Quantifying the accuracies of six 30-m cropland datasets over China: A comparison and evaluation analysis
文献类型：期刊论文
| 作者 | Zhang, Chao1,2; Dong, Jinwei1; Ge, Quansheng1 |
| 刊名 | COMPUTERS AND ELECTRONICS IN AGRICULTURE
 |
| 出版日期 | 2022-06-01 |
| 卷号 | 197页码:14 |
| 关键词 | Cropland dataset China 30-m resolution Accuracy assessment Agriculture planning Food security |
| ISSN号 | 0168-1699 |
| DOI | 10.1016/j.compag.2022.106946 |
| 通讯作者 | Dong, Jinwei(dongjw@igsnrr.ac.cn) |
| 英文摘要 | With the development of remote sensing technology, more and more fine-resolution cropland datasets have emerged as powerful tools for agriculture planning and food security evaluation. But questions about their accuracy and reliability must be answered before using them, making evaluations necessary. So far, little attention has been paid to the performance of fine-resolution (e.g., 30 m) and cropland-specific products at continental or regional scales. This study implemented a comparison analysis and accuracy assessment for six cropland products with a 30-m resolution in China circa 2015, including FROM-GLC, GLC_FCS, CLCD, AGLC, GFSAD, GLAD. Their similarities and disparities were delineated at national, provincial, meridional, and zonal scales. 33,713 ground truth points were then collected through visual interpretation of Google Earth images and from existing available validation datasets, to evaluate the pixel-wise accuracy of them across China. In terms of spatial consistency, high agreement among the six products could be found in North China Plain and Northeast China, and low agreement was found in Southern, Southwest, and Northwest China. Topography including elevation and slope were important factors influencing spatial consistency. As for provincial area accuracy, CLCD and AGLC were most correlated with statistical data (r(2) > 0.9), followed by GLAD (0.88) and AGLC (0.87). FROM-GLC had the lowest correlation (r(2) = 0.37) with statistics. The relative area differences between each product and statistics also demonstrated that CLCD had the best area accuracies in most provinces. By contrast, GLC_FCS had a severe overestimation and FROM-GLC suffered from a large underestimation of cropland area. Last, the pixel-wise validation results indicated that CLCD and GLAD had the highest overall accuracy (OA) of 0.88, followed by AGLC (0.85) and GFSAD (0.84). FROM-GLC and GLC_FCS had the lowest OAs of less than 0.70. The comparison and evaluation results in this study can provide insights into the national and provincial performances of these fine-resolution cropland products and give valuable references for guiding data usage and help to improve future land use/cover mapping. |
| WOS关键词 | LAND-COVER DATASETS ; DATA SET ; CLASSIFICATION ; AREA ; VALIDATION ; PRODUCTS ; MODIS ; GLC |
| 资助项目 | Strategic Priority Research Program of the Chinese Academy of Sciences[XDA23100400] ; National Natural Science Foundation of China[41871349] |
| WOS研究方向 | Agriculture ; Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000830894300003 |
| 出版者 | ELSEVIER SCI LTD |
| 资助机构 | Strategic Priority Research Program of the Chinese Academy of Sciences ; National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/181173]  |
| 专题 | 陆地表层格局与模拟院重点实验室_外文论文 |
| 通讯作者 | Dong, Jinwei |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Chao,Dong, Jinwei,Ge, Quansheng. Quantifying the accuracies of six 30-m cropland datasets over China: A comparison and evaluation analysis[J]. COMPUTERS AND ELECTRONICS IN AGRICULTURE,2022,197:14. |
| APA | Zhang, Chao,Dong, Jinwei,&Ge, Quansheng.(2022).Quantifying the accuracies of six 30-m cropland datasets over China: A comparison and evaluation analysis.COMPUTERS AND ELECTRONICS IN AGRICULTURE,197,14. |
| MLA | Zhang, Chao,et al."Quantifying the accuracies of six 30-m cropland datasets over China: A comparison and evaluation analysis".COMPUTERS AND ELECTRONICS IN AGRICULTURE 197(2022):14. |
入库方式： OAI收割
来源：地理科学与资源研究所
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