Machine learning for digital soil mapping: Applications, challenges and suggested solutions
The uptake of machine learning (ML) algorithms in digital soil mapping (DSM) is
transforming the way soil scientists produce their maps. Within the past two decades, soil …
transforming the way soil scientists produce their maps. Within the past two decades, soil …
陆表定量遥感反演方法的发展新动态.
梁顺林， 程洁， 贾坤， 江波， 刘强… - Journal of Remote …, 2016 - search.ebscohost.com
随着获取的遥感数据越来越多, 定量遥感正处于一个飞速发展的时期. 本文从反演方法和遥感
数据产品生成两个主要方面对近期陆表定量遥感的发展进行评述. 由于大气 …
数据产品生成两个主要方面对近期陆表定量遥感的发展进行评述. 由于大气 …
Modelling and mapping soil organic carbon stocks in Brazil
Brazil has extensive forests and savannas on deep weathered soils and plays a key role in
the discussions about carbon sequestration, but the distribution of soil organic carbon (SOC) …
the discussions about carbon sequestration, but the distribution of soil organic carbon (SOC) …
High resolution mapping of soil properties using remote sensing variables in south-western Burkina Faso: a comparison of machine learning and multiple linear …
Accurate and detailed spatial soil information is essential for environmental modelling, risk
assessment and decision making. The use of Remote Sensing data as secondary sources of …
assessment and decision making. The use of Remote Sensing data as secondary sources of …
Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation
Importance of target-oriented validation strategies for spatio-temporal prediction models is
illustrated using two case studies:(1) modelling of air temperature (T air) in Antarctica, and …
illustrated using two case studies:(1) modelling of air temperature (T air) in Antarctica, and …
Integration of high resolution remotely sensed data and machine learning techniques for spatial prediction of soil properties and corn yield
Widespread adoption of precision agriculture requires timely acquisition of low-cost, high
quality soil and crop yield maps. Integration of remotely sensed data and machine learning …
quality soil and crop yield maps. Integration of remotely sensed data and machine learning …
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 …
Digital mapping of soil organic carbon using ensemble learning model in Mollisols of Hyrcanian forests, northern Iran
This study was conducted to evaluate the efficacy of the ensemble machine learning model
to predict the spatial variation of soil organic carbon (SOC) concentration in a deciduous …
to predict the spatial variation of soil organic carbon (SOC) concentration in a deciduous …
Estimating soil organic carbon stocks using different modelling techniques in the semi-arid rangelands of eastern Australia
Soil organic carbon (SOC) is pivotal for biological, chemical and physical processes and
provides vital information on changes in soil fertility and land degradation. Rangelands …
provides vital information on changes in soil fertility and land degradation. Rangelands …
Modelling and mapping soil organic carbon stocks under future climate change in south-eastern Australia
Soil organic carbon (SOC) plays a key role in the sequestration of carbon that could
otherwise be warming the atmosphere. Climate change including increased temperature …
otherwise be warming the atmosphere. Climate change including increased temperature …