Document Type

Article

Publication Date

1-1-2025

Department

Michigan Tech Research Institute

Abstract

Leaf Area Index (LAI) and Plant Area Index (PAI) are critical biophysical parameters for quantifying foliage density of crops and trees. As such they may also be effective metrics to estimate the impact of leafy vegetation on microwave signals, like the radiometer on board NASA’s Soil Moisture Active Passive (SMAP) satellite. This study presents a comprehensive analysis of PAI and LAI measurements acquired through in situ and remotely sensed (RS) methods in diverse forest ecosystems of northeastern America, including deciduous, mixed, and coniferous forests. We compare RS LAI and ground-truth PAI data to understand the variation between the RS LAI products and the impacts of RS spatial resolution on analysis. We perform these comparisons in the context of understanding SMAP’s Vegetation Optical Depth (VOD) retrievals, and compare RS LAI with SMAP VOD measurements. We find strong (R2 > 0.83) positive relationships between in situ PAI and the tested RS LAI products, with improved positive relationships (R2 > 0.92) for higher spatial resolution (< 30m) RS LAI products. We also discuss considerations for the LAI algorithms. The higher resolution RS products showed a consistently low bias (by ~1 unit) in both spring and summer compared to in situ PAI measurements, while the coarse resolution LAI products matched in situ PAI values in spring but overestimated LAI by ~ 1 unit in the summer. Smaller y-intercept values (< -0.56) associated with the higher resolution products relative to the coarse resolution products (> -0.29) could indicate greater influence of woody biomass on higher resolution RS LAI algorithms relative to low resolution RS LAI algorithms. Comparisons of RS LAI and VOD showed generally positive relationships that varied by satellite sensor.

Publisher's Statement

© 2025 The Authors. Publisher’s version of record: https://doi.org/10.1109/JSTARS.2025.3588091

Publication Title

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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