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Ning Yang
Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China

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Journal article
Published: 16 September 2019 in Sustainability
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Accurate, year-by-year crop distribution information is a key element in agricultural production regulation and global change governance. However, due to the high sampling costs and insufficient use of historical samples, a supervised classifying method for sampling every year is unsustainable for mapping crop types over time. Therefore, this paper proposes a method for the generation and screening of new samples for 2018 based on historical crop samples, and then it builds a crop mapping model for that current season. Pixels with the same crop type in the historical year (2013–2017) were extracted as potential samples, and their spectral features and spatial information in the current year (2018) were used to generate new samples based on clustering screening. The research result shows that when the clustering number is different, the number and structure of new generated sample also changes. The sample structure generated in Luobei County was not balanced, with the ‘other crop’ representing less than 3.97%, but the structure of southwest Hulin City was more balanced. Based on the newly generated samples and the ground reference data of classified year, the classification models were constructed. The average classification accuracies of Luobei County in 2018 based on new generated samples and field samples were 69.35% and 77.59%, respectively, while those of southwest Hulin City were 80.44% and 82.94%, respectively. Combined with historical samples and the spectral information of the current year, this study proposes a method to generate new samples. It can overcome the problem of crop samples only being collected in the field due to the difficulty of visual interpretation, effectively improve the use of historical data, and also provide a new idea for sustainable crop mapping in many regions lacking seasonal field samples.

ACS Style

Lin Zhang; Zhe Liu; Diyou Liu; Quan Xiong; Ning Yang; Tianwei Ren; Chao Zhang; Xiaodong Zhang; Shaoming Li. Crop Mapping Based on Historical Samples and New Training Samples Generation in Heilongjiang Province, China. Sustainability 2019, 11, 5052 .

AMA Style

Lin Zhang, Zhe Liu, Diyou Liu, Quan Xiong, Ning Yang, Tianwei Ren, Chao Zhang, Xiaodong Zhang, Shaoming Li. Crop Mapping Based on Historical Samples and New Training Samples Generation in Heilongjiang Province, China. Sustainability. 2019; 11 (18):5052.

Chicago/Turabian Style

Lin Zhang; Zhe Liu; Diyou Liu; Quan Xiong; Ning Yang; Tianwei Ren; Chao Zhang; Xiaodong Zhang; Shaoming Li. 2019. "Crop Mapping Based on Historical Samples and New Training Samples Generation in Heilongjiang Province, China." Sustainability 11, no. 18: 5052.

Journal article
Published: 25 June 2019 in Remote Sensing
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Large-scale crop mapping provides important information in agricultural applications. However, it is a challenging task due to the inconsistent availability of remote sensing data caused by the irregular time series and limited coverage of the images, together with the low spatial resolution of the classification results. In this study, we proposed a new efficient method based on grids to address the inconsistent availability of the high-medium resolution images for large-scale crop classification. First, we proposed a method to block the remote sensing data into grids to solve the problem of temporal inconsistency. Then, a parallel computing technique was introduced to improve the calculation efficiency on the grid scale. Experiments were designed to evaluate the applicability of this method for different high-medium spatial resolution remote sensing images and different machine learning algorithms and to compare the results with the widely used nonparallel method. The computational experiments showed that the proposed method was successful at identifying large-scale crop distribution using common high-medium resolution remote sensing images (GF-1 WFV images and Sentinel-2) and common machine learning classifiers (the random forest algorithm and support vector machine). Finally, we mapped the croplands in Heilongjiang Province in 2015, 2016, 2017, which used a random forest classifier with the time series GF-1 WFV images spectral features, the enhanced vegetation index (EVI) and normalized difference water index (NDWI). Ultimately, the accuracy was assessed using a confusion matrix. The results showed that the classification accuracy reached 88%, 82%, and 85% in 2015, 2016, and 2017, respectively. In addition, with the help of parallel computing, the calculation speed was significantly improved by at least seven-fold. This indicates that using the grid framework to block the data for classification is feasible for crop mapping in large areas and has great application potential in the future.

ACS Style

Ning Yang; Diyou Liu; Quanlong Feng; Quan Xiong; Lin Zhang; Tianwei Ren; Yuanyuan Zhao; Dehai Zhu; Jianxi Huang. Large-Scale Crop Mapping Based on Machine Learning and Parallel Computation with Grids. Remote Sensing 2019, 11, 1500 .

AMA Style

Ning Yang, Diyou Liu, Quanlong Feng, Quan Xiong, Lin Zhang, Tianwei Ren, Yuanyuan Zhao, Dehai Zhu, Jianxi Huang. Large-Scale Crop Mapping Based on Machine Learning and Parallel Computation with Grids. Remote Sensing. 2019; 11 (12):1500.

Chicago/Turabian Style

Ning Yang; Diyou Liu; Quanlong Feng; Quan Xiong; Lin Zhang; Tianwei Ren; Yuanyuan Zhao; Dehai Zhu; Jianxi Huang. 2019. "Large-Scale Crop Mapping Based on Machine Learning and Parallel Computation with Grids." Remote Sensing 11, no. 12: 1500.