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Improving the Spatiotemporal Resolution of Soil Moisture through a Synergistic Combination of MODIS and LANDSAT8 Data

Author

Listed:
  • Sanaz Negahbani

    (University of Isfahan)

  • Mehdi Momeni

    (University of Isfahan)

  • Mina Moradizadeh

    (University of Isfahan)

Abstract

Accurate soil moisture (SM) data with continuous spatiotemporal distribution has greatly contributed to various analyses in the fields of agricultural dryness and irrigation, regional water cycle, soil erosion, and energy exchange. While, spatial and temporal resolutions are practically in conflict with each other, data fusion is considered to be efficient for accessing spatiotemporally high resolution data. In the present research to obtain daily surface SM at a spatial resolution of 100 m, an Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) was used by combining Landsat8 and Moderate resolution Imaging Spectroradiometer (MODIS) data. Furthermore, to improve the accuracy of SM retrieval, a novel scheme land surface temperature (LST)-vegetation index (VI) universal triangle was introduced to increase the LST retrieval accuracy using the TOPSIS method. This algorithm was also examined within two regions in Fars province in Iran. Simultaneously with the satellite passing through the study areas, SM of several points was measured by time-domain reflectometry (TDR). To evaluate the performance of the proposed method, the error metrics including the coefficient of determination (R2) and Root Mean Square Error (RMSE) were calculated between the in-situ SM measurements and those estimated. The resulted fusion SM was compared with the Landsat-derived and in-situ SM which reported lower (R2 = 0.73 and RMSE = 0.005cm3/cm3) and higher (R2 = 0.38 and RMSE = 0.048cm3/cm3) error values, respectively. The outcomes of the study indicated the high ability of the proposed fusion approach for achieving accurate and consistent SM monitoring by using the specified ESTARFM model, especially when the LST was obtained using the weighted average of several LST determination methods with TOPSIS method.

Suggested Citation

  • Sanaz Negahbani & Mehdi Momeni & Mina Moradizadeh, 2022. "Improving the Spatiotemporal Resolution of Soil Moisture through a Synergistic Combination of MODIS and LANDSAT8 Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(6), pages 1813-1832, April.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:6:d:10.1007_s11269-022-03108-1
    DOI: 10.1007/s11269-022-03108-1
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    References listed on IDEAS

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    1. Muhammad Awais & Wei Li & Sajjad Hussain & Muhammad Jehanzeb Masud Cheema & Weiguo Li & Rui Song & Chenchen Liu, 2022. "Comparative Evaluation of Land Surface Temperature Images from Unmanned Aerial Vehicle and Satellite Observation for Agricultural Areas Using In Situ Data," Agriculture, MDPI, vol. 12(2), pages 1-19, January.
    2. Prashant Srivastava & Dawei Han & Miguel Ramirez & Tanvir Islam, 2013. "Machine Learning Techniques for Downscaling SMOS Satellite Soil Moisture Using MODIS Land Surface Temperature for Hydrological Application," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(8), pages 3127-3144, June.
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