scholarly journals Forward Simulation of Multi-Frequency Microwave Brightness Temperature over Desert Soils in Kuwait and Comparison with Satellite Observations

2019 ◽  
Vol 11 (14) ◽  
pp. 1647 ◽  
Author(s):  
Hala K. AlJassar ◽  
Marouane Temimi ◽  
Dara Entekhabi ◽  
Peter Petrov ◽  
Hussain AlSarraf ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Brightness Temperature ◽  
Surface Soil ◽  
Dynamic Range ◽  
European Space Agency ◽  
Mixing Model ◽  
Surface Soil Moisture ◽  
Horizontal Polarization ◽  
Brightness Temperatures ◽  
The Impact

In this study, we address the variations of bare soil surface microwave brightness temperatures and evaluate the performance of a dielectric mixing model over the desert of Kuwait. We use data collected in a field survey and data obtained from NASA Soil Moisture Active Passive (SMAP), European Space Agency Soil Moisture and Ocean Salinity (SMOS), Advanced Microwave Scanning Radiometer 2 (AMSR2), and Special Sensor Microwave/Imager (SSM/I). In situ measurements are collected during two intensive field campaigns over bare, flat, and homogeneous soil terrains in the desert of Kuwait. Despite the prevailing dry desert environment, a large range of soil moisture values was monitored, due to precedent rain events and subsequent dry down. The mean relative difference (MRD) is within the range of ±0.005 m3·m−3 during the two sampling days. This reflects consistency of soil moisture in space and time. As predicted by the model, the higher frequency channels (18 to 19 GHz) demonstrate reduced sensitivity to surface soil moisture even in the absence of vegetation, topography and heterogeneity. In the 6.9 to 10.7 GHz range, only the horizontal polarization is sensitive to surface soil moisture. Instead, at the frequency of 1.4 GHz, both polarizations are sensitive to soil moisture and span a large dynamic range as predicted by the model. The error statistics of the difference between observed satellite brightness temperature (Tb) (excluding SMOS data due to radio frequency interference, RFI) and simulated brightness temperatures (Tbs) show values of Root Mean Square Deviation (RMSD) of 5.05 K at vertical polarization and 4.88 K at horizontal polarization. Such error could be due to the performance of the dielectric mixing model, soil moisture sampling depth and the impact of parametrization of effective temperature and roughness.

The Computation of the Effective Temperature of the Land Surface Covered with Canopy

2014 ◽  
Vol 607 ◽  
pp. 830-834
Author(s):  
Hong Zhang Ma ◽  
Su Mei Liu
Keyword(s):  
Soil Moisture ◽  
Brightness Temperature ◽  
Effective Temperature ◽  
Land Surface ◽  
Surface Soil ◽  
Passive Microwave ◽  
Vegetation Canopy ◽  
Surface Soil Moisture ◽  
Remote Sensors ◽  
Energy Exchanges

—Surface soil moisture is an important parameter in describing the water and energy exchanges at the land surface/atmosphere interface. Passive microwave remote sensors have great potential for monitoring surface soil moisture over land surface. The objective of this study is going to establish a model for estimating the effective temperature of land surface covered with vegetation canopy and to investigate how to compute the microwave radiative brightness temperature of land surface covered with vegetation canopy in considering of the canopy scatter effect.

scholarly journals Assimilation of SMOS brightness temperatures or soil moisture retrievals into a land surface model

2016 ◽  
Vol 20 (12) ◽  
pp. 4895-4911 ◽  
Author(s):  
Gabriëlle J. M. De Lannoy ◽  
Rolf H. Reichle
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Surface Soil ◽  
Root Zone ◽  
Incidence Angle ◽  
Land Surface Model ◽  
Surface Model ◽  
Model Errors ◽  
Surface Soil Moisture ◽  
Brightness Temperatures

Abstract. Three different data products from the Soil Moisture Ocean Salinity (SMOS) mission are assimilated separately into the Goddard Earth Observing System Model, version 5 (GEOS-5) to improve estimates of surface and root-zone soil moisture. The first product consists of multi-angle, dual-polarization brightness temperature (Tb) observations at the bottom of the atmosphere extracted from Level 1 data. The second product is a derived SMOS Tb product that mimics the data at a 40° incidence angle from the Soil Moisture Active Passive (SMAP) mission. The third product is the operational SMOS Level 2 surface soil moisture (SM) retrieval product. The assimilation system uses a spatially distributed ensemble Kalman filter (EnKF) with seasonally varying climatological bias mitigation for Tb assimilation, whereas a time-invariant cumulative density function matching is used for SM retrieval assimilation. All assimilation experiments improve the soil moisture estimates compared to model-only simulations in terms of unbiased root-mean-square differences and anomaly correlations during the period from 1 July 2010 to 1 May 2015 and for 187 sites across the US. Especially in areas where the satellite data are most sensitive to surface soil moisture, large skill improvements (e.g., an increase in the anomaly correlation by 0.1) are found in the surface soil moisture. The domain-average surface and root-zone skill metrics are similar among the various assimilation experiments, but large differences in skill are found locally. The observation-minus-forecast residuals and analysis increments reveal large differences in how the observations add value in the Tb and SM retrieval assimilation systems. The distinct patterns of these diagnostics in the two systems reflect observation and model errors patterns that are not well captured in the assigned EnKF error parameters. Consequently, a localized optimization of the EnKF error parameters is needed to further improve Tb or SM retrieval assimilation.

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.

Soil Hydraulic Parameters and Surface Soil Moisture of a Tilled Bare Soil Plot Inversely Derived from L-Band Brightness Temperatures

2014 ◽  
Vol 13 (1) ◽  
pp. vzj2013.04.0075 ◽  
Author(s):  
M. Dimitrov ◽  
J. Vanderborght ◽  
K. G. Kostov ◽  
K. Z. Jadoon ◽  
L. Weihermüller ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Bare Soil ◽  
Hydraulic Parameters ◽  
Surface Soil Moisture ◽  
Brightness Temperatures ◽  
Soil Hydraulic Parameters ◽  
L Band ◽  
Soil Hydraulic

A semi-empirical inversion model for assessing surface soil moisture using AMSR-E brightness temperatures

2012 ◽  
Vol 456-457 ◽  
pp. 1-11 ◽  
Author(s):  
Xiu-zhi Chen ◽  
Shui-sen Chen ◽  
Ruo-fei Zhong ◽  
Yong-xian Su ◽  
Ji-shan Liao ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Surface Soil Moisture ◽  
Inversion Model ◽  
Brightness Temperatures ◽  
Semi Empirical

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>

scholarly journals Irrigation and Precipitation Hydrological Consistency with SMOS, SMAP, ESA-CCI, Copernicus SSM1km, and AMSR-2 Remotely Sensed Soil Moisture Products

2020 ◽  
Vol 12 (22) ◽  
pp. 3737
Author(s):  
Nicola Paciolla ◽  
Chiara Corbari ◽  
Ahmad Al Bitar ◽  
Yann Kerr ◽  
Marco Mancini
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
European Space Agency ◽  
Irrigation Season ◽  
Surface Soil Moisture ◽  
Irrigation Period ◽  
Space Agency ◽  
Ocean Salinity ◽  
Passive Data ◽  
Active Data

Numerous Surface Soil Moisture (SSM) products are available from remote sensing, encompassing different spatial, temporal, and radiometric resolutions and retrieval techniques. Notwithstanding this variety, all products should be coherent with water inputs. In this work, we have cross-compared precipitation and irrigation with different SSM products: Soil Moisture Ocean Salinity (SMOS), Soil Moisture Active Passive (SMAP), European Space Agency (ESA) Climate Change Initiative (ESA-CCI) products, Copernicus SSM1km, and Advanced Microwave Scanning Radiometer 2 (AMSR2). The products have been analyzed over two agricultural sites in Italy (Chiese and Capitanata Irrigation Consortia). A Hydrological Consistency Index (HCI) is proposed as a means to measure the coherency between SSM and precipitation/irrigation. Any time SSM is available, a positive or negative consistency is recorded, according to the rainfall registered since the previous measurement and the increase/decrease of SSM. During the irrigation season, some agreements are labeled as “irrigation-driven”. No SSM dataset stands out for a systematic hydrological coherence with the rainfall. Negative consistencies cluster just below 50% in the non-irrigation period and lose 20–30% in the irrigation period. Hybrid datasets perform better (+15–20%) than single-technology measurements, among which active data provide slightly better results (+5–10%) than passive data.

scholarly journals Monitoring Surface Soil Moisture Content over the Vegetated Area by Integrating Optical and SAR Satellite Observations in the Permafrost Region of Tibetan Plateau

2020 ◽  
Vol 12 (1) ◽  
pp. 183 ◽  
Author(s):  
Chenyang Xu ◽  
John J. Qu ◽  
Xianjun Hao ◽  
Di Wu
Keyword(s):  
Soil Moisture ◽  
Tibetan Plateau ◽  
Water Content ◽  
Vegetation Index ◽  
Surface Soil ◽  
Cloud Model ◽  
Incidence Angle ◽  
Permafrost Region ◽  
Surface Soil Moisture ◽  
The Impact

Surface soil moisture (SSM), the average water content of surface soil (up to 5 cm depth), plays a key role in the energy exchange within the ecosystem. We estimated SSM in areas with vegetation cover (grassland) by combining microwave and optical satellite measurements in the central Tibetan Plateau (TP) in 2015. We exploited TERRA moderate resolution imaging spectroradiometer (MODIS) and Sentinel-1A synthetic aperture radar (SAR) observations to estimate SSM through a simplified water-cloud model (sWCM). This model considers the impact of vegetation water content (VWC) to SSM retrieval by integrating the vegetation index (VI), the normalized difference water index (NDWI), or the normalized difference infrared index (NDII). Sentinel-1 SAR C-band backscattering coefficients, incidence angle, and NDWI/NDII were assimilated in the sWCM to monitor SSM. The soil moisture and temperature monitoring network on the central TP (CTP-SMTMN) measures SSM within the study area, and ground measurements were applied to train and validate the model. Via the proposed methods, we estimated the SSM in vegetated area with an R2 of 0.43 and a ubRMSE of 0.06 m3/m3 when integrating the NDWI and with an R2 of 0.45 and a ubRMSE of 0.06 m3/m3 when integrating the NDII.

scholarly journals Evaluating Hydrological Processes of the Atmosphere–Vegetation Interaction Model and MERRA-2 at Global Scale

Atmosphere ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 16
Author(s):  
Meizhao Lv ◽  
Zhongfeng Xu ◽  
Meixia Lv
Keyword(s):  
Soil Moisture ◽  
Spatial Pattern ◽  
Annual Cycle ◽  
Surface Runoff ◽  
Surface Soil ◽  
European Space Agency ◽  
Interaction Model ◽  
Hydrological Processes ◽  
Surface Soil Moisture ◽  
Global Land

Hydrological processes are a key component of land surface models and link to the energy budget and carbon cycle. This study assessed the global hydrological processes of the Atmosphere–Vegetation Interaction Model (AVIM) using multiple datasets, including the Global Land Data Assimilation System (GLDAS), the University of New Hampshire and Global Runoff Data Centre (UNH-GRDC), the European Space Agency (ESA) Climate Change Initiative (CCI), the Global Land Evaporation Amsterdam Model (GLEAM), and the Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) datasets. The comparisons showed that the AVIM gives a reasonable spatial pattern for surface soil moisture and surface runoff, but a less satisfactory spatial pattern for evapotranspiration. The AVIM clearly underestimates surface runoff worldwide and overestimates the surface soil moisture in the high latitudes of the Northern Hemisphere, while yielding moderately higher evapotranspiration in arid areas and lower evapotranspiration in low-latitude areas near the equator. The annual cycle of evapotranspiration in the AVIM shows good agreement with the GLEAM dataset, whereas the surface soil moisture in the AVIM has a poor annual cycle relative to the CCI dataset. The AVIM simulates a late start time for snowmelt, which leads to a two-month delay in the peak surface runoff. These results clearly point out the directions required for improvements in the AVIM, which will support future investigations of water–carbon–atmosphere interactions. In addition, the evapotranspiration in the MERRA-2 dataset had an overall good performance comparable with that of the GLEAM dataset, but its surface soil moisture did not perform well when validated against the CCI dataset.

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