Results 171 to 180 of about 13,133 (266)

Environmental coherence framework for multi-sensor remote sensing: water hyacinth assessment in Lake Tana. [PDF]

Sci Rep
Mahmoud MR, Garcia LA, Abd Elhamid A, Aboelkhear M.   +3 more
europepmc   +1 more source

Integrating Grain to Ground Motion Data for Critical Rock Bridge and Slope Failure Analysis

International Journal for Numerical and Analytical Methods in Geomechanics, Volume 50, Issue 16, Page 5891-5918, November 2026.
ABSTRACT Rock masses can fail catastrophically with little warning, yet their stability depends on hidden internal connections that channel forces and localise damage. Among these, rock bridges–a “known unknown” in slope mechanics–lock fractured rock and control how failure unfolds, but their concealed geometry and uncertain strength obscure where and ...
Antoinette Tordesillas, Shuo Zhou, Leonnardo Probst, Pat Bellett, Michal Nitka, Jacek Tejchman   +5 more
wiley   +1 more source

A hybrid framework combining microwave sensing and deep learning for real-time soil volumetric water content estimation. [PDF]

Sci Rep
Soliman AM, Elbrashy A, Hussien AH, Elmorsy A.   +3 more
europepmc   +1 more source

A Deep‐Learning Model Cascade for Automating Large‐Scale Shoreline Mapping From Satellite Imagery

Earth and Space Science, Volume 13, Issue 11, November 2026.
Abstract This study describes a machine learning model cascade for automating large‐scale shoreline mapping from time‐series of coastal satellite imagery. Our model cascade consists of independent supervised deep learning models for image suitability, image segmentation, and image segmentation suitability.
Mark A. Lundine, Daniel D. Buscombe, Sharon F. Batiste, Catherine N. Janda, Jonathan Warrick   +4 more
wiley   +1 more source

Smart Animal Welfare: A Review of Sensing Technologies, Deployment Challenges, and AI-Driven Insights. [PDF]

Sensors (Basel)
Mason SP, Wang N, Salak-Johnson JL.
europepmc   +1 more source

Estimating Regional Snow Water Equivalent Using a Physics‐Informed Graph Convolution Network Model

Earth and Space Science, Volume 13, Issue 11, November 2026.
Abstract Snow water equivalent (SWE), which quantifies the amount of water in a snowpack, is critical for water supply forecasting. This study explores machine learning (ML) models' capability of representing the spatial distribution of SWE across the western United States.
Wei‐Ting Hung, Ziheng Sun, Bowen Cui, Keren Zhou, Nicoleta Cristea, Justin Pflug, Annie Burgess, Daniel Tong   +7 more
wiley   +1 more source

Assessing and quantifying the saturation effect of forest aboveground biomass mapping using Landsat 8, Sentinel 1/2. [PDF]

Carbon Balance Manag
Liu Z, Wang Y, Li S, Mu G, Song K.
europepmc   +1 more source

Do Shallow Slip Deficits Reflect Fault Structural Maturity or Modeling Choices? A Comparative Analysis of Tibetan Strike‐Slip Earthquakes

Journal of Geophysical Research: Solid Earth, Volume 131, Issue 11, November 2026.
Abstract InSAR‐constrained finite fault slip models are often used to characterize fault geometries and slip distributions of large continental earthquakes. A common feature of these models is a decrease in slip near the surface, commonly expressed as a percentage shallow slip deficit (SSD).
É. Gaudreau, E. Nissen, J. Liu
wiley   +1 more source

Geological mapping of the Adrar Souttouf mafic complex in the Northern West African Craton using remote sensing and geophysical data. [PDF]

Sci Rep
Haous EHE, Fekkak A, Houali Y, Haidatte S, Bouasria A, Boumehdi M, Achkouch L, Genske F, Haissen F, Jouhari A, Berrada I, Berkat NE, Errami A, Sadki O, Shebl A.   +14 more
europepmc   +1 more source

Integrating Information From Hydrologic Models and SWOT Remote Sensing Measurements to Estimate Discharge Across River Networks

Water Resources Research, Volume 62, Issue 11, November 2026.
Abstract The Surface Water and Ocean Topography (SWOT) satellite has the potential to improve knowledge of river discharge globally. However, SWOT discharge algorithms are informed by global hydrological model data, and a portion of the model's systematic bias is passed along to the SWOT discharge time series.
M. T. Durand, D. Gomez, T. M. Pavelsky, S. Coss, P. Bates, D. Bjerklie, C. David, R. W. Dudley, R. P. d. M. Frasson, P. A. Garambois, C. J. Gleason, K. Larnier, P. O. Malaterre, J. Monnier, H. Oubanas, T. Simmons, L. C. Smith, N. Tebladi, M. J. Tourian, Y. Zhang   +19 more
wiley   +1 more source