Using Hyperspectral Remote Sensing for Smarter Grassland Monitoring

Over the past several years, hyperspectral remote sensing and imaging applications have been used to monitor grasslands around the world. A recent study explored this issue further. This new study, conducted by researchers from several European institutions, investigated whether hyperspectral remote sensing and machine learning (ML) models can accurately predict grassland forage quality and biomass across vastly different climate zones using a single universal model (1). The study’s findings were published in Ecological Informatics (1).

Why are grasslands important?

Covering approximately one-third of Earth’s land surface, grasslands are vital for global food production, especially through livestock forage (1). There are three types of grasslands: natural, semi-natural, and improved, with each featuring different types of grasses, making them biodiverse (2).

Although sometimes overlooked, grasslands are critical to feeding the planet for generations to come. Consider livestock foraging—precisely measuring forage quantity and quality allows farmers and land managers to optimize grazing, prevent overuse, monitor ecosystem health, and protect biodiversity (1). Although hyperspectral satellite and field sensors offer promising tools for measuring grassland properties without destructive sampling, current models trained in one region rarely perform well elsewhere. There are several considerations researchers can highlight to explain why. For one, differences in canopy structure can impact how these devices monitor the grassland. There are also factors, such as species traits and land-use practices, that contribute to preventing the production of scalable, global prediction frameworks (1).

Read More: Deep Learning Classification of Grassland Desertification in China via Low-Altitude UAV Hyperspectral Remote Sensing

What did the researchers do in their study?

In their study, the researchers compiled one of the most comprehensive hyperspectral data sets ever assembled for grassland monitoring. The data spans temperate, humid tropical, and dry subtropical grasslands across Europe and Africa, capturing full growing seasons and contrasting management regimes (1). Testing several ML approaches, the researchers predicted three key metrics: metabolizable energy (ME), aboveground biomass (AGB), and metabolizable energy yield (MEY) (1).

Out of the ML approaches tested, the researchers found that random forest regression performed the best across all climate zones. It achieved high accuracy for metabolizable energy (nRMSE = 0.108, R² = 0.68), aboveground biomass (nRMSE = 0.145, R² = 0.53), and metabolizable energy yield (nRMSE = 0.153, R² = 0.58) (1). Neural networks had the highest global-to-regional transferability, sometimes maintaining accuracy even when trained outside the target region (1). Partial least squares models, despite their simplicity, performed better globally than regionally in several cases (1).

What is the most important finding of the study?

The most notable finding was that the researchers determined that forage quality (metabolizable energy) is significantly easier to predict than forage quantity (biomass). The authors attribute this to the consistency of functional plant traits that determine chemical composition across grassland ecosystems (1). These traits produce stable spectral signatures, allowing machine-learning models to identify clear relationships between reflectance bands and forage quality (1). Conversely, biomass varies more strongly across climate zones because of factors such as canopy height, density, species mixtures, and pigment levels, reducing model generalizability (1).

Despite strong regional results, the study confirms that no model achieved fully reliable global transferability. Models trained across continents faltered when tested on highly localized environmental and vegetation conditions. According to the authors, achieving true global accuracy will require models that incorporate local variation rather than treat it as noise (1). Expanded global spectral databases, improved sensor technology, and standardized protocols for sampling and processing could be decisive next steps (1).

What are the key takeaways from this study?

The main takeaway from this study is that spectral models will need to be built to understand the causal links between reflectance signatures, plant physiology, and environmental change. By combining hyperspectral satellite data from upcoming space missions with detailed biochemical and structural grassland information, researchers believe that this could dramatically increase prediction power (1).

“Expanding the exploration of global grasslands with hyperspectral remote sensing imagery from new satellite missions combined with improved data on forage quality and quantity, could increase model accuracy and predictive reliability beyond the current training datasets,” the authors wrote in their study (1).

Beyond agriculture, the findings demonstrate the broader environmental value of spectroscopy-based monitoring. More accurate global predictions of grassland properties would support conservation efforts, help quantify biodiversity-ecosystem service relationships, and inform sustainable land-use planning in agroecological landscapes (1).

This presentation was made with the help of Gamma AI.

References

1. Manner, F. A.; Muro, J.; Dubovyk, O.; et al. Field Spectroscopy and Machine Learning Successfully Predict Grassland Forage Quality and Quantity Across Climate Zones. Ecol. Info. 2025, 92, 103426. DOI: 10.1016/j.ecoinf.2025.103426
2. Planet Wild, Let’s Talk Grass: Why Do We Need Grasslands? Planet Wild. Available at: https://planetwild.com/blog/why-do-we-need-grasslands (accessed 2025-11-21).