Journal of Geo-information Science ›› 2019, Vol. 21 ›› Issue (6): 918-927.doi: 10.12082/dqxxkx.2019.180424

Previous Articles     Next Articles

Identifying Soybean Cropped Area with Sentinel-2 Data and Multi-Layer Neural Network

Fuyou TIAN1,2(), Bingfang WU1,2,*(), Hongwei ZENG1, Zhaoxin HE1,2, Miao ZHANG1, Bofana José1,2   

  1. 1. State Key Laboratory of Remote Sensing Science , Institute of Remote Sensing and Digital Earth, Chinese Academy of Science, Beijing 100101, China
    2. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2018-08-30 Revised:2018-12-27 Online:2019-06-15 Published:2019-06-15
  • Contact: Bingfang WU E-mail:tianfy@radi.ac.cn;wubf@radi.ac.cn
  • Supported by:
    Science and Technology Service Network Initiative, No.KFJ-STS-ZDTP-009;the National Natural Science Foundation of China, No.41561144013, 41861144019, 41701496

Abstract:

As the most important oil crop in the world, soybean is a large-scale agricultural product that China imports. The accurate identification of its planting area is the basis for decision-making and planting structure adjustment, and is of great significance to national food security. Sentinel-2 was used as a data source and multi-layer neural network was employed to map soybean cropped area. Besides, visible and infrared bands, three red-edge bands were also selected after radiation and atmospheric correction using the Sentinel-2 Toolbox. According to our test, 8-hidden-layer conducted using Scikit-learn package in Python2.7 was the optimal structure for identifying soybean and other crops. Simple linear iterative clustering (SLIC), the state-of-art segmentation algorithm, was performed to segment the remote sensed image. This method combined five-dimensional color and the image plane space to efficiently generate compact and nearly uniform super pixels. To remove the “salt and pepper effect”, the pixel-based result was integrated with the object output from the SLIC. If the pixel as soybean in an object accounted for less than 50%, this object was eliminated in the fusion map. The results showed that the overall accuracy of multi-layer neural network was 93.95%, which was highest and followed by the support vector machine, decision tree, and random forest. Then, the neural network classification was selected as the best result to integrate with SLIC object-oriented segmentation, and the results ignored the small differences of the same land and distinguish the crop differences of different blocks compared with the segmentation in eCognition software. Sentinel-2 data is an appropriate data source for large-scale soybean planting mapping. According to feature importance derived from the random forest classifier, near-infrared band is the most critical feature for classification, followed by third red edge band (Band 7), fourth red edge 4 band (Band 8), red band, and second red edge band (Band 6). The reflectance values of soybeans and other crops in the second red edge band were different, indicating a huge potential in crop type identifying. In the future, the red edge band can be introduced more into crop type even landscape classification. The multi-layer neural network method performs well in the image classification task and had similar or better overall accuracy value compared with other outstanding machine learning classifier including SVM, decision tree, and random forest. Combined with the image segmentation algorithm, such as SLIC, multi-layer neural network can map soybean cropped area with an accuracy high up to 95.51%, which can serve for soybean planting area monitoring in a large area.

Key words: Soybean mapping, multi-layer neural network, SLIC segmentation, Sentinel-2, red edge band.