Global 30 m annual cropland extent dynamics (2000–2024): a harmonized baseline of structural evolution and regional disparities
Accurate annual information on cropland extent is essential for monitoring agricultural change, yet existing global products are often limited to snapshots or multiyear epochs and differ in their cropland definitions. Here we present GACED30, which, to our knowledge, is the first dedicated global 30 m annual cropland-extent dataset covering 2000–2024. The mapping framework combines gap-free SDC30 observations, spectral-semantic alignment of expert-annotated and spatially augmented samples, and rule-based spatial and temporal refinement. Independent classifiers were trained for each year using a common observation, feature, sample-construction, and modeling protocol, and the resulting annual record was used to derive pixel-level Cropping Frequency and 1 km Structural Evolution Indicators. Against independent stable-site FAST-Crop samples, GACED30 achieved an overall accuracy of 0.965 and an F 1 score of 0.844. Multitemporal assessments using GLAD Cropland and LUCAS reference samples showed higher recall and F 1 scores than GLC_FCS30D for both crop gain and crop loss, although the absolute transition scores remained modest. In a matched regional assessment in China, GACED30 achieved OA and F1 scores of 0.946 and 0.840, respectively, showing performance comparable with CACD. At the national scale, GACED30 agreed closely with definition-reconciled FAOSTAT statistics ( R 2 : 0.95; area-weighted direction match rate: 83.1 %). The sample-adjusted global cropland area was estimated at 1488.5 Mha in 2024, approximately 30.0 Mha higher than in 2000. Persistent expansion was concentrated in parts of Africa and South America, whereas stability and reduction were more widespread across much of the Global North. GACED30 provides a harmonized baseline for monitoring global cropland-extent dynamics and is publicly available at https://doi.org/10.5281/zenodo.18199675 (Chen et al., 2026).
Authors
- Yuqi Bai (ORCID: https://orcid.org/0000-0003-0908-6499)
- Yuanhong Liao (ORCID: https://orcid.org/0009-0004-6123-7391)
- Shuang Chen
- Peng Gong
- Jie Wang
Institutions
- Peng Cheng Laboratory (CN)
- University of Hong Kong (HK)
- Tsinghua University (CN)
Publication Details
- Journal
- Earth system science data
- Published
- 2026-09-25
- DOI
- https://doi.org/10.5194/essd-18-7071-2026
- Primary Topic
- Remote Sensing in Agriculture
- Type
- article
- Field-Weighted Citation Impact
- 0.00