Integration of remote sensing, county-level census, and machine learning for century-long regional cropland distribution data reconstruction-Reference-Cited by-同舟云学术

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Integration of remote sensing, county-level census, and machine learning for century-long regional cropland distribution data reconstruction

Published:2020-09 Issue: Volume:91 Page:102151
ISSN:0303-2434
Container-title:International Journal of Applied Earth Observation and Geoinformation
language:en
Short-container-title:International Journal of Applied Earth Observation and Geoinformation

Author:

Yang Jia,Tao Bo,Shi Hao,Ouyang Ying,Pan Shufen,Ren Wei,Lu Chaoqun

Funder

U.S. Department of Agriculture

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Computers in Earth Sciences,Earth-Surface Processes,Global and Planetary Change

Reference67 articles.

1. Biogeophysical effects of land use on climate: model simulations of radiative forcing and large-scale temperature change;Betts,2007

2. Monitoring US agriculture: the US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program;Boryan;Geocarto Int.,2011

3. Factors affecting methane emissions from rice production in the Lower Mississippi river valley, USA;Brye;Geoderma Reg.,2016

4. Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: a review;Chlingaryan;Comput. Electron. Agric.,2018

5. Biogeophysical versus biogeochemical feedbacks of large-scale land cover change;Claussen;Geophys. Res. Lett.,2001

Cited by 12 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. Annual time-series 1 km maps of crop area and types in the conterminous US (CropAT-US): cropping diversity changes during 1850–2021;Earth System Science Data;2024-07-31

2. CCropLand30: High-resolution hybrid cropland maps of China created through the synergy of state-of-the-art remote sensing products and the latest national land survey;Computers and Electronics in Agriculture;2024-03

3. Variables and a Validation Data Analysis to Improve the Prehistoric Cultivated Land Predictive Precision of Yulin, Northern Shaanxi, China;Land;2024-01-28

4. Marginal agricultural land identification in the Lower Mississippi Alluvial Valley based on remote sensing and machine learning model;International Journal of Applied Earth Observation and Geoinformation;2023-12

5. Effectiveness of land use and disturbance measures, compared to climate, soil, and topography, for modeling relative abundances of tree species in the eastern United States;Ecological Informatics;2023-07

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