Spatial and temporal characteristics of soil moisture in an intensively monitored agricultural field (OPE3)

Lannoy, Gabriëlle J. M. De; Verhoest, Niko; Houser, Paul R.; Gish, T. J.; Meirvenne, Marc Van

doi:10.1016/j.jhydrol.2006.06.016

Cited by 126 publications

(127 citation statements)

References 30 publications

(26 reference statements)

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“…The larger CV S in deeper depth, however, was probably due to the relatively high spatial variability of soil properties and root system (Li et al, 2015). In contrast with the upper depths, a higher SD S and CV S was generally found for the deeper depths, in agreement with the findings by other studies (De Lannoy et al, 2006;Gao and Shao, 2012a).…”

Section: Variablessupporting

confidence: 91%

Profile distribution of soil–water content and its temporal stability along a 1340-m long transect on the Loess Plateau, China

Shao

Jia

et al. 2016

CATENA

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Information on the profile characteristics of soil-water content (SWC) and its temporal stability is essential for an accurate understanding of hydrological processes. This study investigated changes of spatial variation and temporal stability of SWC in soil profiles and estimated mean SWC based on direct and indirect methods. SWCs were measured at 20-cm intervals in the soil profiles to a depth of 3 m using neutron probes at 135 locations along a 1340-m long transect on 18 sampling dates from 2012 to 2013 on the Loess Plateau in China. The coefficient of variation over space (CV S ) of SWC first decreased and then increased vertically. The coefficient of variation over time (CV T ) of SWC decreased along the soil profiles. The spatial patterns of SWC strongly persisted vertically and temporally. Mean Spearman's rank correlation coefficients decreased from a depth of 10 to a depth of 20 cm, fluctuated from 20 to 180 cm, and then increased below 200 cm. Temporal stability increased along the soil profiles based on the standard deviation of mean relative difference (SDRD) and the mean absolute bias error (MABE). The number of locations with an SDRD and/or MABE b 5% increased along the soil profile, and the number of locations with a mean relative difference within ±5% and/or representative locations were variably dependent on depth. Both direct and indirect methods could accurately estimate the mean SWC for each depth based on the mean absolute relative errors and root mean square errors. The driest and wettest locations tended to remain representative for more depths than did locations with mean-moistures. The driest locations were more likely to be the most temporally stable. These findings should improve our understanding of soil-water dynamics in soil profiles.

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Section: Variablessupporting

confidence: 91%

Profile distribution of soil–water content and its temporal stability along a 1340-m long transect on the Loess Plateau, China

Shao

Jia

et al. 2016

CATENA

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“…Previous studies [61][62][63] used soil moisture measurements recorded in several in situ stations aiming at the understanding of soil moisture variability over relatively small distances. However, contradictory results have been found, highlighting the complexity of the phenomenon and thus the difficulty of accurately predicting the dynamics of the soil moisture in different areas.…”

Section: Analysis Of Sm Spatial Variabilitymentioning

confidence: 99%

Evaluation of a Global Soil Moisture Product from Finer Spatial Resolution SAR Data and Ground Measurements at Irish Sites

et al. 2014

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Abstract:In the framework of the European Space Agency Climate Change Initiative, a global, almost daily, soil moisture (SM) product is being developed from passive and active satellite microwave sensors, at a coarse spatial resolution. This study contributes to its validation by using finer spatial resolution ASAR Wide Swath and in situ soil moisture data taken over three sites in Ireland, from 2007 to 2009. This is the first time a comparison has been carried out between three sets of independent observations from different sensors at very different spatial resolutions for such a long time series. Furthermore, the SM spatial distribution has been investigated at the ASAR scale within each Essential Climate Variable (ECV) pixel, without adopting any particular model or using a densely distributed network of in situ stations. This approach facilitated an understanding of the extent to which geophysical factors, such as soil texture, terrain composition and altitude, affect the retrieved ECV SM product values in temperate grasslands. Temporal and spatial variability analysis provided high levels of correlation (p < 0.025) and low errors between the three datasets, leading to confidence in the new ECV SM global product, despite limitations in its ability to track the driest and wettest conditions.

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“…However, Kumar et al (2009) stressed that it is important to assess vertical variability, especially the strength of coupling between surface and subsurface soil moisture for improvement of data assimilation (DA) results. Analyses of vertical soil moisture distributions also have important implications for modeling studies, as they could be used for calibration or validation of model parameters (De Lannoy et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Using lagged dependence to identify (de)coupled surface and subsurface soil moisture values

Carranza

Ploeg

Torfs

2018

Hydrol. Earth Syst. Sci.

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Abstract. Recent advances in radar remote sensing popularized the mapping of surface soil moisture at different spatial scales. Surface soil moisture measurements are used in combination with hydrological models to determine subsurface soil moisture values. However, variability of soil moisture across the soil column is important for estimating depthintegrated values, as decoupling between surface and subsurface can occur. In this study, we employ new methods to investigate the occurrence of (de)coupling between surface and subsurface soil moisture. Using time series datasets, lagged dependence was incorporated in assessing (de)coupling with the idea that surface soil moisture conditions will be reflected at the subsurface after a certain delay. The main approach involves the application of a distributed-lag nonlinear model (DLNM) to simultaneously represent both the functional relation and the lag structure in the time series. The results of an exploratory analysis using residuals from a fitted loess function serve as a posteriori information to determine (de)coupled values. Both methods allow for a range of (de)coupled soil moisture values to be quantified. Results provide new insights into the decoupled range as its occurrence among the sites investigated is not limited to dry conditions.

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Spatial and temporal characteristics of soil moisture in an intensively monitored agricultural field (OPE3)

Cited by 126 publications

References 30 publications

Profile distribution of soil–water content and its temporal stability along a 1340-m long transect on the Loess Plateau, China

Profile distribution of soil–water content and its temporal stability along a 1340-m long transect on the Loess Plateau, China

Evaluation of a Global Soil Moisture Product from Finer Spatial Resolution SAR Data and Ground Measurements at Irish Sites

Using lagged dependence to identify (de)coupled surface and subsurface soil moisture values

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