Crop classification based on the spectrotemporal signature derived from vegetation indices and accumulated temperature-Reference-Cited by-同舟云学术

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Crop classification based on the spectrotemporal signature derived from vegetation indices and accumulated temperature

Published:2022-03-08 Issue:1 Volume:15 Page:626-652
ISSN:1753-8947
Container-title:International Journal of Digital Earth
language:en
Short-container-title:International Journal of Digital Earth

Author:

Zhang Lifu¹²,Gao Liaoran¹³,Huang Changping¹³,Wang Nan¹,Wang Sa¹³,Peng Mingyuan¹³,Zhang Xia¹,Tong Qingxi¹

Affiliation:

1. Chinese Academy of Science, Aerospace Information Research Institute, Beijing, People’s Republic of China

2. Key Laboratory of Oasis Eco-Agriculture, Xinjiang Production and Construction Group, Shihezi University, Shihezi, People’s Republic of China

3. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, People’s Republic of China

Funder

National Natural Science Foundation of China

Key Research Program of Frontier Sciences, CAS

Major Science and Technology Projects of XPCC

Innovation Team of XPCCs Key Area

Youth Innovation Promotion Association, CAS

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences,Computer Science Applications,Software

Link

https://www.tandfonline.com/doi/pdf/10.1080/17538947.2022.2036832

Reference81 articles.

1. The effects of climate change on normalized difference vegetation index (NDVI) in the Northeast of Iran

2. Estimating Regional Soil Moisture Distribution Based on NDVI and Land Surface Temperature Time Series Data in the Upstream of the Heihe River Watershed, Northwest China

3. Phenology from Landsat when data is scarce: Using MODIS and Dynamic Time-Warping to combine multi-year Landsat imagery to derive annual phenology curves

4. Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI

5. Comparison of Different Cropland Classification Methods under Diversified Agroecological Conditions in the Zambezi River Basin

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4. ENHANCED U-NET ALGORITHM FOR TYPICAL CROP CLASSIFICATION USING GF-6 WFV REMOTE SENSING IMAGES;Engenharia Agrícola;2024

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