Fan, Jinlong
[National Satellite Meteorological Center, Beijing 100081, China]
Defourny, Pierre
[UCL]
Dong, Qinghan
[Flemish Institute for Technological Research, Mol 2400, Belgium]
ZHANG, Xiaoyu
[Ningxia Institute of Meteorological Sciences, Yinchuan 750000, China]
De Vroey, Mathilde
[UCL]
Bellemans, Nicolas
[UCL]
XU, Qi
[National Satellite Meteorological Center, Beijing 100081, China]
Li, Qiliang
[National Satellite Meteorological Center, Beijing 100081, China]
Zhang, Lei
[Ningxia Institute of Meteorological Sciences, Yinchuan 750000, China]
Gao, Hao
[National Satellite Meteorological Center, Beijing 100081, China]
et al. [show all ]
Agricultural monitoring is essential for adequate management of food production and distribution. Crop land and crop type classificationꎬ using remote sensing time seriesꎬ form an important tool to capture the agricultural production information. The recently launched Sentinel ̄2 satellites provide unprecedented monitoring capacities in terms of spatial resolutionꎬ swath widthꎬ and revisit frequency. The Sentinel ̄2 for Agriculture (Sen2 ̄Agri) system has been developed to fully exploit those capacitiesꎬ by providing four relevant earth observation products for agricultural monitoring. Under the Dragon 4 Programꎬ the crop mapping with various satellite images and a specific focus on the Yellow River irrigated agricultural area in the Ningxia Hui Autonomous Region in China was carried out with the Sentinel ̄2 for Agriculture system (Sent2Agri). 9 types of crops were classified and the crop type map in 2017 was produced based on 35 scenes Sentinel 2A/ B images. The overall accuracy computed from the error confusion matrix is 88%ꎬ which includes the cropped and uncropped types. After the removal of the uncropped areaꎬ the overall accuracy for a cropped decrease to 73%. In order to further improve the crop classification accuracyꎬ the training dataset should be further improved and tuned.
Bibliographic reference
Fan, Jinlong ; Defourny, Pierre ; Dong, Qinghan ; ZHANG, Xiaoyu ; De Vroey, Mathilde ; et. al. Sent2Agri System Based Crop Type Mapping in Yellow River Irrigation Area. In: Journal of Geodesy and Geoinformation Science , Vol. 3, no. 4, p. 110-117 (2020)
Permanent URL
http://hdl.handle.net/2078.1/243049