Identification of soybean based on Sentinel-1/2 SAR and MSI imagery under a complex planting structure |
| |
| Affiliation: | 1. School of Spatial Informatics and Geomatics Engineering, Anhui University of Science and Technology, Huainan 232001, China;2. National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China;3. School of Information Engineering, Suzhou University, Suzhou 234000, China;1. Shaanxi Meteorological Service Center of Agricultural Remote Sensing and Economic Crops, Xi''an 710014, China;2. National Tibetan Plateau Data Center, State Key Laboratory of Tibetan Plateau Earth System Science, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China;3. Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China;1. Department of Health Science and Biostatistics, School of Health Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia;2. Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia;3. Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria, Australia;1. School of Resources, Environment and Materials, Guangxi University, 530004 Nanning, China;2. State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, China;1. Department of Zoology, University of Calcutta, Kolkata, India;2. Department of Zoology, Shibpur Dinobundhoo Institution (College), Shibpur, Howrah, India |
| |
| Abstract: | Soybeans, as the major global oil seed crops, are among the large-scale agricultural products imported into China. In this way, the accurate identification of their growing areas is the basis for agricultural decision-making and planting structure adjustment, which are of great significance to the national food security. Currently, there is insufficient research on the identification of soybean, using remote sensing, under complex agricultural planting structures. In this paper, Guoyang County, a typical soybean-producing region in the north of Anhui Province, China, was selected as the study area, and then the multi-temporal Sentinel-1/2 (S-1/2) microwave and optical multispectral data were integrated to obtain the spatial distribution of the planting areas, via a stepwise hierarchical extraction strategy. Considering the Jeffries-Matusita (JM) distance, August 18 (i.e., the early pod setting stage) was correspondingly determined as more appropriate for soybean extraction, compared with other time phases. For this purpose, a set of rules were established to eliminate the non-cropland pixels, and the total distribution of the field vegetation was derived accordingly. In total, 30 candidate features (viz. 10 spectral bands, 11 vegetation indices, 4 texture features, and 5 microwave polarization features) were selected, and their importance was evaluated based on the ReliefF algorithm. With the support of the typical cover type samples, the weight assessment method for the ReliefF feature was further bundled with three machine-learning (ML) algorithms, namely, the random forest (RF), the back-propagation neural network (BPNN), and the support vector machine (SVM), to single out the optimum subset of the features for soybean identification. The results finally revealed that the ReliefF-RF model outperformed the other two. With nine optimum features, Kappa coefficient also reached up to 0.77–0.85. Furthermore, the extraction accuracy produced by the optimum subset of the features was significantly greater than the original 10 spectral bands (Kappa coefficient of 0.75–0.84), albeit slightly lower than the effect of the total 30 features (Kappa coefficient of 0.78–0.85). |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
