IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v193y2017icp55-70.html
   My bibliography  Save this article

Evaluation of SWAT simulated soil moisture at catchment scale by field measurements and Landsat derived indices

Author

Listed:
  • Uniyal, Bhumika
  • Dietrich, Jörg
  • Vasilakos, Christos
  • Tzoraki, Ourania

Abstract

The quantification of soil moisture under different soils and crops at regional scale is a challenging task. Hence, such studies are limited by the availability of ground based measurements. The current study evaluates the spatial and temporal patterns of daily soil moisture simulated by the Soil and Water Assessment Tool (SWAT) for the upper 30cm of the soil profile with indirect soil moisture estimates from Landsat for 2016. The Thermal Vegetation Difference Index (TVDI), was calculated based on the Normalized Difference Vegetation Index (NDVI) and the brightness temperature (BT) using Landsat images, from which regression models were trained by using field measurements from Time Domain Reflectometer (TDR) to calculate soil moisture. Two agricultural catchments namely, Gerdau and Wipperau in Germany were satisfactorily calibrated using SWAT for daily streamflow (1975–2000) with NSE (Nash-Sutcliffe-Efficiency) >0.55 and PBIAS (Percent bias) <5.5%. The parameter uncertainty assessment during the irrigation season (Mar–Sept, 2016) for soil moisture revealed that the uncertainty band is narrow (p-factor=0.57–0.83; r-factor=0.52−1.3). Spatial and temporal patterns of soil moisture from Landsat and SWAT were evaluated by using boxplots and absolute soil moisture difference maps. Results revealed that the overall spatial and temporal patterns of boxplots matched better for the dry period (correlation, r≥0.90) compared to the wet period (r≥0.57). The mean absolute difference between soil moisture from Landsat and SWAT ranged between 0.9–10% for most soils. In addition to it, the soil map was refined to match soil moisture patterns shown in Landsat images for one sandy soil, which further improved the mean absolute difference (1.06–6%). The current study provides an approach to use remotely sensed soil moisture for verifying hydrological modeling results and for optimizing the parameterization of soils, which may bridge the gap between global, regional and field studies in agricultural water management.

Suggested Citation

  • Uniyal, Bhumika & Dietrich, Jörg & Vasilakos, Christos & Tzoraki, Ourania, 2017. "Evaluation of SWAT simulated soil moisture at catchment scale by field measurements and Landsat derived indices," Agricultural Water Management, Elsevier, vol. 193(C), pages 55-70.
    • Handle: RePEc:eee:agiwat:v:193:y:2017:i:c:p:55-70
    DOI: 10.1016/j.agwat.2017.08.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377417302585
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agwat.2017.08.002?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Sha Li & Wei Liang & Weibin Zhang & Qinghua Liu, 2016. "Response of Soil Moisture to Hydro-meteorological Variables Under Different Precipitation Gradients in the Yellow River Basin," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(6), pages 1867-1884, April.
    2. N. Maier & J. Dietrich, 2016. "Using SWAT for Strategic Planning of Basin Scale Irrigation Control Policies: a Case Study from a Humid Region in Northern Germany," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(9), pages 3285-3298, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Uniyal, Bhumika & Dietrich, Jörg, 2019. "Modifying Automatic Irrigation in SWAT for Plant Water Stress scheduling," Agricultural Water Management, Elsevier, vol. 223(C), pages 1-1.
    2. Mohammad Zare & Shahid Azam & David Sauchyn, 2023. "Simulation of Climate Change Impacts on Crop Yield in the Saskatchewan Grain Belt Using an Improved SWAT Model," Agriculture, MDPI, vol. 13(11), pages 1-21, November.
    3. 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.
    4. Du, Ruiqi & Xiang, Youzhen & Zhang, Fucang & Chen, Junying & Shi, Hongzhao & Liu, Hao & Yang, Xiaofei & Yang, Ning & Yang, Xizhen & Wang, Tianyang & Wu, Yuxiao, 2024. "Combing transfer learning with the OPtical TRApezoid Model (OPTRAM) to diagnosis small-scale field soil moisture from hyperspectral data," Agricultural Water Management, Elsevier, vol. 298(C).

    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. Junyu Qi & Sheng Li & Qiang Li & Zisheng Xing & Charles P.-A. Bourque & Fan-Rui Meng, 2016. "Assessing an Enhanced Version of SWAT on Water Quantity and Quality Simulation in Regions with Seasonal Snow Cover," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(14), pages 5021-5037, November.
    2. Junyu Qi & Sheng Li & Qi Yang & Zisheng Xing & Fan-Rui Meng, 2017. "SWAT Setup with Long-Term Detailed Landuse and Management Records and Modification for a Micro-Watershed Influenced by Freeze-Thaw Cycles," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(12), pages 3953-3974, September.
    3. McNamara, Ian & Flörke, Martina & Uschan, Thorben & Baez-Villanueva, Oscar M. & Herrmann, Frank, 2024. "Estimates of irrigation requirements throughout Germany under varying climatic conditions," Agricultural Water Management, Elsevier, vol. 291(C).
    4. Qiang Feng & Siyan Dong & Baoling Duan, 2021. "The Effects of Land-Use Change/Conversion on Trade-Offs of Ecosystem Services in Three Precipitation Zones," Sustainability, MDPI, vol. 13(23), pages 1-16, December.
    5. Umutoni, Lisa & Samadi, Vidya, 2024. "Application of machine learning approaches in supporting irrigation decision making: A review," Agricultural Water Management, Elsevier, vol. 294(C).
    6. Prashant K. Srivastava, 2017. "Satellite Soil Moisture: Review of Theory and Applications in Water Resources," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(10), pages 3161-3176, August.
    7. J. Yazdi & A . Moridi, 2017. "Interactive Reservoir-Watershed Modeling Framework for Integrated Water Quality Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(7), pages 2105-2125, May.

    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:eee:agiwat:v:193:y:2017:i:c:p:55-70. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/agwat .

    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.