Using machine learning to generate an open-access cropland map from satellite images time series in the Indian Himalayan region-Reference-Cited by-同舟云学术

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Using machine learning to generate an open-access cropland map from satellite images time series in the Indian Himalayan region

Published:2023-11 Issue: Volume:32 Page:101057
ISSN:2352-9385
Container-title:Remote Sensing Applications: Society and Environment
language:en
Short-container-title:Remote Sensing Applications: Society and Environment

Author:

Li Danya^ORCID,Gajardo Joaquin^ORCID,Volpi Michele,Defraeye Thijs

Funder

Rockefeller Foundation

Publisher

Elsevier BV

Subject

Computers in Earth Sciences,Geography, Planning and Development

Reference40 articles.

1. Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis;Belgiu;Remote Sens. Environ.,2018

2. Random forests;Breiman;Mach. Learn.,2001

3. Copernicus global land service: land cover 100m: collection 3: epoch 2015: globe;Buchhorn;Zenodo,2020

4. MSI Level-1C TOA Reflectance Product,2021

5. Hierarchical classification of Sentinel 2-a images for land use and land cover mapping and its use for the CORINE system;Demirkan;J. Appl. Remote Sens.,2020

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1. Machine learning for food security: current status, challenges, and future perspectives;Artificial Intelligence Review;2023-10-27

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