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Global area boom for greenhouse cultivation revealed by satellite mapping

Nature Food (2024)Cite this article

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Abstract

Greenhouse cultivation has been expanding rapidly in recent years, yet little knowledge exists on its global extent and expansion. Using commercial and freely available satellite data combined with artificial intelligence techniques, we present a global assessment of greenhouse cultivation coverage and map 1.3 million hectares of greenhouse infrastructures in 2019, a much larger extent than previously estimated. Our analysis includes both large (61%) and small-scale (39%) greenhouse infrastructures. Examining the temporal development of the 65 largest clusters (>1,500 ha), we show a recent upsurge in greenhouse cultivation in the Global South since the 2000s, including a dramatic increase in China, accounting for 60% of the global coverage. We emphasize the potential of greenhouse infrastructures to enhance food security but raise awareness of the uncertain environmental and social implications that may arise from this expansion. We further highlight the gap in spatio-temporal datasets for supporting future research agendas on this critical topic.

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Fig. 1: A global inventory of greenhouse cultivation.
Fig. 2: Spatial characteristics of greenhouse cultivation in China.
Fig. 3: Dynamics of areal expansion in greenhouse cultivation for the Global North and South.

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Data availability

PlanetScope imagery was partly purchased via a departmental licence, and the images cannot be distributed. Planetscope imagery in tropical areas via Norway’s International Climate and Forest Initiative (NICFI) satellite data Level 2 programme is available for non-commercial purposes from Planet Labs at https://www.planet.com/nicfi/. The global 3-m greenhouse cultivation product can be viewed at https://rs-cph.projects.earthengine.app/view/greenhouse. The product can be downloaded at https://zenodo.org/records/10907151. Any usage must be solely for Noncommercial education or scientific research purposes, and publication in academic or scientific research journals. Licensee agrees that all such publications must include an attribution that clearly and conspicuously identifies ‘Planet Labs PBC’. The Global Aridity Index database is available at https://doi.org/10.6084/m9.figshare.7504448.v5. The ETOPO Global Relief data are available at https://www.ncei.noaa.gov/products/etopo-global-relief-model. The GFSAD30 Cropland Extent data are available at https://lpdaac.usgs.gov/news/release-of-gfsad-30-meter-cropland-extent-products/. The GHS Urban Centre database 2015 is available at https://data.jrc.ec.europa.eu/dataset/53473144-b88c-44bc-b4a3-4583ed1f547e. Source data are provided with this paper.

Code availability

The code for image segmentation based on U-Net is publicly available at https://doi.org/10.5281/zenodo.3978185. The code for preparation of imagery from PlanetScope raw scenes is available at https://doi.org/10.5281/zenodo.7764360. The code for image classification is built on publicly open-source framework and custom code is available at https://github.com/sizhuoli/greenhouse_classification. The Python libraries used in the image preparation and prediction pipelines are publicly available and include GDAL v3.1.2, rasterio v1.2, tensorflow v2.5, geopandas v0.9 and the Planet python API v1.4.7.

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Acknowledgements

X.T. and M.B. acknowledge funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 947757 TOFDRY). M.B. also acknowledges the funding from the DFF Sapere Aude grant (no. 9064–00049B). X.T. and R.F. acknowledge funding from Villum Fonden through the project Deep Learning and Remote Sensing for Unlocking Global Ecosystem Resource Dynamics (DeReEco, grant no. 34306). F.T. acknowledges funding from the Nation Natural Science Foundation of China (grant no. 42001299) and the Seed Fund Program for Sino-Foreign Joint Scientific Research Platform of Wuhan University (no. WHUZZJJ202205). Additionally, we acknowledge the exceptional remote sensing efforts being conducted in Almeria, Spain and China. We thank Norway’s International Climate and Forest Initiative (NICFI) satellite data Level 2 programme for providing parts of the commercial satellite imagery for the study. We also thank all the developers of global datasets and deep-learning pipelines.

Author information

Authors and Affiliations

  1. Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark

    Xiaoye Tong, Xiaoxin Zhang, Rasmus Fensholt, Sizhuo Li, Marianne Nylandsted Larsen, Florian Reiner & Martin Brandt

  2. Department of Human Geography, Lund University, Lund, Sweden

    Peter Rosendal Dau Jensen

  3. Université Paris-Saclay, Gif-sur-Yvette, France

    Sizhuo Li

  4. Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China

    Feng Tian

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Contributions

X.T., X.Z., R.F., P.R.D.J., M.N.L., F.T. and M.B. designed the study. F.R. developed the code for the PlanetScope imagery generation pipeline. P.R.D.J. prepared the trade and production data, processed and analysed by X.T. S.L. wrote the codes for the deep-learning classification framework. Interpretations were done by X.T. and X.Z. X.T. and X.Z. conducted the analyses. X.T. wrote the original draft with contributions from all authors. X.T., X.Z. and S.L. designed the figures.

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Correspondence to Xiaoye Tong, Rasmus Fensholt or Martin Brandt.

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Nature Food thanks Fernando Aguilar, Jay Ram Lamichhane, Thomas Wanger and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Schematic workflow of global input data preparation, the deep learning models and subsequent analysis.

Human settlements are defined by the global urban footprint. Google Earth (Imagery © 2024 CNES / Airbus, Landsat / Copernicus, Maxar Technologies, Map data ©2024).

Extended Data Fig. 2 Example of the largest major greenhouse cultivation cluster (Weifang, China).

The cluster is defined by 1 km grids of significant (p-value < 0.05) greenhouse cultivation percentage cover. A zoom-in (orange square) on a 8x8 km grid structure, showing the spatial details of the 3 m predictions.

Extended Data Fig. 3 Examples showing common types of greenhouses.

a, Heilongjiang, northern China; b, Shandong, eastern China; c, Hainan, southern China; d, Xianyang, central China; e, Odense, Denmark; f, Almeria, Spain. The first row shows field photos, the second to fourth rows show true color composites of PlanetScope (Image © 2019 Planet Labs PBC), Sentinel-2 (Copernicus Sentinel data) and Landsat images (Landsat image courtesy of the U.S. Geological Survey) from 2019. The example in c illustrates the seasonal mulching activities for low tunnel that was excluded from PlanetScope imagery and therefore not mapped in our study.

Extended Data Fig. 4

Incidence rate of greenhouse cultivation in cropland areas at county level in China.

Extended Data Fig. 5 Examples showing the determination of starting year using Google Earth Timelapse.

a, True color composites of Landsat images from 1988 to 1999; the year 1990 (in red color) was the starting year for the significant cluster of Weifang, China (36.7123N, 118.747E). b, The year of 2006 was the starting year for the significant cluster of Lake Chapala, Mexico (19.885N, -102.207E). The criterion is to identify the first occurrence of visible patterns of semi-transparent whitish features. Google Earth Timelapse (Google, Landsat, Copernicus, https://earthengine.google.com/timelapse/).

Extended Data Fig. 6 FAOSTAT statistics on trade and production for tomatoes, including fresh or chilled tomatoes (HS1996 code 70200).

a, Top importers of tomato from exporters (E) of Spain, Mexico, China, Turkey and Italy to its top importers (I). The percentage of export weight to each of the total vegetable production weight is shown on the secondary y-axis as a dashed line. b, Domestic production of four types of vegetables (GP: green pepper, CG: cucumber, EP: eggplant, TM: tomato) in China.

Extended Data Fig. 7 Full collection of trajectories of areal expansion during 1985–2021 for the largest clusters of the top five greenhouse cultivation countries.

From top to bottom rows are Weifang, China; Almería, Spain; Bari, Italy; Antalya, Turkey; and Chapala, Mexico.

Extended Data Fig. 8 Sentinel-2 derived advanced plastic greenhouse index (APGI) along with Google Earth and PlanetScope images for three locations.

According to the literature18, optimal APGI is 0.63–0.66 for Almería, Spain and is 0.3 for Weifang, China. a, where the index can misinterpret a hilly terrain as greenhouses in Almería, Spain; b, where greenhouses can be mapped using APGI, in Almería, Spain; c, where greenhouses can be mapped using a lower APGI in Weifang, China. The last two columns are the Sentinel-2 APGI predictions using optimal thresholds and the predictions of current study. The second and third columns consists of images from Google Earth (Imagery © 2024 CNES / Airbus, Landsat / Copernicus, Maxar Technologies, Map data ©2024) and PlanetScope (Image © 2019 Planet Labs PBC), respectively.

Extended Data Fig. 9 Difference in the predicted areal extent of greenhouse cultivation using PlanetScope and Landsat images, caused primarily by the difference in spatial resolution, along with Google Earth.

a, Weifang, China. b, Almería, Spain. The second column consists of images from Google Earth (Imagery © 2024 CNES / Airbus, Landsat / Copernicus, Maxar Technologies, Map data ©2024).

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Tong, X., Zhang, X., Fensholt, R. et al. Global area boom for greenhouse cultivation revealed by satellite mapping. Nat Food (2024). https://doi.org/10.1038/s43016-024-00985-0

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