Chlorophyll Concentration Retrieval by Training Convolutional Neural Network for Stochastic Model of Leaf Optical Properties (SLOP) Inversion

Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

(2018) Jochem Verrelst et al.   SURVEYS IN GEOPHYSICS

Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows

(2018) Helge Aasen et al.   Remote Sensing

Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification

(2017) Justin Salamon et al.   IEEE SIGNAL PROCESSING LETTERS

Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging

(2017) Olli Nevalainen et al.   Remote Sensing

Evaluating leaf chlorophyll content prediction from multispectral remote sensing data within a physically-based modelling framework

(2015) H. Croft et al.   ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING

Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties – A review

(2015) Jochem Verrelst et al.   ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING

Deep learning

(2015) Yann LeCun et al.   NATURE

Variation in 13 leaf morphological and physiological traits within a silver birch (Betula pendula) stand and their relation to growth

(2014) Boy J.H.M. Possen et al.   CANADIAN JOURNAL OF FOREST RESEARCH

Hyperspectral Remote Sensing Data Analysis and Future Challenges

(2013) Jose M. Bioucas-Dias et al.   IEEE Geoscience and Remote Sensing Magazine

A Large and Persistent Carbon Sink in the World's Forests

(2011) Y. Pan et al.   SCIENCE

PROSPECT+SAIL models: A review of use for vegetation characterization

(2009) Stéphane Jacquemoud et al.   REMOTE SENSING OF ENVIRONMENT

Soil moisture retrieval from remotely sensed data: Neural network approach versus Bayesian method

(2008) Claudia Notarnicola et al.   IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING

Multi-temporal vegetation canopy water content retrieval and interpretation using artificial neural networks for the continental USA☆

(2007) M TROMBETTI et al.   REMOTE SENSING OF ENVIRONMENT