Estimating surface soil moisture with the scanning low frequency microwave radiometer (SLFMR) during the Southern Great Plains 1997 (SGP97) hydrology experiment

Abstract Near-surface soil moisture retrieved from Soil Moisture and Ocean Salinity (SMOS)-type data is downscaled and assimilated into a distributed soil–vegetation–atmosphere transfer (SVAT) model with the ensemble Kalman filter. Because satellite-based meteorological data (notably rainfall) are not currently available at finescale, coarse-scale data are used as forcing in both the disaggregation and the assimilation. Synthetic coarse-scale observations are generated from the Monsoon ‘90 data by aggregating the Push Broom Microwave Radiometer (PBMR) pixels covering the eight meteorological and flux (METFLUX) stations and by averaging the meteorological measurements. The performance of the disaggregation/assimilation coupling scheme is then assessed in terms of surface soil moisture and latent heat flux predictions over the 19-day period of METFLUX measurements. It is found that the disaggregation improves the assimilation results, and vice versa, the assimilation of the disaggregated microwave soil moisture improves the spatial distribution of surface soil moisture at the observation time. These results are obtainable regardless of the spatial scale at which solar radiation, air temperature, wind speed, and air humidity are available within the microwave pixel and for an assimilation frequency varying from 1/1 day to 1/5 days.

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Spatial and temporal patterns of surface soil moisture over India estimated using surface wetness index from SSM/I microwave radiometer

International Journal of Remote Sensing ◽

10.1080/01431160412331330284 ◽

2005 ◽

Vol 26 (6) ◽

pp. 1269-1276 ◽

Cited By ~ 19

Author(s):

R. P. Singh ◽

S. R. Oza ◽

K. N. Chaudhari ◽

V. K. Dadhwal

Keyword(s):

Soil Moisture ◽

Surface Soil ◽

Microwave Radiometer ◽

Temporal Patterns ◽

Surface Soil Moisture ◽

Spatial And Temporal Patterns ◽

Wetness Index ◽

Surface Wetness

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Remote Sensing of Surface Soil Moisture from FengYun MicroWave Radiation Imager (MWRI) Data Using a Machine Learning Technique

10.5194/egusphere-egu2020-311 ◽

2020 ◽

Author(s):

Sibo Zhang ◽

Wei Yao

Keyword(s):

Machine Learning ◽

Soil Moisture ◽

Tibetan Plateau ◽

Microwave Radiation ◽

Missing Values ◽

Surface Soil ◽

Low Frequency ◽

Global Scale ◽

Western China ◽

Surface Soil Moisture

In the past, soil moisture can be retrieved from microwave imager over most of land conditions. However, the algorithm performances over Tibetan Plateau and the Northwest China vary greatly from one to another due to frozen soils and surface volumetric scattering. The majority of western Chinese region is often filled with invalid retrievals. In this study, Soil Moisture Operational Products System (SMOPS) products from NOAA are used as the learning objectives to train a&#160; machine learning (random forest) model for FY-3C microwave radiation imager (MWRI) data with multivariable inputs: brightness temperatures from all 10 MWRI channels from 10 to 89 GHz, brightness temperature polarization ratios at 10.65, 18.7 and 23.8 GHz, height in DEM (digital elevation model) and statistical soil porosity map data. Since the vegetation penetration of MWRI observations is limited, we exclude forest, urban and snow/ice surfaces in this work. It is shown that our new method performs very well and derives the surface soil moisture over Tibetan Plateau without major missing values. Comparing to other soil moisture data, the volumetric soil moisture (VSM) from this study correlates with SMOPS products much better than the MWRI operational L2 VSM products. R2 score increases from 0.3 to 0.6 and ubRMSE score improves significantly from 0.11 m3 m-3 to 0.04 m3 m-3 during the time period from 1 August 2017 to 31 May 2019. The spatial distribution of our MWRI VSM estimates is also much improved in western China. Moreover, our MWRI VSM estimates are in good agreement with the top 7 cm soil moisture of ECMWF ERA5 reanalysis data: R2 = 0.62, ubRMSD = 0.114 m3 m-3 and mean bias = -0.014 m3 m-3 for a global scale. We note that there is a risk of data gap of AMSR2 from the present to 2025. Obviously, for satellite low frequency microwave observations, MWRI observations from FY-3 series satellites can be a benefit supplement to keep the data integrity and increase the data density, since FY-3B\-3C\-3D satellites launched in November 2010\September 2013\November 2017 are still working today, and FY-3D is designed to work until November 2022.

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Assimilation of space-based passive microwave soil moisture retrievals and the correction for a dynamic open water fraction

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-9-1013-2012 ◽

2012 ◽

Vol 9 (1) ◽

pp. 1013-1039 ◽

Cited By ~ 1

Author(s):

B. T. Gouweleeuw ◽

A. I. J. M. van Dijk ◽

J. P. Guerschman ◽

P. Dyce ◽

R. A. M. de Jeu ◽

...

Keyword(s):

Soil Moisture ◽

Surface Soil ◽

Low Frequency ◽

Open Water ◽

Water Bodies ◽

Surface Soil Moisture ◽

Near Surface ◽

Small Areas ◽

Water Fraction ◽

Moderate Resolution Imaging Spectroradiometer

Abstract. The large observation footprint of low-frequency satellite microwave emissions complicates the interpretation of near-surface soil moisture retrievals. While the effect of sub-footprint lateral heterogeneity is relatively limited under unsaturated conditions, open water bodies, if not accounted for, cause a strong positive bias in the satellite-derived soil moisture retrieval. This bias is generally assumed static and associated with large, continental lakes and coastal areas. Temporal changes in the extent of smaller water bodies as small as a few percent of the sensor footprint size, however, can cause significant and dynamic biases. We analysed the influence of such small open water bodies near-surface soil moisture retrieval data from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) for three areas in Oklahoma, USA. Differences between on-ground observations, model estimates and AMSR-E retrievals were compared to dynamic estimates of open water fraction, one retrieved from a global daily record based on higher frequency AMSR-E data and another derived from the Moderate Resolution Imaging Spectroradiometer (MODIS). The comparisons demonstrates that seasonally varying biases of up to 30 vol.% soil water content can be attributed to the presence of relatively small areas (<5%) of open water in or near the sensor footprint. These errors need to be addressed, either through elimination or accurate characterization, if the soil moisture retrievals are to be used effectively in a data assimilation scheme.

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Estimating root zone soil moisture using near-surface observations from SMOS

Hydrology and Earth System Sciences ◽

10.5194/hess-18-139-2014 ◽

2014 ◽

Vol 18 (1) ◽

pp. 139-154 ◽

Cited By ~ 77

Author(s):

T. W. Ford ◽

E. Harris ◽

S. M. Quiring

Keyword(s):

United States ◽

Soil Moisture ◽

Great Plains ◽

Surface Soil ◽

Root Zone ◽

The United States ◽

Surface Soil Moisture ◽

Near Surface ◽

Cross Correlation Analysis

Abstract. Satellite-derived soil moisture provides more spatially and temporally extensive data than in situ observations. However, satellites can only measure water in the top few centimeters of the soil. Root zone soil moisture is more important, particularly in vegetated regions. Therefore estimates of root zone soil moisture must be inferred from near-surface soil moisture retrievals. The accuracy of this inference is contingent on the relationship between soil moisture in the near-surface and the soil moisture at greater depths. This study uses cross correlation analysis to quantify the association between near-surface and root zone soil moisture using in situ data from the United States Great Plains. Our analysis demonstrates that there is generally a strong relationship between near-surface (5–10 cm) and root zone (25–60 cm) soil moisture. An exponential decay filter is used to estimate root zone soil moisture using near-surface soil moisture derived from the Soil Moisture and Ocean Salinity (SMOS) satellite. Root zone soil moisture derived from SMOS surface retrievals is compared to in situ soil moisture observations in the United States Great Plains. The SMOS-based root zone soil moisture had a mean R2 of 0.57 and a mean Nash–Sutcliffe score of 0.61 based on 33 stations in Oklahoma. In Nebraska, the SMOS-based root zone soil moisture had a mean R2 of 0.24 and a mean Nash–Sutcliffe score of 0.22 based on 22 stations. Although the performance of the exponential filter method varies over space and time, we conclude that it is a useful approach for estimating root zone soil moisture from SMOS surface retrievals.

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Estimation of Landscape Soil Water Losses from Satellite Observations of Soil Moisture

Journal of Hydrometeorology ◽

10.1175/jhm-d-17-0200.1 ◽

2018 ◽

Vol 19 (5) ◽

pp. 871-889 ◽

Cited By ~ 19

Author(s):

Ruzbeh Akbar ◽

Daniel J. Short Gianotti ◽

Kaighin A. McColl ◽

Erfan Haghighi ◽

Guido D. Salvucci ◽

...

Keyword(s):

United States ◽

Soil Moisture ◽

Soil Water ◽

Great Plains ◽

Loss Function ◽

Surface Soil ◽

Soil Water Storage ◽

Surface Soil Moisture ◽

Water Losses ◽

Hydrologic Regimes

Abstract This study presents an observation-driven technique to delineate the dominant boundaries and temporal shifts between different hydrologic regimes over the contiguous United States (CONUS). The energy- and water-limited evapotranspiration regimes as well as percolation to the subsurface are hydrologic processes that dominate the loss of stored water in the soil following precipitation events. Surface soil moisture estimates from the NASA Soil Moisture Active Passive (SMAP) mission, over three consecutive summer seasons, are used to estimate the soil water loss function. Based on analysis of the rates of soil moisture dry-downs, the loss function is the conditional expectation of negative increments in the soil moisture series conditioned on soil moisture itself. An unsupervised classification scheme (with cross validation) is then implemented to categorize regions according to their dominant hydrological regimes based on their estimated loss functions. An east–west divide in hydrologic regimes over CONUS is observed with large parts of the western United States exhibiting a strong water-limited evapotranspiration regime during most of the times. The U.S. Midwest and Great Plains show transitional behavior with both water- and energy-limited regimes present. Year-to-year shifts in hydrologic regimes are also observed along with regional anomalies due to moderate drought conditions or above-average precipitation. The approach is based on remotely sensed surface soil moisture (approximately top 5 cm) at a resolution of tens of kilometers in the presence of soil texture and land cover heterogeneity. The classification therefore only applies to landscape-scale effective conditions and does not directly account for deeper soil water storage.

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