# Quantile regression as a generic approach for estimating uncertainty of digital soil maps produced from machine-learning

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/quantile-regression-as-a-generic-approach-for-estimating-uncertainty-of-digital/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Babak Kasraei,Brandon Heung,Daniel D. Saurette,Margaret Schmidt,Chuck Bulmer,William Bethel |
| Citations | 101 |
| DOI | 10.1016/j.envsoft.2021.105139 |
| Fields | Computer Science,Environmental Science |
| Open Access | false |
| OA Status | closed |
| OpenAlex ID | https://openalex.org/W3186399057 |
| Type | article |
| Year | 2021 |

## Paper authors

- [Margaret Schmidt](https://scholariq.org/researchers/margaret-schmidt/)

## Paper primary topic

- [Soil Geostatistics and Mapping](https://scholariq.org/topics/soil-geostatistics-and-mapping/)

## Paper topics

- [Soil Geostatistics and Mapping](https://scholariq.org/topics/soil-geostatistics-and-mapping/)
- [Image Processing and 3D Reconstruction](https://scholariq.org/topics/image-processing-and-3d-reconstruction/)
- [Remote Sensing and LiDAR Applications](https://scholariq.org/topics/remote-sensing-and-lidar-applications/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.