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A common data basis is the prerequisite for efficient and informed action. Up to now, this has, however, been lacking for plant health, which is of great importance for global agriculture and climate research.

Researchers at the Technical University of Munich (TUM) have developed a method to record plant health worldwide. Based on satellite images, the method uses a hybrid intelligence approach to deliver reliable data on a weekly basis. This not only helps science, but also agriculture and climate planning. The work is in Remote Sensing of Environment.

Hybrid intelligence

The two-step method employs physical models based on light-plant interactions and artificial intelligence. First, a physical model is used to determine the relationship between chlorophyll and canopy reflectance data. In a second step, this data then serves as training material for artificial intelligence models that automate calculations for plant health measurements.

"By using this hybrid intelligence approach, our method overcomes the limitations of typically used methods that rely on reflectance datasets from the Earth's surface," explains Dong Li, first author of the publication. The trained AI models can estimate the canopy chlorophyll content directly from satellite-observed reflectance data and thus circumvent the usual issues with surface data such as partially cloudy conditions.

Observing dynamics in plant health and growth

Chlorophyll content is directly proportional to a plant's metabolic activity as it is the central molecule of photosynthesis. Its abundance is therefore an indicator of plant health and growth, which aids in estimating biomass production, i.e., yields.

Since the method is based on nearly weekly updated satellite data, the index offers insights into dynamic changes. "We can observe how the plants respond to varying conditions such as temporal variations due to climate change," explains Prof. Kang Yu from the chair of Precision Agriculture at TUM.

The indicator may therefore be of interest for modelers to quantify crop vitality and Earth surface processes, which in return can be used to inform global decision-making in agriculture and climate adaptation.