Photosynthesis From Space
Photosynthesis is central to ecosystem function, supplying energy and regulating carbon dioxide levels in the atmosphere. Tracking how much carbon ecosystems absorb, known as gross primary productivity, or GPP, is a helpful metric for understanding the global carbon cycle, especially in the context of climate change.
Existing satellite systems like NASA’s MODIS (Moderate Resolution Imaging Spectroradiometer) have been used to estimate plant productivity. However, many of these instruments lack the spectral resolution, spatial coverage, or revisit frequency needed for detailed ecosystem monitoring. That’s where PACE’s OCI comes in.
Launched in February 2024, PACE was primarily designed to study atmospheric and oceanic processes. But its hyperspectral imaging capability, spanning visible, near-infrared (NIR), and shortwave infrared (SWIR) bands, also makes it well suited for tracking plant physiological changes across different landscapes and seasons.
In the study, NASA researchers focused on land surface reflectance captured by OCI from March to September 2024. The instrument, operating in a sun-synchronous polar orbit, collected surface reflectance data every eight days using its Version 2 Level 3 SFREFL product, which corrects for Rayleigh scattering and gas absorption, but not aerosols.
The dataset included 52 spectral bands, with 44 bands between 339 and 882 nm and five SWIR bands centered at longer wavelengths. A few bands affected by residual atmospheric effects were excluded from analysis.
The reflectance data, mapped at approximately four kilometre resolution, was aligned with the National Ecological Observatory Network (NEON), which operates 47 eddy covariance flux towers across 20 eco-climatic domains in the United States. These towers record carbon, water, and energy fluxes in ecosystems ranging from grasslands to tropical forests.
For each NEON site, researchers compared satellite data with ground-based measurements of carbon exchange, specifically using eddy covariance methods to calculate GPP. These estimates were averaged across eight-day periods to match OCI’s temporal resolution, obtaining more than a thousand paired observations over the 2024 growing season.
Spectral Data Into Plant Health Metrics
To interpret the OCI data, scientists used partial least squares regression (PLSR) models and a simple vegetation index approach. Even a basic chlorophyll index using just two spectral bands was able to estimate GPP with a root mean square error of 4.92 micromoles per square metre per second.
Examining the data, the researchers found that when agricultural sites, where irrigation, fertilization, and other interventions introduce variability, were excluded, model performance improved, with a higher coefficient of determination (R2 = 0.74) and a lower RMSE of 4.22.
Using the full set of 49 available spectral bands in the PLSR model further improved accuracy. Wavelengths in the red-edge region (around 719 and 779 nanometers) were particularly important for detecting chlorophyll content.
SWIR bands, such as those at 1249 and 2131 nm, provided insight into leaf structure and water content, while a blue band at 425 nm helped indicate pigment absorption. A band at around 530 nm was associated with photoprotective pigments like carotenoids, which play a role in plant stress responses.
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Ecosystem-Specific Models Improve Accuracy
Researchers found that GPP predictions improved even further when models were tailored to specific eco-climatic domains within the NEON network.
Vegetation in different regions showed distinct spectral responses, likely due to variations in leaf physiology, pigment composition, and canopy structure. These domain-specific models also reduced residual errors in agricultural zones, suggesting that separating managed landscapes from natural ones may be key to more accurate monitoring.
The variability in how different ecosystems responded to spectral data highlights the value of using a full-spectrum approach when estimating plant productivity at global scales. While simple indices provide quick estimates, high-resolution spectral models offer a deeper, more nuanced picture of how ecosystems function throughout the growing season.
A Change in Year-Round Plant Monitoring
This study demonstrates that PACE’s ocean-focused instrument can be a powerful tool for monitoring land-based ecosystems. With its broad spectral coverage and frequent observations, OCI enables researchers to track plant growth, detect early signs of stress, and monitor photosynthetic activity across a range of environments in near real time.
The team showed that OCI-based GPP estimates are on par with those derived from solar-induced fluorescence, a leading technique in photosynthesis monitoring. As a result, PACE adds a valuable new asset to the growing toolkit for ecosystem science, especially for understanding carbon cycling, biodiversity, and the impacts of climate change.
Journal Reference
- Huemmrich, K. F., Caplan, S., Gamon, J. A., & Campbell, P. (2025). Determining Terrestrial Ecosystem Gross Primary Productivity from PACE OCI. IEEE Geoscience and Remote Sensing Letters, 22. DOI: 10.1109/LGRS.2025.3587584, https://ieeexplore.ieee.org/document/11075694
- NASA Scientists Map Plant Productivity with Data from Ocean Satellite [Online] Available at https://science.nasa.gov/blogs/science-news/2025/08/29/nasa-scientists-map-plant-productivity-with-data-from-ocean-satellite/ (Accessed on 11 September 2025)
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