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• W4319862417 abstract "Rapeseed mapping is important for national food security and government regulation of land use. Various methods, including empirical index-based and machine learning-based methods, have been developed to identify rapeseed using remote sensing. Empirical index-based methods commonly employed empirically designed indices to enhance rapeseed’s bright yellow spectral feature during the flowering period, which is straightforward to implement. Unfortunately, the heavy cloud cover in the flowering period of China would lead to serious omission errors; and the required flowering period varies spatially and yearly, which often cannot be acquired accurately. Machine learning-based methods mitigate the reliance on clear observations during the flowering period by inputting all-season imagery to adaptively learn features. However, it is difficult to collect sufficient samples across all of China considering the large intraclass variation in both land cover types of rapeseed and non-rapeseed. This study proposed an automated rapeseed mapping approach integrating rule-based sample generation and a one-class classifier (RSG-OC) to overcome the shortcomings of the two types of methods. First, a set of sample selection rules based on empirical indices of rapeseed were developed to automatically generate samples in cloud-free pixels during the predicted flowering period throughout China. Second, all available features composited based on the rapeseed phenological calendar were used for classification to eliminate the phenology differences in different regions. Third, a specific sample augmentation that removes the observation in the flowering period was employed to improve the generalization to the pixels without cloud-free observation in the flowering period. Finally, to avoid the need for diverse samples of nonrapeseed classes, a typical one-class classifier, positive unlabeled learning implemented by random forest (PUL-RF) trained by the generated samples, was applied to map rapeseed. With the proposed method, China rapeseed was mapped at 20 m resolution during 2017–2021 based on the Google Earth Engine (GEE). Validation on six typical rapeseed planting areas demonstrates that RSG-OC achieves an average accuracy of 94.90%. In comparison, the average accuracy of the other methods ranged from 83.33% to 88.25%, all of which were poorer than the proposed method. Additional experiments show that the performance of RSG-OC was not sensitive to cloud contamination, inaccurate predicted flowering time and the threshold of sample selection rule. These results indicate that the rapeseed maps produced in China are overall reliable and that the proposed method is an effective and robust method for annual rapeseed mapping across China." @default.
• W4319862417 created "2023-02-11" @default.
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• W4319862417 date "2023-01-10" @default.
• W4319862417 modified "2023-10-03" @default.
• W4319862417 title "Mapping rapeseed in China during 2017-2021 using Sentinel data: an automated approach integrating rule-based sample generation and a one-class classifier (RSG-OC)" @default.
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• W4319862417 cites W1993585210 @default.
• W4319862417 cites W1998046114 @default.
• W4319862417 cites W1998281138 @default.
• W4319862417 cites W2004611847 @default.
• W4319862417 cites W2006387357 @default.
• W4319862417 cites W2006929658 @default.
• W4319862417 cites W2017976213 @default.
• W4319862417 cites W2024964814 @default.
• W4319862417 cites W2032598182 @default.
• W4319862417 cites W2036459681 @default.
• W4319862417 cites W2047122864 @default.
• W4319862417 cites W2048308312 @default.
• W4319862417 cites W2056435747 @default.
• W4319862417 cites W2104896032 @default.
• W4319862417 cites W2121025662 @default.
• W4319862417 cites W2123958887 @default.
• W4319862417 cites W2124431629 @default.
• W4319862417 cites W2124706543 @default.
• W4319862417 cites W2140136934 @default.
• W4319862417 cites W2153633421 @default.
• W4319862417 cites W2290326488 @default.
• W4319862417 cites W2390200613 @default.
• W4319862417 cites W2469111149 @default.
• W4319862417 cites W2480997172 @default.
• W4319862417 cites W2498526479 @default.
• W4319862417 cites W2500026632 @default.
• W4319862417 cites W2551843886 @default.
• W4319862417 cites W2555988103 @default.
• W4319862417 cites W2557992764 @default.
• W4319862417 cites W2592712793 @default.
• W4319862417 cites W2605906441 @default.
• W4319862417 cites W2611891293 @default.
• W4319862417 cites W2725897987 @default.
• W4319862417 cites W2745131289 @default.
• W4319862417 cites W2793927960 @default.
• W4319862417 cites W2799805920 @default.
• W4319862417 cites W2807393992 @default.
• W4319862417 cites W2886759672 @default.
• W4319862417 cites W2892469959 @default.
• W4319862417 cites W2943472941 @default.
• W4319862417 cites W2954994501 @default.
• W4319862417 cites W2964421288 @default.
• W4319862417 cites W2968521248 @default.
• W4319862417 cites W2979497319 @default.
• W4319862417 cites W2983376237 @default.
• W4319862417 cites W2999439435 @default.
• W4319862417 cites W3001402238 @default.
• W4319862417 cites W3024842265 @default.
• W4319862417 cites W3037944168 @default.
• W4319862417 cites W3045585619 @default.
• W4319862417 cites W3087384841 @default.
• W4319862417 cites W3088036223 @default.
• W4319862417 cites W3089038181 @default.
• W4319862417 cites W3091952054 @default.
• W4319862417 cites W3108009668 @default.
• W4319862417 cites W3109770820 @default.
• W4319862417 cites W3112557529 @default.
• W4319862417 cites W3119258537 @default.
• W4319862417 cites W3158568413 @default.
• W4319862417 cites W3169072044 @default.
• W4319862417 cites W3173665920 @default.
• W4319862417 cites W3183668645 @default.
• W4319862417 cites W3185118158 @default.
• W4319862417 cites W3186597588 @default.
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• W4319862417 doi "https://doi.org/10.1080/15481603.2022.2163576" @default.
• W4319862417 hasPublicationYear "2023" @default.
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