Remote Sensing of Environment最新文献

{"title":"LFSR: Low-resolution Filling then Super-resolution Reconstruction framework for gapless all-weather MODIS-like land surface temperature generation","authors":"Chan Li ,&nbsp;Penghai Wu ,&nbsp;Si-Bo Duan ,&nbsp;Yixuan Jia ,&nbsp;Shuai Sun ,&nbsp;Chunxiang Shi ,&nbsp;Zhixiang Yin ,&nbsp;Huifang Li ,&nbsp;Huanfeng Shen","doi":"10.1016/j.rse.2025.114637","DOIUrl":"10.1016/j.rse.2025.114637","url":null,"abstract":"<div><div>Due to the great advancements in land surface models (LSMs), integrating data from thermal infrared (TIR) and LSMs is a promising way for obtaining gapless all-weather land surface temperature (LST). However, the differences of spatial resolution and discrepancy of data acquisition ways between TIR LST and model-simulated LST usually brought great challenges to traditional methods in terms of accuracy and texture details. This study proposes a low-resolution filling then super-resolution reconstruction (LFSR) framework for generating gapless all-weather LST using Moderate Resolution Imaging Spectroradiometer (MODIS) LST and China Meteorological Administration Land Data Assimilation System (CLDAS) LST. For the LFSR, a multi-source multi-temporal low-resolution filling (MSMTLF) network is first designed to alleviate the discrepancy of data acquisition ways between the MODIS LST and CLDAS LST, and generate gapless low-resolution degraded LSTs. A multi-scale multi-temporal super-resolution reconstruction (MSMTSR) network is then used to reconstruct the gapless low-resolution degraded LSTs into gapless high-resolution MODIS-like LSTs with rich-texture, which is mainly used to deal with resolution differences between the two LSTs. The experiments suggested that the LFSR achieved satisfactory results, and the maximal RMSE is less 2.5 K in the simulated experiments. When validated against the in-situ LST data under clear and cloudy skies, the small difference of the overall average bias (−0.91 K for clear skies VS -0.88 K for cloudy skies) and overall average RMSE (4.15 K for clear skies VS 5.68 K for cloudy skies) were obtained. Compared with results from the different input data, the other strategies and the other methods, the generated gapless all-weather MODIS-like LSTs from the LFSR were closer to the actual labels or have better consistency and spatial details. These results indicated the LFSR achieves impressive performance for fusing MODIS and CLDAS data. The LFSR actually provides a new framework for fusing TIR LST and simulation-based LST with considerable data inconsistency, and has the potential for generating gapless all-weather TIR LST records.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"319 ","pages":"Article 114637"},"PeriodicalIF":11.1,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143371518","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":"Spatiotemporal evolution characteristics of ground deformation in the Beijing Plain from 1992 to 2023 derived from a novel multi-sensor InSAR fusion method","authors":"Yuanzhao Fu ,&nbsp;Jili Wang ,&nbsp;Yi Zhang ,&nbsp;Honglei Yang ,&nbsp;Lu Li ,&nbsp;Zhengzhao Ren","doi":"10.1016/j.rse.2025.114635","DOIUrl":"10.1016/j.rse.2025.114635","url":null,"abstract":"<div><div>The Multiple Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) technology is capable of effectively generating ground deformation information derived from high-precision and continuous observation by satellites. However, due to the limited operational lifespan of a single SAR satellite, the derived ground deformation result of the study area cannot be ensured long-term (several decades), and merely a few years. With the increasing number of SAR satellite launches, it has become possible to conduct long-term continuous monitoring of ground deformation by combining data from multiple platforms. Nevertheless, several existing methods (e.g., model fitting method, predictive splicing method, etc.) have lower fusion accuracy and are limited to specific deformation patterns. In this study, a Piecewise Exponential Fitting with Weighted Average (PEFWA) method is proposed, which takes into account both the trend and accuracy of the preceding and following deformation time series in the fusion. The experimental results on the simulation data prove that the accuracy and robustness of this method are higher than several other methods. We applied the proposed method to characterize the evolution of ground deformation in the Beijing Plain from 1992 to 2023 using data from four different SAR satellites. The results show that: (1) With the implementation of various policies (e.g., the South-to-North Water Diversion Project, the Ecological Water Replenishment Project, etc.), ground subsidence has generally followed a trend of “worsening initially, then improving”. (2) The spatial variability of ground subsidence is primarily influenced by the locations of fault zones. (3) The periodic changes in the ground deformation time series are mainly driven by fluctuations in groundwater levels. The above findings indicate that the method proposed in this study can effectively integrate deformation series with temporal discontinuities, which helps detect the long-term trends and formation mechanisms of ground deformation.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"319 ","pages":"Article 114635"},"PeriodicalIF":11.1,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143350779","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":"Joint utilization of closure phase and closure amplitude for soil moisture change using interferometric synthetic aperture radar","authors":"Xujing Zeng,&nbsp;Shisheng Guo,&nbsp;Guolong Cui","doi":"10.1016/j.rse.2025.114620","DOIUrl":"10.1016/j.rse.2025.114620","url":null,"abstract":"<div><div>The sensitivity of microwave data in soil moisture is attributed to radar wave penetration depth and signal attenuation. However, current soil moisture models rarely consider the simultaneous effects of amplitude and phase induced by soil moisture. This study proposes an innovative InSAR Bias Soil Moisture Model (IBSMM) that jointly exploits closure phase and closure amplitude. Compared with traditional models, IBSMM considers the dual physical change process of microwave signals in soil moisture change. The IBSMM includes a three-step framework to estimate soil moisture. First, conventional repeat-pass InSAR datasets are generated. Second, the bias in closure characteristics is estimated using Regularized Maximum Likelihood Estimation (RMLE) and a dynamic nested sampling strategy. Third, a forward model for soil moisture change is constructed based on the backscattering field. The simulation results indicate that the dynamic nested sampling strategy has a deviation of only 0.042 from the logarithm evidence value. Moreover, the insensitivity and saturation thresholds in the soil moisture model are quantified. Subsequently, the results of two practical case experiments in different land cover types confirm the effectiveness of IBSMM. In Castrejón de Trabancos, Spain, from January 12, 2020 to February 5, 2020, the model had an overall average correlation coefficient (R value) of 0.57 and a root mean square error (RMSE) of 3.39%. Similarly, in Guyuan County, China, from October 5, 2018 to October 29, the model recorded an average R value of 0.46 and an RMSE of 3.1% in grassland. The proposed IBSMM effectively enhances soil moisture estimation accuracy and explains the physical process of soil moisture change.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"319 ","pages":"Article 114620"},"PeriodicalIF":11.1,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143258012","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":"Non-linear spectral unmixing for monitoring rapidly salinizing coastal landscapes","authors":"Manan Sarupria ,&nbsp;Rodrigo Vargas ,&nbsp;Matthew Walter ,&nbsp;Jarrod Miller ,&nbsp;Pinki Mondal","doi":"10.1016/j.rse.2025.114642","DOIUrl":"10.1016/j.rse.2025.114642","url":null,"abstract":"<div><div>Coastal farmlands in the eastern United States of America (USA) are increasingly suffering from rising soil salinity, rendering them unsuitable for economically productive agriculture. Saltwater intrusion (SWI) into the groundwater reservoir or soil salinization can result in land cover modification (e.g. reduced plant growth) or land cover conversion. Two primary examples of such land cover conversion are farmland to marsh or farmland to salt patches with no vegetation growth. However, due to varying spatial granularity of these conversions, it is challenging to quantify these land covers over a large geographic scale. To address this challenge, we evaluated a non-linear spectral unmixing approach with a Random Forest (RF) algorithm to quantify fractional abundance of salt patch and marshes. Using Sentinel-2 imagery from 2022, we generated gridded datasets for salt patches and marshes across the Delmarva Peninsula, and the associated uncertainty. Moreover, we developed two new spectral indices to enhance the spectral unmixing accuracy: the Normalized Difference Salt Patch Index (NDSPI) and the Modified Salt Patch Index (MSPI). We constructed two sets of ten RF models: one for salt patches and the other for marshes, achieving high (&gt;99 %) training and testing accuracies for classification. The consistently high accuracy and low error values across different model runs demonstrate the method's reliability for classifying spectrally similar land cover classes in the mid-Atlantic region and beyond. Validation metrics for sub-pixel fractional abundances in the salt model revealed a moderate R-squared value of 0.50, and a high R-squared value of 0.90 for the marsh model<em>.</em> Our method complements labor-intensive field-based salinity measurements by offering a reproducible method that can be repeated annually and scaled up to cover large geographic regions.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"319 ","pages":"Article 114642"},"PeriodicalIF":11.1,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143258015","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":"Linear integrated mass enhancement: A method for estimating hotspot emission rates from space-based plume observations","authors":"Janne Hakkarainen ,&nbsp;Iolanda Ialongo ,&nbsp;Daniel J. Varon ,&nbsp;Gerrit Kuhlmann ,&nbsp;Maarten C. Krol","doi":"10.1016/j.rse.2025.114623","DOIUrl":"10.1016/j.rse.2025.114623","url":null,"abstract":"<div><div>In this paper, we propose a new methodology for plume inversion emission estimation termed <em>linear integrated mass enhancement (LIME)</em>. As the name implies, this approach is based on the integrated mass enhancement (IME) method and on the linear relationship between IME and the distance from the source. The proposed approach accounts for the information coming from different portions of the plume, and it can be seen as a “combination” of the cross-sectional flux (CSF) method and IME. The method offers a straightforward way to estimate the source strength by determining the slope of the linear fit. We test the LIME approach with both real (OCO-3, S5P/TROPOMI, Sentinel-2) and simulated (MicroHH, SMARTCARB) satellite data. We apply the method to the simulated carbon dioxide (CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>) observations for the upcoming CO2M mission over the Matimba and Jänschwalde power stations with known source rates. We use the OCO-3 data to estimate the CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> emissions originating from the Bełchatów power station in Poland (between 72 and 103<!--> <!-->ktCO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>/d). We also estimate the emissions from two methane (CH<span><math><msub><mrow></mrow><mrow><mn>4</mn></mrow></msub></math></span>) leaking sites in Algeria based on S5P/TROPOMI (77 and 47<!--> <!-->tCH<span><math><msub><mrow></mrow><mrow><mn>4</mn></mrow></msub></math></span>/h for two days) and Sentinel-2 (7.7<!--> <!-->tCH<span><math><msub><mrow></mrow><mrow><mn>4</mn></mrow></msub></math></span>/h) observations. Finally, we apply the LIME method to the Sentinel-2 retrievals from a controlled CH<span><math><msub><mrow></mrow><mrow><mn>4</mn></mrow></msub></math></span> release in Arizona. Across all case studies, the LIME emission estimates are in agreement with the expected values. The LIME estimates are also aligned with the state-of-the-art IME emission estimates, which are calculated as byproducts in the LIME emission estimation process.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"319 ","pages":"Article 114623"},"PeriodicalIF":11.1,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143192068","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":"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}