Deriving corn and soybeans fractions with Land Remote-Sensing Satellite (System, Landsat) imagery by accounting for endmember variability on Google Earth Engine-Reference-Cited by-同舟云学术

Deriving corn and soybeans fractions with Land Remote-Sensing Satellite (System, Landsat) imagery by accounting for endmember variability on Google Earth Engine

Published:2021-03-10 Issue:12 Volume:42 Page:4493-4513
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Li Ke¹,Wang Le¹,Yin Dameng¹²^ORCID

Affiliation:

1. Department of Geography, The State University of New York at Buffalo, Buffalo, NY, United States

2. Institute of Crop Sciences, Chinese Academy of Agricultural Sciences/Key Laboratory of Crop Physiology and Ecology, Ministry of Agriculture, Beijing, China

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2021.1897184

Reference56 articles.

1. Spectral mixture modeling: A new analysis of rock and soil types at the Viking Lander 1 Site

2. American Soybeans Association. “American Soybeans Production.” http://soystats.com/archives-list/

3. Landsat-based classification in the cloud: An opportunity for a paradigm shift in land cover monitoring

4. Monitoring US agriculture: the US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program

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