Mapping tea plantation area using phenology algorithm, time-series Sentinel-2 and Landsat images-Reference-Cited by-同舟云学术

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Mapping tea plantation area using phenology algorithm, time-series Sentinel-2 and Landsat images

Published:2023-05-03 Issue:9 Volume:44 Page:2826-2846
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Xia Haoming¹²³⁴,Bian Xiqing¹,Pan Li¹,Li Rumeng¹

Affiliation:

1. College of Geography and Environmental Science, Henan University, Kaifeng, China

2. Xinyang Academy of Ecological Research, Henan Dabieshan National Field Observation and Research Station of Forest Ecosystem, Kaifeng, China

3. Henan University, Henan Key Laboratory of Integrated Air Pollution Control and Ecological Security, Kaifeng, China

4. Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Henan University, Kaifeng, China

Funder

National Natural Science Foundation of China

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2023.2208713

Reference53 articles.

1. Integrating multiple texture methods and NDVI to the Random Forest classification algorithm to detect tea and hazelnut plantation areas in northeast Turkey

2. A new method for crop classification combining time series of radar images and crop phenology information

3. Phenology-based sample generation for supervised crop type classification

4. Prediction of Maize Yield at the City Level in China Using Multi-Source Data

5. A Comparative Analysis of Machine Learning with WorldView-2 Pan-Sharpened Imagery for Tea Crop Mapping

Cited by 4 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. An orchard mapping index and mapping algorithm coupling orchard phenology and green-holding characteristics from time-series sentinel-2 images;Computers and Electronics in Agriculture;2024-11

2. Class imbalance: A crucial factor affecting the performance of tea plantations mapping by machine learning;International Journal of Applied Earth Observation and Geoinformation;2024-05

3. Where is tea grown in the world: A robust mapping framework for agroforestry crop with knowledge graph and sentinels images;Remote Sensing of Environment;2024-03

4. Dynamic monitoring of surface water areas of nine plateau lakes in Yunnan Province using long time-series Landsat imagery based on the Google Earth Engine platform;Geocarto International;2023-09-05

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