Integrating UAV and Landsat data: A two-scale approach to topsoil moisture mapping in coastal wetlands by Ricardo Martínez Prentice, Miguel Villoslada, Raymond D. Ward, Kalev Sepp

“…These maps were aggregated to train and test XGBoost models using Landsat-derived predictors.While UAV data captured fine-scale SSM variability, Landsat-based predictions provided consistency at lower spatial scales (30 m of spatial resolution from Collection-2 Level-2), with RMSE values below 10 %. …”
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Development of a Comprehensive Approach to Quality Control of Dermorphin Derivative—Representative of Synthetic Opioid Peptides with Non-Narcotic Type of Analgesia by Vasilisa A. Sukhanova, Elena V. Uspenskaya, Safdari Ainaz, Hoang Thi Ngoc Quynh, Aleksey A. Timofeev

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Mapping global leaf inclination angle (LIA) based on field measurement data by S. Li, S. Li, S. Li, H. Fang, H. Fang

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Comparison of Fuzzy-AHP and ANP Decision Making Methods to Assess the Ecological Capability of Ecotourism Application (Case study: Dehloran National Natural Monuments) by Mohsen Tavakoli

“…Then, to combine the layers, the model of overlapping index or linear weight composition was used. 3-Results and Discussion Using the GIS on the slope of the linear reduction subscription function, the trajectory linear membership function layer and the temperature layer, an incremental linear membership function was applied. …”
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Mapping clay fraction oxides in Brazil using Earth observation strategy by Jorge Tadeu Fim Rosas, José A.M. Demattê, Nícolas Augusto Rosin, Raul Roberto Poppiel, Nélida E.Q. Silvero, Merilyn Taynara Accorsi Amorim, Heidy S. Rodríguez-Albarracín, Letícia Guadagnin Vogel, Bruno dos Anjos Bartsch, José João Lelis Leal de Souza, Lucas de Carvalho Gomes, Danilo César de Mello

“…Moreover, this satellite-based technique, supported by ML, presents a plausible approach to predict oxide fraction at high spatial resolution for large areas.…”
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Deep learning-driven 3D marine nitrate estimation: uncertainty mitigation through underwater signal exploitation and label augmentation by Xiang Yu, Guodong Fan, Jinjiang Li

“…Quantitative evaluations using BGC-Argo and cruise measurement data demonstrate notable improvements in spatial and temporal generalization, with RMSE reductions of approximately 15% and 28%, respectively, particularly in under-sampled areas and complex upper ocean regions. …”
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Time-series high spatio-temporal resolution vegetation leaf area index estimation based on NDVI trends by Chen Li, Hongmin Zhou, Jia Tang, Changjing Wang, Ziyu Wang, Jinlin Qi, Bihong Yang, Ruojing Fang

“…To overcome these challenges, a novel data assimilation approach, the ENKF-NDVI (Ensemble Kalman Filter-NDVI) model, was developed to estimate LAI data with 10-m spatial and 5-day temporal resolution. …”
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Aboveground biomass relationship with canopy cover and vegetation to improve carbon change monitoring in rangelands by Chiara Pasut, Jacqueline R. England, Melissa Piper, Stephen H. Roxburgh, Keryn I. Paul

“…Here, results were compiled from extensive field measurements across 431 Australian rangeland sites (covering an area of ~6 million km2) to develop empirical relationships to predict BAG from C and other structural variables. …”
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A method integrating a non-stationary random field for constrained inversion of CSAMT data by Qianwei DAI, Luyao GUO, Yun WU, Zhexian XIONG, Dan DUAN, Zhonglin BAO, Hongfei WU, Fengyun HAO

“…Accordingly, this study developed a model covariance matrix meeting the non-stationary assumption. …”
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Unraveling the spatiotemporal dynamics and drivers of surface and tropospheric ozone in China by Shuai Yin, Chong Shi, Husi Letu, Zhijun Jin, Qingnan Chu, Huazhe Shang, Dabin Ji, Meng Guo, Kunpeng Yi, Xin Zhao, Tangzhe Nie, Zhongyi Sun

“…The meteorology correction model indicates that the downward trend is primarily attributed to the implementation of effective control plans (the Three-Year Action Plan for Cleaner Air) and the reduction of anthropogenic emissions. …”
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Arsenic health risk in shallow groundwater of the alluvial plains in the lower Yellow River, China: driving mechanisms of climate change and human activities by Wengeng Cao, Yu Fu, Yu Ren, Xiangzhi Li, Yanyan Wang, Le Song

“…In this study, we developed a robust machine learning model framework to predict the spatial variation of arsenic levels in shallow groundwater within the alluvial plains of the lower Yellow River. …”
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An integrative systems-biology approach defines mechanisms of Alzheimer’s disease neurodegeneration by Matthew J. Leventhal, Camila A. Zanella, Byunguk Kang, Jiajie Peng, David Gritsch, Zhixiang Liao, Hassan Bukhari, Tao Wang, Ping-Chieh Pao, Serwah Danquah, Joseph Benetatos, Ralda Nehme, Samouil Farhi, Li-Huei Tsai, Xianjun Dong, Clemens R. Scherzer, Mel B. Feany, Ernest Fraenkel

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PBC-Transformer: Interpreting Poultry Behavior Classification Using Image Caption Generation Techniques by Jun Li, Bing Yang, Jiaxin Liu, Felix Kwame Amevor, Yating Guo, Yuheng Zhou, Qinwen Deng, Xiaoling Zhao

“…The model employs a multi-head concentrated attention mechanism, Head Spatial Position Coding (HSPC), to enhance spatial information; a learnable sparse mechanism (LSM) and RNorm function to reduce noise and strengthen feature correlation; and a depth-wise separable convolutional network for improved local feature extraction. …”
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The characteristics and functional significance of disulfidptosis-related genes in head and neck squamous cell carcinoma by Haiqian Zhu, Chifeng Zhao, Haoran Zhu, Xuhui Xu, Conglin Hu, Zhenxing Zhang

“…The relative compositions of cells in the tumor microenvironment (TME), mutant landscape, lasso regression analysis, and predicted clinical outcome were performed by analyzing bulk RNA-sequencing data. …”
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Small scale, elevation- and environmental-related differences in life history strategies in a temperate resident songbird by Benjamin R. Sonnenberg, Carrie L. Branch, Angela M. Pitera, Virginia K. Heinen, Lauren E. Whitenack, Joseph F. Welklin, Vladimir V. Pravosudov

“…Due to the harsher and less predictable environmental conditions at higher elevations, this investment strategy in this resident species likely leads to the production of offspring with greater chances of survival. …”
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Study of the dynamics of herbs productivity based on long-term monitoring data by D. A. Ivanov, O. V. Karaseva, M. V. Rublyuk

“…It has been determined that different groups of observation years differ in productivity and in the nature of its spatio-temporal variability, as well as in the factors that determine them and in the conditions that affect these factors. This makes, when predicting the yield of grasses of different ages, to create mathematical models of its dependence on landscape conditions for different time clusters.…”
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A Near-Real-Time Operational Live Fuel Moisture Content (LFMC) Product to Support Decision-Making at the National Level by Akli Benali, Giuseppe Baldassarre, Carlos Loureiro, Florian Briquemont, Paulo M. Fernandes, Carlos Rossa, Rui Figueira

“…Live fuel moisture content (LFMC) significantly influences fire activity and behavior over different spatial and temporal scales. The ability to estimate LFMC is important to improve our capability to predict when and where large wildfires may occur. …”
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Comprehensive geospatial analysis of urban expansion dynamic in Lahore, Pakistan (1998–2023) by Sona Karim, Yaning Chen, Patient Mindje Kayumba, Ishfaq Ahmad, Hassan Iqbal

“…To contribute, we used high-resolution Landsat imagery to analyze the spatial diverging patterns of urban extent from 1998 to 2023 in Lahore. …”
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Yield gap in cowpea plants as function of water déficits during reproductive stage by Paulo J. O. P. Souza, Vivian D. da S. Farias, João V. N. Pinto, Hildo G. G. C. Nunes, Everaldo B. de Souza, Clyde W. Fraisse

“…The total deficiencies in the reproductive phase were spatialized considering the 30 locations in order to assess the temporal and spatial seasonality of water availability and the sowing period in the study region. …”
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FlexiNet: An Adaptive Feature Synthesis Network for Real-Time Ego Vehicle Speed Estimation by Abdalrahaman Ibrahim, Kyandoghere Kyamakya, Wolfgang Pointner

“…On the nuImages dataset, our model achieves an RMSE of 1.1358 m/s and an MAE of 0.9599 m/s, while on the KITTI dataset it records an RMSE of 1.9542 m/s and an MAE of 1.0610 m/s—reductions in error of up to 27.6% and 75.5% compared to baseline methods. …”
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