Towards soil moisture profile estimation in the root zone using L- and P-band radiometer observations: A coherent modelling approach

Brakhasi, Foad; Walker, Jeffrey P.; Ye, Nan; Wu, Xiaoling; Shen, Xiaoji; Yeo, In‐Young; Boopathi, Nithyapriya; Kim, Edward; Kerr, Yann H.; Jackson, Thomas J.

doi:10.1016/j.srs.2023.100079

Cited by 8 publications

(5 citation statements)

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“…The calibration accuracy of both PPMR and PLMR was found to be less than 1.5 K. Weekly measurements of near-surface soil moisture (top 5 cm) were taken by a Hydra-probe Data Acquisition System (HDAS; [28]) throughout the quadrants to confirm the representativeness of the station. For a detailed description of the experiment and comprehensive information about the dataset, readers are referred to the publications authored by Shen [11], [15] and the PRISM (P-band Radiometer Inferred Soil Moisture) project website (www.prism.monash.edu).…”

Section: Datamentioning

confidence: 99%

“…To address this need, remote sensing technologies operating at lower frequencies such as P-band (∼40-cm wavelength) have been developed [14]. In addition to providing information about the moisture content distribution in a deeper layer of soil [15], microwave signals at P-band may be better suited for soil moisture retrieval under densely vegetated and topographically complex environments, due to its reduced sensitivity to vegetation [16] and surface roughness [11].…”

Section: Introductionmentioning

confidence: 99%

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A Comparison of Passive Microwave Emission Models for Estimating Brightness Temperature at L- and P-Bands Under Bare and Vegetated Soil Conditions

Brakhasi,

Walker,

Judge

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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P-band radiometry has been demonstrated to have a deeper sensing depth than L-band, making the consideration of multilayer microwave interactions necessary. In addition, the scattering and phase interference effects are different at the Pband, requiring a reconsideration of the need for coherent models. However, the impact remains to be clarified, and understanding the validity and limitations of these models at both L-and P-bands is crucial for their refinement and application. Therefore, two general categories of microwave emission models, including two stratified coherent models (Njoku and Wilhite) and four incoherent models (conventional tau-omega model and three multilayer models being zero-order, first-order, and incoherent solution), were intercompared for the first time on the same dataset. This evaluation utilized observations of L-and P-bands radiometry under different land cover conditions from a tower-based experiment in Victoria,

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Section: Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Comparison of Passive Microwave Emission Models for Estimating Brightness Temperature at L- and P-Bands Under Bare and Vegetated Soil Conditions

Brakhasi,

Walker,

Judge

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Self Cite

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“…In addition, the advanced satellite technology incorporating the existing passive microwave satellite missions, Soil Moisture and Ocean Salinity (SMOS), and Soil Moisture Active Passive (SMAP), can solely estimate soil moisture at different scales (local, regional, global) in the topmost 5 cm layer. The combined use of different bands in satellite images showed the potential to determine the global soil moisture profile precisely up to a depth of 30 cm [32].…”

Section: Soil Moisture Profile (Smp) Calculationmentioning

confidence: 99%

Simulating Tree Root Water Uptake in the Frame of Sustainable Agriculture for Extreme Hyper-Arid Environments Using Modeling and Geophysical Techniques

Pradipta,

Kourgialas,

Mustafa

et al. 2024

Sustainability

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In order to ensure sustainability in the agricultural sector and to meet global food needs, a particularly important challenge for our time is to investigate the possibility of increasing agricultural production in areas with extreme hyper-arid environments. Warming air temperatures and sandy soils significantly influence tree root water uptake (RWU) dynamics, making accurate estimation of RWU depth distribution and magnitude crucial for effective resource management, particularly in the context of precision irrigation within agroecosystems. This study employed two non-invasive techniques, namely HYDRUS 1D and electrical resistivity tomography (ERT), to simulate RWU under controlled experimental conditions and under an extreme hyper-arid environment. The results revealed that the highest RWU rates occurred during the morning (08:00–11:00). RWU activity predominantly concentrated in the upper soil profile (0–30 cm), and the soil water content in this area was notably lower compared to the deeper soil layers. With increasing temperature, there was a tendency for the RWU zone to shift to lower depths within the soil profile. The findings of this study could have important implications for farmers, providing valuable insights to implement irrigation water management strategies.

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“…Profile soil moisture (PSM), which refers to the soil water content in the whole soil layer, is the key variable in ecological, agricultural, and hydrological systems since it controls the major processes related to biological interaction, vegetation growth, and runoff generation [1][2][3]. In recent years, abundant studies have been conducted to investigate the temporal change in PSM based on in situ soil moisture datasets or other long time-series soil moisture datasets, but less attention is paid to its spatial distribution during a specific time period [4][5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Identifying the Spatial Heterogeneity and Driving Factors of Satellite-Based and Hydrologically Modeled Profile Soil Moisture

Yang,

Zhang,

Yuan

et al. 2024

Remote Sensing

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Profile soil moisture (PSM), the soil water content in the whole soil layer, directly controls the major processes related to biological interaction, vegetation growth, and runoff generation. Its spatial heterogeneity, which refers to the uneven distribution and complexity in space, influences refined spatial management and decision-making in ecological, agricultural, and hydrological systems. Satellite instruments and hydrological models are two important sources of spatial information on PSM, but there is still a gap in understanding their potential mechanisms that affect spatial heterogeneity. This study is designed to identify the spatial heterogeneity and the driving factors of two PSM datasets; one is preprocessed from a satellite product (European Space Agency Climate Change Initiative, ESA CCI), and the other is simulated from a distributed hydrological model (the DEM-based distributed rainfall-runoff model, DDRM). Three catchments with different climate conditions were chosen as the study area. By considering the scale dependence of spatial heterogeneity, the profile saturation degree (PSD) datasets from different sources (shown as ESA CCI PSD and DDRM PSD, respectively) during 2017 that are matched in terms of spatial scale and physical properties were acquired first based on the calibration data from 2014–2016, and then the spatial heterogeneity of the PSD from different sources was identified by using spatial statistical analysis and the semi-variogram method, followed by the geographic detector method, to investigate the driving factors. The results indicate that (1) ESA CCI and DDRM PSD are similar for seasonal changes and are overall consistent and locally different in terms of the spatial variations in catchment with different climate conditions; (2) based on spatial statistical analysis, the spatial heterogeneity of PSD reduces after spatial rescaling; at the same spatial scale, DDRM PSD shows higher spatial heterogeneity than ESA CCI PSD, and the low-flow period shows higher spatial heterogeneity than the high-flow period; (3) based on the semi-variogram method, both ESA CCI and DDRM PSD show strong spatial heterogeneity in most cases, in which the proportion of C/(C0 + C) is higher than 0.75, and the spatial data in the low-flow period mostly show larger spatial heterogeneity, in which the proportion is higher than 0.9; the spatial heterogeneity of PSD is higher in the semi-arid catchment; (4) the first three driving factors of the spatial heterogeneity of both ESA CCI and DDRM PSD are DEM, precipitation, and soil type in most cases, contributing more than 50% to spatial heterogeneity; (5) precipitation contributes most to ESA CCI PSD in the low-flow period, and there is no obvious high contribution of precipitation to DDRM PSD. The research provides insights into the spatial heterogeneity of PSM, which helps develop refined modeling and spatial management strategies for soil moisture in ecological, agricultural, and hydrological fields.

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Towards soil moisture profile estimation in the root zone using L- and P-band radiometer observations: A coherent modelling approach

Cited by 8 publications

References 50 publications

A Comparison of Passive Microwave Emission Models for Estimating Brightness Temperature at L- and P-Bands Under Bare and Vegetated Soil Conditions

A Comparison of Passive Microwave Emission Models for Estimating Brightness Temperature at L- and P-Bands Under Bare and Vegetated Soil Conditions

Simulating Tree Root Water Uptake in the Frame of Sustainable Agriculture for Extreme Hyper-Arid Environments Using Modeling and Geophysical Techniques

Identifying the Spatial Heterogeneity and Driving Factors of Satellite-Based and Hydrologically Modeled Profile Soil Moisture

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