[HTML][HTML] Machine learning and soil sciences: A review aided by machine learning tools
The application of machine learning (ML) techniques in various fields of science has
increased rapidly, especially in the last 10 years. The increasing availability of soil data that …
increased rapidly, especially in the last 10 years. The increasing availability of soil data that …
Practical guide to measuring wetland carbon pools and fluxes
Wetlands cover a small portion of the world, but have disproportionate influence on global
carbon (C) sequestration, carbon dioxide and methane emissions, and aquatic C fluxes …
carbon (C) sequestration, carbon dioxide and methane emissions, and aquatic C fluxes …
Spatial predictions and associated uncertainty of annual soil respiration at the global scale
Soil respiration (Rs), the soil‐to‐atmosphere CO2 flux produced by microbes and plant
roots, is a critical but uncertain component of the global carbon cycle. Our current …
roots, is a critical but uncertain component of the global carbon cycle. Our current …
Reducing greenhouse gas emissions and improving net ecosystem economic benefit through long-term conservation tillage in a wheat-maize multiple crop** …
W Wang, H Zhang, F Mo, Y Liao, X Wen - European Journal of Agronomy, 2022 - Elsevier
Reducing greenhouse gas emissions and loss of soil fertility, while ensuring stable yield, is
crucial to achieving “Carbon Peak” and “Carbon Neutrality” in grain production but is …
crucial to achieving “Carbon Peak” and “Carbon Neutrality” in grain production but is …
Downscaling satellite soil moisture using geomorphometry and machine learning
Annual soil moisture estimates are useful to characterize trends in the climate system, in the
capacity of soils to retain water and for predicting land and atmosphere interactions. The …
capacity of soils to retain water and for predicting land and atmosphere interactions. The …
Modelling carbon dioxide emissions under a maize-soy rotation using machine learning
Climatic parameters influence CO 2 emissions and the complexity of the relationship is not
fully captured in biophysical models. Machine learning (ML) is now being applied to …
fully captured in biophysical models. Machine learning (ML) is now being applied to …
[HTML][HTML] The paradox of assessing greenhouse gases from soils for nature-based solutions
Quantifying the role of soils in nature-based solutions requires accurate estimates of soil
greenhouse gas (GHG) fluxes. Technological advances allow us to measure multiple GHGs …
greenhouse gas (GHG) fluxes. Technological advances allow us to measure multiple GHGs …
[HTML][HTML] Identifying landscape hot and cold spots of soil greenhouse gas fluxes by combining field measurements and remote sensing data
Upscaling chamber measurements of soil greenhouse gas (GHG) fluxes from point scale to
landscape scale remain challenging due to the high variability in the fluxes in space and …
landscape scale remain challenging due to the high variability in the fluxes in space and …
Tree-Structured Parzan Estimator–Machine Learning–Ordinary Kriging: An Integration Method for Soil Ammonia Spatial Prediction in the Typical Cropland of Chinese …
Y Song, M Ye, Z Zheng, D Zhan, W Duan, M Lu… - Remote Sensing, 2023 - mdpi.com
Spatial prediction of soil ammonia (NH3) plays an important role in monitoring climate
warming and soil ecological health. However, traditional machine learning (ML) models do …
warming and soil ecological health. However, traditional machine learning (ML) models do …
Simultaneous tree stem and soil greenhouse gas (CO2, CH4, N2O) flux measurements: a novel design for continuous monitoring towards improving flux estimates …
Tree stems and soils can act as sources and sinks for the greenhouse gases (GHG) carbon
dioxide (CO2), methane (CH4), and nitrous oxide (N2O). Since both uptake and emission …
dioxide (CO2), methane (CH4), and nitrous oxide (N2O). Since both uptake and emission …