Abstract
This study proposed a feature-based decision method for the mapping of rice cultivation by using the time-series C-band synthetic aperture radar (SAR) data provided by Sentinel-1A. In this study, a model related to crop growth was first established. The model was developed based on a cubic polynomial function which was fitted by the complete time-series SAR backscatters during the rice growing season. From the developed model, five rice growth-related features were introduced, including backscatter difference (BD), time interval (TI) between vegetative growth and maturity stages, backscatter variation rate (BVR), average normalized backscatter (ANB) and maximum backscatter (MB). Then, a decision method based on the combination of the five extracted features was proposed to improve the rice detection accuracy. In order to verify the detection performance of the proposed method, the test data set of this study consisted of 50,000 rice and non-rice fields which were randomly sampled from a research area in Taiwan for simulation verification. From the experimental results, the proposed method can improve overall accuracy in rice detection by 6% compared with the method using feature BD. Furthermore, the rice detection efficiency of the proposed method was compared with other four classifiers, including decision tree (DT), support vector machine (SVM), K-nearest neighbor (KNN) and quadratic discriminant analysis (QDA). The experimental results show that the proposed method has better rice detection accuracy than the other four classifiers, with an overall accuracy of 91.9%. This accuracy is 3% higher than fine SVM, which performs best among the other four classifiers. In addition, the consistency and effectiveness of the proposed method in rice detection have been verified for different years and studied regions.
Highlights
Rice is the primary staple food for a majority of the world’s population, especially in Asia [1,2]
Since this study mainly focused on the first-stage rice-field detection, the data were downloaded from early February to late July in 2017
There are no crops in the east, because these areas are the highlands and hills of the two counties
Summary
Rice is the primary staple food for a majority of the world’s population, especially in Asia [1,2]. In 2018, the statistics from the FAO (Food and Agriculture Organization of the United Nations) show that rice is the second largest grain crop in the world and accounts for more than 12% of global cultivated land [3]. As the population has grown, the demand for rice has increased substantially [4]. In recent years, global warming has greatly changed the temperature and rainfall [5,6,7,8], and has led to environmental and food security issues, such as land degradation and reduction of crop yields [9,10,11,12]. Considering the protection of the ecological environment and the development of rice crops, it is essential to monitor rice production and accurately detect the distribution of rice fields
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