scholarly journals Research of Soil Moisture Retrieval Algorithms for Processing Radiometry Data

2016 ◽  
Vol 25 (3) ◽  
pp. 283
Author(s):  
Vo Thi Lan Anh ◽  
Doan Minh Chung ◽  
Ngo Tuan Ngoc ◽  
K. G. Kostov
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Field Experiments ◽  
Sandy Loam ◽  
Microwave Remote Sensing ◽  
Ground Truth ◽  
Microwave Emission ◽  
Space Technology ◽  
L Band ◽  
Microwave Radiometers

Since 2012, the experts of Space Technology Institute  have carried out the field experiments to obtain a high-resolution dataset  of microwave radiometers for land surface parameters (soil moisture, soil  temperature, vegetation water content), in order to improve the soil  moisture retrieval methodology. L-band radiometers were used for measuring  the brightness temperature of the bare soil. Field experiments for passive  microwave remote sensing of soil moisture were carried out in Hoai Duc  District in 2012. L-band microwave radiometers were used for measuring the  microwave emission of bare agricultural fields. The radiometers, which are  used for soil moisture measurement, worked well during the experimental  campaign and produced volumetric soil moisture estimates that compared well  with the ground-truth measurements. Explanations for the observed  discrepancies are presented. The experimental results showed that the model  of Choudhury et al. for surface roughness correction provides a better fit  to radiometric data over the angular range between 20° and 50° for  \(n = 0\) (i.e., the \(\cos ^{2}\theta\)  factor in the exponential in (15) is suppressed).  Based on the results of the experiments conducted over two experimental  sites with different soils, namely sandy loam at Hoai Duc Agrometeorologyl  Center, it may be concluded that the testing of both the radiometric  equipment and the method for soil moisture retrieval was very successful,  and the main goal of the experiments was fulfilled.

scholarly journals Required sampling-density of ground-based soil moisture and brightness temperature observations for calibration/validation of L-band satellite observations based on a virtual reality

2019 ◽  
Author(s):  
Shaoning Lv ◽  
Bernd Schalge ◽  
Pablo Saavedra Garfias ◽  
Clemens Simmer
Keyword(s):  
Soil Moisture ◽  
Brightness Temperature ◽  
Confidence Level ◽  
Land Surface ◽  
Microwave Remote Sensing ◽  
Global Scale ◽  
Microwave Emission ◽  
Near Surface ◽  
Temperature Observations ◽  
Better Than

Abstract. Microwave remote sensing is the most promising tool for monitoring global-scale near-surface soil moisture distributions. With the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions in orbit, considerable efforts are made to evaluate their soil moisture products via ground observations, forward microwave transfer simulation, and retrievals. Due to the large footprint of the satellite radiometers of about 40 km in diameter and the spatial heterogeneity of soil moisture, minimum sampling densities for soil moisture are required to challenge the targeted precision. Here we use 400 m resolution simulations with the regional terrestrial system model TerrSysMP and its coupling with the Community Microwave Emission Modelling platform (CMEM) to quantify sampling distance required for soil moisture and brightness temperature validation. Our analysis suggests that an overall sampling resolution of better than 6 km is required to validate the targeted accuracy of 0.04 cm3/cm3 (70 % confidence level) in SMOS and SMAP over typical midlatitude European regions. The minimum sampling resolution depends on the land-surface inhomogeneity and the meteorological situation, which influence the soil moisture patterns, and ranges from about 7 km to 17 km for a 70 % confidence level for a typical year. At the minimum sampling resolution for a 70 % confidence level also the accuracy of footprint-averaged brightness temperature estimates is equal or better than 15 K/10 K for H/V polarization. Estimates strongly deteriorate with sparser sampling densities, e.g., at 3/9 km with 3/5 sampling sites the confidence level of derived footprint estimates can reach about 0.5–0.6 for soil moisture which is much less than the standard 0.7 requirements for ground measurements. The representativeness of ground-based soil moisture and brightness temperature observations – and thus their required minimum sampling densities – are only weakly correlated in space and time. This study provides a basis for a better understanding of sometimes strong mismatches between derived satellite soil moisture products and ground-based measurements.

The implementation of Community Microwave Emission Model (CMEM) as the observation operator in TerrsysMP-PDAF

2020 ◽  
Author(s):  
Shaoning Lv ◽  
Stefan Poll ◽  
Bernd Schalge ◽  
Pablo Garfias ◽  
Clemens Simmer
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Passive Microwave ◽  
Microwave Emission ◽  
Emission Model ◽  
Ideal Case ◽  
Observation Operator ◽  
Community Land Model ◽  
L Band

<p>Studies with satellite-based passive microwave L-band observations have been fostered strongly by the launch of NASA's Soil Moisture Active Passive (SMAP) satellite on January 31, 2015 (Entekhabi et al. 2010), which complements and extends the observations at L-band by the ESA's Soil Moisture Ocean Salinity (SMOS) mission in orbit since 2009 (Kerr et al. 2001, Mecklenburg et al. 2012, Lievens et al. 2014). SMOS and SMAP data assimilation studies started during their pre- and post-launch period. Flores et al. (2012) used an Ensemble Kalman Filter to constrain the uncertainties of the simulated soil moisture fields from physical-based hydrological models. Our work intends to explore the use and value of passive L-band satellite observations for ensemble-based data assimilation with fully-coupled terrestrial system models for mesoscale catchments. An observation operator for satellite-based passive microwave (PMW) observations based on the community microwave emission model (CMEM) (de Rosnay et al. 2009, Drusch et al. 2009) has been modified, applied and tested in an ideal case developed within the FOR2131 (Schalge et al. 2016) with the coupled subsurface-land surface-atmosphere simulation platform TerrSysMP (Shrestha et al. 2014), which couples ParFlow (subsurface), Community Land Model (CLM, surface), and COSMO (atmosphere). We achieve the development of a satellite simulator for passive L-band observations of the satellite missions SMAP and SMOS and its adaptation to the ideal case, and the lower-resolution TerrSysMP model applied for data assimilation (TerrSysMP-PDAF).</p>

scholarly journals Required sampling density of ground-based soil moisture and brightness temperature observations for calibration and validation of L-band satellite observations based on a virtual reality

2020 ◽  
Vol 24 (4) ◽  
pp. 1957-1973
Author(s):  
Shaoning Lv ◽  
Bernd Schalge ◽  
Pablo Saavedra Garfias ◽  
Clemens Simmer
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Brightness Temperature ◽  
Confidence Level ◽  
Land Surface ◽  
System Modeling ◽  
Microwave Remote Sensing ◽  
Microwave Emission ◽  
Near Surface ◽  
Sampling Distance

Abstract. Microwave remote sensing is the most promising tool for monitoring near-surface soil moisture distributions globally. With the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions in orbit, considerable efforts are being made to evaluate derived soil moisture products via ground observations, microwave transfer simulation, and independent remote sensing retrievals. Due to the large footprint of the satellite radiometers of about 40 km in diameter and the spatial heterogeneity of soil moisture, minimum sampling densities for soil moisture are required to challenge the targeted precision. Here we use 400 m resolution simulations with the regional Terrestrial System Modeling Platform (TerrSysMP) and its coupling with the Community Microwave Emission Modelling platform (CMEM) to quantify the maximum sampling distance allowed for soil moisture and brightness temperature validation. Our analysis suggests that an overall sampling distance of finer than 6 km is required to validate the targeted accuracy of 0.04 cm3 cm−3 with a 70 % confidence level in SMOS and SMAP estimates over typical mid-latitude European regions. The maximum allowed sampling distance depends on the land-surface heterogeneity and the meteorological situation, which influences the soil moisture patterns, and ranges from about 6 to 17 km for a 70 % confidence level for a typical year. At the maximum allowed sampling distance on a 70 % confidence level, the accuracy of footprint-averaged soil moisture is equal to or better than brightness temperature estimates over the same area. Estimates strongly deteriorate with larger sampling distances. For the evaluation of the smaller footprints of the active and active–passive products of SMAP the required sampling densities increase; e.g., when a grid resolution of 3 km diameter is sampled by three sites of footprints of 9 km sampled by five sites required, only 50 %–60 % of the pixels have a sampling error below the nominal values. The required minimum sampling densities for ground-based radiometer networks to estimate footprint-averaged brightness temperature are higher than for soil moisture due to the non-linearities of radiative transfer, and only weakly correlated in space and time. This study provides a basis for a better understanding of the sometimes strong mismatches between derived satellite soil moisture products and ground-based measurements.

Retrieval of basic soil physical properties by assimilating radiometry observations in the community land surface model

2020 ◽  
Author(s):  
Hong Zhao ◽  
Yijian Zeng ◽  
Bob Su ◽  
Xujun Han
Keyword(s):  
Soil Moisture ◽  
Soil Properties ◽  
Land Surface ◽  
Land Surface Model ◽  
Microwave Remote Sensing ◽  
Microwave Emission ◽  
Surface Model ◽  
Emission Model ◽  
The Impact ◽  
Parameter Retrieval

<p>Accurate basic soil properties information is fundamental for obtaining reliable soil moisture using land surface models. In view of the passive microwave remote sensing, basic soil properties have an impact on soil dielectric constant, together with soil moisture and temperature. The common link enables to use coupled land surface model with microwave emission model for retrieving basic soil properties in space, especially in remote areas such as the third pole region. The Maqu site in the eastern Tibetan Plateau, including ELBARA-III radiometry observations, was taken as the case. This paper employed an improved observation operator— a discrete scattering-emission model of L-band radiometry with an air-to-soil transition model embedded in, which considers both geometric and dielectric roughness impacts from heterogeneous topsoil structure on surface emission. Community Land Model 4.5 together with Local Ensemble Transform Kalman Filter algorithm were used by mean of the Open Source Multivariate Land Data Assimilation Framework. The retrieved basic soil properties were compared to in situ measurements, as well as the update soil moisture and temperature and energy fluxes. The impacts from surface roughness consideration and polarization configuration on parameter retrieval were also evaluated. To gain an insight on the impact from time interval of observations on parameter retrieval, results using observations at SMAP descending and ascending time were discussed.</p>

scholarly journals Influence of Land Cover and Soil Moisture on the Horizontal Distribution of Sensible and Latent Heat Fluxes in Southeast Kansas during IHOP_2002 and CASES-97

2007 ◽  
Vol 8 (1) ◽  
pp. 68-87 ◽  
Author(s):  
Margaret A. LeMone ◽  
Fei Chen ◽  
Joseph G. Alfieri ◽  
Mukul Tewari ◽  
Bart Geerts ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Latent Heat ◽  
Land Surface ◽  
Field Experiments ◽  
Time Of Day ◽  
Horizontal Distribution ◽  
Heat Fluxes ◽  
Surface Model ◽  
Green Vegetation ◽  
The Impact

Abstract Analyses of daytime fair-weather aircraft and surface-flux tower data from the May–June 2002 International H2O Project (IHOP_2002) and the April–May 1997 Cooperative Atmosphere Surface Exchange Study (CASES-97) are used to document the role of vegetation, soil moisture, and terrain in determining the horizontal variability of latent heat LE and sensible heat H along a 46-km flight track in southeast Kansas. Combining the two field experiments clearly reveals the strong influence of vegetation cover, with H maxima over sparse/dormant vegetation, and H minima over green vegetation; and, to a lesser extent, LE maxima over green vegetation, and LE minima over sparse/dormant vegetation. If the small number of cases is producing the correct trend, other effects of vegetation and the impact of soil moisture emerge through examining the slope ΔxyLE/ΔxyH for the best-fit straight line for plots of time-averaged LE as a function of time-averaged H over the area. Based on the surface energy balance, H + LE = Rnet − Gsfc, where Rnet is the net radiation and Gsfc is the flux into the soil; Rnet − Gsfc ∼ constant over the area implies an approximately −1 slope. Right after rainfall, H and LE vary too little horizontally to define a slope. After sufficient drying to produce enough horizontal variation to define a slope, a steep (∼−2) slope emerges. The slope becomes shallower and better defined with time as H and LE horizontal variability increases. Similarly, the slope becomes more negative with moister soils. In addition, the slope can change with time of day due to phase differences in H and LE. These trends are based on land surface model (LSM) runs and observations collected under nearly clear skies; the vegetation is unstressed for the days examined. LSM runs suggest terrain may also play a role, but observational support is weak.

scholarly journals Impact of ASAR soil moisture data on the MM5 precipitation forecast for the Tanaro flood event of April 2009

2011 ◽  
Vol 11 (12) ◽  
pp. 3135-3149 ◽  
Author(s):  
G. Panegrossi ◽  
R. Ferretti ◽  
L. Pulvirenti ◽  
N. Pierdicca
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Land Surface ◽  
Ground Truth ◽  
Rain Gauge ◽  
Precipitation Forecast ◽  
Flood Event ◽  
Strong Impact ◽  
Moisture Field ◽  
The Impact

Abstract. The representation of land-atmosphere interactions in weather forecast models has a strong impact on the Planetary Boundary Layer (PBL) and, in turn, on the forecast. Soil moisture is one of the key variables in land surface modelling, and an inadequate initial soil moisture field can introduce major biases in the surface heat and moisture fluxes and have a long-lasting effect on the model behaviour. Detecting the variability of soil characteristics at small scales is particularly important in mesoscale models because of the continued increase of their spatial resolution. In this paper, the high resolution soil moisture field derived from ENVISAT/ASAR observations is used to derive the soil moisture initial condition for the MM5 simulation of the Tanaro flood event of April 2009. The ASAR-derived soil moisture field shows significantly drier conditions compared to the ECMWF analysis. The impact of soil moisture on the forecast has been evaluated in terms of predicted precipitation and rain gauge data available for this event have been used as ground truth. The use of the drier, highly resolved soil moisture content (SMC) shows a significant impact on the precipitation forecast, particularly evident during the early phase of the event. The timing of the onset of the precipitation, as well as the intensity of rainfall and the location of rain/no rain areas, are better predicted. The overall accuracy of the forecast using ASAR SMC data is significantly increased during the first 30 h of simulation. The impact of initial SMC on the precipitation has been related to the change in the water vapour field in the PBL prior to the onset of the precipitation, due to surface evaporation. This study represents a first attempt to establish whether high resolution SAR-based SMC data might be useful for operational use, in anticipation of the launch of the Sentinel-1 satellite.

scholarly journals Towards an integrated soil moisture drought monitor for East Africa

2012 ◽  
Vol 9 (4) ◽  
pp. 4587-4631 ◽  
Author(s):  
W. B. Anderson ◽  
B. F. Zaitchik ◽  
C. R. Hain ◽  
M. C. Anderson ◽  
M. T. Yilmaz ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
East Africa ◽  
Remote Monitoring ◽  
Vegetation Cover ◽  
Land Surface ◽  
Microwave Remote Sensing ◽  
Surface Model ◽  
Monitoring Methods ◽  
Data Scarcity

Abstract. Drought in East Africa is a recurring phenomenon with significant humanitarian impacts. Given the steep climatic gradients, topographic contrasts, general data scarcity, and, in places, political instability that characterize the region, there is a need for spatially distributed, remotely derived monitoring systems to inform national and international drought response. At the same time, the very diversity and data scarcity that necessitate remote monitoring also make it difficult to evaluate the reliability of these systems. Here we apply a suite of remote monitoring techniques to characterize the temporal and spatial evolution of the 2010–2011 Horn of Africa drought. Diverse satellite observations allow for evaluation of meteorological, agricultural, and hydrological aspects of drought, each of which is of interest to different stakeholders. Focusing on soil moisture, we apply triple collocation analysis (TCA) to three independent methods for estimating soil moisture anomalies to characterize relative error between products and to provide a basis for objective data merging. The three soil moisture methods evaluated include microwave remote sensing using the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) sensor, thermal remote sensing using the Atmosphere-Land Exchange Inverse (ALEXI) surface energy balance algorithm, and physically-based land surface modeling using the Noah land surface model. It was found that the three soil moisture monitoring methods yield similar drought anomaly estimates in areas characterized by extremely low or by moderate vegetation cover, particularly during the below-average 2011 long rainy season. Systematic discrepancies were found, however, in regions of moderately low vegetation cover and high vegetation cover, especially during the failed 2010 short rains. The merged, TCA-weighted soil moisture composite product takes advantage of the relative strengths of each method, as judged by the consistency of anomaly estimates across independent methods. This approach holds potential as a remote soil moisture-based drought monitoring system that is robust across the diverse climatic and ecological zones of East Africa.

scholarly journals Comparison of measured brightness temperatures from SMOS with modelled ones from ORCHIDEE and H-TESSEL over the Iberian Peninsula

2015 ◽  
Vol 12 (12) ◽  
pp. 13019-13067
Author(s):  
A. Barella-Ortiz ◽  
J. Polcher ◽  
P. de Rosnay ◽  
M. Piles ◽  
E. Gelati
Keyword(s):  
Soil Moisture ◽  
Radiative Transfer ◽  
Iberian Peninsula ◽  
Land Surface ◽  
Function Analysis ◽  
Microwave Emission ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Brightness Temperatures

Abstract. L-Band radiometry is considered to be one of the most suitable techniques to estimate surface soil moisture by means of remote sensing. Brightness temperatures are key in this process, as they are the main input in the retrieval algorithm. The work exposed compares brightness temperatures measured by the Soil Moisture and Ocean Salinity (SMOS) mission to two different sets of modelled ones, over the Iberian Peninsula from 2010 to 2012. The latter were estimated using a radiative transfer model and state variables from two land surface models: (i) ORganising Carbon and Hydrology In Dynamic EcosystEms (ORCHIDEE) and (ii) Hydrology – Tiled ECMWF Scheme for Surface Exchanges over Land (H-TESSEL). The radiative transfer model used is the Community Microwave Emission Model (CMEM). A good agreement in the temporal evolution of measured and modelled brightness temperatures is observed. However, their spatial structures are not consistent between them. An Empirical Orthogonal Function analysis of the brightness temperature's error identifies a dominant structure over the South-West of the Iberian Peninsula which evolves during the year and is maximum in Fall and Winter. Hypotheses concerning forcing induced biases and assumptions made in the radiative transfer model are analysed to explain this inconsistency, but no candidate is found to be responsible for it at the moment. Further hypotheses are proposed at the end of the paper.

scholarly journals Inter-calibrating SMMR brightness temperatures over continental surfaces

2020 ◽  
Author(s):  
Samuel Favrichon ◽  
Carlos Jimenez ◽  
Catherine Prigent
Keyword(s):  
Land Surface ◽  
Microwave Remote Sensing ◽  
Calibration Method ◽  
Diurnal Cycles ◽  
Brightness Temperatures ◽  
Linear Correction ◽  
Long Time ◽  
Microwave Imager ◽  
Microwave Radiometers ◽  
Global Precipitation

Abstract. Microwave remote sensing can be used to monitor the time evolution of some key parameters over land, such as land surface temperature or surface water extent. Observations are made with instrument such as the Scanning Microwave Multichannel Radiometer (SMMR) before 1987, the Special Sensor Microwave/Imager (SSM/I) and the following Special Sensor Microwave Imager/Sounder (SSMIS) from 1987 and still operating, to the more recent Global Precipitation Mission Microwave Imager (GMI). As these instruments differ on some of their characteristics and use different calibration schemes, they need to be inter-calibrated before long time series products can be derived from the observations. Here an inter-calibration method is designed to remove major inconsistencies between the SMMR and other microwave radiometers for the 18 GHz and 37 GHz channels over continental surfaces. Because of a small overlap in observations and a ~6 h difference in overpassing times between SMMR and SSM/I, GMI was chosen as a reference despite the lack of a common observing period. The diurnal cycles from three years of GMI brightness temperatures are first calculated, and then used to evaluate SMMR differences. Based on a statistical analysis of the differences, a simple linear correction is implemented to calibrate SMMR on GMI. This correction is shown to also reduce the biases between SMMR and SSM/I, and can then be applied to SMMR observations to make them more coherent with existing data record of microwave brightness temperatures over continental surfaces.

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