A novel approach to improve spatial detail in modeled soil moisture through the integration of remote sensing data

Greifeneder, Felix; Notarnicola, Claudia; Bertoldi, Giacomo; Brenner, Johannes; Wagner, Wolfgang

doi:10.1109/igarss.2015.7326187

Cited by 1 publication

(1 citation statement)

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“…Assimilation of measured soil water content (SWC) into model runs is frequently used to improve the prediction of soil water at future times by land surface models (e.g., Liu & Gupta, 2007; Montaldo & Albertson, 2003; Reichle, Crow, & Keppenne, 2008; Reichle, Koster, Dong, & Berg, 2004) and hydraulic models (e.g., Crosson, Laymon, Inguva, & Schamschula, 2002; Sun, Zhang, & Li, 2011; Tian et al., 2017). In general, the surface soil water content (2–10 cm) derived from remote sensing is assimilated, which considers both measurement and prediction uncertainty (e.g., Greifeneder, Notarnicola, Bertoldi, Brenner, & Wagner, 2015; Montaldo, Albertson, & Mancini, 2007). Many studies have demonstrated improvements in predicting vertical soil water distribution by assimilating remotely‐sensed surface soil water content in hydrological models (e.g., Liu, Wang, & Hu, 2017), but the improvement varied greatly with soil depth, e.g., smaller improvement in deeper soil layers (Heathman, Starks, Ahuja, & Jackson, 2003; Wang, Liu, Kou, & Lu, 2012).…”

Section: Introductionmentioning

confidence: 99%

Direct assimilation of measured soil water content in Root Zone Water Quality Model calibration for deficit‐irrigated maize

Sima

Fang

et al. 2020

Agronomy Journal

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Correct soil water simulation is critical for water balance and plant growth in agricultural systems. Crop production simulation errors have often been attributed to a lack of accuracy in soil water content (SWC) estimates. However, only a few studies have quantified the effects of SWC estimate errors on crop production and evapotranspiration (ET), especially under different irrigation treatments. The objective of this study was to investigate the impacts of direct assimilation of measured SWC during model calibration for deficit irrigated maize (Zea mays L.) on simulated ET, leaf area index (LAI), biomass, and yield. The CERES-Maize model within the Root Zone Water Quality Model (RZWQM) was calibrated using the automatic parameter estimation (PEST) software. Simulation results showed that, using PEST-optimized crop parameters, RZWQM was able to adequately predict crop yield (relative root mean squared error, rRMSE, of 4.8%) and biomass (rRMSE of 7.1%) in response to irrigation levels, in spite of the bias in SWC and ET simulation. However, with the same crop parameters but replacing simulated SWC with measured data, simulations of crop yield and biomass became worse, with higher rRMSE values (14.5% for yield and 21.5% for biomass). This unexpected model performance with SWC assimilation was mainly associated with the water addition and removal from the soil, which was improved only by recalibration of both soil and crop parameters. This study suggested compensating effects between soil and crop parameters during model calibration. Caution should be applied when using measured SWC as model inputs, especially under water stress conditions. Abbreviations: ET, evapotranspiration; LAI, leaf area index; PEST, parameter estimation software; RMSE, root mean squared error; rRMSE, relative root mean squared error; RZWQM, root zone water quality model; SWC, soil water content.

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