Mapping sugarcane in complex landscapes by integrating multi-temporal Sentinel-2 images and machine learning algorithms-Reference-Cited by-同舟云学术

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Mapping sugarcane in complex landscapes by integrating multi-temporal Sentinel-2 images and machine learning algorithms

Published:2019-11 Issue: Volume:88 Page:104190
ISSN:0264-8377
Container-title:Land Use Policy
language:en
Short-container-title:Land Use Policy

Author:

Wang Ming,Liu Zhengjia,Ali Baig Muhammad Hasan,Wang Yongsheng,Li Yurui,Chen Yuanyan

Funder

Strategic Priority Research Program of the Chinese Academy of Sciences

National Natural Science Foundation of China

National Key Research and Development Program of China

Start-up Research Program of IGSNRR

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Nature and Landscape Conservation,Geography, Planning and Development,Forestry

Reference74 articles.

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2. Gross energy yields and the support energy requirements for the production of sugar from beet and cane; a study of four production areas;Austin;J. Agr. Sci-cambridge,1978

3. Using satellite and field data with crop growth modeling to monitor and estimate corn yield in Mexico;Baez-Gonzalez;Crop Sci.,2002

4. Mapping ecosystem service capacity, flow and demand for landscape and urban planning: a case study in the Barcelona metropolitan region;Baró;Land Use Policy,2016

5. Artificial neural networks: fundamentals, computing, design, and application;Basheer;J. Microbiol. Methods,2000

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