Comparison of physical-based, data-driven and hybrid modeling approaches for evapotranspiration estimation

The data-driven solution of energy imbalance-induced structural error in evapotranspiration models

(2021) Xiaolong Hu et al.   JOURNAL OF HYDROLOGY

Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations

(2020) Maziar Raissi et al.   SCIENCE

Deep learning of subsurface flow via theory-guided neural network

(2020) Nanzhe Wang et al.   JOURNAL OF HYDROLOGY

Increasing contribution of peatlands to boreal evapotranspiration in a warming climate

(2020) Manuel Helbig et al.   Nature Climate Change

Improving surface roughness lengths estimation using machine learning algorithms

(2020) Xiaolong Hu et al.   AGRICULTURAL AND FOREST METEOROLOGY

Improving Evapotranspiration Model Performance by Treating Energy Imbalance and Interaction

(2020) Guoxiao Wei et al.   WATER RESOURCES RESEARCH

A Column Canopy-Air Turbulent Diffusion Method for Different Canopy Structures

(2019) Xuelong Chen et al.   JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES

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

(2019) Markus Reichstein et al.   NATURE

Temporal and spatial variations of energy balance closure across FLUXNET research sites

(2019) Wenhui Cui et al.   AGRICULTURAL AND FOREST METEOROLOGY

A dynamic data-driven method for dealing with model structural error in soil moisture data assimilation

(2019) Qiuru Zhang et al.   ADVANCES IN WATER RESOURCES

Optimization of a remote sensing energy balance method over different canopy applied at global scale

(2019) Xuelong Chen et al.   AGRICULTURAL AND FOREST METEOROLOGY

Physics‐Constrained Machine Learning of Evapotranspiration

(2019) Wen Li Zhao et al.   GEOPHYSICAL RESEARCH LETTERS

Optical-based and thermal-based surface conductance and actual evapotranspiration estimation, an evaluation study in the North China Plain

(2018) Xiaolong Hu et al.   AGRICULTURAL AND FOREST METEOROLOGY

Evaluating Different Machine Learning Methods for Upscaling Evapotranspiration from Flux Towers to the Regional Scale

(2018) Tongren Xu et al.   JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

(2018) M. Raissi et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data

(2017) Anuj Karpatne et al.   IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING

The future of evapotranspiration: Global requirements for ecosystem functioning, carbon and climate feedbacks, agricultural management, and water resources

(2017) Joshua B. Fisher et al.   WATER RESOURCES RESEARCH

An Improvement of Roughness Height Parameterization of the Surface Energy Balance System (SEBS) over the Tibetan Plateau

(2012) Xuelong Chen et al.   Journal of Applied Meteorology and Climatology

Estimating evapotranspiration and drought stress with ground-based thermal remote sensing in agriculture: a review

(2012) W. H. Maes et al.   JOURNAL OF EXPERIMENTAL BOTANY

Integration of soil moisture in SEBS for improving evapotranspiration estimation under water stress conditions

(2012) M. Gokmen et al.   REMOTE SENSING OF ENVIRONMENT

Evaluation of optical remote sensing to estimate actual evapotranspiration and canopy conductance

(2012) Marta Yebra et al.   REMOTE SENSING OF ENVIRONMENT

ET come home: potential evapotranspiration in geographical ecology

(2010) Joshua B. Fisher et al.   GLOBAL ECOLOGY AND BIOGEOGRAPHY

Prediction of hourly actual evapotranspiration using neural networks, genetic programming, and statistical models

(2010) Zohreh Izadifar et al.   HYDROLOGICAL PROCESSES

On the consequences of the energy imbalance for calculating surface conductance to water vapour

(2009) Georg Wohlfahrt et al.   AGRICULTURAL AND FOREST METEOROLOGY

A Hypercube-Based Encoding for Evolving Large-Scale Neural Networks

(2009) Kenneth O. Stanley et al.   ARTIFICIAL LIFE