Estimation of Penetration Depth from Soil Effective Temperature in Microwave Radiometry

Lv, Shan; Zeng, Yijian; Wen, Jun; Hong, Zhao; Su, Zhongbo

doi:10.3390/rs10040519

Cited by 52 publications

(58 citation statements)

References 45 publications

(74 reference statements)

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“…Journal of Advances in Modeling Earth Systems assumption of a constant Tb sensing depth in the L4_SM analysis likely causes bias in the L4_SM soil moisture estimates. Estimating the actual sensing depth and using this information in soil moisture assimilation systems are subjects of ongoing research (Lv et al, 2018(Lv et al, , 2019. Figure 2 further demonstrates that the Version 4 L4_SM estimates are better than estimates from the corresponding (model-only) Nature Run simulation, which again highlights the beneficial impact of the SMAP Tb data.…”

Section: 1029/2019ms001729mentioning

confidence: 94%

Version 4 of the SMAP Level‐4 Soil Moisture Algorithm and Data Product

Reichle

Liu

Koster

et al. 2019

J Adv Model Earth Syst

151

127

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The NASA Soil Moisture Active Passive (SMAP) mission Level‐4 Soil Moisture (L.4_SM) product provides global, 3‐hourly, 9‐km resolution estimates of surface (0–5 cm) and root zone (0–100 cm) soil moisture with a mean latency of ~2.5 days. The underlying L4_SM algorithm assimilates SMAP radiometer brightness temperature (Tb) observations into the NASA Catchment land surface model using a spatially distributed ensemble Kalman filter. In Version 4 of the L4_SM modeling system the upward recharge of surface soil moisture from below under nonequilibrium conditions was reduced, resulting in less bias and improved dynamic range of L4_SM surface soil moisture compared to earlier versions. This change and additional technical modifications to the system reduce the mean and standard deviation of the observation‐minus‐forecast Tb residuals and overall soil moisture analysis increments while maintaining the skill of the L4_SM soil moisture estimates versus independent in situ measurements; the average, bias‐adjusted root‐mean‐square error in Version 4 is 0.039 m3/m3 for surface and 0.026 m3/m3 for root zone soil moisture. Moreover, the coverage of assimilated SMAP observations in Version 4 is near global owing to the use of additional satellite Tb records for algorithm calibration. L4_SM soil moisture uncertainty estimates are biased low (by 0.01–0.02 m3/m3) against actual errors (computed versus in situ measurements). L4_SM runoff estimates, an additional product of the L4_SM algorithm, are biased low (by 35 mm/year) against streamflow measurements. Compared to Version 3, bias in Version 4 is reduced by 46% for surface soil moisture uncertainty estimates and by 33% for runoff estimates.

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Section: 1029/2019ms001729mentioning

confidence: 94%

Version 4 of the SMAP Level‐4 Soil Moisture Algorithm and Data Product

Reichle

Liu

Koster

et al. 2019

J Adv Model Earth Syst

151

127

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“…Air temperature was measured with a Platinum resistance thermometer, type HPM 45C, installed 1.5 m above the ground and precipitation (both rain and snow) was measured with a weight-based rain gauge, type T-200B. The depth profile of volumetric soil moisture content m v (m 3 m −3 ) was measured with an array of 20 capacitance sensors, type 5TM, that were installed at 135 depths ranging from 2.5 cm to 1 m (Lv et al, 2018). All sensors in the array are also equipped with a thermistor, enabling the measurement of the soil temperature depth profile T soil ( • C).…”

Section: Vegetationmentioning

confidence: 99%

Year-long, broad-band, microwave backscatter observations of an Alpine Meadow over the Tibetan Plateau with a ground-based scatterometer

Hofste¹,

Velde²,

Wen³

et al. 2020

Preprint

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Abstract. A ground-based scatterometer was installed on an alpine meadow over the Tibetan Plateau to study the soil moisture and -temperature dynamics of the top soil layer and air-soil interface during the period August 2017–August 2018. The deployed system measured the amplitude and phase of the ground surface radar return at hourly and half-hourly intervals over 1–10 GHz in the four linear polarization combinations (vv, hh, hv, vh). In this paper we describe the developed scatterometer system, gathered datasets, retrieval method for the backscattering coefficient (σ0), and results of (σ0) for co-polarization. The system was installed on a 5 m high tower and designed using only commercially available components: a Vector Network Analyser (VNA), four coaxial cables, and two dual polarization broadband gain horn antennas at a fixed position and orientation. We provide a detailed description on how to retrieve the co-polarized backscattering coefficients σ0vv & σ0hh for this specific scatterometer design. To account for the particular effects caused by wide antenna radiation patterns (G) at lower frequencies, σ0 was calculated using the narrow-beam approximation combined with a mapping the function G2/R4 over the ground surface. (R is the distance between antennas and the infinitesimal patches of ground surface.) This approach allowed for a proper derivation of footprint positions and -areas, and incidence angle ranges. The frequency averaging technique was used to reduce the effects of fading on the σ0 uncertainty. Absolute calibration of the scatterometer was achieved with measured backscatter from a rectangular metal plate as reference target. In the retrieved time-series of σ0vv & σ0hh for S-band (2.5–3.0 GHz), C-band (4.5–5.0 GHz), and X-band (9.0–10.0 GHz) we observed characteristic changes or features that can be attributed to seasonal or diurnal changes in the soil. For example a fully frozen top soil, diurnal freeze-thaw changes in the top soil, emerging vegetation in spring, and drying of soil. Our preliminary analysis on the collected σ0 time-series data set demonstrates that it contains valuable information on water- and energy exchange directly below the air-soil interface. Information which is difficult to quantify, at that particular position, with in-situ measurements techniques alone. Availability of backscattering data for multiple frequency bands allows for studying scattering effects at different depths within the soil and vegetation canopy during the spring and summer periods. Hence further investigation of this scatterometer data set provides an opportunity to gain new insights in hydro-meteorological processes, such as freezing and thawing, and how these can be monitored with multi-frequency scatterometer observations. The data set is available via https://doi.org/10.17026/dans-zc5-skyg (Hofste and Su, 2020). The effects of fading, calibration, and system stability on the uncertainty in σ0 are estimated to vary from ± 1.3 dB for X-band with vv-polarization to ± 2.7 dB for S-band with hh-polarization through the campaign. The low angular resolution of the antennas result in additional σ0 uncertainty, one that is more difficult to quantify. Estimations point out that it probably will not exceed ± 2 dB with C-band. Despite these uncertainties, we believe that the strength of our approach lies in the capability of measuring σ0 dynamics over a broad frequency range, 1–10 GHz, with high temporal resolution over a full-year period.

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“…A wide range of measurement techniques and protocols exist for setting up and performing ground-based observations for such evaluations. SMAP Cal/Val suggests that volumetric soil moisture should be observed in situ at 5 and 100 cm depth; optimal sensing and mounting depths are, however, still debated (Lv et al, 2016a(Lv et al, , 2018(Lv et al, , 2019. For core validation sites a minimum of six stations should cover one SMAP grid cell or footprint (O'Neill et al, 2015;Famiglietti et al, 2008); but this value has not yet been shown to guarantee the nominal accuracy by a thorough analysis (Jackson et al, 2012;.…”

Section: Introductionmentioning

confidence: 99%

Required sampling density of ground-based soil moisture and brightness temperature observations for calibration and validation of L-band satellite observations based on a virtual reality

Schalge

Garfias

et al. 2020

Hydrol. Earth Syst. Sci.

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Abstract. Microwave remote sensing is the most promising tool for monitoring near-surface soil moisture distributions globally. With the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions in orbit, considerable efforts are being made to evaluate derived soil moisture products via ground observations, microwave transfer simulation, and independent remote sensing retrievals. Due to the large footprint of the satellite radiometers of about 40 km in diameter and the spatial heterogeneity of soil moisture, minimum sampling densities for soil moisture are required to challenge the targeted precision. Here we use 400 m resolution simulations with the regional Terrestrial System Modeling Platform (TerrSysMP) and its coupling with the Community Microwave Emission Modelling platform (CMEM) to quantify the maximum sampling distance allowed for soil moisture and brightness temperature validation. Our analysis suggests that an overall sampling distance of finer than 6 km is required to validate the targeted accuracy of 0.04 cm3 cm−3 with a 70 % confidence level in SMOS and SMAP estimates over typical mid-latitude European regions. The maximum allowed sampling distance depends on the land-surface heterogeneity and the meteorological situation, which influences the soil moisture patterns, and ranges from about 6 to 17 km for a 70 % confidence level for a typical year. At the maximum allowed sampling distance on a 70 % confidence level, the accuracy of footprint-averaged soil moisture is equal to or better than brightness temperature estimates over the same area. Estimates strongly deteriorate with larger sampling distances. For the evaluation of the smaller footprints of the active and active–passive products of SMAP the required sampling densities increase; e.g., when a grid resolution of 3 km diameter is sampled by three sites of footprints of 9 km sampled by five sites required, only 50 %–60 % of the pixels have a sampling error below the nominal values. The required minimum sampling densities for ground-based radiometer networks to estimate footprint-averaged brightness temperature are higher than for soil moisture due to the non-linearities of radiative transfer, and only weakly correlated in space and time. This study provides a basis for a better understanding of the sometimes strong mismatches between derived satellite soil moisture products and ground-based measurements.

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Estimation of Penetration Depth from Soil Effective Temperature in Microwave Radiometry

Cited by 52 publications

References 45 publications

Version 4 of the SMAP Level‐4 Soil Moisture Algorithm and Data Product

Version 4 of the SMAP Level‐4 Soil Moisture Algorithm and Data Product

Year-long, broad-band, microwave backscatter observations of an Alpine Meadow over the Tibetan Plateau with a ground-based scatterometer

Required sampling density of ground-based soil moisture and brightness temperature observations for calibration and validation of L-band satellite observations based on a virtual reality

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