Sensors, Measurements, and Software
Developing novel approaches that illuminate ecohydrological patterns and processes through advanced remote sensing and monitoring technologies
Environmental Sensing Research
Our sensors research focuses on creating innovative measurement techniques, developing open-source software tools, and advancing environmental monitoring capabilities. We design and deploy sensor networks, develop remote sensing applications, and create analytical tools that enable new scientific discoveries.
Research Focus
Environmental monitoring in remote and resource-limited settings requires innovative technological solutions. We develop cost-effective, robust sensor systems that can operate reliably in challenging field conditions.
Our work bridges the gap between technological innovation and scientific application, creating tools that enable new research questions and improve our understanding of environmental processes.
Methodological Approach
We combine hardware development, software engineering, and data science to create integrated monitoring solutions. Our approach emphasizes open-source development and community-driven innovation.
From satellite-based remote sensing to ground-based sensor networks, we work across scales to develop comprehensive monitoring systems for environmental research and management.
Key Research Areas
Our sensors research spans multiple interconnected areas of technology development
Remote Sensing
Satellite and airborne remote sensing of vegetation dynamics, water cycles, and land cover change, including hyperspectral and thermal infrared applications.
Sensor Networks
Wireless sensor networks for environmental monitoring, including soil moisture, weather stations, and plant physiological measurements in remote locations.
IoT Systems
Internet of Things (IoT) applications for agricultural monitoring, including crop sensing, irrigation management, and real-time data transmission from field sites.
Open-Source Software
Development of open-source software tools for environmental data analysis, including R packages, Python libraries, and web-based applications for the research community.
Machine Learning
Machine learning applications for environmental monitoring, including image classification, time series analysis, and predictive modeling for agricultural and ecological systems.
Data Integration
Methods for integrating multi-source environmental data, including sensor fusion, data quality assessment, and creating comprehensive datasets from diverse monitoring systems.
Technology Highlights
Examples of innovative sensing technologies developed by our lab
Arable Crop Intelligence Systems
Integrated crop monitoring platforms (Arable Mark 3) that capture weather, plant, soil, and irrigation data along with daily crop imagery. These systems provide comprehensive environmental sensing at the field scale, combining multiple measurement modalities in a single integrated device.
Used across our field research sites to monitor soil moisture, temperature, weather conditions, and vegetation status. The integrated camera enables visual crop phenology monitoring alongside precise environmental measurements.
Multi-Modal Sensing
Combines soil, weather, plant, and imagery data in integrated platforms
Autonomous Robotics
Robotic systems for automated, high-frequency spatial measurements
Fluxbot: Automated Soil Carbon Flux Measurement
Autonomous robotic soil carbon flux chambers that enable high-frequency, spatially distributed measurement of soil respiration and carbon cycling. Fluxbots can be deployed as arrays to capture soil process heterogeneity at centimeter to meter scales.
Our fluxbot arrays deployed in East African savanna ecosystems enable novel insights into ecosystem carbon dynamics and heterogeneity. The open-source, wireless design makes this technology accessible for collaborative research globally.
Unmanned Aerial Vehicle (UAV) Remote Sensing
Drone-based platforms for high-resolution aerial surveys, including thermal imaging, multispectral photography, and structure-from-motion photogrammetry. UAVs enable spatial resolution and revisit frequency that bridge ground sensors and satellite platforms.
We use UAVs for vegetation mapping, water stress assessment, thermal remote sensing of riparian systems, and landscape-scale environmental monitoring. UAV data is integrated with ground-based measurements to provide multi-scale understanding of ecohydrological processes.
Multi-Scale Integration
UAV data bridges ground sensors and satellite observations for comprehensive monitoring
Recent Sensors Publications
Latest research in environmental sensing and measurement technologies
Using hyperspectral and thermal imagery to monitor stress of Southern California plant species during the 2013–2015 drought
Susan K. Meerdink, Dar A. Roberts, Jennifer Y. King, K. Roth, Paul D. Gader, Kelly K. Caylor (2025) • Isprs Journal of Photogrammetry and Remote Sensing
Fluxbot: The Next Generation - Design and Validation of a Wireless, Open-Source Mechatronic CO2 Flux Sensing Chamber
Connor Pan, Vatsal V. Patel, Jonathan Gewirtzman, Ian Richardson, Ravish Dubey, Kelly K. Caylor, A. Dollar, Elizabeth S. Forbes (2024) • The Compass
Precision gas analyzers are widely used in ecological research for manual measurement of soil carbon flux, a key metric used in the study of climate change. We present a generational update to the first low-cost, autonomous, closed-chamber style soil CO2 flux sensors (Fluxbots). Fluxbot 2.0 is the first such low-cost autonomous flux chamber capable of real-time wireless data transmission, which enables ecologists conducting in situ soil carbon flux surveys to set up their own wireless sensor arrays, reporting carbon flux data in real time at a very high level of temporal resolution. The system’s low cost (less than 500 USD per unit) and long-range cellular data transmission capabilities also allow for greatly improved spatial resolution. Additionally, the updated system consumes significantly less power, resulting in the ability to be deployed for longer than 10 × the battery lifetime of the original version on a single charge.
Estimating Fine‐Scale Transpiration From UAV‐Derived Thermal Imagery and Atmospheric Profiles
Bryn E. Morgan, Kelly Caylor (2023) • Water Resources Research
Accurate and timely observations of individual‐scale transpiration are critical for predicting ecosystem responses to climate change. Existing remote sensing methods for measuring transpiration lack the spatial resolution needed to resolve individual plants, and their sources of uncertainty are not well‐constrained. We present two novel approaches for independently quantifying fine‐scale transpiration using thermal imagery and a suite of environmental sensors mounted on an unmanned aerial vehicle (UAV) platform. The first is a surface energy balance (SEB) approach designed for fine‐scale thermal imagery; the second uses profiles of air temperature (Ta) and humidity (hr) to calculate transpiration from the Bowen Ratio. Both approaches derive the energy equivalent of transpiration, latent heat flux (λE), solely using data acquired from the UAV. We compare the two approaches and their sources of uncertainty using data from several flights at a grassland eddy covariance site in 2021 and 2022 and using typical diurnal conditions to evaluate the uncertainty of λE estimates for each approach. The SEB approach generated independent, UAV‐based estimates of λE within ∼20% of eddy covariance measurements and was most sensitive to surface temperature and resistance to heat transfer. λE calculated from the Bowen Ratio approach was ∼30% higher than tower values due to inaccuracies in Ta and hr, the main sources of uncertainty in this approach. The Bowen Ratio approach has a lower overall potential uncertainty, indicating its potential for improvement over the SEB approach. Our results are the first physically‐based observations of transpiration derived solely from a UAV platform, with no ancillary data inputs.
Explore Our Research
Learn more about our other research themes and discover how sensors and measurements connect with ecohydrology and human systems research.