From space to soil: Advancing crop mapping and ecosystem insights for smallholder agriculture
This project centers on in-season crop type mapping in Nandi County, Kenya, utilizing time-series Sentinel-2 imagery and supervised machine learning techniques. The objective is to produce accurate crop-type maps to support agricultural management activities such as yield estimation, acreage statist...
| Main Author: | |
|---|---|
| Format: | Brief |
| Language: | Inglés |
| Published: |
CGIAR System Organization
2024
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/168470 |
| _version_ | 1855531694985052160 |
|---|---|
| author | Guo, Zhe |
| author_browse | Guo, Zhe |
| author_facet | Guo, Zhe |
| author_sort | Guo, Zhe |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | This project centers on in-season crop type mapping in Nandi County, Kenya, utilizing time-series Sentinel-2 imagery and supervised machine learning techniques. The objective is to produce accurate crop-type maps to support agricultural management activities such as yield estimation, acreage statistics, disaster damage assessment, and ecosystem evaluation. The approach leverages cloud-based computing, offering a customized and flexible solution that requires no prior knowledge of cloud infrastructure. |
| format | Brief |
| id | CGSpace168470 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | CGIAR System Organization |
| publisherStr | CGIAR System Organization |
| record_format | dspace |
| spelling | CGSpace1684702025-12-03T15:32:09Z From space to soil: Advancing crop mapping and ecosystem insights for smallholder agriculture Guo, Zhe crops cartography smallholders agriculture satellite imagery data ecosystem management This project centers on in-season crop type mapping in Nandi County, Kenya, utilizing time-series Sentinel-2 imagery and supervised machine learning techniques. The objective is to produce accurate crop-type maps to support agricultural management activities such as yield estimation, acreage statistics, disaster damage assessment, and ecosystem evaluation. The approach leverages cloud-based computing, offering a customized and flexible solution that requires no prior knowledge of cloud infrastructure. 2024-12-16 2025-01-02T15:13:31Z 2025-01-02T15:13:31Z Brief https://hdl.handle.net/10568/168470 en Open Access application/pdf CGIAR System Organization Guo, Zhe. 2024. From space to soil: Advancing crop mapping and ecosystem insights for smallholder agriculture. Low-Emission Food Systems Initiative Brief. CGIAR System Organization. https://hdl.handle.net/10568/168470 |
| spellingShingle | crops cartography smallholders agriculture satellite imagery data ecosystem management Guo, Zhe From space to soil: Advancing crop mapping and ecosystem insights for smallholder agriculture |
| title | From space to soil: Advancing crop mapping and ecosystem insights for smallholder agriculture |
| title_full | From space to soil: Advancing crop mapping and ecosystem insights for smallholder agriculture |
| title_fullStr | From space to soil: Advancing crop mapping and ecosystem insights for smallholder agriculture |
| title_full_unstemmed | From space to soil: Advancing crop mapping and ecosystem insights for smallholder agriculture |
| title_short | From space to soil: Advancing crop mapping and ecosystem insights for smallholder agriculture |
| title_sort | from space to soil advancing crop mapping and ecosystem insights for smallholder agriculture |
| topic | crops cartography smallholders agriculture satellite imagery data ecosystem management |
| url | https://hdl.handle.net/10568/168470 |
| work_keys_str_mv | AT guozhe fromspacetosoiladvancingcropmappingandecosysteminsightsforsmallholderagriculture |