Assimilation of surface soil moisture to estimate profile soil water content

Heathman, Gary C.; Starks, Patrick J.; Ahuja, Lajpat R.; Jackson, Thomas J.

doi:10.1016/s0022-1694(03)00088-x

Cited by 125 publications

(94 citation statements)

References 46 publications

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“…Measuring the water content only in the surface soil would reduce costs and resources (Biswas and Si, 2011a) and would also provide information necessary for adopting dataassimilation techniques for integrating remote sensing and soilwater modeling (Houser et al, 1998;Walker et al, 2001;Heathman et al, 2003). The depth persistence of SWS within soil profiles could also be incorporated into hydrological models, be applied to water management, and contribute to our understanding of the hydrological dynamics of landscapes.…”

Section: Discussionmentioning

confidence: 99%

Depth persistence of the spatial pattern of soil–water storage along a small transect in the Loess Plateau of China

Shao

Jia

et al. 2015

Journal of Hydrology

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Keywords:Depth persistence Soil-water storage Spatial pattern Wavelet coherency The Loess Plateau s u m m a r y Knowledge of the spatial patterns of soil-water storage (SWS) in soil profiles is important for understanding the dynamics of soil water between surface and subsurface soil layers in semiarid area. We investigated the depth persistence of the overall and scale-specific spatial patterns of SWS for different soil layers during different seasons. Soil-water contents were measured using a neutron probe on 22 occasions in 2012 and 2013 along a 1340-m transect over several sub-watersheds in the Liudaogou catchment on the Loess Plateau of China. Similarities in the spatial patterns of SWS were analyzed by Spearman's rank correlations and wavelet coherency. A spatiotemporal analysis indicated that the temporal evolution of the SWS profiles differed between the growing and non-growing seasons and that landscape position and soil texture determined the amount of SWS at each sampling location. Spearman's rank correlations were significant between any two layers within different seasons, and the correlation coefficients decreased as the distance between layers increased. Clay content controlled the spatial pattern of SWS between layers at large scales. The SWS spatial pattern had a higher depth persistence during the non-growing season than during the growing season, and the soil layer had a larger effect than season on the similarity in SWS spatial patterns. These results can improve our understanding of the hydrological processes in soil profiles and can be of considerable value in the application of hydrological models and in water management.

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Section: Discussionmentioning

confidence: 99%

Depth persistence of the spatial pattern of soil–water storage along a small transect in the Loess Plateau of China

Shao

Jia

et al. 2015

Journal of Hydrology

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“…There is a scarcity of published studies on the effects of nanocarbon on soil water holding capacity, especially in sandy soils. Soil water holding capacity is a major factor that can affect soil erosion by water, as well as plant growth (Heathman et al, 2003;She et al, 2014). Tan et al (2014) found that nano carbon could increase the water contents effectively in columns of the disturbed soil column.…”

Section: Introductionmentioning

confidence: 99%

Effect of Nano-Carbon on Water Holding Capacity in a Sandy Soil of the Loess Plateau

Zhou¹,

Chen²

2017

Earth sci. res. j.

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The poor water retention capacity of sandy soils commonly aggregate soil erosion and ecological environment on the Chinese Loess Plateau. Due to its strong capacity for absorption and large specific surface area, the use of nanocarbon made of coconut shell as a soil amendment that could improve water retention was investigated. Soil column experiments were conducted in which a layer of nanocarbon mixed well with the soil was formed at a depth of 20 cm below the soil surface. Four different nanocarbon contents by weight (0%, 0.1%, 0.5%, and 1%) and five thicknesses of the nanocarbon- soil mixture layer ranging from 1 to 5 cm were considered. Cumulative infiltration and soil water content distributions were determined when water was added to soil columns. Soil Water Characteristic Curves (SWCC) were obtained using the centrifuge method. The principal results showed that the infiltration rate and cumulative infiltration increased with the increases of nanocarbon contents, to the thicknesses of the nano carbon-soil mixture layer. Soil water contents that below the soil-nano carbon layer decreased sharply. Both the Brooks-Corey and van Genuchten models could describe well the SWCC of the disturbed sandy soil with various nano carbon contents. Both the saturated water content (θs), residual water content (θr) and empirical parameter (α) increased with increasing nano carbon content, while the pore-size distribution parameter (n) decreased. The available soil water contents were efficiently increased with the increase in nanocarbon contents.

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“…Thus, it influences both water availability and energy balances [111]. Accurate, spatially-and temporally-distributed information about the concentration of soil moisture is of great importance in hydrological applications, such as flood prediction related to extreme rainfall events, watershed management during dry periods, irrigation scheduling, precision farming, in addition in Earth sciences, such as climate change analysis and meteorology [112,113].…”

Section: Retrieval Of Essential Variables: Soil Moisturementioning

confidence: 99%

Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data

et al. 2015

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Abstract:The enormous increase of remote sensing data from airborne and space-borne platforms, as well as ground measurements has directed the attention of scientists towards new and efficient retrieval methodologies. Of particular importance is the consideration of the large extent and the high dimensionality (spectral, temporal and spatial) of remote sensing data. Moreover, the launch of the Sentinel satellite family will increase the availability of data, especially in the temporal domain, at no cost to the users. To analyze these data and to extract relevant features, such as essential climate variables (ECV), specific methodologies need to be exploited. Among these, greater attention is devoted to machine learning methods due to their flexibility and the capability to process large number of inputs and to handle non-linear problems. The main objective of this paper is to provide a review of research that is being carried out to retrieve two critically important terrestrial biophysical quantities (vegetation biomass and soil moisture) from remote sensing data using machine learning methods.

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Assimilation of surface soil moisture to estimate profile soil water content

Cited by 125 publications

References 46 publications

Depth persistence of the spatial pattern of soil–water storage along a small transect in the Loess Plateau of China

Depth persistence of the spatial pattern of soil–water storage along a small transect in the Loess Plateau of China

Effect of Nano-Carbon on Water Holding Capacity in a Sandy Soil of the Loess Plateau

Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data

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