干旱区生态环境知识资源中心(Arid): Crop type identification and spatial mapping using Sentinel-2 satellite data with focus on field-level information

Arid

DOI	10.1080/10106049.2020.1805029
	Crop type identification and spatial mapping using Sentinel-2 satellite data with focus on field-level information
	Gumma, Murali Krishna; Tummala, Kimeera; Dixit, Sreenath; Collivignarelli, Francesco; Holecz, Francesco; Kolli, Rao N.; Whitbread, Anthony M.
通讯作者	Gumma, MK
来源期刊	GEOCARTO INTERNATIONAL
ISSN	1010-6049
EISSN	1752-0762
英文摘要	Accurate monitoring of croplands helps in making decisions (for insurance claims, crop management and contingency plans) at the macro-level, especially in drylands where variability in cropping is very high owing to erratic weather conditions. Dryland cereals and grain legumes are key to ensuring the food and nutritional security of a large number of vulnerable populations living in the drylands. Reliable information on area cultivated to such crops forms part of the national accounting of food production and supply in many Asian countries, many of which are employing remote sensing tools to improve the accuracy of assessments of cultivated areas. This paper assesses the capabilities and limitations of mapping cultivated areas in the Rabi (winter) season and corresponding cropping patterns in three districts characterized by small-plot agriculture. The study used Sentinel-2 Normalized Difference Vegetation Index (NDVI) 15-day time-series at 10 m resolution by employing a Spectral Matching Technique (SMT) approach. The use of SMT is based on the well-studied relationship between temporal NDVI signatures and crop phenology. The rabi season in India, dominated by non-rainy days, is best suited for the application of this method, as persistent cloud cover will hamper the availability of images necessary to generate clearly differentiating temporal signatures. Our study showed that the temporal signatures of wheat, chickpea and mustard are easily distinguishable, enabling an overall accuracy of 84%, with wheat and mustard achieving 86% and 94% accuracies, respectively. The most significant misclassifications were in irrigated areas for mustard and wheat, in small-plot mustard fields covered by trees and in fragmented chickpea areas. A comparison of district-wise national crop statistics and those obtained from this study revealed a correlation of 96%.
英文关键词	Cropping pattern Sentinel-2 matching technique small-plot agriculture semi-arid-conditions
类型	Article ; Early Access
语种	英语
开放获取类型	Green Accepted
收录类别	SCI-E
WOS记录号	WOS:000560122900001
WOS关键词	LAND-COVER CLASSIFICATION ; TIME-SERIES DATA ; AREA ; BASIN ; INTENSITY ; ACCURACY ; IMAGERY
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
来源机构	International Crops Research Institute for the Semi-Arid Tropics
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/328164
作者单位	[Gumma, Murali Krishna; Tummala, Kimeera; Dixit, Sreenath] Int Crops Res Inst Semi Arid Trop, RS GIS Lab, Innovat Syst Drylands, Patancheru, Andhra Pradesh, India; [Collivignarelli, Francesco; Holecz, Francesco] Sarmap, Caslano, Switzerland; [Kolli, Rao N.] Int Reinsurance & Insurance Consultancy & Broking, Mumbai, Maharashtra, India; [Whitbread, Anthony M.] Int Crops Res Inst Semi Arid Trop, Dodoma, Tanzania
推荐引用方式 GB/T 7714	Gumma, Murali Krishna,Tummala, Kimeera,Dixit, Sreenath,et al. Crop type identification and spatial mapping using Sentinel-2 satellite data with focus on field-level information[J]. International Crops Research Institute for the Semi-Arid Tropics.
APA	Gumma, Murali Krishna.,Tummala, Kimeera.,Dixit, Sreenath.,Collivignarelli, Francesco.,Holecz, Francesco.,...&Whitbread, Anthony M..
MLA	Gumma, Murali Krishna,et al."Crop type identification and spatial mapping using Sentinel-2 satellite data with focus on field-level information".GEOCARTO INTERNATIONAL