Estimation of surface soil moisture using the Thermal-Optical TRApezoid Model with Landsat-8 data.

Nouraki, Atefeh; Golabi, Mona; Albaji, Mohammad; Naseri, Abd Ali et Homayouni, Saeid ORCID: https://orcid.org/0000-0002-0214-5356 (2025). Estimation of surface soil moisture using the Thermal-Optical TRApezoid Model with Landsat-8 data. Journal of Water and Irrigation Management , vol. 14 , nº 4. pp. 827-844. DOI: 10.22059/JWIM.2024.376802.1163.

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Résumé

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.

Type de document:	Article
Mots-clés libres:	optical and thermal remote sensing; soil moisture; sugarcane; vegetation index
Centre:	Centre Eau Terre Environnement
Date de dépôt:	01 avr. 2025 20:08
Dernière modification:	01 avr. 2025 20:08
URI:	https://espace.inrs.ca/id/eprint/16365

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