Sent2Agri system based crop type mapping in Yellow River irrigation area

FAN Jinlong; DEFOURNY Pierre; DONG Qinghan; ZHANG Xiaoyu; DE VROEY Mathilde; BELLEMANS Nicolas; XU Qi; LI Qiliang; ZHANG Lei; GAO Hao

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Sent2Agri system based crop type mapping in Yellow River irrigation area
Vol. 24, Issue S1, Pages: 10-16(2020)
Received：21 October 2019，

Accepted：16 April 2020，

Published：07 June 2020
DOI：
稿件说明：

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DOI：

Fan J L,Defourny P,Dong Q H,Zhang X Y,De Vroey Mathilde,Bellemans Nicolas,Xu Q,Li Q L,Zhang L and Gao H. 2020. Sent2Agri system based crop type mapping in Yellow River irrigation area. Journal of Remote Sensing(Chinese). 24(S1): 10-16 DOI：

摘要

Abstract

Agricultural monitoring is essential for an adequate management of food production and distribution. Crop land and crop type classification

using remote sensing time series

form an important tool to capture the agricultural production information. The recently launched Sentinel-2 satellites provide unprecedented monitoring capacities in terms of spatial resolution

swath width and revisit frequency. The Sentinel-2 for Agriculture (Sen2Agri) system has been developed to fully exploit those capacities

by providing four relevant earth observation products for agricultural monitoring. Under the Dragon4 Program

the crop mapping with various satellite images and a specific focus on 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 crop 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%

which include the cropped and uncropped types. After the removal of the uncropped area

the overall accuracy for cropped decrease to 73%. In order to further improve the crop classification accuracy

training dataset should be further improved and tuned.

关键词

Keywords

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