GCI30: a global dataset of 30-m cropping intensity using multisource remote sensing imagery
- 1State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, PR China
- 2University of Chinese Academy of Sciences, Beijing 100049, PR China
- 3School of Geospatial Engineering and Science, Sun Yat-Sen University, Guangzhou, 510275, PR China
- 4Graduate School of Geography, Clark University, Worcester, MA 01610, USA
- 5Department of Earth and Environment, Boston University, Boston, MA 02215, USA
- 6Frederick S. Pardee Center for the Study of Longer-Range Future, Frederick S. Pardee School of Global Studies, Boston University, Boston, MA 02215, USA
- 7Division of Agriculture Applications, Soils, and Marine (AASMD), National Authority for Remote Sensing & Space Sciences (NARSS), Cairo, New Nozha, Alf Maskan,1564, Egypt
- 8Center for Agricultural and Sustainable Development Research (CIADS), Catholic University of Mozambique-Faculty of Agricultural Sciences, Cuamba 3305, Mozambique
- 9Tigray Agricultural Research Institute, P.O. Box 492, Mekelle 251, Ethiopia
- 10Department of Natural Resources, Faculty of African Postgraduate Studies, Cairo University, Giza 12613, Egypt
Abstract. The global distribution of cropping intensity (CI) is essential to our understanding of agricultural land use management on Earth. Optical remote sensing has revolutionized our ability to map CI over large areas in a repeated and cost-efficient manner. Previous studies have mainly focused on investigating the spatiotemporal patterns of CI ranging from regions to the entire globe with the use of coarse-resolution data, which are inadequate for characterizing farming practices within heterogeneous landscapes. To fill this knowledge gap, in this study, we utilized multiple satellite data to develop a global, spatially continuous CI map dataset at 30-m resolution (GCI30). Accuracy assessments indicated that GCI30 exhibited high agreement with visually interpreted validation samples and in situ observations from the PhenoCam network. We carried out both statistical and spatial comparisons of GCI30 with existing global CI estimates. Based on GCI30, we estimated that the global average annual CI during 2016–2018 was 1.05, which is close to the mean (1.04) and median (1.13) CI values of the existing six estimates, although the spatial resolution and temporal coverage vary significantly among products. A spatial comparison with two other satellite based land surface phenology products further suggested that GCI30 was not only capable of capturing the overall pattern of global CI but also provided many spatial details. GCI30 indicated that single cropping was the primary agricultural system on Earth, accounting for 81.57 % (12.28 million km2) of the world’s cropland extent. Multiple-cropping systems, on the other hand, were commonly observed in South America and Asia. We found large variations across countries and agroecological zones, reflecting the joint control of natural and anthropogenic drivers on regulating cropping practices. As the first global coverage, fine-resolution CI product, GCI30 can facilitate ongoing efforts to achieve sustainable development goals (SDGs) by improving food production while minimizing environmental impacts. The data are available on Harvard Dataverse: https://doi.org/10.7910/DVN/86M4PO (Zhang et al, 2020).
Miao Zhang et al.
Miao Zhang et al.
GCI30: Global Cropping Intensity at 30m resolution https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/86M4PO
Model code and software
GEE code of GCI30 core algorithm https://code.earthengine.google.com/f23108c6c47025c4acbd90b57c0753f7
Miao Zhang et al.
Viewed (geographical distribution)