Water and Irrigation Management

Water and Irrigation Management

Estimation of Surface Soil Moisture Using the Thermal-Optical TRApezoid Model with Landsat-8 Data

Document Type : Research Paper

Authors
Atefeh Nouraki 1
Mona Golabi 1
Mohammad Albaji 1
Abd Ali Naseri 1
Saeid Homayouni 2
1 Department of Irrigation and Drainage, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
2 Centre Eau Terre Environnement, Institut National de la Recherche Scientifique (INRS), 490 Couronne St, Quebec, QC G1K 9A9, Canada.
10.22059/jwim.2024.376802.1163
Abstract
Soil moisture is a critical variable for land-atmosphere interactions. It measures drought conditions in agricultural areas and significantly impacts surface water and agricultural production. This study aims to evaluate the Thermal-Optical TRApezoid Model (TOTRAM) in estimating surface soil moisture at a farm scale using Landsat-8 imagery in the Hakim Farabi sugarcane agro-industrial company lands in Khuzestan, Iran. For this purpose, 16 Landsat-8 images were used during the sugarcane growing season in the agricultural year 2019-2020, and simultaneously, surface soil moisture was measured at 27 ground control points at a depth of 0-10 cm. Additionally, to investigate the potential of various vegetation indices in the TOTRAM model, NDVI, SAVI, and kNDVI were used in soil moisture modeling. Subsequently, the wet and dry edges were determined based on the distribution of pixels in the different LST-NDVI, LST-SAVI, and LST-kNDVI spaces. The distribution of pixels in various LST-VI spaces showed significant changes in land surface temperature from November 11, 2019, to October 28, 2020. These temperature changes led to significant variations in the distribution of pixels and the equations of the wet and dry edges over the studied period. The results also indicated a better correlation of soil moisture with TOTRAM-SAVI (0.56) compared to TOTRAM-kNDVI (0.46). Moreover, examining the soil moisture maps derived from the TOTRAM model showed that with increased plant growth, soil moisture increased, and soil moisture distribution heterogeneity decreased in the sugarcane fields. Overall, despite the need for local calibration, the TOTRAM model can estimate soil moisture with acceptable accuracy over large geographical areas.
Keywords
Optical and thermal remote sensing
Soil moisture
Sugarcane
Vegetation index
Subjects
  1. Alavi, M., Albaji, M., Golabi, M., Naseri, A. A., & Homayouni, S. (2024). Estimation of sugarcane evapotranspiration from remote sensing and limited meteorological variables using machine learning models. Journal of Hydrology, 629, 130605.
  2. Alavi, M., Albaji, M., Golabi, M., Naseri, A. A., & Homayouni, S. (2023). Evaluating remote sensing technique and machine learning algorithms in estimating sugarcane evapotranspiration. Water and Irrigation Management, 13(4), 965-982.‏ (In Persian)
  3. Babaeian, E., Sidike, P., Newcomb, M. S., Maimaitijiang, M., White, S. A., Demieville, J., Ward, R. W., Sadeghi, M., LeBauer, D. S., & Jones, S. B. (2019). A new optical remote sensing technique for high-resolution mapping of soil moisture. Frontiers in big Data, 2, 37.
  4. Baghdadi, N., El Hajj, M., Zribi, M., & Bousbih, S. (2017). Calibration of the water cloud model at C-band for winter crop fields and grasslands. Remote Sensing, 9(9), 969.
  5. Blake, G. (1965). Bulk density. Methods of Soil Analysis: Part 1 Physical and Mineralogical Properties, Including Statistics of Measurement and Sampling, 9, 374-390.
  6. Burdun, I., Bechtold, M., Aurela, M., De Lannoy, G., Desai, A. R., Humphreys, E., Kareksela, S., Komisarenko, V., Liimatainen, M., & Marttila, H. (2023). Hidden becomes clear: Optical remote sensing of vegetation reveals water table dynamics in northern peatlands. Remote sensing of environment, 296, 113736.
  7. Carlson, T. N., Gillies, R. R., & Perry, E. M. (1994). A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation cover. Remote sensing reviews, 9(1-2), 161-173.
  8. Day, P. R. (1965). Particle fractionation and particle‐size analysis. Methods of Soil Analysis: Part 1 Physical and Mineralogical Properties, Including Statistics of Measurement and Sampling, 9, 545-567.
  9. Goward, S. N., & Hope, A. (1989). Evapotranspiration from combined reflected solar and emitted terrestrial radiation: Preliminary FIFE results from AVHRR data. Advances in Space Research, 9(7), 239-249.
  10. Huete, A., Justice, C., & Liu, H. (1994). Development of vegetation and soil indices for MODIS-EOS. Remote sensing of environment, 49(3), 224-234.
  11. Jiang, L., & Islam, S. (1999). A methodology for estimation of surface evapotranspiration over large areas using remote sensing observations. Geophysical research letters, 26(17), 2773-2776.
  12. Jimenez-Munoz, J. C., Sobrino, J. A., Skoković, D., Mattar, C., & Cristobal, J. (2014). Land surface temperature retrieval methods from Landsat-8 thermal infrared sensor data. IEEE Geoscience and remote sensing letters, 11(10), 1840-1843.
  13. Krishnan, S., & Indu, J. (2023). Assessing the potential of temperature/vegetation index space to infer soil moisture over Ganga Basin. Journal of Hydrology, 621, 129611.
  14. Mohammadi Moalezade, J., Hamzeh, S., & Naseri, A. (2022). Estimating Soil Surface Moisture Content and Investigating Irrigation Schedule of Sugarcane Fields Using Thermal Trapezoidal Model. Iranian Journal of Soil and Water Research, 53(10), 2209-2223.(In Persian)
  15. Moran, M., Clarke, T., Inoue, Y., & Vidal, A. (1994). Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index. Remote sensing of environment, 49(3), 246-263.
  16. Nouraki, A., Golabi, M., Albaji, M., Naseri, A., & Homayouni, S. (2023). Spatial-temporal modeling of soil moisture using optical and thermal remote sensing data and machine learning algorithms. Iranian Journal of Soil and Water Research, 54(4), 637-653. (In Persian)
  17. Nouraki, A., Akhavan, S., Rezaei, Y., & Fuentes, S. (2021). Assessment of sunflower water stress using infrared thermometry and computer vision analysis. Water Supply, 21(3), 1228-1242.‏
  18. Quintana-Molina, J. R., Sánchez-Cohen, I., Jiménez-Jiménez, S. I., Marcial-Pablo, M. d. J., Trejo-Calzada, R., & Quintana-Molina, E. (2023). Calibration of volumetric soil moisture using Landsat-8 and Sentinel-2 satellite imagery by Google Earth Engine. Revista de Teledetección(62), 21-38.
  19. Rahimzadeh-Bajgiran, P., Berg, A. A., Champagne, C., & Omasa, K. (2013). Estimation of soil moisture using optical/thermal infrared remote sensing in the Canadian Prairies. ISPRS journal of photogrammetry and remote sensing, 83, 94-103.
  20. Sadeghi, M., Babaeian, E., Tuller, M., & Jones, S. B. (2017). The optical trapezoid model: A novel approach to remote sensing of soil moisture applied to Sentinel-2 and Landsat-8 observations. Remote sensing of environment, 198, 52-68.
  21. Sandholt, I., Rasmussen, K., & Andersen, J. (2002). A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote sensing of environment, 79(2-3), 213-224.
  22. Taddeo, S., Dronova, I., & Depsky, N. (2019). Spectral vegetation indices of wetland greenness: Responses to vegetation structure, composition, and spatial distribution. Remote sensing of environment, 234, 111467.
  23. Tuller, M., Babaeian, E., Jones, S., Montzka, C., Vereecken, H., & Sadeghi, M. (2019). The paramount societal impact of soil moisture. Eos, 100.
  24. Wang, W., Huang, D., Wang, X.-G., Liu, Y.-R., & Zhou, F. (2011). Estimation of soil moisture using trapezoidal relationship between remotely sensed land surface temperature and vegetation index. Hydrology and Earth System Sciences, 15(5), 1699-1712. 
Water and Irrigation Management
Volume 14, Issue 4
Winter 2025
Pages 827-844