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Estimation of 100 m root zone soil moisture by downscaling 1 km soil water index with machine learning and multiple geodata.
Mahmood, Talha; Löw, Johannes; Pöhlitz, Julia; Wenzel, Jan Lukas; Conrad, Christopher.
Afiliación
  • Mahmood T; Department of Geoecology, Institute of Geosciences and Geography, Martin Luther University Halle-Wittenberg, 06120, Halle (Saale), Germany. talha.mahmood@student.uni-halle.de.
  • Löw J; Department of Geoecology, Institute of Geosciences and Geography, Martin Luther University Halle-Wittenberg, 06120, Halle (Saale), Germany.
  • Pöhlitz J; Department of Geoecology, Institute of Geosciences and Geography, Martin Luther University Halle-Wittenberg, 06120, Halle (Saale), Germany.
  • Wenzel JL; Department of Geoecology, Institute of Geosciences and Geography, Martin Luther University Halle-Wittenberg, 06120, Halle (Saale), Germany.
  • Conrad C; Department of Geoecology, Institute of Geosciences and Geography, Martin Luther University Halle-Wittenberg, 06120, Halle (Saale), Germany.
Environ Monit Assess ; 196(9): 823, 2024 Aug 19.
Article en En | MEDLINE | ID: mdl-39158616
ABSTRACT
Root zone soil moisture (RZSM) is crucial for agricultural water management and land surface processes. The 1 km soil water index (SWI) dataset from Copernicus Global Land services, with eight fixed characteristic time lengths (T), requires root zone depth optimization (Topt) and is limited in use due to its low spatial resolution. To estimate RZSM at 100-m resolution, we integrate the depth specificity of SWI and employed random forest (RF) downscaling. Topographic synthetic aperture radar (SAR) and optical datasets were utilized to develop three RF models (RF1 SAR, RF2 optical, RF3 SAR + optical). At the DEMMIN experimental site in northeastern Germany, Topt (in days) varies from 20 to 60 for depths of 10 to 30 cm, increasing to 100 for 40-60 cm. RF3 outperformed other models with 1 km test data. Following residual correction, all high-resolution predictions exhibited strong spatial accuracy (R ≥ 0.94). Both products (1 km and 100 m) agreed well with observed RZSM during summer but overestimated in winter. Mean R between observed RZSM and 1 km (100 m; RF1, RF2, and RF3) SWI ranges from 0.74 (0.67, 0.76, and 0.68) to 0.90 (0.88, 0.81, and 0.82), with the lowest and highest R achieved at 10 cm and 30 cm depths, respectively. The average RMSE using 1 km (100 m; RF1, RF2, and RF3) SWI increased from 2.20 Vol.% (2.28, 2.28, and 2.35) at 30 cm to 3.40 Vol.% (3.50, 3.70, and 3.60) at 60 cm. These negligible accuracy differences underpin the potential of the proposed method to estimate RZSM for precise local applications, e.g., irrigation management.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Suelo / Agua / Monitoreo del Ambiente / Aprendizaje Automático País/Región como asunto: Europa Idioma: En Revista: Environ Monit Assess Asunto de la revista: SAUDE AMBIENTAL Año: 2024 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Países Bajos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Suelo / Agua / Monitoreo del Ambiente / Aprendizaje Automático País/Región como asunto: Europa Idioma: En Revista: Environ Monit Assess Asunto de la revista: SAUDE AMBIENTAL Año: 2024 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Países Bajos