Decoding the inconsistency of six cropland maps in China

Cui, Yifeng; Liu, Ronggao; Li, Zhichao; Zhang, Chao; Song, Xiao-Peng; Yang, Jilin; Yu, Le; Chen, Mengxi; Dong, Jinwei

doi:10.1016/j.cj.2023.11.011

Cited by 2 publications

(1 citation statement)

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“…These studies collectively show that global precision does not necessarily indicate better demonstrate local performance at the regional level 39 . A similar situation was reported in the forest evaluation for seven global land cover datasets of 2010 34 and six cropland maps of 2020 40 in China, as well as the accuracy quantification of six 30-m cropland datasets in circa 2015 41 . These studies have carried out assessment work on administrative scales such as national, provincial, and other scales, based on datasets updated in 2020 or earlier with spatial resolutions equal to or l coarser than 30 m. Since 2020, more than ten newly released and continuously updated datasets with cropland categories provide unprecedented detail of EO at 10-m or higher spatial resolution 24 , 42 .…”

Section: Background and Summarysupporting

confidence: 80%

Validation and refinement of cropland map in southwestern China by harnessing ten contemporary datasets

Cui,

Dong,

Zhang

et al. 2024

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Accurate cropland map serves as the cornerstone of effective agricultural monitoring. Despite the continuous enrichment of remotely sensed cropland maps, pervasive inconsistencies have impeded their further application. This issue is particularly evident in areas with limited valid observations, such as southwestern China, which is characterized by its complex topography and fragmented parcels. In this study, we constructed multi-sourced samples independent of the data producers, taking advantage of open-source validation datasets and sampling to rectify the accuracy of ten contemporary cropland maps in southwestern China, decoded their inconsistencies, and generated a refined cropland map (CroplandSyn) by leveraging ten state-of-the-art remotely sensed cropland maps released from 2021 onwards using the self-adaptive threshold method. Validations, conducted at both prefecture and county scales, underscored the superiority of the refined cropland map, aligning more closely with national land survey data. The refined cropland map and samples are publicly available to users. Our study offers valuable insights for improving agricultural practices and land management in under-monitored areas by providing high-quality cropland maps and validation datasets.

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