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.

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.

scholarly journals A bare ground evaporation revision in the ECMWF land-surface scheme: evaluation of its impact using ground soil moisture and satellite microwave data

2012 ◽  
Vol 16 (10) ◽  
pp. 3607-3620 ◽  
Author(s):  
C. Albergel ◽  
G. Balsamo ◽  
P. de Rosnay ◽  
J. Muñoz-Sabater ◽  
S. Boussetta
Keyword(s):  
Soil Moisture ◽  
Brightness Temperature ◽  
Land Surface ◽  
The United States ◽  
Microwave Emission ◽  
Bare Ground ◽  
New Formulation ◽  
The Impact ◽  
Surface Fields

Abstract. In situ soil moisture data from 122 stations across the United States are used to evaluate the impact of a new bare ground evaporation formulation at ECMWF. In November 2010, the bare ground evaporation used in ECMWF's operational Integrated Forecasting System (IFS) was enhanced by adopting a lower stress threshold than for the vegetation, allowing a higher evaporation. It results in more realistic soil moisture values when compared to in situ data, particularly over dry areas. Use was made of the operational IFS and offline experiments for the evaluation. The latter are based on a fixed version of the IFS and make it possible to assess the impact of a single modification, while the operational analysis is based on a continuous effort to improve the analysis and modelling systems, resulting in frequent updates (a few times a year). Considering the field sites with a fraction of bare ground greater than 0.2, the root mean square difference (RMSD) of soil moisture is shown to decrease from 0.118 m3 m−3 to 0.087 m3 m−3 when using the new formulation in offline experiments, and from 0.110 m3 m−3 to 0.088 m3 m−3 in operations. It also improves correlations. Additionally, the impact of the new formulation on the terrestrial microwave emission at a global scale is investigated. Realistic and dynamically consistent fields of brightness temperature as a function of the land surface conditions are required for the assimilation of the SMOS data. Brightness temperature simulated from surface fields from two offline experiments with the Community Microwave Emission Modelling (CMEM) platform present monthly mean differences up to 7 K. Offline experiments with the new formulation present drier soil moisture, hence simulated brightness temperature with its surface fields are larger. They are also closer to SMOS remotely sensed brightness temperature.

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.

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.

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 Assessment of the SMAP Level-4 Surface and Root-Zone Soil Moisture Product Using In Situ Measurements

2017 ◽  
Vol 18 (10) ◽  
pp. 2621-2645 ◽  
Author(s):  
Rolf H. Reichle ◽  
Gabrielle J. M. De Lannoy ◽  
Qing Liu ◽  
Joseph V. Ardizzone ◽  
Andreas Colliander ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Brightness Temperature ◽  
Land Surface ◽  
Root Zone ◽  
Land Surface Model ◽  
In Situ Measurements ◽  
Surface Model ◽  
Accuracy Requirement ◽  
Temperature Observations

Abstract The Soil Moisture Active Passive (SMAP) mission Level-4 Surface and Root-Zone Soil Moisture (L4_SM) data product is generated by assimilating SMAP L-band brightness temperature observations into the NASA Catchment land surface model. The L4_SM product is available from 31 March 2015 to present (within 3 days from real time) and provides 3-hourly, global, 9-km resolution estimates of surface (0–5 cm) and root-zone (0–100 cm) soil moisture and land surface conditions. This study presents an overview of the L4_SM algorithm, validation approach, and product assessment versus in situ measurements. Core validation sites provide spatially averaged surface (root zone) soil moisture measurements for 43 (17) “reference pixels” at 9- and 36-km gridcell scales located in 17 (7) distinct watersheds. Sparse networks provide point-scale measurements of surface (root zone) soil moisture at 406 (311) locations. Core validation site results indicate that the L4_SM product meets its soil moisture accuracy requirement, specified as an unbiased RMSE (ubRMSE, or standard deviation of the error) of 0.04 m3 m−3 or better. The ubRMSE for L4_SM surface (root zone) soil moisture is 0.038 m3 m−3 (0.030 m3 m−3) at the 9-km scale and 0.035 m3 m−3 (0.026 m3 m−3) at the 36-km scale. The L4_SM estimates improve (significantly at the 5% level for surface soil moisture) over model-only estimates, which do not benefit from the assimilation of SMAP brightness temperature observations and have a 9-km surface (root zone) ubRMSE of 0.042 m3 m−3 (0.032 m3 m−3). Time series correlations exhibit similar relative performance. The sparse network results corroborate these findings over a greater variety of climate and land cover conditions.

scholarly journals Evaluation of AMSR-E-Derived Soil Moisture Retrievals Using Ground-Based and PSR Airborne Data during SMEX02

2005 ◽  
Vol 6 (6) ◽  
pp. 864-877 ◽  
Author(s):  
M. F. McCabe ◽  
H. Gao ◽  
E. F. Wood
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Microwave Emission ◽  
Moisture Distribution ◽  
Emission Model ◽  
Near Surface ◽  
The North ◽  
Microwave Sounding ◽  
Land Data Assimilation System ◽  
Land Surface Parameters

Abstract A Land Surface Microwave Emission Model (LSMEM) is used to derive soil moisture estimates over Iowa during the Soil Moisture Experiment 2002 (SMEX02) field campaign, using brightness temperature data from the Advanced Microwave Sounding Radiometer (AMSR)-E satellite. Spatial distributions of the near-surface soil moisture are produced using the LSMEM, with data from the North American Land Data Assimilation System (NLDAS), vegetation and land surface parameters estimated through recent Moderate Imaging Spectroradiometer (MODIS) land surface products, and standard soil datasets. To assess the value of soil moisture estimates from the 10.7-GHz X-band sensor on the AMSR-E instrument, retrievals are evaluated against ground-based sampling and soil moisture estimates from the airborne Polarimetric Scanning Radiometer (PSR) operating at C band. The PSR offers high-resolution detail of the soil moisture distribution, which can be used to analyze heterogeneity within the scale of the AMSR-E pixel. Preliminary analysis indicates that retrievals from the AMSR-E instrument at 10.7 GHz using the LSMEM are surprisingly robust, with accuracies within 3% vol/vol compared with in situ samples. Results from these AMSR-E comparisons also indicate potential in determining soil moisture patterns over regional scales, even in the presence of vegetation. Assessment of soil moisture determined through local-scale sampling within the larger-scale AMSR-E footprint reveals a consistent level of agreement over a range of meteorological and surface conditions, offering promise for improved land surface hydrometeorological characterization.

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 A bare ground evaporation revision in the ECMWF land-surface scheme: evaluation of its impact using ground soil moisture and satellite microwave data

2012 ◽  
Vol 9 (5) ◽  
pp. 6715-6752 ◽  
Author(s):  
C. Albergel ◽  
G. Balsamo ◽  
P. de Rosnay ◽  
J. Muñoz-Sabater ◽  
S. Boussetta
Keyword(s):  
Soil Moisture ◽  
Brightness Temperature ◽  
Land Surface ◽  
The United States ◽  
Microwave Emission ◽  
Bare Ground ◽  
New Formulation ◽  
The Impact ◽  
Surface Fields

Abstract. In situ soil moisture data from 122 stations across the United States are used to evaluate the impact of a new bare ground evaporation formulation at ECMWF. In November 2010 the bare ground evaporation used in ECMWF's operational Integrated Forecasting System (IFS) was enhanced by adopting a lower stress threshold than for the vegetation, allowing a higher evaporation. It results in more realistic soil moisture values when compared to in situ data, particularly over dry areas. Use was made of the operational IFS and offline experiments for the evaluation. The latter are based on a fixed version of the IFS and make it possible to assess the impact of a single modification while the operational analysis is based on a continuous effort to improve the analysis and modelling systems, resulting in frequent updates (few times a year). Considering the field sites with a fraction of bare ground greater than 0.2, the root mean square difference (RMSD) of soil moisture is shown to decrease from 0.118 m3 m−3 to 0.087 m3 m−3 when using the new formulation in offline experiments, and from 0.110 m3 m−3 to 0.088 m3 m−3 in operations. It also improves correlations. Additionally the impact of the new formulation on the terrestrial microwave emission at a global scale is investigated. Realistic and dynamically consistent fields of brightness temperature as a function of the land surface conditions are required for the assimilation of the SMOS data. Brightness temperature simulated from surface fields from two offline experiments with the Community Microwave Emission Modelling (CMEM) platform present monthly mean differences up to 7 K. Offline experiment with the new formulation presents drier soil moisture, hence simulated brightness temperature with its surface fields are larger. They are also closer to SMOS remotely sensed brightness temperature.

Sign in / Sign up

Export Citation Format

Share Document