Evidence map›Paper›PMID 33531510›Full record

ArticleScientific data2021

The 10-m crop type maps in Northeast China during 2017-2019.

Nanshan You, Jinwei Dong, Jianxi Huang, Guoming Du, Geli Zhang, Yingli He, Tong Yang, Yuanyuan Di, Xiangming Xiao

Abstract read
In one paragraph

Article in Scientific data, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

33 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Nanshan YouKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
Jinwei DongKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China. dongjw@igsnrr.ac.cn.ORCID http://orcid.org/0000-0001-5687-803X
Jianxi HuangCollege of Land Science and Technology, China Agricultural University, Beijing, 100083, China.
Guoming DuSchool of Public Administration and Law, Northeast Agricultural University, Harbin, 150030, China.
Geli ZhangCollege of Land Science and Technology, China Agricultural University, Beijing, 100083, China.ORCID http://orcid.org/0000-0002-0386-5646
Yingli HeKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
Tong YangCollege of Land Science and Technology, China Agricultural University, Beijing, 100083, China.
Yuanyuan DiCollege of Land Science and Technology, China Agricultural University, Beijing, 100083, China.
Xiangming XiaoDepartment of Microbiology and Plant Biology, University of Oklahoma, Norman, OK, 73019, USA. xiangming.xiao@ou.edu.ORCID http://orcid.org/0000-0003-0956-7428

Funding

National Natural Science Foundation of China (National Science Foundation of China) 41871349
6 · The paper itself

Abstract

Northeast China is the leading grain production region in China where one-fifth of the national grain is produced; however, consistent and reliable crop maps are still unavailable, impeding crop management decisions for regional and national food security. Here, we produced annual 10-m crop maps of the major crops (maize, soybean, and rice) in Northeast China from 2017 to 2019, by using (1) a hierarchical mapping strategy (cropland mapping followed by crop classification), (2) agro-climate zone-specific random forest classifiers, (3) interpolated and smoothed 10-day Sentinel-2 time series data, and (4) optimized features from spectral, temporal, and texture characteristics of the land surface. The resultant maps have high overall accuracies (OA) spanning from 0.81 to 0.86 based on abundant ground truth data. The satellite estimates agreed well with the statistical data for most of the municipalities (R

Identifiers

PMID33531510
PMCPMC7854749

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.