Wavelet geographically weighted regression for spectroscopic modelling of soil properties
Abstract Soil properties, such as organic carbon, pH and clay content, are critical indicators of ecosystem function. Visible–near infrared (vis–NIR) reflectance spectroscopy has been widely used to cost-efficiently estimate such soil properties. Multivariate modelling, such as partial least squares...
Guardado en:
Autores principales: | Yongze Song, Zefang Shen, Peng Wu, R. A. Viscarra Rossel |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/fa39f1282e1d420abebb5f318f389f06 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Automated spectroscopic modelling with optimised convolutional neural networks
por: Zefang Shen, et al.
Publicado: (2021) -
Spatial prediction of flood-prone areas using geographically weighted regression
por: Jia Min Lin, et al.
Publicado: (2021) -
Explaining the longevity characteristics in China from a geographical perspective: A multi-scale geographically weighted regression analysis
por: Renfei Yang, et al.
Publicado: (2021) -
Determinants of the incidence of hand, foot and mouth disease in China using geographically weighted regression models.
por: Maogui Hu, et al.
Publicado: (2012) -
Geographic weighted regression analysis of hot spots of anemia and its associated factors among children aged 6-59 months in Ethiopia: A geographic weighted regression analysis and multilevel robust Poisson regression analysis.
por: Getayeneh Antehunegn Tesema, et al.
Publicado: (2021)