Remote Sensing of Environment最新文献

{"title":"LiDAR-derived Lorenz-entropy metric for vertical structural complexity: A comparative study of tropical dry and moist forests","authors":"Nooshin Mashhadi ,&nbsp;Arturo Sanchez-Azofeifa ,&nbsp;Ruben Valbuena","doi":"10.1016/j.rse.2024.114545","DOIUrl":"10.1016/j.rse.2024.114545","url":null,"abstract":"<div><div>This study introduces an Entropy-based index: the Lorenz-entropy (LE) index, which we have developed by integrating Light Detection And Ranging (LiDAR), econometrics, and forest ecology. The main goal of the LE is to bridge the gap between theoretical entropy concepts and their practical applications in monitoring vertical structural complexity of tropical forest ecosystems. The LE index quantifies entropy by analyzing Relative Height (RH) metrics (representing a one-dimensional (1D) canopy structure metric) distributions from full-waveform LiDAR across successional stages in a tropical dry forest (TDF) and a tropical rainforest. To validate the LE trends derived from LiDAR, we extended the analysis using inventory-based two-dimensional (2D) and three-dimensional (3D) metrics, specifically basal area and biomass. The consistency of trends between the 1D LiDAR-derived LE and the inventory-based 2D and 3D metrics reinforces the LE's ability to capture and monitor structural complexity reliably across different measurement dimensions.</div><div>Our findings demonstrated that LE captures the changes in entropy as a function of successional stages, reflecting how canopy structure evolves towards homogeneity and complexity. Our statistical analysis revealed significant differences between successional stages (ANOVA, α = 0.05, <em>p</em> &lt; 2e-16), with LE increasing substantially from early to late stages and plateauing at climax, where vertical structure (entropy) stabilizes. The mean LE increased by 1.70<span><math><mo>×</mo><msup><mn>10</mn><mrow><mo>−</mo><mn>2</mn></mrow></msup></math></span> between late and climax stages, with a small effect size (Cohen's d = 0.25), indicating minor differences in complexity. The LE index, calculated from biomass and basal area, confirming that as forests mature, entropy and vertical structural complexity increase. Furthermore, the sensitivity analysis showed that LE is most responsive to RHs variability during intermediate stages, suggesting that structural development is most dynamic during this phase. These results demonstrate the potential of the LE index as a tool for ecological analysis and monitoring forest dynamics.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"318 ","pages":"Article 114545"},"PeriodicalIF":11.1,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142788585","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":"Assessing and attributing flood potential in Brazil using GPS 3D deformation","authors":"Xinghai Yang ,&nbsp;Linguo Yuan ,&nbsp;Miao Tang ,&nbsp;Zhongshan Jiang","doi":"10.1016/j.rse.2024.114535","DOIUrl":"10.1016/j.rse.2024.114535","url":null,"abstract":"<div><div>Global Positioning System (GPS) instruments capture the daily crustal 3D deformation responding elastically to terrestrial water storage (TWS) variations, providing a powerful tool for hydrological studies. Here, we further expand the application of GPS in flood potential assessment. GPS vertical and horizontal crustal deformation are inverted into TWS variations using a 3D-Inversion model, and then a novel GPS-based modified flood potential index (GMFPI) is developed to assess and attribute the spatiotemporal patterns of flood potential in Brazil. The 3D-Inversion-derived TWS estimates show more spatial details compared to those derived from vertical deformation (1D-Inversion), with annual water thickness amplitudes of about 900 mm in the middle Amazon River, which is consistent with the Gravity Recovery and Climate Experiment Mascon solutions but is greater than the 1D-Inversion estimates. The comparison between in-situ discharge data and GMFPI indicates that GMFPI performs well in monitoring flood potential, showing Accuracy (ACC) values greater than 0.8 at basin scales. In four reported flood events, the spatial patterns of GMFPI and discharge show that the locations of those floods are accurately characterized by GMFPI. The attribution analysis of flood dynamics shows that precipitation in coastal regions can rapidly increase flood potential, while a large amount of precipitation in inland regions first replenishes unsaturated soil water and groundwater. Additionally, the daily GMFPI exhibits good consistency with daily discharge, demonstrating a capacity for monitoring floods at a sub-monthly scale. Our study highlights the improvement of 3D-Inversion to TWS estimates and the novel application of GPS in flood potential assessing with high spatiotemporal resolution, providing valuable insights for flood early warning and prevention.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"318 ","pages":"Article 114535"},"PeriodicalIF":11.1,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142782875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to “HIDYM: A high-resolution gross primary productivity and dynamic harvest index based crop yield mapper” [Remote Sensing of Environment, 2024, 114301]","authors":"Weiguo Yu ,&nbsp;Dong Li ,&nbsp;Hengbiao Zheng ,&nbsp;Xia Yao ,&nbsp;Yan Zhu ,&nbsp;Weixing Cao ,&nbsp;Lin Qiu ,&nbsp;Tao Cheng ,&nbsp;Yongguang Zhang ,&nbsp;Yanlian Zhou","doi":"10.1016/j.rse.2024.114548","DOIUrl":"10.1016/j.rse.2024.114548","url":null,"abstract":"","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"318 ","pages":"Article 114548"},"PeriodicalIF":11.1,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142763602","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 text-based, generative deep learning model for soil reflectance spectrum simulation in the solar range (400–2499 nm)","authors":"Tong Lei,&nbsp;Brian N. Bailey","doi":"10.1016/j.rse.2024.114527","DOIUrl":"10.1016/j.rse.2024.114527","url":null,"abstract":"<div><div>Soil spectral reflectance is a necessary input for land surface and radiative transfer models, and can be used to infer soil properties. Numerous soil reflectance inversion models have been developed based on mechanistic approaches, each with their own limitations. Mechanistic models based on radiative transfer theory are usually based on only a few input soil properties, whereas data-driven approaches are limited by high non-uniformity of available published datasets that severely limits the amount of data usable for model calibration. To address these limitations, a fully data-driven soil optics generative model (SOGM) for simulation of soil reflectance spectra from soil property inputs was developed based on the denoising diffusion probabilistic model (DDPM). The model was trained on an extensive dataset comprising nearly 180,000 soil spectra-property set pairs from 17 published datasets. The model generates soil reflectance spectra from text-based inputs describing soil properties and their values rather than only numerical values and labels in binary vector format, which means the model can handle variable formats for property reporting. Because the model is generative, it can simulate reasonable output spectra based on an incomplete set of available input properties, which becomes more reliable as the input property set becomes more complete. Two additional sub-models were also built to complement the SOGM: a spectral padding model that can fill in the gaps for spectra shorter than the target solar range (400 to 2499 nm), and a wet soil spectra model that can estimate the effects of water content on soil reflectance spectra given the dry spectrum predicted by the SOGM. It can also be easily integrated with other soil–plant radiation models used for remote sensing research such as PROSAIL and Helios 3D plant modeling software. The testing results of the SOGM on new datasets not included in model training demonstrated that the model can generate reasonable soil reflectance spectra based on available property inputs. Results also show soil clay/sand/silt fraction, organic carbon content, nitrogen content, and iron content tended to be important properties for spectra simulation. Inclusion of some trace minerals like nickel as model inputs decreased model performance because of their low concentrations and large propensity for ground-truth measurement error.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"318 ","pages":"Article 114527"},"PeriodicalIF":11.1,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142763601","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":"Estimating actual evapotranspiration across China by improving the PML algorithm with a shortwave infrared-based surface water stress constraint","authors":"Yongmin Yang","doi":"10.1016/j.rse.2024.114544","DOIUrl":"10.1016/j.rse.2024.114544","url":null,"abstract":"<div><div>Accurate estimation of evapotranspiration (ET) is essential for the precise quantification of energy and water budgets under climate change. Remote sensing ET models provide an effective way to map ET across different spatial and temporal scales. However, conductance-based ET models such as PML_V2 are associated with limited or no water stress constraints on soil evaporation and canopy transpiration that could cause significant bias for sparse vegetation in arid and semi-arid areas. To meet this challenge for using conductance-based ET models, we proposed to use shortwave infrared information to serve as a water stress constraint to vegetation transpiration and soil evaporation, and an improved ET model (PML_SWIR) was proposed. The PML_SWIR model was calibrated with ET measurements from 21 eddy covariance flux towers distributed across China, and showed good performance for estimating ET (R² = 0.70 and RMSE = 0.72 mm/day) for the cross-validation dataset. PML_SWIR outperformed PML_V2 in estimating ET for arid and semi-arid areas, indicated by RMSE being 7.86 and 25.93 mm/year lower and bias being 4.74 and 16.63 % less compared with PML_V2(China) and PML_V2(Global) for ET estimation over Xinjiang Province. In addition, PML_SWIR was noticeably better than PML_V2 for depicting the ET patterns for these seasonal rivers in the arid areas. The ET values estimated by PML_SWIR were further compared with other ET products. The results indicated that PML_SWIR well characterized the ET pattern in arid and semi-arid areas, and the estimated ET values showed good agreement with the water balance-based ET (R² = 0.87, RMSE = 91.37 mm/year) in major river basins of China. The PML_SWIR ET estimates indicated that 20.2 % of the area of China increased significantly in ET over the study period, mainly due to vegetation greening caused by cropland expansion and the large-scale afforestation program. Overall, our results demonstrated that the incorporation of SWIR-based water stress constraints into the conductance-based ET model was a very promising way for accurately mapping ET in arid and semi-arid areas, and that the PML_SWIR model was highly applicable to regional high spatiotemporal ET mapping.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"318 ","pages":"Article 114544"},"PeriodicalIF":11.1,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142760401","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":"Archetypal crop trait dynamics for enhanced retrieval of biophysical parameters from Sentinel-2 MSI","authors":"Feng Yin ,&nbsp;Philip E. Lewis ,&nbsp;Jose L. Gómez-Dans ,&nbsp;Thomas Weiß","doi":"10.1016/j.rse.2024.114510","DOIUrl":"10.1016/j.rse.2024.114510","url":null,"abstract":"&lt;div&gt;&lt;div&gt;We present a new method for estimating biophysical parameters from Earth Observation (EO) data using a crop-specific empirical model based on the PROSAIL Radiative Transfer (RT) model, called an ‘archetype’ model. The first-order model presented uses maximum biophysical parameter magnitude, phenological and soil parameters to describe the spectral reflectance (400–2500 nm) of vegetation over time. The approach assumes smooth variation and archetypical coordination of crop biophysical parameters over time for a given crop. The form of coordination is learned from a large sample of observations. Using Sentinel-2 observations of maize from Northeast China in 2019, we map reflectance to biophysical parameters using an inverse model operator, synchronise the parameters to a consistent time frame using a double logistic model of &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;L&lt;/mi&gt;&lt;mi&gt;A&lt;/mi&gt;&lt;mi&gt;I&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;, then derive the model archetypes as the median value of the synchronised samples. We apply the model to estimate time series of biophysical parameters for different cereal crops using an ensemble framework with a weighted K-nearest neighbour solution, and validate the results with ground measurements of different crops collected near Munich, Germany in 2017 and 2018. The results show &lt;span&gt;&lt;math&gt;&lt;mi&gt;R&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt; values greater than 0.8 for leaf area index (&lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;L&lt;/mi&gt;&lt;mi&gt;A&lt;/mi&gt;&lt;mi&gt;I&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;) and leaf brown pigment content (&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;C&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;b&lt;/mi&gt;&lt;mi&gt;r&lt;/mi&gt;&lt;mi&gt;o&lt;/mi&gt;&lt;mi&gt;w&lt;/mi&gt;&lt;mi&gt;n&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;), with an RMSE of 0.94 &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mtext&gt;m&lt;/mtext&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;mo&gt;/&lt;/mo&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mtext&gt;m&lt;/mtext&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; for &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;L&lt;/mi&gt;&lt;mi&gt;A&lt;/mi&gt;&lt;mi&gt;I&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; and 0.15 for &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;C&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;b&lt;/mi&gt;&lt;mi&gt;r&lt;/mi&gt;&lt;mi&gt;o&lt;/mi&gt;&lt;mi&gt;w&lt;/mi&gt;&lt;mi&gt;n&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;. The chlorophyll content (&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;C&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;a&lt;/mi&gt;&lt;mi&gt;b&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;) and canopy water content (&lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;C&lt;/mi&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;C&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;w&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;) were retrieved at a higher level of accuracy, with &lt;span&gt;&lt;math&gt;&lt;mi&gt;R&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt; values around 0.9 and an RMSE of &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mn&gt;6&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;59&lt;/mn&gt;&lt;mi&gt;μ&lt;/mi&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mtext&gt;g/cm&lt;/mtext&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; for &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;C&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;a&lt;/mi&gt;&lt;mi&gt;b&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; and 0.03 &lt;span&gt;&lt;math&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mtext&gt;g/cm&lt;/mtext&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;/math&gt;&lt;/span&gt; for &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;C&lt;/mi&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;C&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;w&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;. Comparison of forward-modelled hyperspectral reflectance with independent ground measures shows that the retrieved paramete","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"318 ","pages":"Article 114510"},"PeriodicalIF":11.1,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142758647","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":"An advanced dorsiventral leaf radiative transfer model for simulating multi-angular and spectral reflection: Considering asymmetry of leaf internal and surface structure","authors":"Dongjie Ran ,&nbsp;Zhongqiu Sun ,&nbsp;Shan Lu ,&nbsp;Kenji Omasa","doi":"10.1016/j.rse.2024.114531","DOIUrl":"10.1016/j.rse.2024.114531","url":null,"abstract":"<div><div>Understanding the optical properties of dorsiventral leaves and quantifying leaf biochemical traits through physical models are important for interpreting canopy radiative transfer and monitoring plant growth. Previous models, such as the dorsiventral leaf model (DLM), have effectively accounted for the inner asymmetry of the leaf but neglected the asymmetry of surface structures between the upper and lower epidermis. In this study, we found marked differences in bidirectional reflectance factors (BRF) between the adaxial and abaxial surfaces of leaves under multi-angular measurements due to surface structural distinctions. To address this asymmetry in both internal and surface leaf structures, we subsequently proposed an advanced DLM model (MADLM) for simulating both multi-angular and spectral BRF of two leaf sides, linking the angular reflection of leaf adaxial and abaxial sides to surface structural parameters (roughness and refractive index) based on microfacet theory. Results show that MADLM accurately simulates multi-angular and spectral BRF for both sides of dorsiventral leaves, and yields satisfactory retrieval accuracy of leaf traits from all observation geometries. For close-range hyperspectral imaging applications, we further introduced a simplified version, sMADLM, which characterizes the surface reflection of two leaf sides in terms of the product of a leaf-side dependent parameter and the wavelength-dependent Fresnel factor. The sMADLM improves the mapping accuracy of leaf biochemical traits by effectively reducing the surface reflection effects in dorsiventral leaves. The MADLM and sMADLM deepen our understanding of the optical properties of dorsiventral leaves and provide practical methods for retrieving leaf biochemical traits via optical remote sensing.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"318 ","pages":"Article 114531"},"PeriodicalIF":11.1,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142753404","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":"Angular normalization of GOES-16 and GOES-17 land surface temperature over overlapping region using an extended time-evolving kernel-driven model","authors":"Boxiong Qin ,&nbsp;Shuisen Chen ,&nbsp;Biao Cao ,&nbsp;Yunyue Yu ,&nbsp;Peng Yu ,&nbsp;Qiang Na ,&nbsp;Enqing Hou ,&nbsp;Dan Li ,&nbsp;Kai Jia ,&nbsp;Yingpin Yang ,&nbsp;Tian Hu ,&nbsp;Zunjian Bian ,&nbsp;Hua Li ,&nbsp;Qing Xiao ,&nbsp;Qinhuo Liu","doi":"10.1016/j.rse.2024.114532","DOIUrl":"10.1016/j.rse.2024.114532","url":null,"abstract":"<div><div>Land surface temperature (LST) is an important parameter that critically contributes to Earth’ s climate. Thermal anisotropy is a major challenge that must be addressed while generating long-term LST products from satellites. For instance, the differences between GOES-16 and GOES-17 LST products caused by thermal anisotropy have not yet been resolved, which impacts the high-frequency monitoring of the land surface. The coupled contributions of the gap fraction and hotspot effects in the thermal infrared domain result in the existence of thermal anisotropy effect. The time-evolving kernel-driven model (TEKDM) is a recently proposed practical tool for conducting LST angular normalization for geostationary satellites. However, the existing six-parameter TEKDM considers only the hotspot effect and ignores the gap fraction effect, which may limit the TEKDM-based angular normalization method. In this study, we proposed an extended seven-parameter TEKDM considering both the gap fraction and hotspot effects and normalized the angular effect of GOES-16 and GOES-17 LST products over the overlapping region using this model. The accuracy of this seven-parameter TEKDM was evaluated using a physically based discrete anisotropic radiative transfer (DART) simulation dataset. Subsequently, the seven-parameter TEKDM-based angular normalization method was evaluated using the GOES-16 and GOES-17 LST products of the overlapping region for one year against ten AmeriFlux sites. The results showed that the seven-parameter TEKDM had a RMSE (MBE) of 0.36 K (0.0019 K). Compared with the RMSE of the NOAA-released GOES LST products, the seven-parameter TEKDM-based normalization method could reduce the RMSE of GOES-16 and GOES-17 LST products from 2.2 K and 2.6 K to 1.7 K, respectively, with a reduction of 0.5 K (22.7 %) and 0.9 K (34.6 %), respectively. Furthermore, the RMSE/MBE of GOES-17 LST exhibited a different diurnal variation pattern than that of GOES-16 LST, which could be explained by the different illumination-viewing geometries of the two satellites. This emphasizes the necessity of conducting angular normalization of current geostationary satellite LST products. The seven-parameter TEKDM provides a feasible method for generating long-term high-quality LST datasets for remote sensing communities.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"318 ","pages":"Article 114532"},"PeriodicalIF":11.1,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142753405","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":"Unsupervised object-based spectral unmixing for subpixel mapping","authors":"Chengyuan Zhang ,&nbsp;Qunming Wang ,&nbsp;Peter M. Atkinson","doi":"10.1016/j.rse.2024.114514","DOIUrl":"10.1016/j.rse.2024.114514","url":null,"abstract":"<div><div>Subpixel mapping (SPM) addresses the widespread mixed pixel problem in remote sensing images by predicting the spatial distribution of land cover <em>within</em> mixed pixels. However, conventional pixel-based spectral unmixing, a key pre-processing step for SPM, neglects valuable spatial contextual information and struggles with spectral variability, ultimately undermining SPM accuracy. Additionally, while extensively utilized, supervised spectral unmixing is labor-intensive and user-unfriendly. To address these issues, this paper proposes a fully automatic, unsupervised object-based SPM (UO-SPM) model that exploits object-scale information to reduce spectral unmixing errors and subsequently enhance SPM. Given that mixed pixels are typically located at the edges of objects (i.e., the inner part of objects is characterized by pure pixels), segmentation and morphological erosion are employed to identify pure pixels within objects and mixed pixels at the edges. More accurate endmembers are extracted from the identified pure pixels for the secondary spectral unmixing of the remaining mixed pixels. Experimental results on 10 study sites demonstrate that the proposed unsupervised object (UO)-based analysis is an effective model for enhancing both spectral unmixing and SPM. Specifically, the spectral unmixing results of UO show an average increase of 3.65 % and 1.09 % in correlation coefficient (<em>R</em>) compared to Fuzzy-<em>C</em> means (FCM) and linear spectral mixture model (LSMM)-derived coarse proportions, respectively. Moreover, the UO-derived results of four SPM methods (i.e., Hopfield neural network (HNN), Markov random field (MRF), pixel swapping (PSA) and radial basis function interpolation (RBF)) exhibit an average increase of 5.89 % and 3.04 % in overall accuracy (OA) across the four SPM methods and 10 study sites compared to the FCM and LSMM-based results, respectively. Moreover, the proportions of both mixed and pure pixels are more accurately predicted. The advantage of UO-SPM is more evident when the size of land cover objects is larger, benefiting from more accurate identification of objects.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"318 ","pages":"Article 114514"},"PeriodicalIF":11.1,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping and reconstruct suspended sediment dynamics (1986–2021) in the source region of the Yangtze River, Qinghai-Tibet Plateau using Google Earth Engine","authors":"Jinlong Li,&nbsp;Genxu Wang,&nbsp;Shouqin Sun,&nbsp;Jiapei Ma,&nbsp;Linmao Guo,&nbsp;Chunlin Song,&nbsp;Shan Lin","doi":"10.1016/j.rse.2024.114533","DOIUrl":"10.1016/j.rse.2024.114533","url":null,"abstract":"<div><div>Using remote sensing to measure suspended sediment concentration (SSC) in mountainous rivers can compensate for the scarcity of <em>in situ</em> sediment observations, providing valuable direct supplementation to observational records. However, for inland rivers, remote sensing SSC assessments face challenges such as data quality, long-term water body changes, environmental noise, flood events, and the transferability of local calibrations. Here, we introduce and apply remote sensing big data techniques using 12,445 cloud-free Landsat 5, 7, and 8 satellite images to calibrate SSC in the source region of the Yangtze River (SRYR). Utilizing Google Earth Engine, we implemented a series of image preprocessing techniques and water fraction methods to extract precise inland river water masks. Then we used unsupervised K-Means clustering and machine learning algorithms to model the relationship between water optical properties and SSC. By integrating these methodologies, we achieved an average relative calibration error of 0.26 for each optical cluster, and an average relative station deviation of 0.24 based on <em>in situ</em> measurements, minimizing SSC calibration to acceptable levels. Additionally, our results reveal that geomorphic patterns significantly influence sediment yield and transport by regulating sediment sources and sinks, fluvial morphology, and water-sediment connectivity. Over the past two decades, approximately 35.73 % of the sediment relative to the basin outlet discharge in the SRYR has been temporarily stored or confined within sediment sinks. These methods and findings hold significant implications for assessing and projecting fluvial sediment dynamics and the associated ecological and environmental issues in ungauged cold headwater regions.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"317 ","pages":"Article 114533"},"PeriodicalIF":11.1,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142742598","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}