jianchang Chen ,&nbsp;Weishu Gong ,&nbsp;Eetu Puttonen ,&nbsp;Yunsheng Wang","doi":"10.1016/j.rse.2025.114654","DOIUrl":"10.1016/j.rse.2025.114654","url":null,"abstract":"<div><div>Seasonal phenological transformations alter tree appearances, notably by influencing the size and color of the foliage. It has long been anticipated that such phenology induced characteristics can help address the tree-species recognition problem, a fundamental challenge in forest science. Yet, studies on tree-species recognition using remote sensing and phenological characteristics have been rare, due to the very limited availability of high spatiotemporal resolution observations. Moreover, the interactions between the effectiveness of phenological characteristics, remote sensing data, and the analytical methodologies have not yet been sufficiently explored. The understanding of how to integrate multi-temporal observations and phenological characteristics in tree-species recognition has been lacking. This study aims to identify principles for optimizing species recognition by combining data, methods, and phenological dynamics. This involves understanding the impact factors of various methodologies, and how they interact with phenological characteristics and datasets at different times and/or frequencies. The study was carried out using multi-temporal high-resolution optical images of a temperate forest, which were collected in 2021 during leaf growth and senescence periods between May and October, i.e., three leaf growth (May–August) and three leaf senescence (September–October) periods. The test site comprised 14 different tree classes, including 11 species, 2 genera, and 1 dead tree class. The experimental results showed that, for deep learning approaches, the current main limitations in the tree species recognition lie in sample imbalance as the targeted species number increases. With the state-of-the-art data and methods, distinguishing between species within a same genus is much more challenging than differentiating between species from different genera or families. It is also revealed that the best timing for tree species classification is early autumn (September) or late spring (May) when a single-temporal (one-timepoint) data is applied; all-temporal (six-timepoint) data improves the recognition results in comparison with single-temporal observations; however, the improvements from adding additional timepoints became marginal after two timepoint are used with one from late spring and other from early autumn. Furthermore, prior knowledge of individual crown boundaries, typically obtained through individual tree crown delineation, is essential for efficiently incorporating phenological variations into species recognition.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"323 ","pages":"Article 114654"},"PeriodicalIF":11.1,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synergistic estimation of mangrove canopy height across coastal China: Integrating SDGSAT-1 multispectral data with Sentinel-1/2 time-series imagery","authors":"Mingming Jia ,&nbsp;Rong Zhang ,&nbsp;Chuanpeng Zhao ,&nbsp;Yaming Zhou ,&nbsp;Chunying Ren ,&nbsp;Dehua Mao ,&nbsp;Huiying Li ,&nbsp;Genyun Sun ,&nbsp;Hongsheng Zhang ,&nbsp;Wensen Yu ,&nbsp;Zongming Wang ,&nbsp;Yeqiao Wang","doi":"10.1016/j.rse.2025.114719","DOIUrl":"10.1016/j.rse.2025.114719","url":null,"abstract":"<div><div>Mangrove canopy height (MCH) is a critical indicator used to evaluate blue carbon sequestration and biodiversity conservation. However, mapping MCH is challenging because of the dense tree canopy and fluctuating tide conditions. To solve the issue, this study developed a novel approach to retrieve MCH by training a robust XGBoost regression model using UAV-LiDAR, SDGSAT-1, and time series Sentinel-1 SAR and Sentinel-2 MultiSpectral Instrument imagery. The approach was applied to mangrove forests along China's coast. The study resulted in a 10 m resolution MCH map and so named China's mangrove canopy height (CMCH). The accuracy of CMCH was assessed using in-situ and UAV-LiDAR data, achieving an <em>R</em>² of 0.84 and an RMSE of 1.19 m. Band 6 from SDGSAT-1, the only available 10 m resolution red edge spectral band of current available satellite data, was identified as the most crucial feature for predicting MCH. After analyzing the geographic characteristics of CMCH at species level, we had three innovative and quantitative discoveries. Firstly, the mean height of mangrove forests in China was 6.0 m, significantly lower than the global average of 12.7 m. Secondly, the height of mangrove forests in China was found to decrease with increasing latitude. Thirdly, the exotic <em>S. apetala</em> was identified as the tallest mangrove species in China, with the highest trees in 18.7 m along the coasts of Inner Deep Bay. To the best of our knowledge, this is the first national-scale study to investigate the geographic characteristics of MCH at species level. The resultant CMCH map and species-level findings provide essential information for managing mangrove ecosystems in China. The technical methodology employed has the potential to be expanded globally, thereby enhancing the execution of the UN's Sustainable Development Goals related to coastal and marine ecosystems. Additionally, it can contribute to the safeguarding of nature, fostering the preservation of biodiversity.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"323 ","pages":"Article 114719"},"PeriodicalIF":11.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738650","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":"Changes in GEDI-based measures of forest structure after large California wildfires relative to pre-fire conditions","authors":"Matthew L. Clark ,&nbsp;Christopher R. Hakkenberg ,&nbsp;Tim Bailey ,&nbsp;Patrick Burns ,&nbsp;Scott J. Goetz","doi":"10.1016/j.rse.2025.114718","DOIUrl":"10.1016/j.rse.2025.114718","url":null,"abstract":"<div><div>Forest productivity, biodiversity, and ecosystem services in California and the Western United States are closely tied to fire. However, fire regimes are shifting toward larger, more severe fires driven by factors such as high fuel loads and increased temperature and aridity. While multispectral satellite (e.g., Landsat) burn indices provide valuable insights into fire severity, they primarily capture top-of-canopy greenness, missing important sub-canopy changes in vegetation structure and residual fuels. The Global Ecosystem Dynamics Investigation (GEDI) spaceborne lidar mission provides current and consistent, near-global 3D forest structure measurements, enabling detailed assessment of forest changes from disturbances such as wildfire. This study utilized near-coincident, bitemporal pre- and post-fire GEDI on-orbit measurements to analyze structural changes across thirty-four large California wildfires (2019 to 2021). We examined twelve GEDI-based forest structure metrics representing a variety of 3D fuels properties, including forest height, low-stature fuels, biomass, canopy heterogeneity, volume and cover. Our broad goals were to: 1) assess GEDI's ability to detect structural changes in burned areas relative to control areas; and 2) in burned areas, explore relationships between forest structural change and factors such as pre-fire fuel loads, Landsat-based differenced Normalized Burn Ratio (dNBR), topographic slope, wind speed, vapor pressure deficit, evapotranspiration, and time since fire. Results showed significant structural loss in all GEDI structural metrics for burned areas relative to nearby controls. Pre-fire fuel loads measured by GEDI metrics were the strongest predictors of post-fire structural change, with linear models explaining an average of 46 % of variance. Model slopes showed increasing levels of pre-fire fuels were associated with large, significant post-fire decreases in canopy structure – that is, more fuels lead to higher wildfire severity, particularly for conifer forests of the Klamath, Cascades and Sierra Nevada ecoregions. One metric, measuring the proportion of waveform energy below 10 m height, increased significantly after fire in mixed and conifer forests due to canopy opening, which enhanced lidar penetration toward the ground. In contrast, the widely-used dNBR burn severity index was less correlated with GEDI-based forest structural change than pre-fire fuels, particularly in sub-canopy fuels, with models explaining no more than 32 % of the variance (average 19 %). GEDI overcomes key limitations of airborne lidar, including high cost, limited extent, and data latency, enabling scalable and timely assessments of wildfire impacts needed to manage fuels and track forest resilience and recovery. Further, GEDI metrics are physically-based and ecologically interpretable, providing complimentary information to multispectral burn severity indices.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"323 ","pages":"Article 114718"},"PeriodicalIF":11.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bias correction for near-real-time estimation of snow water equivalent using machine learning algorithms: A case study in the Tuolumne River basin, California","authors":"Kehan Yang ,&nbsp;Thomas H. Painter ,&nbsp;Jeffrey S. Deems ,&nbsp;Noah P. Molotch","doi":"10.1016/j.rse.2025.114693","DOIUrl":"10.1016/j.rse.2025.114693","url":null,"abstract":"<div><div>Accurately estimating snow water equivalent (SWE) in near-real-time is important for water resources management and water supply forecasting in snow-dominant regions. However, conventional SWE estimation approaches have large uncertainties in mountainous regions due to complex terrain, snow-vegetation interactions, and other challenging factors. This study develops a SWE bias correction framework (SWE-BCF) that utilizes the Airborne Snow Observatories (ASO) SWE data and machine learning (ML) algorithms to correct biases in a near-real-time SWE estimation linear regression model (LRM). The spatial distribution of LRM SWE residuals, which are estimated using the ASO SWE, is explicitly modeled using multiple ML algorithms and evaluated using a leave-one-out cross-validation (LOOCV) workflow. A wide range of commonly used ML algorithms is examined to model LRM SWE residuals, including Gaussian Process Regression (GPR), Support Vector Machine (SVM), Bayesian Regularized Neural Networks (BRNN), Random Forest (RF), and Gradient Boosting Machine (GBM). The results show that all ML algorithms substantially improve LRM SWE estimation accuracy. While the Kruskal-Wallis test indicates no significant difference (<em>p</em>-value &gt;0.05) among the bias correction models, the RF model outperforms others, with the highest median R² (0.89), the lowest median RMSE (69 mm), MAE (41 mm), and NRMSE (37.4 %), as well as the second-best median PBIAS (−6.6 %) in the LOOCV for correcting SWE bias. Four performance metrics (R², MAE, RMSE, NRMSE) show significant improvements (<em>p</em>-value &lt;0.05) over the original LRM model, highlighting the effectiveness of SWE-BCF in correcting the spatial patterns of SWE. However, the correction in the basin-wide average SWE, as indicated by the PBIAS values, exhibits high variance and does not show significant improvement (<em>p</em>-value &gt;0.05). Among the three land cover types in the Upper Tuolumne River Basin, the alpine area showed the most substantial SWE improvements with the SWE-BCF. The structural adaptability of the SWE-BCF enables its transferability to various geographic locations and SWE datasets, allowing for an extension of coverage and frequency of more accurate SWE estimates. This potential advancement may improve water management decisions which rely on accurate water supply forecasts.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"323 ","pages":"Article 114693"},"PeriodicalIF":11.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738649","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":"Next generation Arctic vegetation maps: Aboveground plant biomass and woody dominance mapped at 30 m resolution across the tundra biome","authors":"Kathleen M. Orndahl ,&nbsp;Logan T. Berner ,&nbsp;Matthew J. Macander ,&nbsp;Marie F. Arndal ,&nbsp;Heather D. Alexander ,&nbsp;Elyn R. Humphreys ,&nbsp;Michael M. Loranty ,&nbsp;Sarah M. Ludwig ,&nbsp;Johanna Nyman ,&nbsp;Sari Juutinen ,&nbsp;Mika Aurela ,&nbsp;Juha Mikola ,&nbsp;Michelle C. Mack ,&nbsp;Melissa Rose ,&nbsp;Mathew R. Vankoughnett ,&nbsp;Colleen M. Iversen ,&nbsp;Jitendra Kumar ,&nbsp;Verity G. Salmon ,&nbsp;Dedi Yang ,&nbsp;Paul Grogan ,&nbsp;Scott J. Goetz","doi":"10.1016/j.rse.2025.114717","DOIUrl":"10.1016/j.rse.2025.114717","url":null,"abstract":"<div><div>The Arctic is warming faster than anywhere else on Earth, placing tundra ecosystems at the forefront of global climate change. Plant biomass is a fundamental ecosystem attribute that is sensitive to changes in climate, closely tied to ecological function, and crucial for constraining ecosystem carbon dynamics. However, the amount, functional composition, and distribution of plant biomass are only coarsely quantified across the Arctic. Therefore, we developed the first moderate resolution (30 m) maps of live aboveground plant biomass (g m⁻²) and woody plant dominance (%) for the Arctic tundra biome, including the mountainous Oro Arctic. We modeled biomass for the year 2020 using a new synthesis dataset of field biomass harvest measurements, Landsat satellite seasonal synthetic composites, ancillary geospatial data, and machine learning models. Additionally, we quantified pixel-wise uncertainty in biomass predictions using Monte Carlo simulations and validated the models using a robust, spatially blocked and nested cross-validation procedure. Observed plant and woody plant biomass values ranged from 0 to ∼6000 g m⁻² (mean ≈ 350 g m⁻²), while predicted values ranged from 0 to ∼4000 g m⁻² (mean ≈ 275 g m⁻²), resulting in model validation root-mean-squared-error (RMSE) ≈ 400 g m⁻² and R² ≈ 0.6. Our maps not only capture large-scale patterns of plant biomass and woody plant dominance across the Arctic that are linked to climatic variation (e.g., thawing degree days), but also illustrate how fine-scale patterns are shaped by local surface hydrology, topography, and past disturbance. By providing data on plant biomass across Arctic tundra ecosystems at the highest resolution to date, our maps can significantly advance research and inform decision-making on topics ranging from Arctic vegetation monitoring and wildlife conservation to carbon accounting and land surface modeling.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"323 ","pages":"Article 114717"},"PeriodicalIF":11.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143745142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Internal solitary waves in the Banda Sea, a pathway between Indian and Pacific oceans: Satellite observations and physics-AI hybrid forecasting","authors":"Xudong Zhang ,&nbsp;Haoyu Wang ,&nbsp;Xiaofeng Li ,&nbsp;Adi Purwandana ,&nbsp;I Wayan Sumardana Eka Putra","doi":"10.1016/j.rse.2025.114733","DOIUrl":"10.1016/j.rse.2025.114733","url":null,"abstract":"<div><div>Internal solitary waves (ISW) are widespread in global oceans, and satellite/in-situ observations showed that the Banda Sea has frequent ISW activities, characterized by long-wave crests, fast propagation speeds, and large amplitudes exceeding 100 m. In this paper, we conducted a comprehensive ISW study in the Banda Sea to reveal ISW characteristics by collecting 417 synthetic aperture radar and optical images from 2013 to 2019. The constructed dataset comprises 134 pairs of matched satellite images and a total of 12,021 ISW propagation vectors were extracted. Satellite observation reveals that ISWs in the Banda Sea mainly originate from the Ombai Strait and propagate northward, with an average propagation speed of over 2.50 m/s and with seasonal variation of less than 20 %. To forecast ISW propagations, we developed a physics-informed neural network ISW forecast model combining the classic Eikonal Eq. (EE) and the data-driven AI algorithms following a two-step transfer learning scheme. The forecast model employs a three-hidden-layer structure with 512 nodes in each layer. Firstly, the hybrid model includes ISW physics by setting the EE as the loss function. The second step is the data-driven process, which exploits a fully connected neural network and collected ISW dataset to improve EE-based model performance by 61 % with a loss function of the mean squared error. Through the two-step training, the forecast model adopts ISW physics and also benefits from the high accuracy of the data-driven process. We randomly selected 188/118 satellite images from the built dataset to serve as the training/test dataset for the data-driven process. After the second-step training, the root mean square (average) error of the model-predicted ISW propagation time reduced from 2.59 (2.37) h to 1.01 (−0.01) h. Error analysis shows that the data-driven process can efficiently correct the systematic error in the first-step model, which stems from errors in determining the ISW source and the propagation speed distribution map. Using the developed model, we predicted the propagation time of the ISWs and compared these predictions with satellite observations and in-situ observations. The comparison showed a high degree of agreement regarding the ISWs' location and their wave crests' geometry between model predictions and satellite/in-situ observations. Key differences between the proposed model and previous models are discussed.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"323 ","pages":"Article 114733"},"PeriodicalIF":11.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143745215","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":"Combining geometric-optical and spectral invariants theories for modeling canopy fluorescence anisotropy","authors":"Yachang He ,&nbsp;Yelu Zeng ,&nbsp;Dalei Hao ,&nbsp;Nikolay V. Shabanov ,&nbsp;Jianxi Huang ,&nbsp;Gaofei Yin ,&nbsp;Khelvi Biriukova ,&nbsp;Wendi Lu ,&nbsp;Yongyuan Gao ,&nbsp;Marco Celesti ,&nbsp;Baodong Xu ,&nbsp;Si Gao ,&nbsp;Mirco Migliavacca ,&nbsp;Jing Li ,&nbsp;Micol Rossini","doi":"10.1016/j.rse.2025.114716","DOIUrl":"10.1016/j.rse.2025.114716","url":null,"abstract":"<div><div>The spectral invariants theory (<span><math><mi>p</mi></math></span>-theory) has received much attention in the field of quantitative remote sensing over the past few decades and has been adopted for modeling of canopy solar-induced chlorophyll fluorescence (SIF). However, the spectral invariant properties (SIP) in simple analytical formulae have not been applied for modeling canopy fluorescence anisotropy primarily because they are parameterized in terms of leaf total scattering, which precludes the differentiation between forward and backward leaf SIF emissions. In this study, we have developed the canopy-SIP SIF model by combining geometric-optical (GO) theory to account for asymmetric leaf SIF forward and backward emissions at the first-order scattering and by modeling multiple scattering based on the <span><math><mi>p</mi></math></span>-theory, thus avoiding the dependence on radiative transfer models. The applicability of the model simulations especially over 3D heterogeneous canopies was improved by incorporating canopy structure through multi-angular clumping index, and by modeling single scattering from the four components of the scene in view according to the GO approach. The results show good consistency with both the state-of-the-art SIF models and multi-angular field SIF observations over grass and chickpea canopies. The coefficient of determination (<em>R</em>²) between the simulated SIF and field measurements was 0.75 (red) and 0.74 (far-red) for chickpea, and 0.65 (both red and far-red) for grass. The average relative error was approximately 3 % for 1D homogeneous scenes when comparing the canopy-SIP SIF model simulations to the SCOPE model simulations, and around 4 % for the 3D heterogeneous scene when comparing to the LESS model simulations. The results indicate that the proposed approach for separating asymmetric leaf SIF emissions is a robust way to keep a balance between satisfactory simulation accuracy and efficiency. Model simulations suggest that neglecting the leaf SIF asymmetry can lead to an underestimation of canopy red SIF by 6.3 % to 42.6 % for various leaf biochemical and canopy structural parameters. This study presents a simple but efficient analytical approach for canopy fluorescence modeling, with potential for large-scale canopy fluorescence simulations.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"323 ","pages":"Article 114716"},"PeriodicalIF":11.1,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738651","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":"Spatially continuous mapping of pre-fire fuel characteristics with imaging spectroscopy and lidar for fire emissions modeling","authors":"Clare M. Saiki ,&nbsp;Dar A. Roberts ,&nbsp;E. Natasha Stavros ,&nbsp;Andrew T. Hudak ,&nbsp;Nancy H.F. French ,&nbsp;Olga Kalashnikova ,&nbsp;Michael J. Garay ,&nbsp;T. Ryan McCarley ,&nbsp;Mark Corrao","doi":"10.1016/j.rse.2025.114721","DOIUrl":"10.1016/j.rse.2025.114721","url":null,"abstract":"<div><div>Fuels are a large source of uncertainty in fire emissions estimates due to variability in the physical and chemical properties of fuels and how they are represented. These uncertainties can be addressed using imaging spectroscopy and lidar data, that provide observations of the chemical and physical traits and spatial distribution of vegetation. Combined with ground fuel measurements, these data provide information on fuel distribution and quantity important for mapping and modeling fire effects. In this study, we present a methodology to develop models and continuous maps of pre-fire fuel characteristics for use in fire emissions modeling. We first addressed any spatial gaps over fire areas for Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) chemical trait data using Random Forests regression and for derived fractional cover. We used the AVIRIS fractional cover and chemical traits or AVIRIS estimates alongside lidar, multispectral, and topographic variables to build fuel characteristic models informed by ground measurements with partial least squares regression. We derived maps of predictive uncertainty alongside a suite of uncertainty statistics for each fuel characteristic that inform the use of fuels data within fire effects models. We used two study sites: the Williams Flats wildfire in eastern Washington state, USA and three prescribed crown fires in Utah, USA. The results show similar error between calibration and validation sets and NRMSE of around 20 % or lower for a majority of the fuel models. We present fuel characteristic and uncertainty maps for all fires. This study shows that the use of imaging spectroscopy and lidar data have the potential to represent fuel heterogeneity and continuously map fuel characteristics for fire effects modeling.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"323 ","pages":"Article 114721"},"PeriodicalIF":11.1,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725614","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":"Application of satellite and proximal hyperspectral sensing to target lithium mineralization in volcano-sedimentary deposits: A case study from the McDermitt caldera, USA","authors":"F. Corrado ,&nbsp;F. Putzolu ,&nbsp;R.N. Armstrong ,&nbsp;N. Mondillo ,&nbsp;R. Chirico ,&nbsp;B. Casarotto ,&nbsp;M. Massironi ,&nbsp;D. Fuller ,&nbsp;R. Ball ,&nbsp;R.J. Herrington","doi":"10.1016/j.rse.2025.114724","DOIUrl":"10.1016/j.rse.2025.114724","url":null,"abstract":"<div><div>This study provides satellite and proximal hyperspectral analyses of lithium (Li)-bearing volcano-sedimentary environments aimed at determining the target absorption features of alteration assemblages to be used as exploration vectors towards analogous Li-mineralized systems.</div><div>The study was applied at the McDermitt caldera (USA), which hosts volcano-sedimentary Li mineralization in the form of clay minerals originated from the alteration of glass-rich extrusive igneous rocks in endorheic lacustrine basins. The surface-exposed areas of the caldera were investigated using satellite hyperspectral imagery acquired by the German Environmental Mapping and Analysis Program (EnMAP) mission. Satellite data were validated via ground spectroscopy, performed through hyperspectral imaging, complemented by mineralogical and geochemical analyses on specimens deriving from the Jindalee McDermitt Li deposit.</div><div>The Li mineralization in the Jindalee McDermitt deposit is dominated by a Mg(Li)-smectite (hectorite) + amorphous silica assemblage, showing absorption features at 2306 nm and 2200 nm that can be detected in the spectral range covered by the EnMAP sensor. The analysis of the corresponding hyperspectral feature distribution maps and the comparison with ground control samples, confirmed that the above features together can be effectively used as mineralogical-hyperspectral vectors for Li-prospective areas on a caldera-scale. This spectral footprint was used in the analysis of a similar system that lacks Li mineralization (High Rock caldera complex, USA). Results of this test show that the distinctive 2200 + 2306 nm bands association is lacking at the High Rock caldera complex, which suggests that this spectral footprint can be employed as a mappable criteria to target lacustrine sequences with mineralogical features analogous to those of Li-mineralized volcano-sedimentary deposits.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"323 ","pages":"Article 114724"},"PeriodicalIF":11.1,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143723679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sensitivity of sun-induced chlorophyll fluorescence (SIF) and hyperspectral reflectance to drought response in soybean genotypes with contrasting affinities for arbuscular mycorrhizal fungi","authors":"Christine Y. Chang ,&nbsp;Jinyoung Y. Barnaby ,&nbsp;Jude E. Maul","doi":"10.1016/j.rse.2025.114722","DOIUrl":"10.1016/j.rse.2025.114722","url":null,"abstract":"<div><div>Increasing frequency and severity of drought events impact global and domestic agricultural productivity. Monitoring drought in agricultural fields with remote sensing can provide faster, lower-cost decision management support for critical field management activities. We evaluated the application of sun-induced chlorophyll fluorescence (SIF) emitted at red (SIF_Red) and far-red (SIF_FarRed) wavelengths in comparison with chlorophyll- and xanthophyll-sensitive reflectance-based remote sensing indices (NDVI, NIR_V, NIR_VP and PRI) for drought stress monitoring at the canopy scale. To do so, we evaluated impacts of drought stress on two soybean varieties with similar phenology but contrasting affinities for arbuscular mycorrhizal fungi (AMF), which can provide host plants with extended access to water and nutrients in exchange for carbohydrates. Drought response physiology of the two genotypes was further explored using leaf level photosynthetic gas exchange, chlorophyll fluorescence, water potential and phenology. We observed distinct responses, with the low-affinity genotype exhibiting lower SIF_Red and more negative midday leaf water potential, as well as reduced growth and development rate compared with the high-affinity genotype. SIF_FarRed and NIR_VP exhibited the strongest correlation with canopy photosynthesis followed by NIR_V. We also observed different timing of drought response parameters associated with different remote sensing signals. Our findings demonstrate the particular sensitivity of SIF to physiological drought responses, conferred here through AMF associations in the soil, and provide insight to the physiological drought responses tracked by different remote sensing signals.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"323 ","pages":"Article 114722"},"PeriodicalIF":11.1,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143705771","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}