Structural complexity biases vegetation greenness measures

Area of Science:

• Earth Observation
• Ecology
• Remote Sensing

Background:

• Vegetation greenness, measured by spectral vegetation indices (VIs), reflects leaf abundance and health.
• Satellite VIs paradoxically show lower values in Amazon rainforests than US Corn Belt crops, despite greater forest leaf area.

Purpose of the Study:

• Investigate the drivers behind contradictory VIs between forests and crops.
• Assess the impact of shadows on VIs and their implications for global vegetation monitoring.

Main Methods:

• Analyzed satellite-derived VIs over the US Corn Belt and Amazon rainforest.
• Investigated the influence of macroscale shadows from varying structural complexities (forests vs. crops).
• Examined sun-sensor geometry's role in shadow detection.

Main Results:

• Complex forest structures create macroscale shadows that significantly lower VI measurements compared to homogeneous crops.
• This shadow effect is an inherent artifact of satellite observation geometry.
• Land conversion from forest to crops in the Amazon shows increased VIs despite reduced leaf area.

Conclusions:

• Shadows are a critical, often overlooked, factor in interpreting satellite-based VIs.
• Misinterpretations of VIs and solar-induced chlorophyll fluorescence can arise without accounting for shadow impacts.
• Accurate global vegetation assessment requires incorporating shadow effects into remote sensing data analysis.