The Bigger Picture

“Our approach is novel in that we have demonstrated, for the first time, that time-series satellite-based multispectral data is nearly as effective as drone/airborne hyperspectral imaging for high-throughput foliar trait monitoring,” said Professor Wu Jin from the School of Biological Sciences, who led the international team of researchers.

Utilising high-resolution imagery from the Sentinel-2 satellite, which captures multispectral data at weekly intervals with a 10-metre resolution, the team recorded the reflections of light from plant leaves, and gained valuable insights into the physical and biochemical properties of the vegetation. They then compared their observations to the timing of phenological (that is, plant life cycle) events.

“By integrating the data from satellite imagery and phenological observations, the team gained comprehensive information about plant functional traits across high dimensions,” said Professor Wu. “Further, the free cost and global availability of the input data we used indicates that our successful technique provides the scientific community with a completely novel, freely accessed,and large-scale foliar trait data source, revolutionising conventional ways of monitoring Earth’s surface.”

Conventionally, scientists have relied on either field-based methods or drone/airborne hyperspectral imaging systems for plant functional traits monitoring. Field-based approaches are often time-consuming, resource-expensive, labour-intensive, and difficult to scale up to regional, continental, or global levels.

‘Foliar traits’ refer to the key characteristics or features of a plant’s foliage such as leaf morphology, colour, chemical composition and photosynthetic rates, which can reveal a lot about how a plant grows, how it uses water and nutrients, how it photosynthesises and how it might respond to different environmental conditions like drought or high temperatures. In other words, these ‘essential foliar traits’ provide important information about a plant’s health, growth, and overall function in an ecosystem.

In addition to the main breakthrough, the team also found evidence suggesting that the leaf economics spectrum (LES) may be the underlying mechanism driving this technical success.

Strong correlation

“In our research, we found that the satellite-derived seasonal amplitude of the vegetation index, which can be viewed as a proxy for ecosystem-scale leaf turnover rate or leaf lifespan, strongly correlates with multiple essential plant traits of interest,” said Professor Wu. “This finding aligns with previous understandings of the LES theory. Moreover, in our final model that connects satellite-based time-series multispectral data for foliar trait prediction, we observed a significant role for the satellite-derived seasonal amplitude of the vegetation index, although its importance varies considerably among traits.”

The new regional plant trait dataset generated by this novel approach will provide scientists with a wealth of information about plant characteristics across a large geographical area, information which could be used to inform conservation strategies and efforts to restore or rehabilitate damaged ecosystems. Additionally, information on plant traits can also be used to model ecosystem processes, such as nutrient cycling and carbon sequestration, processes that play a critical role in mitigating climate change by removing carbon dioxide from the atmosphere.

“Through understanding how different plant traits influence these processes, scientists can develop strategies to enhance these natural climate solutions,” said Professor Wu. “The dataset could also help in predicting the spread of invasive species or the potential impact of pests and diseases on local vegetation, which can have significant implications for biodiversity, agriculture, and local economies. In essence, this new regional plant trait dataset equips scientists with essential data to inspire and generate novel knowledge needed to tackle environmental problems more effectively and design more sustainable landscapes for the future.”

The initial research using Sentinel-2 was done over ecosystem sites across the US, but in theory, the approach can be applied to any region across the globe. “We began our research in the US because readily available data allowed us to verify and refine our approach effectively,” said Professor Wu. “Our next step involves conducting extensive ground truth sampling in China and other regions worldwide. By doing so, we aim to demonstrate the versatility and adaptability of our methodology. Ultimately, our objective is to make our approach globally applicable.

“By gathering data and validating our approach in various locations around the world, we can contribute to a more comprehensive understanding of ecosystem health, resilience, and vulnerability. This, in turn, can help inform effective strategies for conservation and sustainable management of ecosystems across the planet.”

The team also aim to build upon the technical advances of their current research, which was published in Remote Sensing of Environment, and move forward multiple further research foci that best leverage this technical advance. “Plant traits provide crucial baseline information to further guide effective nature-based solutions for climate change mitigation,” said Professor Wu.