Chinese researchers reveal how vegetation structure biases satellite observation-Xinhua

Chinese researchers reveal how vegetation structure biases satellite observation

Source: Xinhua

Editor: huaxia

2023-09-22 23:16:45

BEIJING, Sept. 22 (Xinhua) -- Vegetation is a crucial part of terrestrial ecosystems and an important indicator of climate change. A team of Chinese researchers has reported that the structural complexity of vegetation may influence satellites' remote sensing monitoring.

Vegetation indices (VIs), derived from satellite data, are widely used for monitoring terrestrial ecosystems and tracking plant properties, vegetation changes, and environmental stresses.

Researchers from China Agricultural University found that several major VIs from satellite observation over the U.S. corn belt are higher than those over the Amazon rainforest, despite the latter having more leaf area. The contradicting pattern underscores the need to understand the underlying drivers and their impacts to prevent misinterpretations.

The researchers attributed the discrepancy to differences in structural complexity. They reported in the journal Nature Ecology & Evolution that complex forest structures cast macroscale shadows, resulting in lower spectral greenness compared to simpler crops.

Zeng Yelu, the lead author of the research, explained that most Earth-observing satellites do not view the Earth in the solar direction, and the impact of shadows is inevitable. However, the shaded parts also contribute to the carbon, water, and energy of the entire vegetation. Ignoring the complexity of vegetation structure may lead to uncertainty in assessing vegetation change and growth trends, hindering accurate quantification of regional and global carbon sinks.

They suggested that it could be beneficial to adjust and make corrections to the VIs observed by satellites.

The researchers said that the study brought new insights into the remote sensing monitoring of vegetation greenness, deepened understanding of the characteristics of remote sensing data, and provided a scientific basis for future research on vegetation and climate change.