Toward a Simple and Generic Approach for Identifying Multi-Year Cotton Cropping Patterns Using Landsat and Sentinel-2 Time Series

被引:14
作者
Li, Qiqi [1 ,2 ]
Liu, Guilin [1 ,2 ]
Chen, Weijia [1 ,2 ]
机构
[1] South China Normal Univ, Sch Geog, Guangzhou 510631, Peoples R China
[2] Minist Nat Resources, Key Lab Nat Resources Monitoring Trop & Subtrop A, Guangzhou 510631, Peoples R China
基金
中国国家自然科学基金;
关键词
multi-year cotton-cropping patterns; classification; temporal trajectory pattern; time series remote sensing; COVER CLASSIFICATION; DISCRIMINATION; PRODUCTIVITY; ALGORITHMS; CROPLANDS; IMAGERY; CHINA; NDVI;
D O I
10.3390/rs13245183
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The sustainable development goals of the United Nations, as well as the era of pandemics have introduced serious challenges for agricultural production and management. Precise management of agricultural practices based on satellite-borne remote sensing has been considered an effective means for monitoring cropping patterns and crop-farming patterns. Therefore, we proposed a simple and generic approach to identify multi-year cotton-cropping patterns based on time series of Landsat and Sentinel-2 images, with few ground samples that covered many years, a simple classification algorithm, and had a high classification accuracy. In this approach, we extended the size of training samples using active learning, and we employed a random forest algorithm to extract multi-year cotton planting patterns based on dense time series of Landsat and Sentinel-2 data from 2014 to 2018. We created annual crop cultivation maps based on training samples with an accuracy greater than 95.69%. The accuracy of multi-year cotton cropping patterns was 96.93%. The proposed approach was effective and robust in identifying multi-year cropping patterns, and it could be applied in other regions.
引用
收藏
页数:17
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