Results 101 to 110 of about 8,883 (219)

Prediction and mapping of tobacco yield with fresh leaf mass using hyperspectral sensing data

Smart Agricultural Technology
Tobacco yield is important for agricultural management and policy making. Hyperspectral remote sensing has advantages in predicting and mapping crop yield, providing both yield values and the spatial distribution of yield.
Junying Li, Weichao Sun, Shuo Liu, Tao Cheng, Liang Tang, Wei Jiang, Bowen Zhou   +6 more
doaj   +1 more source

Current and Future Changes in Earth's Outgoing Infrared Spectrum

Geophysical Research Letters, Volume 53, Issue 11, 16 June 2026.
Abstract Hyperspectral infrared satellite observations provide an information‐rich, global record of climate mean state and change without the structural uncertainties often present satellite retrievals and reanalyses. Current and planned hyperspectral satellite missions will continuously observe changes in Earth's outgoing infrared spectrum into the ...
J. K. Shaw, J. E. Kay, S. DeSouza‐Machado, D. D. Turner, L. L. Strow   +4 more
wiley   +1 more source

Initial Assessment of the Detectability of Cyanotoxin Occurrence Using PACE Satellite Observations

Geophysical Research Letters, Volume 53, Issue 11, 16 June 2026.
Abstract Microcystin is a major public health hazard during freshwater cyanobacterial blooms, yet its remote detection remains challenging because it lacks any direct optical absorption features. We demonstrate that hyperspectral observations from NASA's PACE Ocean Color Instrument (OCI) can be used to detect elevated microcystin concentrations by ...
Denesh Karunakaran, Ardeshir Ebtehaj, Lucas Clark Burnette, Miki Hondzo, Leif Olmanson   +4 more
wiley   +1 more source

Training‐Domain Gaps in Data‐Driven Ocean Color Algorithms

Geophysical Research Letters, Volume 53, Issue 11, 16 June 2026.
Abstract Bio‐optical algorithms serve a critical role, enabling satellite‐based monitoring of the global ocean and its ecosystems. These largely empirical algorithms invert satellite measurements of water leaving reflectance (“ocean color”) to biogeochemical parameters such as chlorophyll a, but often fail to generalize beyond their training data ...
P. C. Gray, C. Begouen Demeaux, A. Chase, R. Frouin, E. S. Boss   +4 more
wiley   +1 more source

Spatial relationship of turfgrass tissue nitrogen content and several vegetation indices on sand‐capped golf course fairways

Agrosystems, Geosciences &Environment, Volume 9, Issue 2, June 2026.
Abstract Vegetation indices (VIs), such as normalized difference vegetation index (NDVI), are widely used to assess nitrogen (N) status in crop systems and are gaining interest in turfgrass management. However, most studies have been conducted in controlled settings, and their relevance under real‐world golf course conditions remains unclear.
Madan Sapkota, Opeyemi E. Alabi, Weston W. Floyd, Sarah G. Hong, Dallas M. Williams, Briana M. Wyatt, A. Peyton Smith, Chase M. Straw   +7 more
wiley   +1 more source

Retrieval of rice biophysical parameters from Sentinel‑2 using parsimonious multi‑output machine learning

Agrosystems, Geosciences &Environment, Volume 9, Issue 2, June 2026.
Abstract Rice (Oryza sativa L.) crop monitoring in Africa requires efficient retrieval of biophysical parameters such as leaf area index (LAI) and leaf chlorophyll content (LCC), which are essential for crop stress detection and nutrient management.
Joseph Kagotho Muriithi, Collins Mito, Giovanni Laneve, John Buers, Elijah Cheruiyot, Jephter Ondieki   +5 more
wiley   +1 more source

Lightweight 3D Dense Autoencoder Network for Hyperspectral Remote Sensing Image Classification. [PDF]

Sensors (Basel), 2023
Bai Y, Sun X, Ji Y, Fu W, Duan X.
europepmc   +1 more source

Conceptual Differences Between Hyperspectral Unmixing, PCA, and Clustering: A Practical Demonstration Using Single‐Cell Raman Imaging

Journal of Chemometrics, Volume 40, Issue 6, June 2026.
ABSTRACT Hyperspectral unmixing is a well‐established approach for analyzing spectroscopic images by recovering chemically interpretable component spectra and their spatial distributions. Despite its distinct analytical objective, unmixing is frequently discussed alongside or directly compared with more familiar multivariate techniques such as ...
Rustam R. Guliev, Ute Neugebauer
wiley   +1 more source

Superpixel-Guided Matrix-Valued Kernel Functions for Multiscale Nonlinear Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Hyperspectral unmixing is a critical challenge in the analysis of hyperspectral remote sensing data. Due to the complex interactions between incident light and materials, which are significantly influenced by the three-dimensional geometry of the scene ...
Xiu Zhao, Meiping Song
doaj   +1 more source

SpectralMAE: Spectral Masked Autoencoder for Hyperspectral Remote Sensing Image Reconstruction. [PDF]

Sensors (Basel), 2023
Zhu L, Wu J, Biao W, Liao Y, Gu D.
europepmc   +1 more source