4.7 Article

A deep-learning-based experiment for benchmarking the performance of global terrestrial vegetation phenology models

Related references

Note: Only part of the references are listed.
Article Computer Science, Interdisciplinary Applications

DATimeS: A machine learning time series GUI toolbox for gap -filling and vegetation phenology trends detection

Santiago Belda et al.

ENVIRONMENTAL MODELLING & SOFTWARE (2020)

Article Meteorology & Atmospheric Sciences

A Semiprognostic Phenology Model for Simulating Multidecadal Dynamics of Global Vegetation Leaf Area Index

Qinchuan Xin et al.

JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS (2020)

Article Multidisciplinary Sciences

Deep learning and process understanding for data-driven Earth system science

Markus Reichstein et al.

NATURE (2019)

Article Multidisciplinary Sciences

Deep learning for multi-year ENSO forecasts

Yoo-Geun Ham et al.

NATURE (2019)

Article Meteorology & Atmospheric Sciences

The Community Land Model Version 5: Description of New Features, Benchmarking, and Impact of Forcing Uncertainty

David M. Lawrence et al.

JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS (2019)

Article Biodiversity Conservation

Simulating the onset of spring vegetation growth across the Northern Hemisphere

Qiang Liu et al.

GLOBAL CHANGE BIOLOGY (2018)

Article Agriculture, Multidisciplinary

QPhenoMetrics: An open source software application to assess vegetation phenology metrics

Lia Duarte et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2018)

Article Geography, Physical

A hybrid training approach for leaf area index estimation via Cubist and random forests machine-learning

Rasmus Houborg et al.

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2018)

Article Computer Science, Artificial Intelligence

Recent advances in convolutional neural networks

Jiuxiang Gu et al.

PATTERN RECOGNITION (2018)

Article Ecology

An integrated phenology modelling framework in R

Koen Hufkens et al.

METHODS IN ECOLOGY AND EVOLUTION (2018)

Article Computer Science, Hardware & Architecture

ImageNet Classification with Deep Convolutional Neural Networks

Alex Krizhevsky et al.

COMMUNICATIONS OF THE ACM (2017)

Article Biodiversity Conservation

Multiscale modeling of spring phenology across Deciduous Forests in the Eastern United States

Eli K. Melaas et al.

GLOBAL CHANGE BIOLOGY (2016)

Article Environmental Sciences

Development of the BIOME-BGC model for the simulation of managed Moso bamboo forest ecosystems

Fangjie Mao et al.

JOURNAL OF ENVIRONMENTAL MANAGEMENT (2016)

Article Biodiversity Conservation

The timing of autumn senescence is affected by the timing of spring phenology: implications for predictive models

Trevor F. Keenan et al.

GLOBAL CHANGE BIOLOGY (2015)

Article Meteorology & Atmospheric Sciences

A global soil data set for earth system modeling

Wei Shangguan et al.

JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS (2014)

Article Engineering, Electrical & Electronic

An Enhanced TIMESAT Algorithm for Estimating Vegetation Phenology Metrics From MODIS Data

Bin Tan et al.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2011)

Article Remote Sensing

Global land cover classification at 1km spatial resolution using a classification tree approach

MC Hansen et al.

INTERNATIONAL JOURNAL OF REMOTE SENSING (2000)