Modelling soil water retention and water‐holding capacity with visible–near‐infrared spectra and machine learning-Reference-Cited by-同舟云学术

Modelling soil water retention and water‐holding capacity with visible–near‐infrared spectra and machine learning

Published:2022-03 Issue:2 Volume:73 Page:
ISSN:1351-0754
Container-title:European Journal of Soil Science
language:en
Short-container-title:European J Soil Science

Author:

Baumann Philipp¹²^ORCID,Lee Juhwan³⁴^ORCID,Behrens Thorsten⁵,Biswas Asim⁶,Six Johan²,McLachlan Gordon⁷,Viscarra Rossel Raphael A.¹³^ORCID

Affiliation:

1. CSIRO Land & Water, Bruce E. Butler Laboratory Canberra Australia

2. Institute of Agricultural Sciences, Department of Environmental Systems Science Swiss Federal Institute of Technology, ETH Zurich Zurich Switzerland

3. Soil & Landscape Science, School of Molecular and Life Sciences, Curtin University Perth Australia

4. Department of Smart Agro‐industry Gyeongsang National University Jinju Republic of Korea

5. Soil and Spatial Data Science Solution GbR Quedlinburg Germany

6. School of Environmental Sciences University of Guelph, School of Environmental Sciences Guelph Canada

7. CSIRO Agriculture Canberra Australia

Funder

Grains Research and Development Corporation

Publisher

Wiley

Subject

Soil Science

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/ejss.13220

Reference62 articles.

1. Effect of Long-Term Irrigation with Treated Wastewater on the Root Zone Environment

2. Towards Retrieving Soil Hydraulic Properties by Hyperspectral Remote Sensing

3. A Comparative Study of Multiple Approaches for Predicting the Soil-Water Retention Curve: Hyperspectral Information vs. Basic Soil Properties

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