scholarly journals An Improved Algorithm for Discriminating Soil Freezing and Thawing Using AMSR-E and AMSR2 Soil Moisture Products

2018 ◽  
Vol 10 (11) ◽  
pp. 1697 ◽  
Author(s):  
Huiran Gao ◽  
Wanchang Zhang ◽  
Hao Chen
Keyword(s):  
Soil Moisture ◽  
Brightness Temperature ◽  
Surface Soil ◽  
Microwave Remote Sensing ◽  
Passive Microwave ◽  
Soil Freezing ◽  
Classification Rate ◽  
Freeze Thaw ◽  
The Impact ◽  
Improved Algorithm

Discriminating between surface soil freeze/thaw states with the use of passive microwave brightness temperature has been an effective approach so far. However, soil moisture has a direct impact on the brightness temperature of passive microwave remote sensing, which may result in uncertainties in the widely used dual-index algorithm (DIA). In this study, an improved algorithm is proposed to identify the surface soil freeze/thaw states based on the original DIA in association with the AMSR-E and AMSR2 soil moisture products to avoid the impact of soil moisture on the brightness temperature derived from passive microwave remotely-sensed soil moisture products. The local variance of soil moisture (LVSM) with a 25-day interval was introduced into this algorithm as an effective indicator for selecting a threshold to update and modify the original DIA to identify surface soil freeze/thaw states. The improved algorithm was validated against in-situ observations of the Soil Moisture/Temperature Monitoring Network (SMTMN). The results suggest that the temporal and spatial variation characteristics of LVSM can significantly discriminate between surface soil freeze/thaw states. The overall discrimination accuracy of the improved algorithm was approximately 89% over a remote area near the town of Naqu on the East-Central Tibetan Plateau, which demonstrated an obvious improvement compared with the accuracy of 82% derived with the original DIA. More importantly, the correct classification rate for the modified pixels was over 96%.

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 USING SMOS PASSIVE MICROWAVE DATA TO DEVELOP SMAP FREEZE/THAW ALGORITHMS ADAPTED FOR THE CANADIAN SUBARCTIC

2015 ◽  
Vol XL-1-W5 ◽  
pp. 365-368 ◽  
Author(s):  
P. Kalantari ◽  
M. Bernier ◽  
K. C. McDonal ◽  
J. Poulin
Keyword(s):  
Land Cover ◽  
Time Series Data ◽  
Passive Microwave ◽  
Soil Freezing ◽  
Series Data ◽  
Freezing And Thawing ◽  
Freeze Thaw ◽  
Thaw Cycle ◽  
Freeze Thaw Cycle ◽  
The Impact

Seasonal terrestrial Freeze/Thaw cycle in Northern Quebec Tundra (Nunavik) was determined and evaluated with passive microwave observations. SMOS time series data were analyzed to examine seasonal variations of soil freezing, and to assess the impact of land cover on the Freeze/Thaw cycle. Furthermore, the soil freezing maps derived from SMOS observations were compared to field survey data in the region near Umiujaq. The objective is to develop algorithms to follow the seasonal cycle of freezing and thawing of the soil adapted to Canadian subarctic, a territory with a high complexity of land cover (vegetation, soil, and water bodies). Field data shows that soil freezing and thawing dates vary much spatially at the local scale in the Boreal Forest and the Tundra. The results showed a satisfactory pixel by pixel mapping for the daily soil state monitoring with a > 80% success rate with in situ data for the HH and VV polarizations, and for different land cover. The average accuracies are 80% and 84% for the soil freeze period, and soil thaw period respectively. The comparison is limited because of the small number of validation pixels.

scholarly journals Application of satellite microwave remote sensed brightness temperature in the regional soil moisture simulation

2009 ◽  
Vol 6 (1) ◽  
pp. 1233-1260 ◽  
Author(s):  
X. K. Shi ◽  
J. Wen ◽  
L. Wang ◽  
T. T. Zhang ◽  
H. Tian ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Brightness Temperature ◽  
Land Surface ◽  
Surface Soil ◽  
Microwave Remote Sensing ◽  
Temperature Data ◽  
Fine Tuning ◽  
Tibet Plateau ◽  
Surface Model ◽  
Satellite Microwave

Abstract. As the satellite microwave remote sensed brightness temperature is sensitive to land surface soil moisture (SM) and SM is a basic output variable in model simulation, it is of great significance to use the brightness temperature data to improve SM numerical simulation. In this paper, the theory developed by Yan et al. (2004) about the relationship between satellite microwave remote sensing polarization index and SM was used to estimate the land surface SM from AMSR-E (Advanced Microwave Scanning Radiometer – Earth Observing System) brightness temperature data. With consideration of land surface soil texture, surface roughness, vegetation optical thickness, and the AMSR-E monthly SM products, the regional daily land surface SM was estimated over the eastern part of the Qinghai-Tibet Plateau. The results show that the estimated SM is lower than the ground measurements and the NCEP (American National Centers for Environmental Prediction) reanalysis data at the Maqu Station (33.85° N, 102.57° E) and the Tanglha Station (33.07° N, 91.94° E), but its regional distribution is reasonable and somewhat better than that from the daily AMSR-E SM product, and its temporal variation shows a quick response to the ground daily precipitations. Furthermore, in order to improve the simulating ability of the WRF (Weather Research and Forecasting) model to land surface SM, the estimated SM was assimilated into the Noah land surface model by the Newtonian relaxation (NR) method. The results indicate that, by fine tuning of the quality factor in NR method, the simulated SM values are improved most in desert area, followed by grassland, shrub and grass mixed zone. At temporal scale, Root Mean Square Error (RMSE) values between simulated and observed SM are decreased 0.03 and 0.07 m3/m3 by using the NR method in the Maqu Station and the Tanglha Station, respectively.

Exploiting the information in soil moisture and vegetation optical depth retrievals from passive microwave radiometry.

2020 ◽  
Author(s):  
Sujay Kumar ◽  
Thomas Holmes ◽  
Rajat bindlish ◽  
Richard de Jeu ◽  
Christa Peters-Lidard
Keyword(s):  
Soil Moisture ◽  
Optical Depth ◽  
Land Surface ◽  
Surface Soil ◽  
Passive Microwave ◽  
Microwave Radiometry ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
Passive Microwave Radiometry ◽  
The Impact

<p>Historically, microwave radiometry has usually been used for retrieving estimates of soil moisture. As these measurements are also sensitive to vegetation, the attenuation of the microwave signal from vegetation, described by the vegetation optical depth (VOD) parameter can be used an analog of above-ground canopy biomass. This study explores the relative and joint utility of assimilating soil moisture and VOD retrievals from passive microwave radiometry within the NoahMP land surface model. The impact of assimilation on key water and carbon budget terms are quantified through comparisons against reference datasets. The results indicate that the assimilation of soil moisture retrievals has a positive impact on the simulation of surface soil moisture and little impact on evaporative fluxes. In contrast, VOD assimilation has significant impacts on the simulation of vegetation conditions, root zone soil moisture, and evapotranspiration (ET). Over water limited domains with sparse vegetation where soil moisture is the primary control on ET, the assimilation of surface soil moisture is more beneficial than VOD DA. In contrast, over regions with dense vegetation and where water availability is not limiting, transpiration has a significant influence on evapotranspiration. The assimilation of VOD is more beneficial in developing improvements in ET over such areas. The results of this study confirm that soil moisture and VOD retrievals provide independent information that can be jointly exploited through their simultaneous assimilation.</p><p> </p><p> </p>

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.

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