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Global area boom for greenhouse cultivation revealed by satellite mapping

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.

References

  1. Mormile, P. et al. in Soil Degradable Bioplastics for a Sustainable Modern Agriculture (ed. Malinconico, M.) 1–21 (Springer, Berlin, Heidelberg, 2017).

  2. van Delden, S. H. et al. Current status and future challenges in implementing and upscaling vertical farming systems. Nat. Food 2, 944–956 (2021). 2021 2:12.

    Article  PubMed  Google Scholar 

  3. Sun, D. et al. An overview of the use of plastic-film mulching in China to increase crop yield and water-use efficiency. Natl Sci. Rev. 7, 1523–1526 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Liu, E. K., He, W. Q. & Yan, C. R. ‘White revolution’ to ‘white pollution’—agricultural plastic film mulch in China. Environ. Res. Lett. 9, 091001 (2014).

    Article  ADS  Google Scholar 

  5. MacLeod, M., Arp, H. P. H., Tekman, M. B. & Jahnke, A. The global threat from plastic pollution. Science 373, 61–65 (2021).

    Article  ADS  CAS  PubMed  Google Scholar 

  6. Zhang, D. et al. Plastic pollution in croplands threatens long-term food security. Global Change Biol. 26, 3356–3367 (2020).

    Article  ADS  Google Scholar 

  7. Fan, X., Chen, H., Xia, X. & Yu, Y. Increase in surface albedo caused by agricultural plastic film. Atmos. Sci. Lett. 16, 291–296 (2015).

    Article  ADS  Google Scholar 

  8. Campra, P., Garcia, M., Canton, Y. & Palacios-Orueta, A. Surface temperature cooling trends and negative radiative forcing due to land use change toward greenhouse farming in southeastern Spain. J. Geophys. Res. Atmos. 113, 18109 (2008).

    Article  ADS  Google Scholar 

  9. Hickman G. W. Greenhouse vegetable statistics. Cuesta Roble Consulting Press www.cuestaroble.com/statistics.html (2019).

  10. Marcelis, L. F. M. in Achieving Sustainable Greenhouse Cultivation (eds Heuvelink, E. & Marcelis, L. F. M.) 1–14 (Burleigh Dodds Science Publishing, 2019).

  11. Kozhikkodan Veettil, B. et al. Remote sensing of plastic-covered greenhouses and plastic-mulched farmlands: current trends and future perspectives. Land Degrad. Dev. 34, 591–609 (2023).

    Article  Google Scholar 

  12. Jiménez-Lao, R., Aguilar, F. J., Nemmaoui, A. & Aguilar, M. A. Remote sensing of agricultural greenhouses and plastic-mulched farmland: an analysis of worldwide research. Remote Sens. 12, 2649 (2020).

    Article  ADS  Google Scholar 

  13. Gao, C., Wu, Q., Dyck, M., Lv, J. & He, H. Greenhouse area detection in Guanzhong Plain, Shaanxi, China: spatio-temporal change and suitability classification. Int. J. Digit. Earth 15, 226–248 (2022).

    Article  ADS  Google Scholar 

  14. Ma, A., Chen, D., Zhong, Y., Zheng, Z. & Zhang, L. National-scale greenhouse mapping for high spatial resolution remote sensing imagery using a dense object dual-task deep learning framework: a case study of China. ISPRS J. Photogramm. Remote Sens. 181, 279–294 (2021).

    Article  ADS  Google Scholar 

  15. Ou, C. et al. Landsat-derived annual maps of agricultural greenhouse in Shandong Province, China from 1989 to 2018. Remote Sens. 13, 4830 (2021).

    Article  ADS  Google Scholar 

  16. Chen, Z. et al. A convolutional neural network for large-scale greenhouse extraction from satellite images considering spatial features. Remote Sens. 14, 4908 (2022).

    Article  ADS  Google Scholar 

  17. Zhang, P. et al. Pixel–scene–pixel–object sample transferring: a labor-free approach for high-resolution plastic greenhouse mapping. IEEE Trans. Geosci. Remote Sens. 61, 1–17 (2023).

    Google Scholar 

  18. Senel, G., Aguilar, M. A., Aguilar, F. J., Nemmaoui, A. & Goksel, C. A comprehensive benchmarking of the available spectral indices based on Sentinel-2 for large-scale mapping of plastic-covered greenhouses. IEEE J. Select. Top. Appl. Earth Obs. Remote Sens. 16, 6601–6613 (2023).

    Article  ADS  Google Scholar 

  19. Aguilar, M. A., Vallario, A., Aguilar, F. J., Lorca, A. G. & Parente, C. Object-based greenhouse horticultural crop identification from multi-temporal satellite imagery: a case study in Almeria, Spain. Remote Sens. 7, 7378–7401 (2015).

    Article  ADS  Google Scholar 

  20. Novelli, A., Aguilar, M. A., Nemmaoui, A., Aguilar, F. J. & Tarantino, E. Performance evaluation of object based greenhouse detection from Sentinel-2 MSI and Landsat 8 OLI data: a case study from Almería (Spain). Int. J. Appl. Earth Obs. Geoinf. 52, 403–411 (2016).

    ADS  Google Scholar 

  21. Nemmaoui, A., Aguilar, M. A., Aguilar, F. J., Novelli, A. & Lorca, A. G. Greenhouse crop identification from multi-temporal multi-sensor satellite imagery using object-based approach: a case study from Almería (Spain). Remote Sens. 10, 1751 (2018).

    Article  ADS  Google Scholar 

  22. Foley, J. A. et al. Global consequences of land use. Science 309, 570–574 (2005).

    Article  ADS  CAS  PubMed  Google Scholar 

  23. Chang, J. et al. Does growing vegetables in plastic greenhouses enhance regional ecosystem services beyond the food supply? Front. Ecol. Environ. 11, 43–49 (2013).

    Article  Google Scholar 

  24. Fischer, J. et al. Should agricultural policies encourage land sparing or wildlife-friendly farming? Front. Ecol. Environ. 6, 380–385 (2008).

    Article  Google Scholar 

  25. Huang, Y., Liu, Q., Jia, W., Yan, C. & Wang, J. Agricultural plastic mulching as a source of microplastics in the terrestrial environment. Environ. Pollut. 260, 114096 (2020).

    Article  CAS  PubMed  Google Scholar 

  26. Dahl, M. et al. A temporal record of microplastic pollution in Mediterranean seagrass soils. Environ. Pollut. 273, 116451 (2021).

    Article  CAS  PubMed  Google Scholar 

  27. Ntinas, G. K., Neumair, M., Tsadilas, C. D. & Meyer, J. Carbon footprint and cumulative energy demand of greenhouse and open-field tomato cultivation systems under Southern and Central European climatic conditions. J. Clean. Prod. 142, 3617–3626 (2017).

    Article  CAS  Google Scholar 

  28. Paeth, H., Born, K., Girmes, R., Podzun, R. & Jacob, D. Regional climate change in tropical and Northern Africa due to greenhouse forcing and land use changes. J. Clim. 22, 114–132 (2009).

    Article  ADS  Google Scholar 

  29. Zhang, J., Zhang, K., Liu, J. & Ban-Weiss, G. Revisiting the climate impacts of cool roofs around the globe using an Earth system model. Environ. Res. Lett. 11, 084014 (2016).

    Article  ADS  Google Scholar 

  30. Assessment of Agricultural Plastics and Their Sustainability—A Call for Action (FAO, 2021).

  31. Kenya’s $800 million flower market is seeing a boost, thanks to China. CNN https://edition.cnn.com/2018/10/08/africa/kenya-china-flower-market/index.html (2018).

  32. Nemali, K. History of controlled environment horticulture: greenhouses. HortScience 57, 239–246 (2022).

    Article  Google Scholar 

  33. Zhou, L. & Xiong, L. Y. Natural topographic controls on the spatial distribution of poverty-stricken counties in China. Appl. Geogr. 90, 282–292 (2018).

    Article  Google Scholar 

  34. Zhang, P. et al. A novel index for robust and large-scale mapping of plastic greenhouse from Sentinel-2 images. Remote Sens. Environ. 276, 113042 (2022).

    Article  Google Scholar 

  35. Orzolek, M. A Guide to the Manufacture, Performance, and Potential of Plastics in Agriculture (Elsevier, 2017).

  36. Gertel, J. & Sippel, S. R. Seasonal Workers in Mediterranean Agriculture: The Social Costs of Eating Fresh (Routledge, 2014).

  37. Li, H., Gan, Y., Wu, Y. & Guo, L. EAGNet: a method for automatic extraction of agricultural greenhouses from high spatial resolution remote sensing images based on hybrid multi-attention. Comput. Electron. Agric. 202, 107431 (2022).

    Article  Google Scholar 

  38. La Cecilia, D., Tom, M., Stamm, C. & Odermatt, D. Pixel-based mapping of open field and protected agriculture using constrained Sentinel-2 data. ISPRS Open J. Photogramm. Remote Sens. 8, 100033 (2023).

    Article  Google Scholar 

  39. Benabderrazik, K., Kopainsky, B., Tazi, L., Jörin, J. & Six, J. Agricultural intensification can no longer ignore water conservation—a systemic modelling approach to the case of tomato producers in Morocco. Agric. Water Manage. 256, 107082 (2021).

    Article  Google Scholar 

  40. Zhang, Y. et al. Oral intake exposure to phthalates in vegetables produced in plastic greenhouses and its health burden in Shaanxi Province, China. Sci. Total Environ. 696, 133921 (2019).

    Article  ADS  CAS  PubMed  Google Scholar 

  41. He, L., Li, Z., Jia, Q. & Xu, Z. Soil microplastics pollution in agriculture. Science 379, 547 (2023).

    Article  ADS  CAS  PubMed  Google Scholar 

  42. Kianpoor Kalkhajeh, Y. et al. Environmental soil quality and vegetable safety under current greenhouse vegetable production management in China. Agricult. Ecosyst. Environ. 307, 107230 (2021).

    Article  CAS  Google Scholar 

  43. Wang, H., Zheng, J., Fan, J., Zhang, F. & Huang, C. Grain yield and greenhouse gas emissions from maize and wheat fields under plastic film and straw mulching: a meta-analysis. Field Crops Res. 270, 108210 (2021).

    Article  Google Scholar 

  44. Hu, Y., Zheng, J., Kong, X., Sun, J. & Li, Y. Carbon footprint and economic efficiency of urban agriculture in Beijing—a comparative case study of conventional and home-delivery agriculture. J. Clean. Prod. 234, 615–625 (2019).

    Article  Google Scholar 

  45. Four Decades of Poverty Reduction in China: Drivers, Insights for the World, and the Way Ahead (World Bank, 2022).

  46. Zhang, X. et al. A large but transient carbon sink from urbanization and rural depopulation in China. Nat. Sustain. 5, 321–328 (2022).

    Article  Google Scholar 

  47. Ge, Y. et al. Mapping annual land use changes in China’s poverty-stricken areas from 2013 to 2018. Remote Sens. Environ. 232, 111285 (2019).

    Article  Google Scholar 

  48. Boulestreau, Y., Peyras, C.-L., Casagrande, M. & Navarrete, M. Tracking down coupled innovations supporting agroecological vegetable crop protection to foster sustainability transition of agrifood systems. Agric. Syst. 196, 103354 (2022).

    Article  Google Scholar 

  49. Wanner, P. Plastic in agricultural soils—a global risk for groundwater systems and drinking water supplies?—a review. Chemosphere 264, 128453 (2021).

    Article  CAS  PubMed  Google Scholar 

  50. Aguilar, M. Á. et al. Evaluation of the consistency of simultaneously acquired Sentinel-2 and Landsat 8 imagery on plastic covered greenhouses. Remote Sens. 12, 2015 (2020).

    Article  ADS  Google Scholar 

  51. Aguilar, M. A., Nemmaoui, A., Aguilar, F. J. & Qin, R. Quality assessment of digital surface models extracted from WorldView-2 and WorldView-3 stereo pairs over different land covers. GISci. Remote Sens. 56, 109–129 (2019).

    Article  Google Scholar 

  52. Mendoza-Fernández, A. J., Peña-Fernández, A., Molina, L. & Aguilera, P. A. The role of technology in greenhouse agriculture: towards a sustainable intensification in Campo de Dalías (Almería, Spain). Agronomy 11, 101 (2021).

    Article  Google Scholar 

  53. Reiner, F. et al. More than one quarter of Africa’s tree cover is found outside areas previously classified as forest. Nat. Commun. 14, 2258 (2023).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  54. Acharki, S. PlanetScope contributions compared to Sentinel-2, and Landsat-8 for LULC mapping. Remote Sens. Appl. Society Environ. 27, 100774 (2022).

    Google Scholar 

  55. Acharki, S. & Kozhikkodan Veettil, B. Mapping plastic-covered greenhouse farming areas using high-resolution PlanetScope and RapidEye imagery: studies from Loukkos perimeter (Morocco) and Dalat City (Vietnam). Environ. Sci. Pollut. Res. 30, 23012–23022 (2023).

    Article  Google Scholar 

  56. Huete, A. R. et al. Amazon rainforests green-up with sunlight in dry season. Geophys. Res. Lett. 33, 6405 (2006).

    Article  ADS  Google Scholar 

  57. Tatem, A. J. WorldPop, open data for spatial demography. Sci. Data 4, 170004 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Pekel, J.-F. F., Cottam, A., Gorelick, N. & Belward, A. S. High-resolution mapping of global surface water and its long-term changes. Nature 540, 418–422 (2016).

    Article  ADS  CAS  PubMed  Google Scholar 

  59. Esch, T. et al. Breaking new ground in mapping human settlements from space—The Global Urban Footprint. ISPRS J. Photogramm. Remote Sens. 134, 30–42 (2017).

    Article  ADS  Google Scholar 

  60. Sumbul, G., Charfuelan, M., Demir, B. & Markl, V. in Bigearthnet: A Large-Scale Benchmark Archive for Remote Sensing Image Understanding 5901–5904 (IEEE, 2019).

  61. Tan, M. & Le, Q. Efficientnet: rethinking model scaling for convolutional neural networks. In International Conference On Machine Learning 6105–6114 (PMLR, 2019).

  62. Global Administrative Unit Layers (GAUL). FAO https://data.apps.fao.org/map/catalog/srv/eng/catalog.search?id=12691#/metadata/9c35ba10-5649-41c8-bdfc-eb78e9e65654 (2015).

  63. Thenkabail, P. S. et al. Global Cropland-Extent Product at 30-m Resolution (GCEP30) derived from landsat satellite time-series data for the year 2015 using multiple machine-learning algorithms on Google Earth Engine Cloud. United States Geological Survey https://doi.org/10.3133/pp1868 (2021).

  64. Zhou, Y., Liu, Z., Wang, H. & Cheng, G. Targeted poverty alleviation narrowed China’s urban–rural income gap: a theoretical and empirical analysis. Appl. Geogr. 157, 103000 (2023).

    Article  Google Scholar 

  65. Zomer, R. J., Xu, J. & Trabucco, A. Version 3 of the Global Aridity Index and Potential Evapotranspiration Database. Sci. Data 9, 1–15 (2022).

    Article  Google Scholar 

  66. NOAA National Centers for Environmental Information. ETOPO 2022 15 Arc-Second Global Relief Model (NOAA National Centers for Environmental Information, 2022).

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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.

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Authors

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.

Corresponding authors

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 5, 513–523 (2024). https://doi.org/10.1038/s43016-024-00985-0

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