National high-resolution cropland classification of Japan with agricultural census information and multi-temporal multi-modality datasets-Reference-Cited by-同舟云学术

National high-resolution cropland classification of Japan with agricultural census information and multi-temporal multi-modality datasets

Published:2023-03 Issue: Volume:117 Page:103193
ISSN:1569-8432
Container-title:International Journal of Applied Earth Observation and Geoinformation
language:en
Short-container-title:International Journal of Applied Earth Observation and Geoinformation

Author:

Xia Junshi,Yokoya Naoto^ORCID,Adriano Bruno^ORCID,Kanemoto Keiichiro

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Computers in Earth Sciences,Earth-Surface Processes,Global and Planetary Change

Reference45 articles.

1. Large-scale crop type and crop area mapping across Brazil using synthetic aperture radar and optical imagery;Ajadi;Int. J. Appl. Earth Obs. Geoinf.,2021

2. A new automatic stratification method for U.S. agricultural area sampling frame construction based on the cropland data layer;Boryan;IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.,2014

3. Monitoring US agriculture: the US department of agriculture, national agricultural statistics service, cropland data layer program;Boryan;Geocarto Int.,2011

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

5. DESTIN: A new method for delineating the boundaries of crop fields by fusing spatial and temporal information from WorldView and Planet satellite imagery;Cheng;Comput. Electron. Agric.,2020

Cited by 7 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A novel and robust method for large-scale single-season rice mapping based on phenology and statistical data;ISPRS Journal of Photogrammetry and Remote Sensing;2024-07

2. Spatially Explicit Active Learning for Crop-Type Mapping from Satellite Image Time Series;Sensors;2024-03-26

3. Agricultural Land Cover Mapping through Two Deep Learning Models in the Framework of EU’s CAP Activities Using Sentinel-2 Multitemporal Imagery;Remote Sensing;2023-09-22

4. A spatiotemporal collaborative approach for precise crop planting structure mapping based on multi-source remote-sensing data;International Journal of Remote Sensing;2023-06-19

5. Cropland Extraction in Southern China from Very High-Resolution Images Based on Deep Learning;Remote Sensing;2023-04-23