Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems
Accurate and cost-effective quantification of the carbon cycle for agroecosystems at decision-
relevant scales is critical to mitigating climate change and ensuring sustainable food …
relevant scales is critical to mitigating climate change and ensuring sustainable food …
X-BASE: the first terrestrial carbon and water flux products from an extended data-driven scaling framework, FLUXCOM-X
Map** in situ eddy covariance measurements of terrestrial land–atmosphere fluxes to the
globe is a key method for diagnosing the Earth system from a data-driven perspective. We …
globe is a key method for diagnosing the Earth system from a data-driven perspective. We …
[HTML][HTML] Super resolution of historic Landsat imagery using a dual generative adversarial network (GAN) model with CubeSat constellation imagery for spatially …
Detailed spatial representations of terrestrial vegetation are essential for precision
agricultural applications and the monitoring of land cover changes in heterogeneous …
agricultural applications and the monitoring of land cover changes in heterogeneous …
[HTML][HTML] Contrasting drought legacy effects on gross primary productivity in a mixed versus pure beech forest
Droughts affect terrestrial ecosystems directly and concurrently and can additionally induce
lagged effects in subsequent seasons and years. Such legacy effects of drought on …
lagged effects in subsequent seasons and years. Such legacy effects of drought on …
Recommendations for develo**, documenting, and distributing data products derived from NEON data
Abstract The National Ecological Observatory Network (NEON) provides over 180 distinct
data products from 81 sites (47 terrestrial and 34 freshwater aquatic sites) within the United …
data products from 81 sites (47 terrestrial and 34 freshwater aquatic sites) within the United …
[HTML][HTML] Using automated machine learning for the upscaling of gross primary productivity
Estimating gross primary productivity (GPP) over space and time is fundamental for
understanding the response of the terrestrial biosphere to climate change. Eddy covariance …
understanding the response of the terrestrial biosphere to climate change. Eddy covariance …
Charting the Future of the FLUXNET Network
FLUXNET is a global network of micrometeorological tower sites that employ eddy
covariance (EC) methods to measure the exchanges of greenhouse gasses, water vapor …
covariance (EC) methods to measure the exchanges of greenhouse gasses, water vapor …
[HTML][HTML] Narrow but robust advantages in two-big-leaf light use efficiency models over big-leaf light use efficiency models at ecosystem level
This study aims to (1) investigate whether two-big-leaf light use efficiency (LUE) models (TL)
outperform big-leaf LUE models (BL) by incorporating different gross primary productivity …
outperform big-leaf LUE models (BL) by incorporating different gross primary productivity …
Learning extreme vegetation response to climate drivers with recurrent neural networks
F Martinuzzi, MD Mahecha… - Nonlinear Processes …, 2024 - npg.copernicus.org
The spectral signatures of vegetation are indicative of ecosystem states and health. Spectral
indices used to monitor vegetation are characterized by long-term trends, seasonal …
indices used to monitor vegetation are characterized by long-term trends, seasonal …
Variability and uncertainty in flux-site scale net ecosystem exchange simulations based on machine learning and remote sensing: A systematic evaluation
Net ecosystem exchange (NEE) is an important indicator of carbon cycling in terrestrial
ecosystems. Many previous studies have combined flux observations, meteorological …
ecosystems. Many previous studies have combined flux observations, meteorological …