scholarly journals Space-based passive microwave soil moisture retrievals and the correction for a dynamic open water fraction

2012 ◽  
Vol 16 (6) ◽  
pp. 1635-1645 ◽  
Author(s):  
B. T. Gouweleeuw ◽  
A. I. J. M. van Dijk ◽  
J. P. Guerschman ◽  
P. Dyce ◽  
M. Owe
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Open Water ◽  
Water Bodies ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Vegetation Density ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
Water Fraction

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 on near-surface soil moisture products derived from actual (non-synthetic) 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 related to dynamic estimates of open water fraction, one retrieved from a global daily record based on higher frequency AMSR-E data, a second derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and a third through inversion of the radiative transfer model, used to retrieve soil moisture. The comparison demonstrates the presence of relatively small areas (<0.05) of open water in or near the sensor footprint, possibly in combination with increased, below-critical vegetation density conditions (optical density <0.8), which contribute to seasonally varying biases in excess of 0.2 (m3 m−3) soil water content. These errors need to be addressed, either through elimination or accurate characterisation, if the soil moisture retrievals are to be used effectively in a data assimilation scheme.

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

2012 ◽  
Vol 9 (1) ◽  
pp. 1013-1039 ◽  
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.

scholarly journals Surface soil moisture estimates from AMSR-E observations over an arid area, Northwest China

2009 ◽  
Vol 6 (1) ◽  
pp. 1055-1087 ◽  
Author(s):  
L. Wang ◽  
J. Wen ◽  
T. Zhang ◽  
Y. Zhao ◽  
H. Tian ◽  
...  
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Surface Soil ◽  
Northwest China ◽  
Arid Area ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Taklimakan Desert ◽  
Surface Soil Moisture ◽  
Good Agreement

Abstract. In this paper, 7 years (during the growing season (April–October) of 2002–2008) of the Advanced Microwave Scanning Radiometer (AMSR-E) data taken at a frequency of 6.9 GHz for night observations at both polarizations are processed and used to conduct 7 years of surface soil moisture dataset for an arid area, in Northwestern China. This soil moisture dataset can contribute to better understanding the climate change and forecast modeling for this area. Based on the first-order radiative transfer model calculation, a unique method for estimating surface soil moisture over the study area is developed. Considering extremely complex topography over the study area, we have to present a parameterization of surface roughness at the 6.9 GHz and spatial resolution of the AMSR-E using the annual minimum MPDI (Microwave Polarization Difference Index). For validation purpose, the comparisons of soil moisture patterns with precipitation fields are made. The results indicate the evolutions of soil moisture estimated from the AMSR-E and antecedent ground daily precipitations are in a good agreement. Furthermore, a series of rainfall traces is captured over the Taklimakan Desert. Comparisons of the estimated values of soil moisture with the ground observations are also made for two reprehensive sites over 2002–2004. The results indicate there has a good agreement between them, with higher correlation coefficients (R=0.649, 0.604) and RMSE (3.5, 5.4%), and the soil moisture product derived from the AMSR-E is realistic and acceptable. This new long time series of estimated soil moisture will prove valuable for other studies of climate change and model evaluation.

scholarly journals The impact of near-surface soil moisture assimilation at subseasonal, seasonal, and inter-annual timescales

2015 ◽  
Vol 19 (12) ◽  
pp. 4831-4844 ◽  
Author(s):  
C. Draper ◽  
R. Reichle
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Surface Soil ◽  
Root Zone ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
In Situ Observations ◽  
The Impact

Abstract. A 9 year record of Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) soil moisture retrievals are assimilated into the Catchment land surface model at four locations in the US. The assimilation is evaluated using the unbiased mean square error (ubMSE) relative to watershed-scale in situ observations, with the ubMSE separated into contributions from the subseasonal (SMshort), mean seasonal (SMseas), and inter-annual (SMlong) soil moisture dynamics. For near-surface soil moisture, the average ubMSE for Catchment without assimilation was (1.8 × 10−3 m3 m−3)2, of which 19 % was in SMlong, 26 % in SMseas, and 55 % in SMshort. The AMSR-E assimilation significantly reduced the total ubMSE at every site, with an average reduction of 33 %. Of this ubMSE reduction, 37 % occurred in SMlong, 24 % in SMseas, and 38 % in SMshort. For root-zone soil moisture, in situ observations were available at one site only, and the near-surface and root-zone results were very similar at this site. These results suggest that, in addition to the well-reported improvements in SMshort, assimilating a sufficiently long soil moisture data record can also improve the model representation of important long-term events, such as droughts. The improved agreement between the modeled and in situ SMseas is harder to interpret, given that mean seasonal cycle errors are systematic, and systematic errors are not typically targeted by (bias-blind) data assimilation. Finally, the use of 1-year subsets of the AMSR-E and Catchment soil moisture for estimating the observation-bias correction (rescaling) parameters is investigated. It is concluded that when only 1 year of data are available, the associated uncertainty in the rescaling parameters should not greatly reduce the average benefit gained from data assimilation, although locally and in extreme years there is a risk of increased errors.

scholarly journals A Data-Driven Approach for Daily Real-Time Estimates and Forecasts of Near-Surface Soil Moisture

2017 ◽  
Vol 18 (3) ◽  
pp. 837-843 ◽  
Author(s):  
Randal D. Koster ◽  
Rolf H. Reichle ◽  
Sarith P. P. Mahanama
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Real Time ◽  
Surface Soil ◽  
Data Driven ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
Time Estimates ◽  
Data Driven Approach ◽  
Global Surface

Abstract NASA’s Soil Moisture Active Passive (SMAP) mission provides global surface soil moisture retrievals with a revisit time of 2–3 days and a latency of 24 h. Here, to enhance the utility of the SMAP data, an approach is presented for improving real-time soil moisture estimates (nowcasts) and for forecasting soil moisture several days into the future. The approach, which involves using an estimate of loss processes (evaporation and drainage) and precipitation to evolve the most recent SMAP retrieval forward in time, is evaluated against subsequent SMAP retrievals themselves. The nowcast accuracy over the continental United States is shown to be markedly higher than that achieved with the simple yet common persistence approach. The accuracy of soil moisture forecasts, which rely on precipitation forecasts rather than on precipitation measurements, is reduced relative to nowcast accuracy but is still significantly higher than that obtained through persistence.

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