Using Sentinel-1, Sentinel-2, and planet imagery to map crop type of smallholder farms
Remote sensing offers a way to map crop types across large spatio-temporal scales at low costs. However, mapping crop types is challenging in heterogeneous, smallholder farming systems, such as those in India, where field sizes are often smaller than the resolution of historically available imagery....
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Journal Article |
| Language: | Inglés |
| Published: |
MDPI
2021
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/164277 |
Similar Items: Using Sentinel-1, Sentinel-2, and planet imagery to map crop type of smallholder farms
- A machine learning algorithm for mapping small reservoirs using Sentinel-2 satellite imagery in Google Earth Engine
- Monitoring spatial-temporal variations of surface areas of small reservoirs in Ghana’s Upper East Region using Sentinel-2 satellite imagery and machine learning
- Smallholder fields crop type mapping based on satellite imagery
- Modeling turbidity of aquaculture pond in the East Kolkata Wetlands using Sentinel-2 bands and ground data through multivariate regression on Google Earth Engine
- Beyond pastures, look at plastic: Using Sentinel-2 imagery to map silage bags to improve understanding of cattle intensity
- Synergy of Sentinel-1 and Sentinel-2 Time Series for Cloud-Free Vegetation Water Content Mapping with Multi-Output Gaussian Processes