Diurnal Variation of Vertical Temperature Gradients Within a Field of Maize: Implications for Satellite Microwave Radiometry

Hornbuckle, B. K.; England, Anthony W.

doi:10.1109/lgrs.2004.841370

Cited by 36 publications

(22 citation statements)

References 6 publications

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“…Compared to DA 2, the increment standard deviation is larger in the root zone for DA 0 and the top-layer increment bias decreases substantially. As for the differences between the ascending and descending orbits, we conclude that they can be partly explained by referring back to the fact that soil moisture retrievals are expected to be of a higher quality for the ascending orbits (Hornbuckle and England, 2005;Kerr et al, 2010).…”

Section: Soil Moisture Incrementsmentioning

confidence: 86%

SMOS brightness temperature assimilation into the Community Land Model

Rains

Han²,

Lievens

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. SMOS (Soil Moisture and Ocean Salinity mission) brightness temperatures at a single incident angle are assimilated into the Community Land Model (CLM) across Australia to improve soil moisture simulations. Therefore, the data assimilation system DasPy is coupled to the local ensemble transform Kalman filter (LETKF) as well as to the Community Microwave Emission Model (CMEM). Brightness temperature climatologies are precomputed to enable the assimilation of brightness temperature anomalies, making use of 6 years of SMOS data (2010)(2011)(2012)(2013)(2014)(2015). Mean correlation R with in situ measurements increases moderately from 0.61 to 0.68 (11 %) for upper soil layers if the root zone is included in the updates. A reduced improvement of 5 % is achieved if the assimilation is restricted to the upper soil layers. Root-zone simulations improve by 7 % when updating both the top layers and root zone, and by 4 % when only updating the top layers. Mean increments and increment standard deviations are compared for the experiments. The longterm assimilation impact is analysed by looking at a set of quantiles computed for soil moisture at each grid cell. Within hydrological monitoring systems, extreme dry or wet conditions are often defined via their relative occurrence, adding great importance to assimilation-induced quantile changes. Although still being limited now, longer L-band radiometer time series will become available and make model output improved by assimilating such data that are more usable for extreme event statistics.

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Section: Soil Moisture Incrementsmentioning

confidence: 86%

SMOS brightness temperature assimilation into the Community Land Model

Rains

Han²,

Lievens

et al. 2017

Hydrol. Earth Syst. Sci.

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“…The SMOS‐A data were collected at 6 AM, whereas the SMOS‐D data were collected at 6 PM; various studies [e.g., O'Neill et al ., (see their Figure )] suggest that at 6 AM, vertical temperature profiles in the soil, upon which retrieval algorithms are based, are roughly uniform, whereas at 6 PM, strong vertical gradients exist that can make soil moisture estimation more difficult. (Note, however, that at least one study [ Hornbuckle and England , ] found the opposite: more vertical uniformity in the evening.) Regardless of reason, assuming (following Lei et al .…”

Section: Resultsmentioning

confidence: 99%

Precipitation estimation using L‐band and C‐band soil moisture retrievals

Koster

Brocca

Crow

et al. 2016

Water Resources Research

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An established methodology for estimating precipitation amounts from satellite‐based soil moisture retrievals is applied to L‐band products from the Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) satellite missions and to a C‐band product from the Advanced Scatterometer (ASCAT) mission. The precipitation estimates so obtained are evaluated against in situ (gauge‐based) precipitation observations from across the globe. The precipitation estimation skill achieved using the L‐band SMAP and SMOS data sets is higher than that obtained with the C‐band product, as might be expected given that L‐band is sensitive to a thicker layer of soil and thereby provides more information on the response of soil moisture to precipitation. The square of the correlation coefficient between the SMAP‐based precipitation estimates and the observations (for aggregations to ∼100 km and 5 days) is on average about 0.6 in areas of high rain gauge density. Satellite missions specifically designed to monitor soil moisture thus do provide significant information on precipitation variability, information that could contribute to efforts in global precipitation estimation.

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“…SMOS has a sun-synchronous orbit with local equatorial crossing times of approximately 6:00 a.m. and 6:00 p.m. in ascending and descending nodes, respectively [64]. The ascending (6:00 a.m.) node is used in the current study because surface soil layer conditions are expected to be closest to thermal equilibrium at this time [58,65,66]. SMOS soil moisture data are discarded when the quality of retrieval is poor (DQX greater than 0.07), the soil moisture value is negative, or the retrieval has failed.…”

Section: Satellite Observations (Smos and Modis)mentioning

confidence: 99%

Evapotranspiration Estimates Derived Using Multi-Platform Remote Sensing in a Semiarid Region

et al. 2017

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Evapotranspiration (ET) is a key component of the water balance, especially in arid and semiarid regions. The current study takes advantage of spatially-distributed, near real-time information provided by satellite remote sensing to develop a regional scale ET product derived from remotely-sensed observations. ET is calculated by scaling PET estimated from Moderate Resolution Imaging Spectroradiometer (MODIS) products with downscaled soil moisture derived using the Soil Moisture Ocean Salinity (SMOS) satellite and a second order polynomial regression formula. The MODis-Soil Moisture ET (MOD-SMET) estimates are validated using four flux tower sites in southern Arizona USA, a calibrated empirical ET model, and model output from Version 2 of the North American Land Data Assimilation System (NLDAS-2). Validation against daily eddy covariance ET indicates correlations between 0.63 and 0.83 and root mean square errors (RMSE) between 40 and 96 W/m 2 . MOD-SMET estimates compare well to the calibrated empirical ET model, with a −0.14 difference in correlation between sites, on average. By comparison, NLDAS-2 models underestimate daily ET compared to both flux towers and MOD-SMET estimates. Our analysis shows the MOD-SMET approach to be effective for estimating ET. Because it requires limited ancillary ground-based data and no site-specific calibration, the method is applicable to regions where ground-based measurements are not available.

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Diurnal Variation of Vertical Temperature Gradients Within a Field of Maize: Implications for Satellite Microwave Radiometry

Cited by 36 publications

References 6 publications

SMOS brightness temperature assimilation into the Community Land Model

SMOS brightness temperature assimilation into the Community Land Model

Precipitation estimation using L‐band and C‐band soil moisture retrievals

Evapotranspiration Estimates Derived Using Multi-Platform Remote Sensing in a Semiarid Region

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