IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0219639.html
   My bibliography  Save this article

Downscaling satellite soil moisture using geomorphometry and machine learning

Author

Listed:
  • Mario Guevara
  • Rodrigo Vargas

Abstract

Annual soil moisture estimates are useful to characterize trends in the climate system, in the capacity of soils to retain water and for predicting land and atmosphere interactions. The main source of soil moisture spatial information across large areas (e.g., continents) is satellite-based microwave remote sensing. However, satellite soil moisture datasets have coarse spatial resolution (e.g., 25–50 km grids); and large areas from regional-to-global scales have spatial information gaps. We provide an alternative approach to predict soil moisture spatial patterns (and associated uncertainty) with higher spatial resolution across areas where no information is otherwise available. This approach relies on geomorphometry derived terrain parameters and machine learning models to improve the statistical accuracy and the spatial resolution (from 27km to 1km grids) of satellite soil moisture information across the conterminous United States on an annual basis (1991–2016). We derived 15 primary and secondary terrain parameters from a digital elevation model. We trained a machine learning algorithm (i.e., kernel weighted nearest neighbors) for each year. Terrain parameters were used as predictors and annual satellite soil moisture estimates were used to train the models. The explained variance for all models-years was >70% (10-fold cross-validation). The 1km soil moisture grids (compared to the original satellite soil moisture estimates) had higher correlations (improving from r2 = 0.1 to r2 = 0.46) and lower bias (improving from 0.062 to 0.057 m3/m3) with field soil moisture observations from the North American Soil Moisture Database (n = 668 locations with available data between 1991–2013; 0-5cm depth). We conclude that the fusion of geomorphometry methods and satellite soil moisture estimates is useful to increase the spatial resolution and accuracy of satellite-derived soil moisture. This approach can be applied to other satellite-derived soil moisture estimates and regions across the world.

Suggested Citation

  • Mario Guevara & Rodrigo Vargas, 2019. "Downscaling satellite soil moisture using geomorphometry and machine learning," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-20, September.
    • Handle: RePEc:plo:pone00:0219639
    DOI: 10.1371/journal.pone.0219639
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0219639
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0219639&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0219639?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Aiguo Dai, 2011. "Drought under global warming: a review," Wiley Interdisciplinary Reviews: Climate Change, John Wiley & Sons, vol. 2(1), pages 45-65, January.
    2. Borra, Simone & Di Ciaccio, Agostino, 2010. "Measuring the prediction error. A comparison of cross-validation, bootstrap and covariance penalty methods," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 2976-2989, December.
    3. Brian Walsh & Philippe Ciais & Ivan A. Janssens & Josep Peñuelas & Keywan Riahi & Felicjan Rydzak & Detlef P. van Vuuren & Michael Obersteiner, 2017. "Pathways for balancing CO2 emissions and sinks," Nature Communications, Nature, vol. 8(1), pages 1-12, April.
    4. Markus Reichstein & Gustau Camps-Valls & Bjorn Stevens & Martin Jung & Joachim Denzler & Nuno Carvalhais & Prabhat, 2019. "Deep learning and process understanding for data-driven Earth system science," Nature, Nature, vol. 566(7743), pages 195-204, February.
    5. L. Samaniego & S. Thober & R. Kumar & N. Wanders & O. Rakovec & M. Pan & M. Zink & J. Sheffield & E. F. Wood & A. Marx, 2018. "Anthropogenic warming exacerbates European soil moisture droughts," Nature Climate Change, Nature, vol. 8(5), pages 421-426, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Ziyue & Zhang, Zhao & Zhang, Lingyan, 2021. "Improving regional wheat drought risk assessment for insurance application by integrating scenario-driven crop model, machine learning, and satellite data," Agricultural Systems, Elsevier, vol. 191(C).
    2. Renee Obringer & Rohini Kumar & Roshanak Nateghi, 2020. "Managing the water–electricity demand nexus in a warming climate," Climatic Change, Springer, vol. 159(2), pages 233-252, March.
    3. Licheng Liu & Wang Zhou & Kaiyu Guan & Bin Peng & Shaoming Xu & Jinyun Tang & Qing Zhu & Jessica Till & Xiaowei Jia & Chongya Jiang & Sheng Wang & Ziqi Qin & Hui Kong & Robert Grant & Symon Mezbahuddi, 2024. "Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    4. Ashenafi Yimam Kassaye & Guangcheng Shao & Xiaojun Wang & Shiqing Wu, 2021. "Quantification of drought severity change in Ethiopia during 1952–2017," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(4), pages 5096-5121, April.
    5. Rozenstein, Offer & Fine, Lior & Malachy, Nitzan & Richard, Antoine & Pradalier, Cedric & Tanny, Josef, 2023. "Data-driven estimation of actual evapotranspiration to support irrigation management: Testing two novel methods based on an unoccupied aerial vehicle and an artificial neural network," Agricultural Water Management, Elsevier, vol. 283(C).
    6. Luong, Tuan Anh & Nguyen, Manh-Hung & Truong, N.T. Khuong & Le, Kien, 2023. "Rainfall variability and internal migration: The importance of agriculture linkage and gender inequality," Economic Analysis and Policy, Elsevier, vol. 77(C), pages 326-336.
    7. Jiang, Hou & Lu, Ning & Huang, Guanghui & Yao, Ling & Qin, Jun & Liu, Hengzi, 2020. "Spatial scale effects on retrieval accuracy of surface solar radiation using satellite data," Applied Energy, Elsevier, vol. 270(C).
    8. Mauricio Marrone & Martina K Linnenluecke, 2020. "Interdisciplinary Research Maps: A new technique for visualizing research topics," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-16, November.
    9. Xiaoliang Shi & Fei Chen & Hao Ding & Mengqi Shi & Yi Li, 2022. "Assessing Vegetation Ecosystem Resistance to Drought in the Middle Reaches of the Yellow River Basin, China," IJERPH, MDPI, vol. 19(7), pages 1-16, March.
    10. Getachew Tegegne & Assefa M. Melesse, 2020. "Multimodel Ensemble Projection of Hydro-climatic Extremes for Climate Change Impact Assessment on Water Resources," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(9), pages 3019-3035, July.
    11. Wen Zhang & Jing Li & Yunhao Chen & Yang Li, 2019. "A Surrogate-Based Optimization Design and Uncertainty Analysis for Urban Flood Mitigation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(12), pages 4201-4214, September.
    12. Wang, Fei & Lai, Hexin & Li, Yanbin & Feng, Kai & Zhang, Zezhong & Tian, Qingqing & Zhu, Xiaomeng & Yang, Haibo, 2022. "Dynamic variation of meteorological drought and its relationships with agricultural drought across China," Agricultural Water Management, Elsevier, vol. 261(C).
    13. Shahzada Adnan & Kalim Ullah, 2020. "Development of drought hazard index for vulnerability assessment in Pakistan," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 103(3), pages 2989-3010, September.
    14. Pere Quintana-Seguí & Anaïs Barella-Ortiz & Sabela Regueiro-Sanfiz & Gonzalo Miguez-Macho, 2020. "The Utility of Land-Surface Model Simulations to Provide Drought Information in a Water Management Context Using Global and Local Forcing Datasets," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(7), pages 2135-2156, May.
    15. Araneda-Cabrera, Ronnie J. & Bermúdez, María & Puertas, Jerónimo, 2021. "Assessment of the performance of drought indices for explaining crop yield variability at the national scale: Methodological framework and application to Mozambique," Agricultural Water Management, Elsevier, vol. 246(C).
    16. Corwin, D.L. & Scudiero, E. & Zaccaria, D., 2022. "Modified ECa – ECe protocols for mapping soil salinity under micro-irrigation," Agricultural Water Management, Elsevier, vol. 269(C).
    17. Moritz von Cossel & Andrea Bauerle & Meike Boob & Ulrich Thumm & Martin Elsaesser & Iris Lewandowski, 2019. "The Performance of Mesotrophic Arrhenatheretum Grassland under Different Cutting Frequency Regimes for Biomass Production in Southwest Germany," Agriculture, MDPI, vol. 9(9), pages 1-17, September.
    18. D. Chiru Naik & Sagar Rohidas Chavan & P. Sonali, 2023. "Incorporating the climate oscillations in the computation of meteorological drought over India," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 117(3), pages 2617-2646, July.
    19. Abbasabadi, Narjes & Ashayeri, Mehdi & Azari, Rahman & Stephens, Brent & Heidarinejad, Mohammad, 2019. "An integrated data-driven framework for urban energy use modeling (UEUM)," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    20. Newton Muhury & Armando A. Apan & Tek N. Marasani & Gebiaw T. Ayele, 2022. "Modelling Floodplain Vegetation Response to Groundwater Variability Using the ArcSWAT Hydrological Model, MODIS NDVI Data, and Machine Learning," Land, MDPI, vol. 11(12), pages 1-23, November.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0219639. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.