Prediction of Soil Organic Carbon Content Using Sentinel-1/2 and Machine Learning Algorithms in Swamp Wetlands in Northeast China

Soil organic carbon (SOC) is a sensitive indicator of climate change, and small changes in the soil carbon pool will affect the carbon balance. Accurate and robust SOC quantitative prediction is of great significance to studying the carbon budget of swamp wetlands and its response to climate change....

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Hlavní autoři:	Honghua Zhang, Luhe Wan, Yang Li
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	IEEE 2023-01-01
Edice:	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Témata:	Extreme gradient boosting with random forest (XGBRF) machine learning (ML) Sentinel-1/2 soil organic carbon (SOC) swamp wetlands
On-line přístup:	https://ieeexplore.ieee.org/document/10138795/
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