The assimilation of remotely sensed soil brightness temperature imagery into a land surface model using Ensemble Kalman filtering: a case study based on ESTAR measurements during SGP97

2003 ◽  
Vol 26 (2) ◽  
pp. 137-149 ◽  
Author(s):  
Wade T Crow ◽  
Eric F Wood
Keyword(s):  
Brightness Temperature ◽  
Kalman Filtering ◽  
Land Surface ◽  
Land Surface Model ◽  
Remotely Sensed ◽  
Surface Model ◽  
Ensemble Kalman Filtering

Examination of Dielectric Models in AMSR2 Soil Moisture Estimation Algorithm for Japanese and Cambodian Soils

2020 ◽  
Author(s):  
Kumiko Tsujimoto ◽  
Tetsu Ohta
Keyword(s):  
Soil Moisture ◽  
Brightness Temperature ◽  
Land Surface ◽  
Surface Soil ◽  
Land Surface Model ◽  
Mixing Model ◽  
Dielectric Behavior ◽  
Moisture Distribution ◽  
Surface Model ◽  
In Situ Observation

<p>The Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the Global Change Observation Mission – Water (GCOM-W) satellite provides global surface soil moisture as well as other water-related variables over the earth. With its brightness temperature observations at 10 and 36 GHz, the global soil moisture product is operationally created by the Japan Aerospace Exploration Agency (JAXA) based on the Koike’s algorithm (Koike et al., 2004) using the Polar Index (PI) and the Index of Soil Wetness (ISW). A land data assimilation system, LDAS-UT, has been also developed by Yang et al. (2007) to retrieve the optimized soil moisture estimates using both the brightness temperature observation and a land surface model.</p><p>In this study, we applied the distributed hydrological model, WEB-DHM (Wang et al., 2009), which incorporates the same land surface model with LDAS-UT, to a river basin in Cambodia and then calculated the brightness temperature at 6.9GHz from the simulated soil moisture distribution, using the same forward model as LDAS-UT. The temporal and spatial distribution of soil moisture was calibrated and validated against in-situ observation through river discharge using WEB-DHM, and the calculated brightness temperature was compared with the AMSR2 observation at 6.9 GHz. In addition to the dielectric mixing model by Dobson (Dobson et al., 1985) which is originally used in the LDAS-UT as well as in the JAXA's soil moisture retrieval algorithm, the performance of the Mironov model (Mironov et al., 2004) was examined as an alternative for the dielectric mixing model in the forward calculation and the calculated results from the two models were compared.</p><p>Along with the hydrological simulation, field measurements and laboratory experiments were conducted in Cambodia and Japan to evaluate the dielectric behavior of wet soils with different soil water content at a point scale. A ground microwave radiometer was temporally installed over a paddy field in Japan to measure the brightness temperature at 6.9GHz directly from the near surface. Soil samples were also taken from this field as well as several other locations in Japan and Cambodia to measure the permittivity with different soil moisture content with a network analyzer in the laboratory, in order to examine the dielectric behavior of wet soils for different soil textures. The measured results were then compared with the Dobson and Mironov models to evaluate their performance for Asian soils.</p>

The Contributions of Gauge-Based Precipitation and SMAP Brightness Temperature Observations to the Skill of the SMAP Level-4 Soil Moisture Product

2020 ◽  
Author(s):  
Rolf H. Reichle ◽  
Qing Liu ◽  
Joseph V. Ardizzone ◽  
Wade T. Crow ◽  
Gabrielle J. M. De Lannoy ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Brightness Temperature ◽  
Land Surface ◽  
Root Zone ◽  
Land Surface Model ◽  
Surface Model ◽  
Meteorological Forcing ◽  
Earth Observing System ◽  
Error Surface

AbstractSoil Moisture Active Passive (SMAP) mission L-band brightness temperature (Tb) observations are routinely assimilated into the Catchment land surface model to generate Level-4 Soil Moisture (L4_SM) estimates of global surface and root-zone soil moisture at 9-km, 3-hourly resolution with ~2.5-day latency. The Catchment model in the L4_SM algorithm is driven with ¼-degree, hourly surface meteorological forcing data from the Goddard Earth Observing System (GEOS). Outside of Africa and the high latitudes, GEOS precipitation is corrected using Climate Prediction Center Unified (CPCU) gauge-based, ½-degree, daily precipitation. L4_SM soil moisture was previously shown to improve over land model-only estimates that use CPCU precipitation but no Tb assimilation (CPCU_SIM). Here, we additionally examine the skill of model-only (CTRL) and Tb assimilation-only (SMAP_DA) estimates derived without CPCU precipitation. Soil moisture is assessed versus in situ measurements in well-instrumented regions and globally through the Instrumental Variable (IV) method using independent soil moisture retrievals from the Advanced Scatterometer. At the in situ locations, SMAP_DA and CPCU_SIM have comparable soil moisture skill improvements relative to CTRL for the unbiased root-mean-square error (surface and root-zone) and correlation metrics (root-zone only). In the global average, SMAP Tb assimilation increases the surface soil moisture anomaly correlation by 0.10-0.11 compared to an increase of 0.02-0.03 from the CPCU-based precipitation corrections. The contrast is particularly strong in central Australia, where CPCU is known to have errors and observation-minus-forecast Tb residuals are larger when CPCU precipitation is used. Validation versus streamflow measurements in the contiguous U.S. reveals that CPCU precipitation provides most of the skill gained in L4_SM runoff estimates over CTRL.

scholarly journals A comparison between remotely-sensed and modelled surface soil moisture (and frozen status) at high latitudes

2013 ◽  
Vol 10 (8) ◽  
pp. 11241-11291 ◽  
Author(s):  
I. Gouttevin ◽  
A. Bartsch ◽  
G. Krinner ◽  
V. Naeimi
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Surface Soil ◽  
Land Surface Model ◽  
Remotely Sensed ◽  
Hydrological Modelling ◽  
Northern Eurasia ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
North Eastern

Abstract. In this study, the combined surface status and surface soil moisture products retrieved by the ASCAT sensor within the ESA-DUE Permafrost project are compared to the hydrological outputs of the land surface model ORCHIDEE over Northern Eurasia. The objective is to derive broad conclusions as to the strengths and weaknesses of hydrological modelling and, to a minor extent, remote sensing of soil moisture over an area where data is rare and hydrological modelling is though crucial for climate and ecological applications. The spatial and temporal resolutions of the ASCAT products make them suitable for comparison with model outputs. Modelled and remotely-sensed surface frozen and unfrozen statuses agree reasonably well, which allows for a seasonal comparison between modelled and observed (liquid) surface soil moisture. The atmospheric forcing and the snow scheme of the land surface model are identified as causes of moderate model-to-data divergence in terms of surface status. For unfrozen soils, the modelled and remotely-sensed surface soil moisture signals are positively correlated over most of the study area. The correlation deteriorates in the North-Eastern Siberian regions, which is consistent with the lack of accurate model parameters and the scarcity of meteorological data. The model shows a reduced ability to capture the main seasonal dynamics and spatial patterns of observed surface soil moisture in Northern Eurasia, namely a characteristic spring surface moistening resulting from snow melt and flooding. We hypothesize that these weak performances mainly originate from the non-representation of flooding and surface ponding in the model. Further identified limitations proceed from the coarse treatment of the hydrological specificities of mountainous areas and spatial inaccuracies in the meteorological forcing in remote, North-Eastern Siberian areas. Investigations are currently underway to determine to which extent plausible inaccuracies in the satellite data could also contribute to the diagnosed model-to-data discrepancies.

scholarly journals Assimilation of SMOS brightness temperature into a large-scale distributed conceptual hydrological model

2019 ◽  
Author(s):  
Renaud Hostache ◽  
Dominik Rains ◽  
Kaniska Mallick ◽  
Marco Chini ◽  
Ramona Pelich ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Brightness Temperature ◽  
Land Surface ◽  
Large Scale ◽  
Hydrological Model ◽  
Land Surface Model ◽  
Surface Model ◽  
Meteorological Model ◽  
Temperature Observations

Abstract. The main objective of this study is to investigate how brightness temperature observations from satellite microwave sensors may help in reducing errors and uncertainties in soil moisture simulations with a large-scale conceptual hydro-meteorological model. In particular, we use as forcings the ERA-Interim public dataset and we couple the CMEM radiative transfer model with a hydro-meteorological model enabling therefore soil moisture and SMOS-like brightness temperature simulations. The hydro-meteorological model is configured using recent developments of the SUPERFLEX framework, which enables tailoring the model structure to the specific needs of the application as well as to data availability and computational requirements. In this case, the model spatial resolution is adapted to the spatial grid of the satellite data, and the soil stratification is tailored to the satellite datasets to be assimilated and the forcing data. The hydrological model is first calibrated using a sample of SMOS brightness temperature observations (period 2010–2011). Next, SMOS-derived brightness temperature observations are sequentially assimilated into the coupled SUPERFLEX-CMEM model (period 2010–2015). For this experiment, a Local Ensemble Transform Kalman Filter is used and the meteorological forcings (ERA interim-based rainfall, air and soil temperature) are perturbed to generate a background ensemble. Each time a SMOS observation is available, the SUPERFLEX state variables related to the water content in the various soil layers are updated and the model simulations are resumed until the next SMOS observation becomes available. Our empirical results show that the SUPERFLEX-CMEM modelling chain is capable of predicting soil moisture at a performance level similar to that obtained for the same study area and with a quasi-identical experimental set up using the CLM land surface model. This shows that a simple model, when carefully calibrated, can yield performance level similar to that of a much more complex model. The correlation between simulated and in situ observed soil moisture ranges from 0.62 to 0.72. The assimilation of SMOS brightness temperature observation into the SUPERFLEX-CMEM modelling chain improves the correlation between predicted and in situ observed soil moisture by 0.03 on average showing improvements similar to those obtained using the CLM land surface model.

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