6 research outputs found
A twenty-year dataset of high-resolution maize distribution in China
No full textAbstract China is the world’s second-largest maize producer, contributing 23% to global production and playing a crucial role in stabilizing the global maize supply. Therefore, accurately mapping the maize distribution in China is of great significance for regional and global food security and international cereals trade. However, it still lacks a long-term maize distribution dataset with fine spatial resolution, because the existing high spatial resolution satellite datasets suffer from data gaps caused by cloud cover, especially in humid and cloudy regions. This study aimed to produce a long-term, high-resolution maize distribution map for China (China Crop Dataset–Maize, CCD-Maize) identifying maize in 22 provinces and municipalities from 2001 to 2020. The map was produced using a high spatiotemporal resolution fused dataset and a phenology-based method called Time-Weighted Dynamic Time Warping. A validation based on 54,281 field survey samples with a 30-m resolution showed that the average user’s accuracy and producer’s accuracy of CCD-Maize were 77.32% and 80.98%, respectively, and the overall accuracy was 80.06% over all 22 provinces
Exploring the effects of training samples on the accuracy of crop mapping with machine learning algorithm
No full textMachine learning algorithms are a frequently used crop classification method and have been applied to identify the distribution of various crops over regional and national scales. Previous studies have underscored that the number of training samples strongly influences the classification accuracy of machine learning algorithms, resulting in extensive training sample collection efforts. This study, taking winter wheat as an example, challenges the above principle by selecting training samples with the time-weighted dynamic time warping (TWDTW) method and finds that the classification accuracy of machine learning algorithms highly relies on the representativeness and proportion of training samples rather than the quantity. With the increase of the representativeness of training samples, i.e. more comprehensively reflected the characteristics of winter wheat, the classification accuracy is continually improved. The best classification accuracy is further achieved when selecting the training samples of winter wheat and non-winter wheat according to the ratio of their statistical areas. On the contrary, only a slight difference was found in overall accuracy (91.26% and 90.74%), producer’s accuracy (86.33% and 86.65%) and user’s accuracy (97.37% and 96.01%) when using 1,000 and 10,000 training samples. Overall, this study demonstrates that the characteristics of training samples have a great impact on the classification accuracy of machine learning algorithms, and the training samples generated by TWDTW method are reliable for crop mapping
High Resolution Distribution Dataset of Double-Season Paddy Rice in China
No full textAlthough China is the largest producer of rice, accounting for about 25% of global production, there are no high-resolution maps of paddy rice covering the entire country. Using time-weighted dynamic time warping (TWDTW), this study developed a pixel- and phenology-based method to identify planting areas of double-season paddy rice in China, by comparing temporal variations of synthetic aperture radar (SAR) signals of unknown pixels to those of known double-season paddy rice fields. We conducted a comprehensive evaluation of the method’s performance at pixel and regional scales. Based on 145,210 field surveyed samples from 2018 to 2020, the producer’s and user’s accuracy are 88.49% and 87.02%, respectively. Compared to county-level statistical data from 2016 to 2019, the relative mean absolute errors are 34.11%. This study produced distribution maps of double-season rice at 10 m spatial resolution from 2016 to 2020 over nine provinces in South China, which account for more than 99% of the planting areas of double-season paddy rice of China. The maps are expected to contribute to timely monitoring and evaluating rice growth and yield
High Resolution Distribution Dataset of Double-Season Paddy Rice in China
No full textAlthough China is the largest producer of rice, accounting for about 25% of global production, there are no high-resolution maps of paddy rice covering the entire country. Using time-weighted dynamic time warping (TWDTW), this study developed a pixel- and phenology-based method to identify planting areas of double-season paddy rice in China, by comparing temporal variations of synthetic aperture radar (SAR) signals of unknown pixels to those of known double-season paddy rice fields. We conducted a comprehensive evaluation of the method’s performance at pixel and regional scales. Based on 145,210 field surveyed samples from 2018 to 2020, the producer’s and user’s accuracy are 88.49% and 87.02%, respectively. Compared to county-level statistical data from 2016 to 2019, the relative mean absolute errors are 34.11%. This study produced distribution maps of double-season rice at 10 m spatial resolution from 2016 to 2020 over nine provinces in South China, which account for more than 99% of the planting areas of double-season paddy rice of China. The maps are expected to contribute to timely monitoring and evaluating rice growth and yield
