Remote Sensing and Artificial Intelligence for Soil Organic Carbon Geospatial Modeling
| Autores principales: | , , , , , , |
|---|---|
| Formato: | Póster |
| Lenguaje: | Español |
| Publicado: |
International Potato Center
2022
|
| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/126802 |
Ejemplares similares: Remote Sensing and Artificial Intelligence for Soil Organic Carbon Geospatial Modeling
- From Rangelands to Cropland, Land-Use Change and its impact on soil organic carbon variables in a Peruvian Andean Highlands: A Machine Learning Modeling approach
- Overview of artificial intelligence and big data analytics for remote sensing
- Towards spatially continuous mapping of soil organic carbon in croplands using multitemporal Sentinel-2 remote sensing
- From rangelands to cropland, land-use change and its impact on soil organic carbon variables in a Peruvian Andean highlands: a machine learning modeling approach
- Modelling the impacts of organic matter quality on soil carbon turnover and storage
- Remote sensing of soil salinity mapping: status and potential