干旱区生态环境知识资源中心(Arid): Mapping Maize Fields by Using Multi-Temporal Sentinel-1A and Sentinel-2A Images in Makarfi, Northern Nigeria, Africa

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DOI	10.3390/su12062539
	Mapping Maize Fields by Using Multi-Temporal Sentinel-1A and Sentinel-2A Images in Makarfi, Northern Nigeria, Africa
	Abubakar, Ghali Abdullahi; Wang, Ke; Shahtahamssebi, AmirReza; Xue, Xingyu; Belete, Marye; Gudo, Adam Juma Abdallah; Mohamed Shuka, Kamal Abdelrahim; Gan, Muye
通讯作者	Wang, Ke
来源期刊	SUSTAINABILITY
EISSN	2071-1050
出版年	2020
卷号	12 期号:6
英文摘要	A timely and accurate crop type mapping is very significant, and a prerequisite for agricultural regions and ensuring global food security. The combination of remotely sensed optical and radar datasets presents an opportunity for acquiring crop information at relative spatial resolution and temporal resolution adequately to capture the growth profiles of various crop species. In this paper, we employed Sentinel-1A (S-1) and Sentinel-2A (S-2) data acquired between the end of June and early September 2016, on a semi-arid area in northern Nigeria. A different set of (VV and VH) SAR and optical (SI and SB) images, illustrating crop phenological development stage, were employed as inputs to the two machines learning Random Forest (RF) and Support Vector Machine (SVM) algorithms to automatically map maize fields. Significant increases in overall classification were shown when the multi-temporal spectral indices (SI) and spectral band (SB) datasets were added with the different integration of SAR datasets (i.e., VV and VH). The best overall accuracy (OA) for maize (96.93%) was derived by using RF classification algorithms with SI-SB-SAR datasets, although the SI datasets for RF and SB datasets for SVM also produced high overall maize classification accuracies, of 97.04% and 97.44%. The outcomes indicate the robustness of the RF or SVM methods to produce high-resolution maps of maize for subsequent application from agronomists, policy planners, and the government, because such information is lacking in our study area.
英文关键词	Sentinel-1 Sentinel-2 smallholder tropical food security maize fields random forest support vector machine
类型	Article
语种	英语
国家	Peoples R China
开放获取类型	gold
收录类别	SCI-E ; SSCI
WOS记录号	WOS:000523751400386
WOS关键词	LAND-COVER CLASSIFICATION ; RANDOM FOREST ; NDVI DATA ; SAR ; IDENTIFICATION ; DISCRIMINATION ; CLASSIFIERS ; INTEGRATION ; PERFORMANCE ; ALGORITHMS
WOS类目	Green & Sustainable Science & Technology ; Environmental Sciences ; Environmental Studies
WOS研究方向	Science & Technology - Other Topics ; Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/315639
作者单位	Zhejiang Univ, Coll Environm & Resource Sci, Inst Appl Remote Sensing & Informat Technol, Hangzhou 310058, Peoples R China
推荐引用方式 GB/T 7714	Abubakar, Ghali Abdullahi,Wang, Ke,Shahtahamssebi, AmirReza,et al. Mapping Maize Fields by Using Multi-Temporal Sentinel-1A and Sentinel-2A Images in Makarfi, Northern Nigeria, Africa[J],2020,12(6).
APA	Abubakar, Ghali Abdullahi.,Wang, Ke.,Shahtahamssebi, AmirReza.,Xue, Xingyu.,Belete, Marye.,...&Gan, Muye.(2020).Mapping Maize Fields by Using Multi-Temporal Sentinel-1A and Sentinel-2A Images in Makarfi, Northern Nigeria, Africa.SUSTAINABILITY,12(6).
MLA	Abubakar, Ghali Abdullahi,et al."Mapping Maize Fields by Using Multi-Temporal Sentinel-1A and Sentinel-2A Images in Makarfi, Northern Nigeria, Africa".SUSTAINABILITY 12.6(2020).