Crop Mapping Based on Historical Samples and New Training Samples Generation in Heilongjiang Province, China

Lin Zhang, Zhe Liu, Diyou Liu, Quan Xiong, Ning Yang, Tianwei Ren, Chao Zhang, Xiaodong Zhang and Shaoming Li
Additional contact information
Lin Zhang: College of Land Science and Technology, China Agricultural University, Beijing 100083, China
Zhe Liu: College of Land Science and Technology, China Agricultural University, Beijing 100083, China
Diyou Liu: College of Land Science and Technology, China Agricultural University, Beijing 100083, China
Quan Xiong: College of Land Science and Technology, China Agricultural University, Beijing 100083, China
Ning Yang: College of Land Science and Technology, China Agricultural University, Beijing 100083, China
Tianwei Ren: College of Land Science and Technology, China Agricultural University, Beijing 100083, China
Chao Zhang: College of Land Science and Technology, China Agricultural University, Beijing 100083, China
Xiaodong Zhang: College of Land Science and Technology, China Agricultural University, Beijing 100083, China
Shaoming Li: College of Land Science and Technology, China Agricultural University, Beijing 100083, China

Sustainability, 2019, vol. 11, issue 18, 1-17

Abstract: 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.

Keywords: historical samples; new samples generation; crop mapping; clustering; GF-1 (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.mdpi.com/2071-1050/11/18/5052/pdf (application/pdf)
https://www.mdpi.com/2071-1050/11/18/5052/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:11:y:2019:i:18:p:5052-:d:267657

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().