Soil Salinity Risks Assessment Using Hybrid Machine Learning Approaches
A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Environmental Sustainability and Applications".
Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 2573
Special Issue Editors
Interests: remote sensing satellite and UAV (multispectral and hyperspectral); soil salinity; digital soil map**; land degradation; ecological hydrology; google earth engine
Special Issues, Collections and Topics in MDPI journals
Interests: crop water deficit; soil salinization; water-salt stress; thermal infrared; UAVs; Sentinel-1/2; coupled model; data fusion; data assimilation; intelligent irrigation district
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing satellite and UAV (multispectral, hyperspectral and radar); geomatic; natural resources; natural hazard; precision agriculture; land degradation; soil salinity; climate change; environmental impact assessment; optical sensor calibration
Special Issues, Collections and Topics in MDPI journals
Interests: hydrology; water management science; soil salinity map**; machine learning in remote sensing and geography
Special Issue Information
Dear Colleagues,
Soil salinization has become a global issue; it affects more than one hundred countries, greatly impeding the achievement of sustainable development goals (SDGs). With the excessive exploitation and utilization of water and land resources, salts continue to accumulate in the soil. This can significantly reduce agricultural productivity, water quality condition and ecosystem diversity. If not addressed, land degradation, ecological degradation, food production reduction, and socioeconomic losses are inevitable. Therefore, there is an urgent need to monitor and assess soil salinity risks. However, this is not an easy task because the processes, drivers, estimation, impacts, and management of soil salinity are not well understood. In dealing with a challenge in soil salinity risk assessment, the application, improvement, or fusion of machine learning provides a new solution for assessing the risk of soil salinization. Machine learning approaches can quantitatively and accurately simulate, analyze, and evaluate soil salinization, as well as improve the shortcomings of traditional methods.
In this Special Issue, we are seeking original research articles and reviews. We encourage contributions from multidisciplinary and multimethod studies. The aim is to provide new insights for the further monitoring, assessment, prevention, and management of soil salinization, thus supporting and advancing SDGs. Research areas may include (but are not limited to) the following:
l Proximal and/or remote sensing monitor and assess soil salinity (optical, microwave, thermal infrared, LIDAR, etc.);
- Digital soil map**;
- Simulation of soil salinization process;
- Soil salinity stress for crop;
- Assessment and remediation of soil salinization in agriculture;
- Soil salinization and hydrological processes;
- Driving factors of soil salinization;
- Effect of climate change on soil salinization;
- Soil salinity and soil variation;
- Quantitative assessment supports the sustainable development goals;
- Effect of land use/cover changes (LUCC) or land management policy.
We look forward to receiving your contribution.
Dr. **angyu Ge
Dr. Zhitao Zhang
Prof. Dr. Abderrazak Bannari
Dr. Haiyang Shi
Guest Editors
Manuscript Submission Information
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Keywords
- soil salinity
- remote sensing
- land degradation
- digital soil map**
- spatio-temporal modelling
- soil variation
- climate change
- hydrological processes
- water management
- intelligent agriculture
- machine learning
- LULC
- water-energy-nexus
- sustainable policy issues
