Remote Sensing of Environment最新文献

{"title":"Dynamic vegetation parameter retrieval algorithm for SMAP L-band radiometer observations","authors":"Preethi Konkathi ,&nbsp;L. Karthikeyan","doi":"10.1016/j.rse.2025.114641","DOIUrl":"10.1016/j.rse.2025.114641","url":null,"abstract":"<div><div>Vegetation Optical Depth (VOD), obtained from passive microwave sensors, quantifies Vegetation Water Content (VWC) and complements conventional vegetation indices. Recent studies on Soil Moisture (SM) and VOD retrieval algorithms identified that VOD is more susceptible to errors due to the Radiative Transfer Model (RTM) and its parameterization than SM. The present work aims to address this limitation. We initially characterized the error propagation from <em>ω</em> and <em>h</em> parameters in VOD through synthetic experiments. These experiments also indicate notable propagation of errors from assuming a temporally constant <em>ω</em> in VOD retrievals, which could be resolved using a time-varying <em>ω</em> parameter.</div><div>To improve the VOD characterization, we proposed a Dynamic Vegetation Parameter retrieval Algorithm (DVPA) to retrieve VOD and <em>ω</em> simultaneously, along with a temporally constant <em>h</em> parameter applied to L-band SMAP brightness temperatures. DPVA is based on the Two-Stream emission model (2S-EM) RTM. Retrievals are obtained using a novel multi-temporal inversion coupled with a regularization scheme. SMAP Level-3 SM is supplied as one of the critical inputs. DVPA, as a proof-of-concept, is applied to ten reference sites with varying vegetation conditions. The retrieved VOD and <em>ω</em> from DVPA are compared with optical vegetation indices and SMAP baseline VOD product (Regularized Dual Channel Algorithm-RDCA). DVPA VOD estimates outperform SMAP RDCA VOD in terms of correlation (R) and lagged correlation with vegetation indices. Regularization ensured optimum filtering of retrieval noise from the VOD retrievals. Retrieval of dynamic <em>ω</em> helped to resolve errors in VOD, resulting in improved correspondence with vegetation growth patterns compared to SMAP baseline VOD retrievals. Given its generic structure, DPVA is scalable and applies to other passive microwave sensors.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"319 ","pages":"Article 114641"},"PeriodicalIF":11.1,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143258138","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":"DART-based temporal and spatial retrievals of solar-induced chlorophyll fluorescence quantum efficiency from in-situ and airborne crop observations","authors":"Omar Regaieg ,&nbsp;Zbyněk Malenovský ,&nbsp;Bastian Siegmann ,&nbsp;Jim Buffat ,&nbsp;Julie Krämer ,&nbsp;Nicolas Lauret ,&nbsp;Valérie Le Dantec","doi":"10.1016/j.rse.2025.114636","DOIUrl":"10.1016/j.rse.2025.114636","url":null,"abstract":"<div><div>Remotely sensed top-of-the-canopy (TOC) SIF is highly impacted by non-physiological structural and environmental factors that are confounding the photosystems' emitted SIF signal. Our proposed method for scaling TOC SIF down to photosystems' (PSI and PSII) level uses a three-dimensional (3D) modeling approach, capable of accounting physically for the main confounding factors, <em>i.e.</em>, SIF scattering and reabsorption within a leaf, by canopy structures, and by the soil beneath. Here, we propose a novel SIF downscaling method that separates the structural component from the functional physiological component of TOC SIF signal by using the 3D Discrete Anisotropic Radiative Transfer (DART) model coupled with the leaf-level fluorescence model Fluspect-CX, and estimates the Fluorescence Quantum Efficiency (FQE) at photosystem level. The method was first applied on <em>in-situ</em> diurnal measurements acquired at the top of the canopy of an alfalfa crop with a near-distance point-measuring FloX system. The retrieved photosystem-level FQE diurnal courses correlated significantly with photosynthetic yield of PSII measured by an active leaf florescence instrument MiniPAM (<em>R</em> = 0.87, R² = 0.76 before and <em>R</em> = −0.82, R² = 0.67 after 2.00 pm local time). Diurnal FQE trends of both photosystems jointly were descending from late morning 9.00 am till afternoon 4.00 pm. A slight late-afternoon increase, observed for three days between 4.00 and 7.00 pm, could be attributed to an increase in FQE of PSI that was retrieved separately from PSII. The method was subsequently extended and applied to airborne SIF images acquired with the HyPlant imaging spectrometer over the same alfalfa field. While the input canopy SIF radiance computed by two different methods, i) a spectral fitting method (SFM) and ii) a spectral fitting method neural network (SFMNN), produce broad and irregularly shaped (skewed) histograms (spatial coefficients of variation: CV = 29–35 % and 14–20 %, respectively), the retrieved HyPlant per-pixel FQE estimates formed significantly narrower and regularly bell-shaped near-Gaussian histograms (CV = 27–34 % and 14–17 %, respectively). The achieved spatial homogeneity of resulting FQE maps confirms successful removal of the TOC SIF radiance confounding impacts. Since our method is based on direct matching of measured and physically modelled canopy SIF radiance, simulated by 3D radiative transfer, it is versatile and transferable to other canopy architectures, including structurally complex canopies such as forest stands.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"319 ","pages":"Article 114636"},"PeriodicalIF":11.1,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143192062","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":"kNDMI: A kernel normalized difference moisture index for remote sensing of soil and vegetation moisture","authors":"Huanyu Xu ,&nbsp;Hao Sun ,&nbsp;Zhenheng Xu ,&nbsp;Yunjia Wang ,&nbsp;Tian Zhang ,&nbsp;Dan Wu ,&nbsp;JinHua Gao","doi":"10.1016/j.rse.2025.114621","DOIUrl":"10.1016/j.rse.2025.114621","url":null,"abstract":"<div><div>Optical remote sensing of soil and vegetation moisture index is widely recognized as a vital indicator for monitoring soil moisture and drought stress. Nevertheless, the traditional soil and vegetation moisture index does not adequately capture enough higher-order relations between spectral channels, leading to limited sensitivity to soil moisture variations in certain value ranges and difficulties in reconciling discrepancies in soil moisture numerical distribution across temporal and spatial scales. In this paper, based on the concept of kernel method, a new soil and vegetation moisture index, Kernel Normalized Difference Moisture Index (kNDMI), was formulated to capture more spectral channel information. Global kNDMI were calculated using MODIS spectral reflectance product. The effectiveness of kNDMI in responding to moisture and drought was evaluated using the European Space Agency (ESA) Climate Change Initiative (CCI) dataset, the Soil Moisture Active and Passive (SMAP) dataset, and meteorological reanalysis data. Results demonstrated that: 1) The kNDMI significantly outperforms traditional remote sensing moisture indices in global soil moisture monitoring on the temporal scale, particularly in monitoring SMAP soil moisture dataset. The performance improvement of kNDMI compared to the best traditional index ranges from 107.1 % to 127.8 %, with the most notable advantages observed in mid-to-high latitude regions and areas with moderate vegetation cover, such as croplands, shrublands, and grasslands. 2) The average spatial correlation between kNDMI and CCI soil moisture exceeds that of the best traditional moisture index, Normalized Difference Infrared Index (NDII SWIR3-based), by approximately 0.02 to 0.04. However, kNDMI's performance in capturing SMAP's spatial distribution is slightly inferior to that of NDII (SWIR3-based). 3) kNDMI proves to be more effective than traditional moisture indices in monitoring short-term meteorological droughts on 1- to 3-months scale. Furthermore, kNDMI significantly outperforms traditional indices in soil drought monitoring, showing an improvement range of 59.09 % to 169.37 %. 4) The optimal sigma parameter for kNDMI on the temporal scale exhibits adaptive characteristics related to the dryness of the pixels; the drier the pixel, the more its numerical distribution resembles a smoother Gaussian Radial Basis Function (RBF) kernel. The maximum parameter setting method, which combines the advantages of both adaptive and fixed parameters, yields the best performance in the kNDMI tuning process on global scale.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"319 ","pages":"Article 114621"},"PeriodicalIF":11.1,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143083920","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":"Estimating global transpiration from TROPOMI SIF with angular normalization and separation for sunlit and shaded leaves","authors":"Chen Zheng ,&nbsp;Shaoqiang Wang ,&nbsp;Jing M. Chen ,&nbsp;Jingfeng Xiao ,&nbsp;Jinghua Chen ,&nbsp;Zhaoying Zhang ,&nbsp;Giovanni Forzieri","doi":"10.1016/j.rse.2024.114586","DOIUrl":"10.1016/j.rse.2024.114586","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Gross primary productivity (GPP) is more accurately estimated by total canopy solar-induced chlorophyll fluorescence (&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;SIF&lt;/mi&gt;&lt;mi&gt;total&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;) compared to raw sensor observed SIF signals (&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;SIF&lt;/mi&gt;&lt;mi&gt;obs&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;). The use of two-leaf strategy, which distinguishes between SIF from sunlit (&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;SIF&lt;/mi&gt;&lt;mi&gt;sunlit&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;) and shaded (&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;SIF&lt;/mi&gt;&lt;mi&gt;shaded&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;) leaves, further improves GPP estimates. However, the two-leaf strategy, along with SIF corrections for bidirectional effects, has not been applied to transpiration (T) estimation. In this study, we used the angular normalization method to correct the bidirectional effects and separate &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;SIF&lt;/mi&gt;&lt;mi&gt;sunlit&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; and &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;SIF&lt;/mi&gt;&lt;mi&gt;shaded&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;. Then we developed &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;SIF&lt;/mi&gt;&lt;mi&gt;sunlit&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; and &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;SIF&lt;/mi&gt;&lt;mi&gt;shaded&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; driven semi-mechanistic and hybrid models, comparing their T estimates with those from a &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;SIF&lt;/mi&gt;&lt;mi&gt;obs&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; driven semi-mechanistic model at both site and global scales. All three types of SIF-driven T models integrate canopy conductance (&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;g&lt;/mi&gt;&lt;mi&gt;c&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;) with the Penman-Monteith model, differing in how &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;g&lt;/mi&gt;&lt;mi&gt;c&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; is derived: from a &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;SIF&lt;/mi&gt;&lt;mi&gt;obs&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; driven semi-mechanistic equation, a &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;SIF&lt;/mi&gt;&lt;mi&gt;sunlit&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; and &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;SIF&lt;/mi&gt;&lt;mi&gt;shaded&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; driven semi-mechanistic equation, and a &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;SIF&lt;/mi&gt;&lt;mi&gt;sunlit&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; and &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;SIF&lt;/mi&gt;&lt;mi&gt;shaded&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; driven machine learning model. When evaluated against partitioned T using the underlying water use efficiency method at 72 eddy covariance sites and two global T remote sensing products, a consistent pattern emerged: &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;SIF&lt;/mi&gt;&lt;mi&gt;sunlit&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; and &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;SIF&lt;/mi&gt;&lt;mi&gt;shaded&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; driven hybrid model &gt; &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;SIF&lt;/mi&gt;&lt;mi&gt;sunlit&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; and &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;SIF&lt;/mi&gt;&lt;mi&gt;shaded&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; driven semi-mechanistic model &gt; &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;SIF&lt;/mi&gt;&lt;mi&gt;obs&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; driven semi-mechanistic model. The &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;SIF&lt;/mi&gt;&lt;mi&gt;sunlit&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; and &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;SIF&lt;/mi&gt;&lt;mi&gt;shaded&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; driven hybrid model demonstrated a notable proficiency under high vapor pressure deficit and low soil water content conditions. The &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;SIF&lt;/mi&gt;&lt;mi&gt;obs&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; driven semi-mec","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"319 ","pages":"Article 114586"},"PeriodicalIF":11.1,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077122","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":"Individual tree crown delineation in high resolution aerial RGB imagery using StarDist-based model","authors":"Fei Tong,&nbsp;Yun Zhang","doi":"10.1016/j.rse.2025.114618","DOIUrl":"10.1016/j.rse.2025.114618","url":null,"abstract":"<div><div>The availability of high spatial resolution remote sensing imagery has facilitated forestry attribute estimation at the individual tree level. However, producing accurate tree crown delineations for practical applications remains challenging, particularly in mixed forests with overlapping tree crowns. In this study, we propose an individual tree crown delineation method leveraging the StarDist model to improve the delineation accuracy in mixed forests. The StarDist model captures tree crown shapes uniquely through star-convex polygons, which are predicted by the U-Net architecture. The final tree crowns are determined by applying non-maximum suppression (NMS) to all identified star-convex polygons. Performance evaluation on two mixed forest areas reveals a delineation accuracy exceeding 92%, notably outperforming the widely used deep learning model MASK R-CNN by over 6%. In terms of tree crown areas estimation, the <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> for both testing areas is higher than 0.85 for both testing areas. Moreover, the evaluations on precision, recall, and F1-score demonstrate that the proposed model can generate tree crowns fitting well with the true crowns. This study marks the first utilization of the StarDist model for tree crown delineation in mixed forests. Our findings demonstrate the effectiveness of the StarDist model for accurately delineating individual tree crowns, thereby advancing the field of forestry research.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"319 ","pages":"Article 114618"},"PeriodicalIF":11.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143071556","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":"Cloud heights retrieval from passive satellite measurements using lapse rate information","authors":"Weiyuan Zhang ,&nbsp;Jiming Li ,&nbsp;Jiayi Li ,&nbsp;Sihang Xu ,&nbsp;Lijie Zhang ,&nbsp;Yang Wang ,&nbsp;Jianping Huang","doi":"10.1016/j.rse.2025.114622","DOIUrl":"10.1016/j.rse.2025.114622","url":null,"abstract":"<div><div>Cloud top and base height (CTH and CBH) are essential in understanding the role of clouds on the weather and climate systems and improving radiation and precipitation simulations. However, inferring accurate cloud heights from passive satellite observations remains more challenging, especially for CBH. This study developed an effective and convenient method for estimating cloud heights for different cloud types on a global scale. The method is based on the mean lapse rate from surface to cloud top (<em>Γ</em>_<em>ct</em>), the lapse rate within (<em>Γ</em>_<em>cb1</em>) and below cloud (<em>Γ</em>_<em>cb2</em>), which are calculated from collocated active and passive satellite observations. The CTH and CBH can be easily derived based on cloud top temperature (CTT), surface temperature (ST), surface height (SH), <em>Γ</em>_<em>ct</em>, <em>Γ</em>_<em>cb1</em> and <em>Γ</em>_<em>cb2</em>. The lapse rate method was applied to polar-orbiting and geostationary passive satellites and the performances were evaluated using cloud heights measurements from CloudSat and CALIPSO satellite. Overall, our retrieval results can achieve high accuracy and stability in estimating both CTH and CBH. For example, our CTH results have significantly improved the retrieval accuracy, with mean bias error (MBE) is 0 km and R is 0.96, and the absolute bias error (MAE) and root mean square error (RMSE) are reduced from 1.12 km and 1.72 km to 0.85 km and 1.33 km, respectively, compared with the MODIS CTH product. Our CBH retrieval results based on MODIS CTT and ST also agree well with CloudSat and CALIPSO observations, the R is 0.91 and the MAE, MBE and RMSE are 0.73 km, 0 km and 1.26 km, respectively. The cloud geometric thickness derived from the cloud heights retrieval results also agrees well with the active satellite observations (MAE = 0.97 km, MBE = 0 km, RMSE = 1.44 km and <em>R</em> = 0.91). In addition, the good performance of cloud heights retrieval during night and for geostationary satellites can further illustrate the excellent accuracy and strong applicability of the lapse rate method. Specifically, compared with SatCORPS Himawari-8 product, the MAE and RMSE of CTH (CBH) are reduced by 41.5 % (44.2 %) and 39.4 % (36.6 %), respectively. These statistical results confirm that our method has comparable performance to other algorithms (e.g., machine learning and other empirical methods), in the meantime, exhibiting the advantages of simplicity and less input parameters. In addition, the lapse rate method can also be employed to provide a supplemental criterion on determining cloud layers from radiosonde data.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"319 ","pages":"Article 114622"},"PeriodicalIF":11.1,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143071584","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":"Risk identification of mangroves facing Spartina alterniflora invasion using data-driven approaches with UAV and machine learning models","authors":"Zhiyi Kan ,&nbsp;Bin Chen ,&nbsp;Weiwei Yu ,&nbsp;Shunyang Chen ,&nbsp;Guangcheng Chen","doi":"10.1016/j.rse.2025.114613","DOIUrl":"10.1016/j.rse.2025.114613","url":null,"abstract":"<div><div>The rapid and large-scale invasion of <em>Spartina alterniflora</em> has led to extensive biodiversity loss and the degradation of essential ecosystem services, particularly in mangroves. Recent studies have shown that the landscape pattern of mangroves is a key indicator of whether <em>Spartina alterniflora</em> can successfully invade. Therefore, assessing the risk of <em>S. alterniflora</em> invasion from the perspective of mangrove landscape patterns is crucial. This study utilizes drone imagery to extract the spatial distribution of mangroves and <em>S. alterniflora</em> in the Jiulong Estuary. Using an interpretable machine learning model, the relationship between mangrove landscape patterns and <em>S. alterniflora</em> coverage was studied at three plot sizes (10 m, 20 m, 30 m). The results show that: (1) The interpretable machine learning model constructed based on UAV (Unmanned Aerial Vehicle) big data can assess <em>S. alterniflora</em> coverage through mangrove landscape patterns. The validation set achieved good results with Adjusted R² (&gt;0.974), MAE (Mean Absolute Error) (&lt;4.8), and RMSE (Root Mean Square Error) (&lt;7) across the three plot sizes, though the SMAPE (Symmetric Mean Absolute Percentage Error) indicator was relatively poor (&gt;20 %). (2) At all three plot sizes, mangrove coverage, mangrove tree height, average patch perimeter, patch density, landscape shape index, and edge density significantly impacted the suppression of <em>S. alterniflora</em> coverage by mangroves. However, the relative importance of these indicators changes with increasing spatial granularity. (3) The SHAP (SHapley Additive exPlanations) Value of the model revealed the nonlinear response of <em>S. alterniflora</em> coverage to changes in mangrove coverage: mangrove coverage inhibits <em>S. alterniflora</em> growth between 41 %–64 %, and completely suppresses it above 65 %. This study effectively assesses the extent of mangrove invasion by <em>S. alterniflora</em> and provides clear spatial evidence of the threshold for mangrove coverage to suppress <em>S. alterniflora</em> growth. It emphasizes that rationally regulating mangrove distribution through landscape indices will more effectively resist the invasion of <em>S. alterniflora.</em></div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"319 ","pages":"Article 114613"},"PeriodicalIF":11.1,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143071583","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":"Cross-scalar analysis of multisensor land surface phenology","authors":"Xiaojie Gao ,&nbsp;Sophia Stonebrook ,&nbsp;Tristan Green ,&nbsp;Minkyu Moon ,&nbsp;Mark A. Friedl","doi":"10.1016/j.rse.2025.114624","DOIUrl":"10.1016/j.rse.2025.114624","url":null,"abstract":"<div><div>Land surface phenology (LSP) metrics derived from remote sensing are widely used to monitor vegetation phenology over large areas and to characterize how the growing seasons of terrestrial ecosystems are responding to climate change. Until recently, however, most LSP studies relied on coarse spatial resolution sensors, which makes assigning direct linkages between LSP metrics and ecological processes and properties challenging due to scale mismatches and because substantial variation in phenology and ecological properties are often present at sub-pixel scale in coarse resolution LSP metrics. In this study, we leverage publicly available LSP data products with three orders of magnitude difference in spatial resolution derived from Moderate Resolution Imaging Spectroradiometer (MODIS, 500 m), Landsat and Sentinel-2 (HLS, 30 m), and PlanetScope (3 m) imagery to examine and characterize the nature, magnitude, and sources of the agreement and disagreement in LSP metrics across spatial scales. Our results provide three key conclusions: (1) LSP metrics from three sensors showed consistently high cross-scalar agreement across sites (r² = 0.70–0.97), suggesting that they all effectively capture geographic variation in LSP; (2) within-site cross-scalar agreement between LSP metrics was systematically lower relative to agreement across sites, but mean absolute differences were consistent across and within sites (generally &lt;14 days for day of year-based metrics, with a few exceptions); and (3) local-scale composition and heterogeneity in land cover is a key factor that controls cross-scalar agreement in LSP metrics. In particular, we found that site-level heterogeneity in land cover (measured via entropy) and the proportion of evergreen versus deciduous land cover types explain up to half of site-to-site variance in local-scale cross-scalar agreement in LSP metrics. Results from this study support the internal consistency and quality of the three LSP data products examined, and more generally, provide guidance regarding the choice of spatial resolution for different applications and land cover conditions, and yield new insights related to how LSP observations scale across different sensors and spatial resolutions.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"319 ","pages":"Article 114624"},"PeriodicalIF":11.1,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143071557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel strategy of full-waveform light detection and ranging bathymetry based on spatial gram angle difference field conversion and deep-learning network architecture","authors":"Yifu Chen ,&nbsp;Lin Wu ,&nbsp;Yue Qian ,&nbsp;Yuan Le ,&nbsp;Yi Yang ,&nbsp;Dongfang Zhang ,&nbsp;Liqin Zhou ,&nbsp;Haichao Guo ,&nbsp;Lizhe Wang","doi":"10.1016/j.rse.2025.114615","DOIUrl":"10.1016/j.rse.2025.114615","url":null,"abstract":"<div><div>The airborne light detection and ranging (LiDAR) bathymetry (ALB) system is a promising and effective approach for surveying nearshore bathymetry and underwater terrain. Deep-learning techniques have been developed to reduce waveform superposition in shallow and deep areas. These methods avoid the complex transmission process of laser pulses in the water column and the intricate determination of various parameters and thresholds in traditional ALB methods. However, studies on ALB bathymetry using deep-learning techniques remain insufficient. To improve the accuracy and reliability of nearshore bathymetry, this study proposes deep-learning bathymetry fusing waveform features and spatial-angular field features (DBWSF). This method utilizes the waveform curvature to construct an energy curve, enhancing the waveform's features. Additionally, it employs a Gram angle difference field to convert the temporal waveform into a two-dimensional Gram angle difference field image, increasing the dimensions and quantity of waveform features. Finally, this method constructs a dual-path neural network with an attention mechanism to extract the water surface and bottom waveform signals precisely to achieve nearshore bathymetry. In comparison to sample data, DBWSF exhibited high bathymetric accuracy across three study areas (Ganquan Island, Lingyang Reef, and Dong Island), achieving a root mean squared error of 0.21 m. The R² in these regions were 99.6 %, 95.1 %, and 99.8 %, respectively. In the above three study areas, compared with the bathymetric results obtained using the waveform decomposition method, DBWSF was more accurate, with improvements in RMSE of 0.33, 0.27, and 0.04 m. Compared with multilayer perceptron (MLP), the corresponding accurate improvements in RMSE with DBWSF were 0.47, 0.40, and 0.25 m. Compared with other two methods, the R² value for DBWSF in the three study areas exceeded 95 % and reached a highest value of 99.8 %. The results demonstrated the bathymetric capability, reliability, and transferability of DBWSF for determining nearshore bathymetry in different water environments. The novel LiDAR bathymetric deep-learning technique can effectively and intelligently produce precise nearshore bathymetry and seafloor topography maps.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"318 ","pages":"Article 114615"},"PeriodicalIF":11.1,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143044440","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 multi-sensor approach allows confident mapping of forest canopy fuel load and canopy bulk density to assess wildfire risk at the European scale","authors":"Elena Aragoneses ,&nbsp;Mariano García ,&nbsp;Hao Tang ,&nbsp;Emilio Chuvieco","doi":"10.1016/j.rse.2024.114578","DOIUrl":"10.1016/j.rse.2024.114578","url":null,"abstract":"<div><div>With the increasing influence of climate and socio-economic changes, crown fires are becoming the main concern of fire managers and civil protection authorities in Europe. Evaluating and mitigating the negative impacts of these fires requires better tools to identify high-risk areas. Prevention and management strategies for crown fires require accurate and cost-effective tools that can parameterise fuel properties. Here, we use a multi-sensor approach integrating satellite Light Detection and Ranging (LiDAR) observations from the Global Ecosystems Dynamics Investigation (GEDI) sensor, with other remote sensing imagery and biophysical variables to provide spatially-explicit estimates of two key descriptors of crown fire behaviour – canopy fuel load (CFL) and canopy bulk density (CBD) – over the entire European territory at 1 km² grid resolution.</div><div>GEDI L1B and L2A level footprints were used to estimate Leaf Area Density, from which CFL and CBD were subsequently derived. The approach was assessed by applying it to regions of the United States, where bioclimatic conditions are similar to those in Europe, and for which LANDFIRE CBD maps are available (CBD <em>r</em> = 0.6–0.86 and RMSE = 33.1–59.6 %). We then extrapolated the estimates to European areas not covered by GEDI using machine learning models with multispectral (Landsat 8) and radar (Phased Array L-band Synthetic Aperture Radar sensor – PALSAR) imagery, and biophysical variables (CFL <em>r</em> = 0.85 and RMSE = 12.98 %; CBD <em>r</em> = 0.75 and RMSE = 21 %). Pixel-level uncertainty for the spatial extrapolation was also estimated.</div><div>The new wall-to-wall maps of crown fuel properties (<span><span>https://doi.org/10.21950/Z6BWQG</span><svg><path></path></svg></span>) provide new insights into the potential for fire risk prevention in Europe, which together with climate and socio-economic models, would greatly improve the prioritisation of management areas and the targeting of mitigation measures in strategic areas to reduce wildfire risk.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"318 ","pages":"Article 114578"},"PeriodicalIF":11.1,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143050397","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}