Upscaling Frameworks Drive Prediction Accuracy and Uncertainty When Mapping Aboveground Biomass Density from the Synergism of Spaceborne LiDAR, SAR, and Passive Optical Data

Upscaling Frameworks Drive Prediction Accuracy and Uncertainty When Mapping Aboveground Biomass Density from the Synergism of Spaceborne LiDAR, SAR, and Passive Optical Data

Accurate mapping of aboveground biomass density (AGBD) is vital for ecological research and carbon cycle monitoring. Integrating multi-source remote sensing data offers significant potential to enhance the accuracy and coverage of AGBD estimates. This study evaluated three upscaling frameworks for i...

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Bibliographic Details
Main Authors: Inacio T. Bueno, Carlos A. Silva, Monique B. Schlickmann, Victoria M. Donovan, Jeff W. Atkins, Kody M. Brock, Jinyi Xia, Denis R. Valle, Jiangxiao Qiu, Jason Vogel, Andres Susaeta, Ajay Sharma, Carine Klauberg, Midhun Mohan, Ana Paula Dalla Corte
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Remote Sensing
Subjects:
GEDI
Random Forest
Harmonized Landsat Sentinel
principal component analysis
wall-to-wall mapping
Online Access:https://www.mdpi.com/2072-4292/17/14/2340
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https://www.mdpi.com/2072-4292/17/14/2340

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