Quantifying Gross Primary Production (GPP) is fundamental for understanding terrestrial carbon dynamics, particularly in forests. The overarching question we address here is whether integrating remote sensing (RS) with deep learning (DL) methodologies can enhance the estimation of daily forest GPP on a European scale.The Eddy Covariance (EC) method, although widely used to infer ecosystem-scale estimates of GPP from in situ CO2 exchange measurements, suffers from limited global coverage. When EC data are not available, RS data are often employed to estimate GPP by establishing statistical relationships with in situ observations. Recently, Machine Learning (ML) strategies, particularly involving RS and meteorological inputs, have been used for estimating GPP continuously in space and time.
%0 Journal Article
%1 montero2024estimating
%A Montero, David
%A Mahecha, Miguel D
%A Martinuzzi, Francesco
%A Aybar, César
%A Klosterhalfen, Anne
%A Knohl, Alexander
%A Koebsch, Franziska
%A Anaya, Jesús
%A Wieneke, Sebastian
%D 2024
%I Copernicus Meetings
%K imported topic_earthenvironment
%N EGU24-6773
%T Estimating Gross Primary Production via Recurrent Neural Networks: A comparative analysis
%X Quantifying Gross Primary Production (GPP) is fundamental for understanding terrestrial carbon dynamics, particularly in forests. The overarching question we address here is whether integrating remote sensing (RS) with deep learning (DL) methodologies can enhance the estimation of daily forest GPP on a European scale.The Eddy Covariance (EC) method, although widely used to infer ecosystem-scale estimates of GPP from in situ CO2 exchange measurements, suffers from limited global coverage. When EC data are not available, RS data are often employed to estimate GPP by establishing statistical relationships with in situ observations. Recently, Machine Learning (ML) strategies, particularly involving RS and meteorological inputs, have been used for estimating GPP continuously in space and time.
@article{montero2024estimating,
abstract = {Quantifying Gross Primary Production (GPP) is fundamental for understanding terrestrial carbon dynamics, particularly in forests. The overarching question we address here is whether integrating remote sensing (RS) with deep learning (DL) methodologies can enhance the estimation of daily forest GPP on a European scale.The Eddy Covariance (EC) method, although widely used to infer ecosystem-scale estimates of GPP from in situ CO2 exchange measurements, suffers from limited global coverage. When EC data are not available, RS data are often employed to estimate GPP by establishing statistical relationships with in situ observations. Recently, Machine Learning (ML) strategies, particularly involving RS and meteorological inputs, have been used for estimating GPP continuously in space and time.},
added-at = {2024-11-29T11:53:34.000+0100},
author = {Montero, David and Mahecha, Miguel D and Martinuzzi, Francesco and Aybar, César and Klosterhalfen, Anne and Knohl, Alexander and Koebsch, Franziska and Anaya, Jesús and Wieneke, Sebastian},
biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/2c4c8eed063bf7ef3189664027e00cd3a/joum576e},
citation = {EGU24, 2024},
interhash = {bebdd8d44675e6b9bd2c013dbb7059b1},
intrahash = {c4c8eed063bf7ef3189664027e00cd3a},
keywords = {imported topic_earthenvironment},
number = {EGU24-6773},
publisher = {Copernicus Meetings},
timestamp = {2024-11-29T11:53:34.000+0100},
title = {Estimating Gross Primary Production via Recurrent Neural Networks: A comparative analysis},
year = 2024
}
