Combining remote sensing and ground census data to develop new maps of the distribution of rice agriculture in China
Open accessAbstract
Large‐scale assessments of the potential for food production and its impact on biogeochemical cycling require the best possible information on the distribution of cropland. This information can come from ground‐based agricultural census data sets and/or spaceborne remote sensing products, both with strengths and weaknesses. Official cropland statistics for China contain much information on the distribution of crop types, but are known to significantly underestimate total cropland areas and are generally at coarse spatial resolution. Remote sensing products can provide moderate to fine spatial resolution estimates of cropland location and extent, but supply little information on crop type or management. We combined county‐scale agricultural census statistics on total cropland area and sown area of 17 major crops in 1990 with a fine‐resolution land‐cover map derived from 1995–1996 optical remote sensing (Landsat) data to generate 0.5° resolution maps of the distribution of rice agriculture in mainland China. Agricultural census data were used to determine the fraction of crop area in each 0.5° grid cell that was in single rice and each of 10 different multicrop paddy rice rotations (e.g., winter wheat/rice), while the remote sensing land‐cover product was used to determine the spatial distribution and extent of total cropland in China. We estimate that there were 0.30 million km2 of paddy rice cropland; 75% of this paddy land was multicropped, and 56% had two rice plantings per year. Total sown area for paddy rice was 0.47 million km2. Paddy rice agriculture occurred on 23% of all cultivated land in China.
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Single cropping dominates the U.S. crop systems, while multiple cropping has been significantly contributing to the overall harvest area and total annual crop production in China.
“On the other, however, single cropping dominates the U.S. crop systems, while multiple cropping has been significantly contributing to the overall harvest area and total annual crop production in China [56,57].”
From Biomass and biofuels in China: Toward bioenergy resource potentials and their impacts on the environment (2018) · cites this paper as [57]
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- Authors
- Frolking, S., Qiu, J., Boles, S., Xiao, X., Liu, J., Zhuang, Y., Li, C., & Qin, X.
- Affiliations
- University of New Hampshire (US)
- Institute of Agricultural Resources and Regional Planning (CN)
- Chinese Academy of Sciences (CN)
- Venue
- Global Biogeochemical Cycles
- Publisher
- American Geophysical Union (AGU)
- Published
- 2002-11-19
- Volume
- 16
- Issue
- 4
- ISSN
- 0886-6236 (ISSN-L)
- 1944-9224
- OpenAlex
- W1891144596
- Cited by
- 348
- References
- 28
- Citations by year
- 2026: 4 · 2025: 11 · 2024: 19 · 2023: 25 · 2022: 23 · 2021: 26 · 2020: 17 · 2019: 16 · 2018: 20 · 2017: 19 · 2016: 19 · 2015: 23 · 2014: 21 · 2013: 15 · 2012: 18
- FWCI
- 7.75
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Global Biogeochemical Cycles (publishedVersion)
- University of New Hampshire Scholars Repository (University of New Hampshire at Manchester) (submittedVersion)
- Funders
- National Science Foundation
- U.S. Department of Energy
- National Aeronautics and Space Administration
- U.S. Department of Agriculture
- Chinese Academy of Sciences
- Chinese Academy of Agricultural Sciences
- Topics
- Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
- Physical Sciences › Environmental Science › Global and Planetary Change › Land Use and Ecosystem Services
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Remote Sensing and Land Use
- Keywords
- rice cultivation, paddy rice, cropland distribution, agricultural census data, Landsat, crop rotation, multiple cropping, sown area, food production potential, China
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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