Comparison of in Situ Plant Area Index and Remotely Sensed Leaf Area Index of Northeastern American Deciduous, Mixed, and Coniferous Forests for SMAPVEX19-22

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 (< -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.

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Publication Details

Journal
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Published
2025-01-01
DOI
https://doi.org/10.1109/jstars.2025.3588091
Citations
2
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
3.43

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article

Comparison of in Situ Plant Area Index and Remotely Sensed Leaf Area Index of Northeastern American Deciduous, Mixed, and Coniferous Forests for SMAPVEX19-22

Simon Kraatz, Andreas Colliander, Michael H. Cosh, Laura Bourgeau‐Chavez et al.
2 citations
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Remote Sensing in Agriculture
3.43
article

Comparison of in Situ Plant Area Index and Remotely Sensed Leaf Area Index of Northeastern American Deciduous, Mixed, and Coniferous Forests for SMAPVEX19-22

Simon Kraatz, Andreas Colliander, Michael H. Cosh, Laura Bourgeau‐Chavez, Christopher Cook, Victoria R. Kelly, Mary Ellen Miller, Dorthea Vander Bilt
article en
2 citations

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 (< -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.

IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingVol. 18
Michigan Technological University (US), Jet Propulsion Laboratory (US), United States Department of Agriculture (US), Cary Institute of Ecosystem Studies (US)
National Aeronautics and Space Administration
Life in Land
Openalex Percentile: Top 8%
Remote Sensing in Agriculture
3.43
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