A cultivated planet in 2010 – Part 1: The global synergy cropland map

Lu, Miao; Wu, Wenbin; You, Liangzhi; See, Linda; Fritz, Steffen; Yu, Qiangyi; Wei, Yanbing; Chen, Di; Yang, Peng; Xue, Bing

doi:https://doi.org/10.5194/essd-12-1913-2020

Articles | Volume 12, issue 3

https://doi.org/10.5194/essd-12-1913-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/essd-12-1913-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 12, issue 3

Data description paper

|

28 Aug 2020

Data description paper |

| 28 Aug 2020

A cultivated planet in 2010 – Part 1: The global synergy cropland map

Miao Lu, Wenbin Wu, Liangzhi You, Linda See, Steffen Fritz, Qiangyi Yu, Yanbing Wei, Di Chen, Peng Yang, and Bing Xue

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Final revised paper (published on 28 Aug 2020)
Preprint (discussion started on 11 Feb 2020)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Comments on paper by Lu et al.', Anonymous Referee #1, 11 Feb 2020
- AC1: 'Response to Anonymous Referee #1', Qiangyi Yu, 22 May 2020
  - AC3: 'A cultivated planet in 2010: 1. the global synergy cropland map', Qiangyi Yu, 22 May 2020
RC2: 'comments to Lu et al's paper', Anonymous Referee #2, 21 Mar 2020
- AC2: 'A cultivated planet in 2010: 1. the global synergy cropland map', Qiangyi Yu, 22 May 2020

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Qiangyi Yu on behalf of the Authors (22 May 2020) Author's response Manuscript

ED: Publish as is (06 Jul 2020) by David Carlson

Short summary

Global cropland distribution is critical for agricultural monitoring and food security. We propose a new Self-adapting Statistics Allocation Model (SASAM) to develop the global map of cropland distribution. SASAM is based on the fusion of multiple existing cropland maps and multilevel statistics of cropland area, which is independent of training samples. The synergy map has higher accuracy than the input datasets and better consistency with the cropland statistics.