The dataset of main grain land changes in China over 1985–2020

Continuous, Accurate, and detailed information on main grain land (MGL) areas is crucial for provisioning food security and making policies affecting sustainable agricultural production. It still lacks a long-term MGL distribution dataset with fine spatial resolution. This study aimed to produce a long-term, high-resolution MGL distribution map for China. Here, we developed the change map of MGL with resolution 30 m in China for the period 1985-2020 using the Landsat image-based random forest algorithm on the GEE platform. Finally, the planting intensity, gain time and loss time of MGL was calculated. Results indicate that our mapping results are highly consistent with the annual planting area of various grain crops according to national statistics. A validation based on 3113 field survey samples with a 30-m resolution showed that the overall accuracy of MGL were 93.57%. The full MGL dataset is freely available at https://doi.org/10.6084/m9.figshare.26212643.v2 .

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Publication Details

Journal
Scientific Data
Published
2024-12-24
DOI
https://doi.org/10.1038/s41597-024-04292-y
Citations
17
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
7.80

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article

The dataset of main grain land changes in China over 1985–2020

Shidong Liu, Li Wang, Jie Zhang
17 citations
Scientific Data
Remote Sensing in Agriculture
7.80
article

The dataset of main grain land changes in China over 1985–2020

Shidong Liu, Li Wang, Jie Zhang
article en
17 citations

Abstract

Continuous, Accurate, and detailed information on main grain land (MGL) areas is crucial for provisioning food security and making policies affecting sustainable agricultural production. It still lacks a long-term MGL distribution dataset with fine spatial resolution. This study aimed to produce a long-term, high-resolution MGL distribution map for China. Here, we developed the change map of MGL with resolution 30 m in China for the period 1985-2020 using the Landsat image-based random forest algorithm on the GEE platform. Finally, the planting intensity, gain time and loss time of MGL was calculated. Results indicate that our mapping results are highly consistent with the annual planting area of various grain crops according to national statistics. A validation based on 3113 field survey samples with a 30-m resolution showed that the overall accuracy of MGL were 93.57%. The full MGL dataset is freely available at https://doi.org/10.6084/m9.figshare.26212643.v2 .

Scientific DataVol. 11(1)
Chinese Academy of Sciences (CN), Institute of Remote Sensing and Digital Earth (CN), Aerospace Information Research Institute (CN), Tsinghua University (CN)
National Natural Science Foundation of China, Chinese Academy of Sciences, China Postdoctoral Science Foundation
Zero hunger
Openalex Percentile: Top 2%
Remote Sensing in Agriculture
7.80
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