Support vector machine versus random forest for remote sensing image classification: A meta-analysis and systematic review
M Sheykhmousa, M Mahdianpari… - IEEE Journal of …, 2020 - ieeexplore.ieee.org
Several machine-learning algorithms have been proposed for remote sensing image
classification during the past two decades. Among these machine learning algorithms …
classification during the past two decades. Among these machine learning algorithms …
An overview and comparison of machine-learning techniques for classification purposes in digital soil mapping
Abstract Machine-learning is the automated process of uncovering patterns in large datasets
using computer-based statistical models, where a fitted model may then be used for …
using computer-based statistical models, where a fitted model may then be used for …
Flood hazard risk assessment model based on random forest
Floods, natural disasters that occur worldwide, have become more and more frequent in
recent decades. Flooding is often unavoidable and unexpected; however, it can be …
recent decades. Flooding is often unavoidable and unexpected; however, it can be …
Using machine learning algorithms to estimate soil organic carbon variability with environmental variables and soil nutrient indicators in an alluvial soil
K John, I Abraham Isong, N Michael Kebonye… - Land, 2020 - mdpi.com
Soil organic carbon (SOC) is an important indicator of soil quality and directly determines
soil fertility. Hence, understanding its spatial distribution and controlling factors is necessary …
soil fertility. Hence, understanding its spatial distribution and controlling factors is necessary …
Machine learning for predicting soil classes in three semi-arid landscapes
Mapping the spatial distribution of soil taxonomic classes is important for informing soil use
and management decisions. Digital soil mapping (DSM) can quantitatively predict the spatial …
and management decisions. Digital soil mapping (DSM) can quantitatively predict the spatial …
Pedology and digital soil mapping (DSM)
Pedology focuses on understanding soil genesis in the field and includes soil classification
and mapping. Digital soil mapping (DSM) has evolved from traditional soil classification and …
and mapping. Digital soil mapping (DSM) has evolved from traditional soil classification and …
Machine learning in precision agriculture: a survey on trends, applications and evaluations over two decades
Precision agriculture represents the new age of conventional agriculture. This is made
possible by the advancement of various modern technologies such as the internet of things …
possible by the advancement of various modern technologies such as the internet of things …
Recent progress and future prospect of digital soil mapping: A review
GL Zhang, LIU Feng, XD Song - Journal of integrative agriculture, 2017 - Elsevier
To deal with the global and regional issues including food security, climate change, land
degradation, biodiversity loss, water resource management, and ecosystem health, detailed …
degradation, biodiversity loss, water resource management, and ecosystem health, detailed …
High resolution mapping of soil organic carbon stocks using remote sensing variables in the semi-arid rangelands of eastern Australia
Efficient and effective modelling methods to assess soil organic carbon (SOC) stock are
central in understanding the global carbon cycle and informing related land management …
central in understanding the global carbon cycle and informing related land management …
Soil organic carbon prediction using phenological parameters and remote sensing variables generated from Sentinel-2 images
It is important to predict the spatial distribution of SOC accurately for migrating carbon
emission and sustainable soil management. Environmental variables influence the accuracy …
emission and sustainable soil management. Environmental variables influence the accuracy …