Comparison of Machine Learning Methods for Predicting Soil Total Nitrogen Content Using Landsat-8, Sentinel-1, and Sentinel-2 Images

Soil total nitrogen (STN) is a crucial component of the ecosystem’s nitrogen pool, and accurate prediction of STN content is essential for understanding global nitrogen cycling processes. This study utilized the measured STN content of 126 sample points and 40 extracted remote sensing variables to p...

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Principais autores:	Qingwen Zhang, Mingyue Liu, Yongbin Zhang, Dehua Mao, Fuping Li, Fenghua Wu, Jingru Song, Xiang Li, Caiyao Kou, Chunjing Li, Weidong Man
Formato:	Artigo
Idioma:	Inglês
Publicado:	MDPI AG 2023-06-01
Series:	Remote Sensing
Assuntos:	soil total nitrogen content random forest gradient boosting machine extreme gradient boosting remote sensing digital soil mapping
Acceso en liña:	https://www.mdpi.com/2072-4292/15/11/2907
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