P-Band Radar Retrieval of Subsurface Soil Moisture Profile as a Second-Order Polynomial: First AirMOSS Results

Tabatabaeenejad, Alireza; Burgin, Mariko; Duan, Xueyang; Moghaddam, Mahta

doi:10.1109/tgrs.2014.2326839

Cited by 116 publications

(80 citation statements)

References 34 publications

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“…Therefore, small changes in c 1 , c 2 , and c 3 can lead to a significant change of the SMP shape. This means that direct derivation of c 1 , c 2 , and c 3 through inversion is not as straightforward as proposed in [14], for example, to find the optimum polynomial parameters. We resolve this problem by replacing the free model parameters (c 1 , c 2 , and c 3 ) with soil moisture values at three arbitrary depths, θ 1 (z 1 ), θ 2 (z 2 ), and θ 3 (z 3 ).…”

Section: Of 18mentioning

confidence: 99%

“…Combining Equations (10), (11), (14), (18) and (19), and denoting the constants b 1 , b 2 , and b 3 , a simple closed-form solution is obtained:…”

Section: New Solution To Richards' Equationmentioning

confidence: 99%

“…Equation (28) implicates that the second order polynomial introduced in [14] based on empirical findings can be approximated based on the physics of unsaturated flow in soils. The accuracy of this approximation is dependent on the SMP shape, which is discussed in the following section.…”

Section: Of 18mentioning

confidence: 99%

“…In order to test the validity of this assumption, soil moisture data from SCAN site number 2026 in the Walnut Gulch watershed in Arizona were investigated. This site located within a USDA Agricultural Research Service (ARS) experimental watershed was selected because it was among the sites applied for AirMOSS evaluation in Tabatabaeenejad et al [14] and it was also a validation site for other satellite missions (e.g., SMEX04, SMAPVEX15) [45].…”

Section: Measured Datamentioning

confidence: 99%

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Advancing NASA’s AirMOSS P-Band Radar Root Zone Soil Moisture Retrieval Algorithm via Incorporation of Richards’ Equation

et al. 2016

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P-band radar remote sensing applied during the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) mission has shown great potential for estimation of root zone soil moisture. When retrieving the soil moisture profile (SMP) from P-band radar observations, a mathematical function describing the vertical moisture distribution is required. Because only a limited number of observations are available, the number of free parameters of the mathematical model must not exceed the number of observed data. For this reason, an empirical quadratic function (second order polynomial) is currently applied in the AirMOSS inversion algorithm to retrieve the SMP. The three free parameters of the polynomial are retrieved for each AirMOSS pixel using three backscatter observations (i.e., one frequency at three polarizations of Horizontal-Horizontal, Vertical-Vertical and Horizontal-Vertical). In this paper, a more realistic, physically-based SMP model containing three free parameters is derived, based on a solution to Richards' equation for unsaturated flow in soils. Evaluation of the new SMP model based on both numerical simulations and measured data revealed that it exhibits greater flexibility for fitting measured and simulated SMPs than the currently applied polynomial. It is also demonstrated that the new SMP model can be reduced to a second order polynomial at the expense of fitting accuracy.

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Section: Of 18mentioning

confidence: 99%

“…Combining Equations (10), (11), (14), (18) and (19), and denoting the constants b 1 , b 2 , and b 3 , a simple closed-form solution is obtained:…”

Section: New Solution To Richards' Equationmentioning

confidence: 99%

Section: Of 18mentioning

confidence: 99%

Section: Measured Datamentioning

confidence: 99%

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Advancing NASA’s AirMOSS P-Band Radar Root Zone Soil Moisture Retrieval Algorithm via Incorporation of Richards’ Equation

et al. 2016

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“…This study evaluated the treatment of multiple coarse grid cells when those cells were relatively small due to the limited spatial extent of the available soil moisture datasets. One could instead compare against remotely-sensed estimates of soil moisture (such as AirMoss [Chapin et al, 2012, Tabatabaeenejad et al, 2015). …”

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confidence: 99%

Downscaling soil moisture over regions that include multiple coarse-resolution grid cells

Hoehn

Niemann

Green

et al. 2017

Remote Sensing of Environment

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Hydrological Storage Length Scales Represented by Remote Sensing Estimates of Soil Moisture and Precipitation

Akbar

Gianotti

McColl

et al. 2018

Water Resources Research

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The soil water content profile is often well correlated with the soil moisture state near the surface. They share mutual information such that analysis of surface‐only soil moisture is, at times and in conjunction with precipitation information, reflective of deeper soil fluxes and dynamics. This study examines the characteristic length scale, or effective depth Δz, of a simple active hydrological control volume. The volume is described only by precipitation inputs and soil water dynamics evident in surface‐only soil moisture observations. To proceed, first an observation‐based technique is presented to estimate the soil moisture loss function based on analysis of soil moisture dry‐downs and its successive negative increments. Then, the length scale Δz is obtained via an optimization process wherein the root‐mean‐squared (RMS) differences between surface soil moisture observations and its predictions based on water balance are minimized. The process is entirely observation‐driven. The surface soil moisture estimates are obtained from the NASA Soil Moisture Active Passive (SMAP) mission and precipitation from the gauge‐corrected Climate Prediction Center daily global precipitation product. The length scale Δz exhibits a clear east‐west gradient across the contiguous United States (CONUS), such that large Δz depths (>200 mm) are estimated in wetter regions with larger mean precipitation. The median Δz across CONUS is 135 mm. The spatial variance of Δz is predominantly explained and influenced by precipitation characteristics. Soil properties, especially texture in the form of sand fraction, as well as the mean soil moisture state have a lesser influence on the length scale.

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P-Band Radar Retrieval of Subsurface Soil Moisture Profile as a Second-Order Polynomial: First AirMOSS Results

Cited by 116 publications

References 34 publications

Advancing NASA’s AirMOSS P-Band Radar Root Zone Soil Moisture Retrieval Algorithm via Incorporation of Richards’ Equation

Advancing NASA’s AirMOSS P-Band Radar Root Zone Soil Moisture Retrieval Algorithm via Incorporation of Richards’ Equation

Downscaling soil moisture over regions that include multiple coarse-resolution grid cells

Hydrological Storage Length Scales Represented by Remote Sensing Estimates of Soil Moisture and Precipitation

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