Remote Sensing of Environment最新文献_第3页

Spatiotemporal evolution characteristics of ground deformation in the Beijing Plain from 1992 to 2023 derived from a novel multi-sensor InSAR fusion method

IF 11.1 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-02-08 DOI: 10.1016/j.rse.2025.114635

Yuanzhao Fu , Jili Wang , Yi Zhang , Honglei Yang , Lu Li , Zhengzhao Ren

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.

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引用次数: 0

Joint utilization of closure phase and closure amplitude for soil moisture change using interferometric synthetic aperture radar

IF 11.1 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-02-07 DOI: 10.1016/j.rse.2025.114620

Xujing Zeng, Shisheng Guo, Guolong Cui

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.

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引用次数: 0

Non-linear spectral unmixing for monitoring rapidly salinizing coastal landscapes

IF 11.1 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-02-07 DOI: 10.1016/j.rse.2025.114642

Manan Sarupria , Rodrigo Vargas , Matthew Walter , Jarrod Miller , Pinki Mondal

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 (>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. 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.

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引用次数: 0

Linear integrated mass enhancement: A method for estimating hotspot emission rates from space-based plume observations

IF 11.1 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-02-06 DOI: 10.1016/j.rse.2025.114623

Janne Hakkarainen , Iolanda Ialongo , Daniel J. Varon , Gerrit Kuhlmann , Maarten C. Krol

In this paper, we propose a new methodology for plume inversion emission estimation termed linear integrated mass enhancement (LIME). 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

_{2}

) 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

_{2}

emissions originating from the Bełchatów power station in Poland (between 72 and 103 ktCO

_{2}

/d). We also estimate the emissions from two methane (CH

_{4}

) leaking sites in Algeria based on S5P/TROPOMI (77 and 47 tCH

_{4}

/h for two days) and Sentinel-2 (7.7 tCH

_{4}

/h) observations. Finally, we apply the LIME method to the Sentinel-2 retrievals from a controlled CH

_{4}

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.

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引用次数: 0

Dynamic vegetation parameter retrieval algorithm for SMAP L-band radiometer observations

IF 11.1 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-02-06 DOI: 10.1016/j.rse.2025.114641

Preethi Konkathi , L. Karthikeyan

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 ω and h parameters in VOD through synthetic experiments. These experiments also indicate notable propagation of errors from assuming a temporally constant ω in VOD retrievals, which could be resolved using a time-varying ω parameter.

To improve the VOD characterization, we proposed a Dynamic Vegetation Parameter retrieval Algorithm (DVPA) to retrieve VOD and ω simultaneously, along with a temporally constant h 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 ω 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 ω 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.

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引用次数: 0

DART-based temporal and spatial retrievals of solar-induced chlorophyll fluorescence quantum efficiency from in-situ and airborne crop observations

IF 11.1 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-02-05 DOI: 10.1016/j.rse.2025.114636

Omar Regaieg , Zbyněk Malenovský , Bastian Siegmann , Jim Buffat , Julie Krämer , Nicolas Lauret , Valérie Le Dantec

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, i.e., 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 in-situ 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 (R = 0.87, R² = 0.76 before and R = −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.

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引用次数: 0

kNDMI: A kernel normalized difference moisture index for remote sensing of soil and vegetation moisture

IF 11.1 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-02-04 DOI: 10.1016/j.rse.2025.114621

Huanyu Xu , Hao Sun , Zhenheng Xu , Yunjia Wang , Tian Zhang , Dan Wu , JinHua Gao

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.

{"title":"kNDMI: A kernel normalized difference moisture index for remote sensing of soil and vegetation moisture","authors":"Huanyu Xu , Hao Sun , Zhenheng Xu , Yunjia Wang , Tian Zhang , Dan Wu , 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}

引用次数: 0

Estimating global transpiration from TROPOMI SIF with angular normalization and separation for sunlit and shaded leaves

IF 11.1 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-02-03 DOI: 10.1016/j.rse.2024.114586

Chen Zheng , Shaoqiang Wang , Jing M. Chen , Jingfeng Xiao , Jinghua Chen , Zhaoying Zhang , Giovanni Forzieri

<div><div>Gross primary productivity (GPP) is more accurately estimated by total canopy solar-induced chlorophyll fluorescence (<span><math><msub><mi>SIF</mi><mi>total</mi></msub></math></span>) compared to raw sensor observed SIF signals (<span><math><msub><mi>SIF</mi><mi>obs</mi></msub></math></span>). The use of two-leaf strategy, which distinguishes between SIF from sunlit (<span><math><msub><mi>SIF</mi><mi>sunlit</mi></msub></math></span>) and shaded (<span><math><msub><mi>SIF</mi><mi>shaded</mi></msub></math></span>) 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 <span><math><msub><mi>SIF</mi><mi>sunlit</mi></msub></math></span> and <span><math><msub><mi>SIF</mi><mi>shaded</mi></msub></math></span>. Then we developed <span><math><msub><mi>SIF</mi><mi>sunlit</mi></msub></math></span> and <span><math><msub><mi>SIF</mi><mi>shaded</mi></msub></math></span> driven semi-mechanistic and hybrid models, comparing their T estimates with those from a <span><math><msub><mi>SIF</mi><mi>obs</mi></msub></math></span> driven semi-mechanistic model at both site and global scales. All three types of SIF-driven T models integrate canopy conductance (<span><math><msub><mi>g</mi><mi>c</mi></msub></math></span>) with the Penman-Monteith model, differing in how <span><math><msub><mi>g</mi><mi>c</mi></msub></math></span> is derived: from a <span><math><msub><mi>SIF</mi><mi>obs</mi></msub></math></span> driven semi-mechanistic equation, a <span><math><msub><mi>SIF</mi><mi>sunlit</mi></msub></math></span> and <span><math><msub><mi>SIF</mi><mi>shaded</mi></msub></math></span> driven semi-mechanistic equation, and a <span><math><msub><mi>SIF</mi><mi>sunlit</mi></msub></math></span> and <span><math><msub><mi>SIF</mi><mi>shaded</mi></msub></math></span> 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: <span><math><msub><mi>SIF</mi><mi>sunlit</mi></msub></math></span> and <span><math><msub><mi>SIF</mi><mi>shaded</mi></msub></math></span> driven hybrid model > <span><math><msub><mi>SIF</mi><mi>sunlit</mi></msub></math></span> and <span><math><msub><mi>SIF</mi><mi>shaded</mi></msub></math></span> driven semi-mechanistic model > <span><math><msub><mi>SIF</mi><mi>obs</mi></msub></math></span> driven semi-mechanistic model. The <span><math><msub><mi>SIF</mi><mi>sunlit</mi></msub></math></span> and <span><math><msub><mi>SIF</mi><mi>shaded</mi></msub></math></span> driven hybrid model demonstrated a notable proficiency under high vapor pressure deficit and low soil water content conditions. The <span><math><msub><mi>SIF</mi><mi>obs</mi></msub></math></span> driven semi-mec

引用次数: 0

Cloud heights retrieval from passive satellite measurements using lapse rate information

IF 11.1 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-01-31 DOI: 10.1016/j.rse.2025.114622

Weiyuan Zhang , Jiming Li , Jiayi Li , Sihang Xu , Lijie Zhang , Yang Wang , Jianping Huang

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 (Γ_ct), the lapse rate within (Γ_cb1) and below cloud (Γ_cb2), 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), Γ_ct, Γ_cb1 and Γ_cb2. 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 R = 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.

{"title":"Cloud heights retrieval from passive satellite measurements using lapse rate information","authors":"Weiyuan Zhang , Jiming Li , Jiayi Li , Sihang Xu , Lijie Zhang , Yang Wang , 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><sub><em>ct</em></sub>), the lapse rate within (<em>Γ</em><sub><em>cb1</em></sub>) and below cloud (<em>Γ</em><sub><em>cb2</em></sub>), 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><sub><em>ct</em></sub>, <em>Γ</em><sub><em>cb1</em></sub> and <em>Γ</em><sub><em>cb2</em></sub>. 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}

引用次数: 0

Cross-scalar analysis of multisensor land surface phenology

IF 11.1 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-01-31 DOI: 10.1016/j.rse.2025.114624

Xiaojie Gao , Sophia Stonebrook , Tristan Green , Minkyu Moon , Mark A. Friedl

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 <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.

{"title":"Cross-scalar analysis of multisensor land surface phenology","authors":"Xiaojie Gao , Sophia Stonebrook , Tristan Green , Minkyu Moon , 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 (r<sup>2</sup> = 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 <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}

引用次数: 0