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Remote Sensing-Guided Sampling Design with Both Good Spatial Coverage and Feature Space Coverage for Accurate Farm Field-Level Soil Mapping
Wang, Yongji1,2; Jiang, Lili1; Qi, Qingwen1; Liu, Ying1; Wang, Jun3
2019-08-01
Source PublicationREMOTE SENSING
Volume11Issue:16Pages:18
Corresponding AuthorJiang, Lili(jiangll@igsnrr.ac.cn)
AbstractWith the increasing requirements of precision agriculture for massive and various kinds of data, remote sensing technology has become indispensable in acquiring the necessary data for precision agriculture. Understanding the spatial variability of a target soil variable (i.e., soil mapping) is a critical issue in solving many agricultural problems. Field sampling is one of the most commonly used technologies for soil mapping, but sample sizes are restricted by resources, such as field labor, soil physicochemical analysis, and funding. In this paper, we proposed a sampling design method with both good spatial coverage and feature space coverage to achieve more precise spatial variability of farm field-level target soil variables for limited sample sizes. The proposed method used the super-grid to achieve good spatial coverage, and it took advantage of remote sensing products that were highly correlated with the target soil property (SOM content) to achieve good feature space coverage. For the experiments, we employed the ordinary kriging (OK) method to map the soil organic matter (SOM) content. The different sized super-grid comparison experiments showed that the 400 x 400 m(2) super-grid had the highest SOM content mapping accuracy. Then, we compared the proposed method to regular grid sampling (good spatial coverage) and k-means sampling (good feature space coverage), and the experimental results indicated that the proposed method had greater potential in the selection of representative samples that could improve the SOM content mapping accuracy.
Keywordsoil sampling spatial coverage feature coverage farm field-level soil mapping super-grid limited sample size
DOI10.3390/rs11161946
WOS KeywordWINTER-WHEAT YIELD ; SENSED VEGETATION INDEXES ; AREA INDEX ; MODIS-LAI ; MODEL ; ASSIMILATION ; CORN ; OPTIMIZATION ; SCHEMES ; WOFOST
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program of China[2017YFB0503500] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19040402]
Funding OrganizationNational Key Research and Development Program of China ; Strategic Priority Research Program of the Chinese Academy of Sciences
WOS Research AreaRemote Sensing
WOS SubjectRemote Sensing
WOS IDWOS:000484387600109
PublisherMDPI
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/69766
Collection中国科学院地理科学与资源研究所
Corresponding AuthorJiang, Lili
Affiliation1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Shandong Univ Sci & Technol, Coll Geometr, Qingdao 266590, Shandong, Peoples R China
Recommended Citation
GB/T 7714
Wang, Yongji,Jiang, Lili,Qi, Qingwen,et al. Remote Sensing-Guided Sampling Design with Both Good Spatial Coverage and Feature Space Coverage for Accurate Farm Field-Level Soil Mapping[J]. REMOTE SENSING,2019,11(16):18.
APA Wang, Yongji,Jiang, Lili,Qi, Qingwen,Liu, Ying,&Wang, Jun.(2019).Remote Sensing-Guided Sampling Design with Both Good Spatial Coverage and Feature Space Coverage for Accurate Farm Field-Level Soil Mapping.REMOTE SENSING,11(16),18.
MLA Wang, Yongji,et al."Remote Sensing-Guided Sampling Design with Both Good Spatial Coverage and Feature Space Coverage for Accurate Farm Field-Level Soil Mapping".REMOTE SENSING 11.16(2019):18.
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