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Obtaining satellite-derived root-zone soil moistureSoil moisture (RZSM) data from surface observations is crucial for advancing the use of remote sensing technology and its contributions to hydrologyHydrology and agricultureAgriculture. Following the advancement of microwave remote sensingRemote sensing, the extent of soil moistureSoil moisture can be estimated from satellite data for large basins. However, satellite data generally provideSoil soil moisture estimates at the first few centimetres of the soil layer. In this study, a nonlinear statistical technique is proposed to extend SMAP surfaceSoil soil moistureSoil moisture estimates to the soil root zone (0–100 cm) over the Abbay River basinAbbay River Basin in EthiopiaEthiopia. The approach is developed by coupling the polynomial regression model and theCumulative Density Function (CDF) cumulative density function (CDF) matching method. When validated using field-observedSoil soil moistureSoil moisture, the prediction model was found to be reliable with a coefficient of correlation (r) ranging from 0.84 to 0.99 and an ubRMSE ranging from 0.002 to 0.039 m3/m3. The results suggested that systematic differences between surface and root-zoneSoil soil moistureSoil moisture can be adjusted statistically by employingCumulative Density Function (CDF) CDF-based observation operators, which can also produce a reasonably accurate prediction of RZSM using only near-surface data. Furthermore, the comparison made between the CDFCumulative Density Function (CDF) matching-based prediction model and SMAP L4 RZSM reveals the greater performance of the proposed model to predict RZSM in the study basin. CDFCumulative Density Function (CDF) matching could be used as an alternative way to extend the surfaceSoil soil moistureSoil moisture generated from remote sensingRemote sensing to the subsurface in areas with similar agro-climatic and soil characteristics to our study location. Thus, remote sensingRemote sensing technology could still provide opportunities and alternatives to estimate RZSM at different scales and with improved spatial and temporal resolutions.

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Root-Zone Soil Moisture Prediction in Rainfed Systems Using Satellite-Derived Product: The Case of Abbay River Basin in Ethiopia

  • 章节

  • 发表日期：

    2025年

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  • Getachew Tesfaye Ayehu,
  • Tsegaye Tadesse,
  • Berhan Gessesse,
  • Wuletawu Abera,
  • Degefie Tibebe,
  • Yonas Getaneh,
  • Lulseged Tamene

摘要

Obtaining satellite-derived root-zone soil moistureSoil moisture (RZSM) data from surface observations is crucial for advancing the use of remote sensing technology and its contributions to hydrologyHydrology and agricultureAgriculture. Following the advancement of microwave remote sensingRemote sensing, the extent of soil moistureSoil moisture can be estimated from satellite data for large basins. However, satellite data generally provideSoil soil moisture estimates at the first few centimetres of the soil layer. In this study, a nonlinear statistical technique is proposed to extend SMAP surfaceSoil soil moistureSoil moisture estimates to the soil root zone (0–100 cm) over the Abbay River basinAbbay River Basin in EthiopiaEthiopia. The approach is developed by coupling the polynomial regression model and theCumulative Density Function (CDF) cumulative density function (CDF) matching method. When validated using field-observedSoil soil moistureSoil moisture, the prediction model was found to be reliable with a coefficient of correlation (r) ranging from 0.84 to 0.99 and an ubRMSE ranging from 0.002 to 0.039 m3/m3. The results suggested that systematic differences between surface and root-zoneSoil soil moistureSoil moisture can be adjusted statistically by employingCumulative Density Function (CDF) CDF-based observation operators, which can also produce a reasonably accurate prediction of RZSM using only near-surface data. Furthermore, the comparison made between the CDFCumulative Density Function (CDF) matching-based prediction model and SMAP L4 RZSM reveals the greater performance of the proposed model to predict RZSM in the study basin. CDFCumulative Density Function (CDF) matching could be used as an alternative way to extend the surfaceSoil soil moistureSoil moisture generated from remote sensingRemote sensing to the subsurface in areas with similar agro-climatic and soil characteristics to our study location. Thus, remote sensingRemote sensing technology could still provide opportunities and alternatives to estimate RZSM at different scales and with improved spatial and temporal resolutions.