Spatial Estimation of Surface Soil Texture Using Machine Learning and Google Earth Engine-Reference-Cited by-同舟云学术

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Spatial Estimation of Surface Soil Texture Using Machine Learning and Google Earth Engine

Published:2024-04-15 Issue: Volume:213 Page:286-290
ISSN:
Container-title:2024 IEEE Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS)
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Author:

Zurqani Hamdi A.¹,Al-Bukhari Abdulsalam²,Alkhboli Mhieldin M.²

Affiliation:

1. University of Arkansas at Monticello,University of Arkansas Agricultural Experiment Station, Arkansas Forest Resources Center, College of Forestry, Agriculture and Natural Resources,Monticello,AR,USA

2. The Libyan Center for Palm Tree Research,The Libyan Ministry of Higher Education and Scientific Research,Tripoli,Libya

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10537232/10537239/10537478.pdf?arnumber=10537478

Reference12 articles.

1. Digital mapping of soil salinity;Taghizadeh-Mehrjardi;Ardakan region, central Iran. Geoderma,2014

2. High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models

3. Integration of Remotely Sensed Data and Machine Learning Technique for Spatial Prediction of Selected Soil Properties in Northwestern Libya

4. A Review of Libyan Soil Databases for Use within an Ecosystem Services Framework

5. Study and evaluation natural vegetation in Al Jabal Al Akhdar area,2005

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