Soil moisture–plant interactions: an ecohydrological review

Wang, Cong; Fu, Bojie; Zhang, Lu; Xu, Zhihong

doi:10.1007/s11368-018-2167-0

Cited by 116 publications

(55 citation statements)

References 98 publications

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“…It can be observed that the variations of multi- To investigate the relationship between the multi-layer soil moisture and meteorological variables, the correlation coefficients between them are calculated ( The correlation between multi-layer soil moisture and environmental variables can reflect some hydrological processes in the soil moisture variation, which has been discussed in many previous studies. C. Wang, Fu, Zhang, and Xu (2018) discussed the advances in understanding soil moisture-plant interactions. They found that soil moisture is affected by changes in rainfall partitioning by plant canopies that regulate water input to the soil.…”

Section: Spatio-temporal Distributions and Variations Of Multi-layer Soil Moisturementioning

confidence: 99%

Improvement of multi‐layer soil moisture prediction using support vector machines and ensemble Kalman filter coupled with remote sensing soil moisture datasets over an agriculture dominant basin in China

Zhu

Wang

Luo

2021

Hydrological Processes

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Soil moisture prediction is of great importance in crop yield forecasting and drought monitoring. In this study, the multi-layer root zone soil moisture (0-5, 0-10, 10-40 and 40-100 cm) prediction is conducted over an agriculture dominant basin, namely the Xiang River Basin, in southern China. The support vector machines (SVM) coupled with dual ensemble Kalman filter (EnKF) technique (SVM-EnKF) is compared with SVM for its potential capability to improve the efficiency of soil moisture prediction. Three remote sensing soil moisture products, namely SMAP, ASCAT and AMSR2, are evaluated for their performance in multi-layer soil moisture prediction with SVM and SVM-EnKF, respectively. Multiple cases are designed to investigate the performance of SVM, the effectiveness of coupling dual EnKF technique and the applicability of the remote sensing products in soil moisture prediction. The main results are as follows: (a) The efficiency of soil moisture prediction with SVM using meteorological variables as inputs is satisfactory for the surface layers (0-5 and 0-10 cm), while poor for the root zone layers (10-40 and 40-100 cm). Adding SMAP as input to SVM can improve its performance in soil moisture prediction, with more than 47% increase in the R-value and at least 11% reduction in RMSE for all layers. However, adding ASCAT or AMSR2 has no improvement for its performance. (b) Coupling dual EnKF can significantly improve the performance of SVM in the soil moisture prediction of both surface and the root zone layers. The increase in R-value is above 80%, while the reduction in BIAS and RMSE is respectively more than 90% and 63%. However, adding remote sensing soil moisture products as inputs can no further improve the performance of SVM-EnKF. (c) The SVM-EnKF can eliminate the influence of remote sensing soil moisture extreme values in soil moisture prediction, therefore, improve its accuracy.

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Section: Spatio-temporal Distributions and Variations Of Multi-layer Soil Moisturementioning

confidence: 99%

Improvement of multi‐layer soil moisture prediction using support vector machines and ensemble Kalman filter coupled with remote sensing soil moisture datasets over an agriculture dominant basin in China

Zhu

Wang

Luo

2021

Hydrological Processes

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“…Moreover, NDVI was significantly correlated with precipitation accumulated during previous periods of the year. Soil water conditions are well known to be one of the foremost drivers of species composition, biomass and plant phenology (Wang et al, 2019). Particularly, soil moisture levels are strongly influenced not only by precipitation accumulated during the current growing season, but also by precipitation accumulated over a relatively long period of time (Wang et al, 2003).…”

Section: Tablementioning

confidence: 99%

Remote sensing depicts riparian vegetation responses to water stress in a humid Atlantic region

Pace

Gutiérrez‐Cánovas

Henriques

et al. 2021

Science of The Total Environment

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“…energy fluxes, groundwater recharge, runoff generation processes and biomass production, which, in turn, influence climatic variables on varying spatiotemporal scales (see e.g. Daly and Porporato, 2005;Vereecken et al, 2008;Seneviratne et al, 2010;Wang et al, 2018). Consequently, observations of soil moisture have a high importance for the estimation of landscape water balances and hydrological modelling.…”

Section: Introductionmentioning

confidence: 99%

Towards disentangling heterogeneous soil moisture patterns in cosmic-ray neutron sensor footprints

Rasche

Köhli

Schrön

et al. 2021

Hydrol. Earth Syst. Sci.

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Abstract. Cosmic-ray neutron sensing (CRNS) allows for non-invasive soil moisture estimations at the field scale. The derivation of soil moisture generally relies on secondary cosmic-ray neutrons in the epithermal to fast energy ranges. Most approaches and processing techniques for observed neutron intensities are based on the assumption of homogeneous site conditions or of soil moisture patterns with correlation lengths shorter than the measurement footprint of the neutron detector. However, in view of the non-linear relationship between neutron intensities and soil moisture, it is questionable whether these assumptions are applicable. In this study, we investigated how a non-uniform soil moisture distribution within the footprint impacts the CRNS soil moisture estimation and how the combined use of epithermal and thermal neutrons can be advantageous in this case. Thermal neutrons have lower energies and a substantially smaller measurement footprint around the sensor than epithermal neutrons. Analyses using the URANOS (Ultra RApid Neutron-Only Simulation) Monte Carlo simulations to investigate the measurement footprint dynamics at a study site in northeastern Germany revealed that the thermal footprint mainly covers mineral soils in the near-field to the sensor while the epithermal footprint also covers large areas with organic soils. We found that either combining the observed thermal and epithermal neutron intensities by a rescaling method developed in this study or adjusting all parameters of the transfer function leads to an improved calibration against the reference soil moisture measurements in the near-field compared to the standard approach and using epithermal neutrons alone. We also found that the relationship between thermal and epithermal neutrons provided an indicator for footprint heterogeneity. We, therefore, suggest that the combined use of thermal and epithermal neutrons offers the potential of a spatial disaggregation of the measurement footprint in terms of near- and far-field soil moisture dynamics.

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Soil moisture–plant interactions: an ecohydrological review

Cited by 116 publications

References 98 publications

Improvement of multi‐layer soil moisture prediction using support vector machines and ensemble Kalman filter coupled with remote sensing soil moisture datasets over an agriculture dominant basin in China

Improvement of multi‐layer soil moisture prediction using support vector machines and ensemble Kalman filter coupled with remote sensing soil moisture datasets over an agriculture dominant basin in China

Remote sensing depicts riparian vegetation responses to water stress in a humid Atlantic region

Towards disentangling heterogeneous soil moisture patterns in cosmic-ray neutron sensor footprints

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