Net ecosystem exchange (NEE) describes the net carbon balance of an ecosystem. It is the net sum of an ecosystem’s CO2 uptake via photosynthesis (gross primary production, or GPP) and its CO2 emissions via microbial and plant respiration (ecosystem respiration, or ER) (Figure 1). Therefore, reliably measuring NEE and its components is key to understanding […]

The eosAC-LT/LO automated soil flux chamber's large footprint and proven design allows researchers to capture soil GHG measurements.

Get the Eosense Newsletter Email Address * You can change your mind at any time by clicking the unsubscribe link in the footer of any email you receive from us, or by contacting us at marketing@eosense.com. We will treat your information with respect. Please visit our Privacy Policy for more information. By clicking below, you […]

ANNOUNCING UP TO $65 MILLION FOR 2025 INDUSTRIAL TRANSFORMATION CHALLENGE TO INCREASE ALBERTA’S GLOBAL COMPETITIVENESS Alberta’s emissions-intensive, trade-exposed industries face challenges to reducing emissions while improving operational efficiencies and increasing global competitiveness amidst geopolitical changes. ERA’s Industrial Transformation Challenge is an annual funding competition designed to improve the economic competitiveness and environmental sustainability of Alberta’s […]

Implements a physics-informed one-dimensional convolutional neural network (CNN1D-PINN) for estimating the complete soil water retention curve (SWRC) as a continuous function of matric potential, from soil texture, organic carbon, bulk density, and depth. The network architecture ensures strict monotonic decrease of volumetric water content with increasing suction by construction, through cumulative integration of non-negative slope outputs (monotone integral architecture). Four physics-based residual constraints adapted from Norouzi et al. (2025) &lt;<a href="https://doi.org/10.1029%2F2024WR038149" target="_top">doi:10.1029/2024WR038149</a>&gt; are embedded in the loss function: (S1) linearity at the dry end (pF in [5, 7.6]); (S2) non-negativity at pF = 6.2; (S3) non-positivity at pF = 7.6; and (S4) a near-zero derivative in the saturated plateau region (pF in [-2, -0.3]). Includes tools for data preparation, model training, dense prediction, performance metrics, texture classification, and publication-quality visualisation.

Implements a physics-informed one-dimensional convolutional neural network (CNN1D-PINN) for estimating the complete soil water retention curve (SWRC) as a continuous function of matric potential, from soil texture, organic carbon, bulk density, and depth. The network architecture ensures strict monotonic decrease of volumetric water content with increasing suction by construction, through cumulative integration of non-negative slope outputs (monotone integral architecture). Four physics-based residual constraints adapted from Norouzi et al. (2025) &lt;<a href="https://doi.org/10.1029%2F2024WR038149" target="_top">doi:10.1029/2024WR038149</a>&gt; are embedded in the loss function: (S1) linearity at the dry end (pF in [5, 7.6]); (S2) non-negativity at pF = 6.2; (S3) non-positivity at pF = 7.6; and (S4) a near-zero derivative in the saturated plateau region (pF in [-2, -0.3]). Includes tools for data preparation, model training, dense prediction, performance metrics, texture classification, and publication-quality visualisation.

This application note provides simplified setup instructions for using the eosMX multiplexer and eosAC soil flux chambers with the Gasmet DX4040, DX4015 and GT5000 Terra Portable FTIR Gas Analyzers. This combined system allows the user to collect multi-species soil gas flux measurements from up to 12 locations at once.

Get the Eosense Newsletter Email Address * You can change your mind at any time by clicking the unsubscribe link in the footer of any email you receive from us, or by contacting us at marketing@eosense.com. We will treat your information with respect. Please visit our Privacy Policy for more information. By clicking below, you […]