Self-adapting extraction of cropland phenological transitions of rotation agroecosystems using dynamically fused NDVI images-Reference-Cited by-同舟云学术

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Self-adapting extraction of cropland phenological transitions of rotation agroecosystems using dynamically fused NDVI images

Published:2020-04-08 Issue:8 Volume:64 Page:1273-1283
ISSN:0020-7128
Container-title:International Journal of Biometeorology
language:en
Short-container-title:Int J Biometeorol

Author:

Tang Jia,Zeng Jingyu,Zhang Qing,Zhang Rongrong,Leng Song,Zeng Yue,Shui Wei,Xu Zhanghua,Wang Qianfeng

Funder

National Natural Science Foundation of China

he Strategic Priority Research Program of the Chinese Academy of Sciences

China Postdoctoral Science Foundation Project

Publisher

Springer Science and Business Media LLC

Subject

Health, Toxicology and Mutagenesis,Atmospheric Science,Ecology

Link

http://link.springer.com/content/pdf/10.1007/s00484-020-01904-1.pdf

Reference42 articles.

1. Baghzouz M, Devitt DA, Fenstermaker LF, Young MH (2010) Monitoring vegetation phenological cycles in two different semi-arid environmental settings using a ground-based NDVI system: a potential approach to improve satellite data interpretation. Remote Sens 2(4):990–1013

2. Balzarolo M, Vicca S, Nguy-Robertson AL, Bonal D, Elbers JA, Fu YH, Grünwald T, Horemans JA, Papale D, Peñuelas J (2016) Matching the phenology of net ecosystem exchange and vegetation indices estimated with MODIS and FLUXNET in-situ observations. Remote Sens Environ 174:290–300

3. Baret F, Guyot G (1991) Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sens Environ 35(2–3):161–173

4. Cai ZZ, Jonsson P, Jin HX, Eklundh L (2017) Performance of smoothing methods for reconstructing NDVI time-series and estimating vegetation phenology from MODIS data. Remote Sens 9(12):1271

5. Cao RY, Chen J, Shen MG, Tang YH (2015) An improved logistic method for detecting spring vegetation phenology in grasslands from MODIS EVI time-series data. Agric For Meteorol 200:9–20

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