Spectral Near-Infrared and Thermal Infrared Imaging for Advanced Estimation of Thermal and Geochemical Soil-Plant -Water Properties
Award last edited on: 6/10/2023

Sponsored Program
Awarding Agency
Total Award Amount
Award Phase
Solicitation Topic Code
Principal Investigator
William Yang

Company Information

BaySpec Inc

1101 McKay Drive
San Jose, CA 95131
   (408) 512-5928

Research Institution

Lawrence Berkeley National Laboratory

Phase I

Contract Number: DE-SC0020478
Start Date: 2/18/2020    Completed: 2/17/2021
Phase I year
Phase I Amount
The spatiotemporal quantification of coupled hydro-biogeochemical processes between soil, plant, and atmosphere requires advances in remote sensing technologies and in understanding the link between the measured signals (ie spectral traits) and the hydro-biochemical properties While current UAV-based remote sensing technologies provide spectral reflectance in the visible- short wave infrared (VSWIR) range, coupling such technology with multi-spectral thermal infrared (TIR) imaging and distributed sensor network is required to enable the spatiotemporal quantification of ecosystem properties and fluxes The TIR region (8-14 ?m) is of strong interest for numerous applications For example, surface temperature of leaves and plants is a key parameter to estimate stomatal conductance, evapotranspiration, and drought stress Other applications consist in identifying surface properties, assessing contaminant spills and water quality, and identify particular thermal behaviors controlled by gas concentration, geochemical compounds or solids (incl, algae bloom) Current limitation of UAV-based thermal imaging is the accuracy of measurement, the lack of integration with other sensors, and difficulties in inferring thermal properties or fluxes across space and time In addition, the current thermal infrared sensors operate in a single broad spectral band while the acquisition of multiple bands is critical for improving the estimation of surface emissivity, surface temperature and for detecting and investigating complex patterns The objective of this STTR project is to develop an integrated UAV-based remote sensing strategy, including hyperspectral SWIR and multi-spectral TIR imaging coupled with ground-based sensor network to provide accurate estimate of properties critical for improving the estimation of heat, water and gas fluxes The project will involve the development of a multi-component sensing platform, algorithms and a data integration framework that enable to improve the estimation of various hydro-biogeochemical properties This STTR will involve Bayspec and Berkeley Lab BaySpec is a leader in manufacturing advanced spectral instruments, including UAV-based hyperspectral sensors The Berkeley Lab team has a strong experience in the estimation of plant, surface and soil properties using airborne hyperspectral imaging and ground-based methods Berkeley Lab is using UAVs at various DOE sites to quantify microtopography, snow thickness, vegetation growth, plant distribution and phenology The proposed effort will concentrate on the following objectives: Multi-spectral TIR : BaySpec will design and produce the hardware and firmware of a multi-spectral TIR sensor based on Fourier Transform Infrared (FTIR) technology This technology enables chemical imaging as well as unparalleled spatial and spectral information about the targets under measurement BaySpec will integrate the novel TIR sensor with their UAV- based multi-/hyperspectral sensors Data processing flow: Berkeley Lab will develop a framework that adequately process spectral TIR data The developed algorithms will enable the separation of canopy from soil pixels; estimation of surface temperature and emissivity; and integration with distributed sensor network to estimate canopy, leaf, and soil surface properties, as well as water and heat fluxes with adequate spatial and temporal resolution Lab and field experiments: Developments will be validated at various sites, including at the East River site near Crested Butte, CO, part of LBNL Watershed Functions Scientific Focus Areas (SFA)The developed capability will be highly valuable to improve ecosystem understanding and will have a strong potential for commercial application in ecosystem management, agriculture, and water resource management among others

Phase II

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Start Date: 00/00/00    Completed: 00/00/00
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