2015-2023年中俄黑龙江跨境流域农作物精细分类数据集
收稿日期: 2024-05-16
录用日期: 2024-07-31
网络出版日期: 2025-02-05
基金资助
ANSO“一带一路”国际科学组织联盟资助项目(ANSO-CR-KP-2022-06);国家科技基础资源调查专项项目(2022FY101902- 02);中国工程科技知识中心建设项目(CKCEST-2023-1-5)
Fine Classification Dataset of Crops in the Transboundary Basin of the Heilongjiang River Between Russia and China, 2015-2023
Received date: 2024-05-16
Accepted date: 2024-07-31
Online published: 2025-02-05
俄罗斯远东和中国东北所在的黑龙江跨境流域地区拥有丰富的自然资源，在农业资源开发利用方面具有巨大潜力。面临全球冲突不断增加和粮食供应链的短缺危机，加强黑龙江跨境流域农业资源的监测和开发利用，对于保障全球粮食安全具有重要意义。该数据集以黑龙江跨境流域作为研究区，运用机器学习和样本迁移方法，构建一套全面的农作物精细分类体系。基于历史遥感影像数据以及Google Earth Engine（GEE）云平台，以Landsat和Sentinel影像为数据源，完成2015、2020和2023年小麦、玉米、大豆、水稻等主要农作物分类，总体精度超过84%，Kappa系数大于0.81。通过时空变化分析，揭示了黑龙江跨境流域农作物的格局与变化特征，对该流域耕地资源优化配置提供决策支持。
数据摘要：
| 项目 | 描述 |
|---|---|
| 数据集名称 | 2015-2023年中俄黑龙江跨境流域农作物精细分类数据集 |
| 所属学科 | 土地资源与信息技术 |
| 研究主题 | 黑龙江跨境流域农作物精细分类 |
| 数据时间范围 | 2015年、2020年、2023年 |
| 时间分辨率 | 年 |
| 数据地理空间覆盖 | 黑龙江跨境流域 |
| 空间分辨率 | 10 m, 30 m |
| 数据类型与技术格式 | .tif |
| 数据库（集）组成 | 本数据集包含2015年、2020年、2023年的黑龙江跨境流域农作物精细分类数据，每一年对应 8个 Tiff 文件，共计 24 条记录 |
| 数据量 | 1.92 GB |
| 主要数据指标 | 黑龙江跨境流域农作物精细分类（小麦、玉米、大豆、水稻） |
| 数据可用性 | https://cstr.cn/17058.11.sciencedb.agriculture.00041 https://doi.org/10.57760/sciencedb.agriculture.00041 |
| 经费支持 | ANSO“一带一路”国际科学组织联盟资助项目（批准号：ANSO-CR-KP-2022-06）、国家科技基础资源调查专项项目（批准号：2022FY101902-02）和中国工程科技知识中心建设项目（批准号：CKCEST-2023-1-5） |
关键词： 作物分类; Sentinel-2; Landsat; 随机森林
刘梦, 王卷乐, 李凯, 江嘉伟, 邹伟豪 . 2015-2023年中俄黑龙江跨境流域农作物精细分类数据集[J]. 农业大数据学报, 2025 , 7(1) : 22 -30 . DOI: 10.19788/j.issn.2096-6369.100035
The Heilongjiang transboundary basin region, where the Russian Far East and northeastern China are located, is rich in natural resources and has great potential for the development and utilization of agricultural resources. Facing the crisis of increasing global conflicts and shortage of food supply chain, strengthening the monitoring and development and utilization of agricultural resources in the Heilongjiang basin is of great significance to guarantee global food security. In this dataset, the Heilongjiang transboundary watershed is used as the study area, and machine learning and sample migration methods are applied to construct a comprehensive set of fine classification system for agricultural crops. Based on historical remote sensing image data and the Google Earth Engine (GEE) cloud platform, the classification of major crops such as wheat, corn, soybean and rice in 2015, 2020 and 2023 was completed with an overall accuracy of more than 84% and a Kappa coefficient of more than 0.81, using Landsat images as the data source. The analysis of spatial and temporal changes reveals the pattern and changing characteristics of crops in the Heilongjiang transboundary watershed, and provides decision-making support for the optimal allocation of arable land resources in this watershed.
Data summary:
| Item | Description |
|---|---|
| Dataset name | |
| Specific subject area | Land resources and information technology |
| Research topic | Fine classification of crops in the transboundary basin of the Heilongjiang River |
| Time range | 2015, 2020, 2023year |
| Temporal resolution | year |
| Geographical scope | Heilongjiang Transboundary Basin |
| Spatial resolution | 10 m, 30 m |
| Data types and technical formats | .tif |
| Dataset structure | This dataset contains fine categorized data of crops in the transboundary basin of Heilongjiang for the years 2015, 2020 and 2023, each year corresponds to 8 Tiff files, totaling 24 records. |
| Volume of dataset | 1.92 GB |
| Key index in dataset | Fine classification of crops (wheat, maize, soybean, rice) in the transboundary basin of the Heilongjiang River |
| Data accessibility | https://cstr.cn/17058.11.sciencedb.agriculture.00041 https://doi.org/10.57760/sciencedb.agriculture.00041 |
| Financial support | The ANSO "Belt and Road" International Alliance of Scientific Organizations (Grant No. AN-SO-CR-KP-2022-06), the China Science and Technology Basic Resource Survey Program (Grant No. 2022FY101902), China Engineering Science and Technology Knowledge Center Construction Project (Grant No. CKCEST-2023-1-5) |
Key words： crop classification; Sentinel-2; Landsat; Random forest
| [1] | WANG Y, ZANG S, TIAN Y. Mapping paddy rice with the random forest algorithm using MODIS and SMAP time series. Chaos Solitons & Fractals, 2020, 140: 110116. DOI:10.1016/j.chaos.2020.110116. |
| [2] | CHEN Y, LU D, MORAN E, et al. Mapping croplands, cropping patterns, and crop types using MODIS time-series data. International Journal of Applied Earth Observation and Geoinformation, 2018, 69:133-147. DOI:10.1016/j.jag.2018.03.005. |
| [3] | RI A, MX A, ZC A, et al. Phenology-based classification of crop species and rotation types using fused MODIS and Landsat data: The comparison of a random-forest-based model and a decision-rule-based model. Soil and Tillage Research, 2021, 206. DOI:10.1016/j.jag.2018.03.005. |
| [4] | 杜保佳, 张晶, 王宗明, 等. 应用 Sentinel-2A NDVI 时间序列和面向对象决策树方法的农作物分类. 地球信息科学学报, 2019, 21(5): 740-751. |
| [5] | 解文欢, 张有智, 张海峰, 等. 县级主要农作物空间分布遥感制图——以同江市为例. 现代农机, 2022(3):67-68. |
| [6] | 宋茜. 农作物空间分布信息提取及其时空格局变化分析研究. 北京: 中国农业科学院, 2018. |
| [7] | 韩冰冰. 吉林省大宗作物分布遥感制图. 长春:吉林大学, 2020. |
| [8] | ZHANG X, LIU K, WANG S, et al. A rapid model (COV_PSDI) for winter wheat mapping in fallow rotation area using MODIS NDVI time-series satellite observations: The case of the Heilonggang Region. Remote Sensing, 2021, 13(23): 4870. |
| [9] | AMANI M, BRISCO B, AFSHAR M, et al. A generalized super-vised classification scheme to produce provincial wetland inventory maps: an application of Google Earth Engine for big GEO data processing. Big Earth Data, 2019, 3(4): 378-394. |
| [10] | HIRD J N, DeLANCEY E R, McDERMID G J, et al. Google Earth Engine, open-access satellite data, and machine learning in support of large-area probabilistic wetland mapping. Remote Sensing, 2017, 9(12): 1315. |
| [11] | AMANI M, GHORBANIAN A, AHMADI S A, et al. Google Earth Engine cloud computing platform for remote sensing big data applications: A comprehensive review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13: 5326-5350. |
| [12] | GORELICK N, HANCHER M, DIXON M, et al. Google Earth Engine: Plane-tary-scale geospatial analysis for everyone. Remote Sensing of Environment, 2017, 2(2): 18-27. |
| [13] | SHELESTOV A, LAVRENIUK M, KUSSUL N, et al. Exploring Google Earth Engine platform for big data processing: Classification of multi- temporal satellite imagery for crop mapping. Frontiers in Earth Science, 2017, 5: 232994. |
| [14] | WANG S, DiTOMMASO S, DEINES J M, et al. Mapping twenty years of corn and soybean across the US Midwest using the Landsat archive. Scientific Data, 2020, 7(1): 307. |
| [15] | CHENG C X, YAN T L, ZHU D H. The method of polygon land use identify supported by GIS-A case study for dynamic monitoring land using. Journal of China Agricultural University, 2001, 6(3): 55-59. |
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