scholarly journals Soil moisture mapping over West Africa with a 30-min temporal resolution using AMSR-E observations and a satellite-based rainfall product

2009 ◽  
Vol 13 (10) ◽  
pp. 1887-1896 ◽  
Author(s):  
T. Pellarin ◽  
T. Tran ◽  
J.-M. Cohard ◽  
S. Galle ◽  
J.-P. Laurent ◽  
...  
Keyword(s):  
Soil Moisture ◽  
West Africa ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Microwave Emission ◽  
Emission Model ◽  
Rainfall Estimation ◽  
Simple Method ◽  
Antecedent Precipitation ◽  
The Difference

Abstract. An original and simple method to map surface soil moisture over large areas has been developed to obtain data with a high temporal and spatial resolution for the study of possible feedback mechanisms between soil moisture and convection in West Africa. A rainfall estimation product based on Meteosat geostationary satellite measurements is first used together with a simple Antecedent Precipitation Index (API) model to produce soil moisture maps at a spatial resolution of 10×10 km2 and a temporal resolution of 30-min. However, given the uncertainty of the satellite-based rainfall estimation product, the resulting soil moisture maps are not sufficiently accurate. For this reason, a technique based on assimilating AMSR-E C-band measurements into a microwave emission model was developed in which the estimated rainfall rates between two successive AMSR-E brightness temperature (TB) measurements are adjusted by multiplying them by a factor between 0 and 7 that minimizes the difference between simulated and observed TBs. Ground-based soil moisture measurements obtained at three sites in Niger, Mali and Benin were used to assess the method which was found to improve the soil moisture estimates on all three sites.

scholarly journals Hourly soil moisture mapping over West Africa using AMSR-E observations and a satellite-based rainfall product

2009 ◽  
Vol 6 (3) ◽  
pp. 4035-4064
Author(s):  
T. Pellarin ◽  
T. Tran ◽  
J.-M. Cohard ◽  
S. Galle ◽  
J.-P. Laurent ◽  
...  
Keyword(s):  
Soil Moisture ◽  
West Africa ◽  
Temporal Frequency ◽  
Microwave Emission ◽  
Emission Model ◽  
Brightness Temperatures ◽  
Antecedent Precipitation ◽  
The Difference ◽  
Satellite Microwave Measurements ◽  
Antecedent Precipitation Index

Abstract. This paper provides an original and simple methodology to map surface soil moisture with a fine temporal and spatial resolution over large areas based on a satellite rainfall accumulation product and soil microwave emission measurements at C-band. The first motivation of this study was to obtain high temporal frequency (~1 h) in order to study the possible feedback mechanisms between soil moisture and convection in West Africa. The use of soil moisture maps derived from satellite microwave measurements was not possible due to the low (at best daily) temporal resolution. Thus, a rainfall accumulation product based on Meteosat geostationary satellite measurements was used together with a simple Antecedent Precipitation Index (API) model to produce soil moisture map at the 10×10 km2 and 30 min resolution. Due to uncertainties on the satellite-based rainfall accumulation product, derived soil moisture maps were found to be erroneous. An assimilation technique based on AMSR-E C-band measurements into a microwave emission model was developed. The assimilation technique described in this study consists of modulating the rainfall accumulation estimate between two successive AMSR-E brightness temperatures (TB) measurements in order to match simulated and observed TB. When a rainfall event happens, the initial rainfall accumulation estimate is modulated using a multiplicative factor ranging from 0 to 7. The best solution is given by the rainfall rate which minimizes the difference between observed and simulated TB. Ground-based soil moisture measurements obtained at three sites in Niger, Mali and Benin were used to assess the methodology which was found to improve the soil moisture estimates over the three sites.

scholarly journals High resolution (1 km) satellite rainfall estimation from SM2RAIN applied to Sentinel-1: Po River Basin as case study

2021 ◽  
Author(s):  
Paolo Filippucci ◽  
Luca Brocca ◽  
Raphael Quast ◽  
Luca Ciabatta ◽  
Carla Saltalippi ◽  
...  
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
River Basin ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
High Spatial Resolution ◽  
Added Value ◽  
Rainfall Estimation ◽  
Po River ◽  
Po River Basin

Abstract. Satellite sensors to infer rainfall measurements have become widely available in the last years, but their spatial resolution usually exceed 10 kilometres, due to technological limitation. This poses an important constraint on their use for application such as water resource management, index insurance evaluation or hydrological models, which require more and more detailed information. In this work, the algorithm SM2RAIN (Soil Moisture to Rain) for rainfall estimation is applied to a high resolution soil moisture product derived from Sentinel-1, named S1-RT1, characterized by 1 km spatial resolution (500 m spacing), and to the 25 km ASCAT soil moisture (12.5 km spacing), resampled to the same grid of S1-RT1, to obtain rainfall products with the same spatial and temporal resolution over the Po River basin. In order to overcome the need of calibration and to allow its global application, a parameterized version of SM2RAIN algorithm was adopted along with the standard one. The capabilities in estimating rainfall of each obtained product were then compared, to assess both the parameterized SM2RAIN performances and the added value of Sentinel-1 high spatial resolution. The results show that good estimates of rainfall are obtainable from Sentinel-1 when considering aggregation time steps greater than 1 day, since to the low temporal resolution of this sensor (from 1.5 to 4 days over Europe) prevents its application to infer daily rainfall. On average, the ASCAT derived rainfall product performs better than S1-RT1 one, even if the performances are equally good when 30 days accumulated rainfall is considered, being the mean Pearson’s correlation of the rainfall obtained from ASCAT and S1-RT1 equal to 0.74 and 0.73, respectively, using the parameterized SM2RAIN. Notwithstanding this, the products obtained from Sentinel-1 outperform those from ASCAT in specific areas, like in valleys inside mountain regions and most of the plains, confirming the added value of the high spatial resolution information in obtaining spatially detailed rainfall. Finally, the parameterized products performances are similar to those obtained with SM2RAIN calibration, confirming the reliability of the parameterized algorithm for rainfall estimation in this area and fostering the possibility to apply SM2RAIN worldwide even without the availability of a rainfall benchmark product.

Comparing ERA-40-Based L-Band Brightness Temperatures with Skylab Observations: A Calibration/Validation Study Using the Community Microwave Emission Model

2009 ◽  
Vol 10 (1) ◽  
pp. 213-226 ◽  
Author(s):  
Matthias Drusch ◽  
Thomas Holmes ◽  
Patricia de Rosnay ◽  
Gianpaolo Balsamo
Keyword(s):  
Soil Moisture ◽  
Dynamic Range ◽  
Microwave Radiometer ◽  
Space Station ◽  
Microwave Emission ◽  
Emission Model ◽  
Soil Database ◽  
Brightness Temperatures ◽  
L Band ◽  
Rms Errors

Abstract The Community Microwave Emission Model (CMEM) has been used to compute global L-band brightness temperatures at the top of the atmosphere. The input data comprise surface fields from the 40-yr ECMWF Re-Analysis (ERA-40), vegetation data from the ECOCLIMAP dataset, and the Food and Agriculture Organization’s (FAO) soil database. Modeled brightness temperatures have been compared against (historic) observations from the S-194 passive microwave radiometer onboard the Skylab space station. Different parameterizations for surface roughness and the vegetation optical depth have been used to calibrate the model. The best results have been obtained for rather simple approaches proposed by Wigneron et al. and Kirdyashev et al. The rms errors after calibration are 10.7 and 9.8 K for North and South America, respectively. Comparing the ERA-40 soil moisture product against the corresponding in situ observations suggests that the uncertainty in the modeled soil moisture is the predominant contributor to these rms errors. Although the bias between model and observed brightness temperatures are reduced after the calibration, systematic differences in the dynamic range remain. For NWP analysis applications, bias correction schemes should be applied prior to data assimilation. The calibrated model has been used to compute a 10-yr brightness temperature climatology based on ERA-40 data.

scholarly journals SMOS brightness temperature assimilation into the Community Land Model

2017 ◽  
Vol 21 (11) ◽  
pp. 5929-5951 ◽  
Author(s):  
Dominik Rains ◽  
Xujun Han ◽  
Hans Lievens ◽  
Carsten Montzka ◽  
Niko E. C. Verhoest
Keyword(s):  
Soil Moisture ◽  
Brightness Temperature ◽  
Extreme Event ◽  
Root Zone ◽  
Incident Angle ◽  
Grid Cell ◽  
Microwave Emission ◽  
Emission Model ◽  
Soil Layers ◽  
Community Land Model

Abstract. SMOS (Soil Moisture and Ocean Salinity mission) brightness temperatures at a single incident angle are assimilated into the Community Land Model (CLM) across Australia to improve soil moisture simulations. Therefore, the data assimilation system DasPy is coupled to the local ensemble transform Kalman filter (LETKF) as well as to the Community Microwave Emission Model (CMEM). Brightness temperature climatologies are precomputed to enable the assimilation of brightness temperature anomalies, making use of 6 years of SMOS data (2010–2015). Mean correlation R with in situ measurements increases moderately from 0.61 to 0.68 (11 %) for upper soil layers if the root zone is included in the updates. A reduced improvement of 5 % is achieved if the assimilation is restricted to the upper soil layers. Root-zone simulations improve by 7 % when updating both the top layers and root zone, and by 4 % when only updating the top layers. Mean increments and increment standard deviations are compared for the experiments. The long-term assimilation impact is analysed by looking at a set of quantiles computed for soil moisture at each grid cell. Within hydrological monitoring systems, extreme dry or wet conditions are often defined via their relative occurrence, adding great importance to assimilation-induced quantile changes. Although still being limited now, longer L-band radiometer time series will become available and make model output improved by assimilating such data that are more usable for extreme event statistics.

scholarly journals Defining a Trade-off Between Spatial and Temporal Resolution of a Geosynchronous SAR Mission for Soil Moisture Monitoring

2018 ◽  
Vol 10 (12) ◽  
pp. 1950 ◽  
Author(s):  
Luca Cenci ◽  
Luca Pulvirenti ◽  
Giorgio Boni ◽  
Nazzareno Pierdicca
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Added Value ◽  
Sar Images ◽  
Radar Images ◽  
Spatio Temporal ◽  
Set Up ◽  
The Impact ◽  
Hydrological Applications

The next generation of synthetic aperture radar (SAR) systems could foresee satellite missions based on a geosynchronous orbit (GEO SAR). These systems are able to provide radar images with an unprecedented combination of spatial (≤1 km) and temporal (≤12 h) resolutions. This paper investigates the GEO SAR potentialities for soil moisture (SM) mapping finalized to hydrological applications, and defines the best compromise, in terms of image spatio-temporal resolution, for SM monitoring. A synthetic soil moisture–data assimilation (SM-DA) experiment was thus set up to evaluate the impact of the hydrological assimilation of different GEO SAR-like SM products, characterized by diverse spatio-temporal resolutions. The experiment was also designed to understand if GEO SAR-like SM maps could provide an added value with respect to SM products retrieved from SAR images acquired from satellites flying on a quasi-polar orbit, like Sentinel-1 (POLAR SAR). Findings showed that GEO SAR systems provide a valuable contribution for hydrological applications, especially if the possibility to generate many sub-daily observations is sacrificed in favor of higher spatial resolution. In the experiment, it was found that the assimilation of two GEO SAR-like observations a day, with a spatial resolution of 100 m, maximized the performances of the hydrological predictions, for both streamflow and SM state forecasts. Such improvements of the model performances were found to be 45% higher than the ones obtained by assimilating POLAR SAR-like SM maps.

scholarly journals Estimation of Near-Ground Propagation Conditions Using Radar Ground Echo Coverage

2011 ◽  
Vol 28 (2) ◽  
pp. 165-180 ◽  
Author(s):  
Shinju Park ◽  
Frédéric Fabry
Keyword(s):  
Spatial Resolution ◽  
Temporal Resolution ◽  
Vertical Gradient ◽  
Simple Method ◽  
Radar Observations ◽  
Beam Bending ◽  
Radar Measurements ◽  
Temporal And Spatial ◽  
Ground Echoes

Abstract The vertical gradient of refractivity (dN/dh) determines the path of the radar beam; namely, the larger the negative values of the refractivity gradient, the more the beam bends toward the ground. The variability of the propagation conditions significantly affects the coverage of the ground echoes and, thus, the quality of the scanning radar measurements. The information about the vertical gradient of refractivity is usually obtained from radiosonde soundings whose use, however, is limited by their coarse temporal and spatial resolution. Because radar ground echo coverage provides clues about how severe the beam bending can be, we have investigated a method that uses radar observations to infer propagation conditions with better temporal resolution than the usual soundings. Using the data collected during the International H2O Project (IHOP_2002), this simple method has shown some skill in capturing the propagation conditions similar to these estimated from soundings. However, the evaluation of the method has been challenging because of 1) the limited resolution of the conventional soundings in time and space, 2) the lack of other sources of data with which to compare the results, and 3) the ambiguity in the separation of ground from weather echoes.

scholarly journals An evaluation of the potential of Sentinel 1 for improving flash flood predictions via soil moisture–data assimilation

2017 ◽  
Vol 44 ◽  
pp. 89-100 ◽  
Author(s):  
Luca Cenci ◽  
Luca Pulvirenti ◽  
Giorgio Boni ◽  
Marco Chini ◽  
Patrick Matgen ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Hydrological Model ◽  
High Spatial Resolution ◽  
Flash Flood ◽  
Soil Moisture Data ◽  
Time Period ◽  
The Impact

Abstract. The assimilation of satellite-derived soil moisture estimates (soil moisture–data assimilation, SM–DA) into hydrological models has the potential to reduce the uncertainty of streamflow simulations. The improved capacity to monitor the closeness to saturation of small catchments, such as those characterizing the Mediterranean region, can be exploited to enhance flash flood predictions. When compared to other microwave sensors that have been exploited for SM–DA in recent years (e.g. the Advanced SCATterometer – ASCAT), characterized by low spatial/high temporal resolution, the Sentinel 1 (S1) mission provides an excellent opportunity to monitor systematically soil moisture (SM) at high spatial resolution and moderate temporal resolution. The aim of this research was thus to evaluate the impact of S1-based SM–DA for enhancing flash flood predictions of a hydrological model (Continuum) that is currently exploited for civil protection applications in Italy. The analysis was carried out in a representative Mediterranean catchment prone to flash floods, located in north-western Italy, during the time period October 2014–February 2015. It provided some important findings: (i) revealing the potential provided by S1-based SM–DA for improving discharge predictions, especially for higher flows; (ii) suggesting a more appropriate pre-processing technique to be applied to S1 data before the assimilation; and (iii) highlighting that even though high spatial resolution does provide an important contribution in a SM–DA system, the temporal resolution has the most crucial role. S1-derived SM maps are still a relatively new product and, to our knowledge, this is the first work published in an international journal dealing with their assimilation within a hydrological model to improve continuous streamflow simulations and flash flood predictions. Even though the reported results were obtained by analysing a relatively short time period, and thus should be supported by further research activities, we believe this research is timely in order to enhance our understanding of the potential contribution of the S1 data within the SM–DA framework for flash flood risk mitigation.

Estimating the best spatial resolution of remotely sensed surface soil moisture based on their uncertainty 

2021 ◽  
Author(s):  
Theresa C. van Hateren ◽  
Marco Chini ◽  
Patrick Matgen ◽  
Luca Pulvirenti ◽  
Nazzareno Pierdicca ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Surface Soil ◽  
Reference Data ◽  
Computing Time ◽  
Ground Truth ◽  
Remotely Sensed ◽  
Surface Soil Moisture ◽  
Trade Offs

<p>Validation of remotely sensed soil moisture is a well-known issue. Reference data with the correct spatial and temporal resolution on large scales are sparse and lack spatial representativeness. Moreover, due to the heterogeneity of soil moisture in both space and time, even reference data cannot be considered to be “ground truth”. As such, uncertainties are difficult to quantify. Additionally, in remotely sensed soil moisture there are trade-offs between spatial resolution and temporal resolution, resolution and accuracy, and resolution and computing time. Here, we try to identify the best spatial resolution for Sentinel-1 based soil moisture estimation, considering the trade-off between product resolution and accuracy. We use the uncertainty  of the soil moisture estimate as a guide parameter, and focus on how product accuracy depends on factors as soil wetness, and characteristics of the vegetated canopy.  To this end, we compare Sentinel-1 soil moisture estimates to both in situ data and global reference data sets with a lower spatial resolution. Remotely sensed surface soil moisture data were obtained by applying the MULESME algorithm  (Pulvirenti et al., 2018) on Sentinel-1 data throughout 2020. An extensive field campaign was performed, where TDR data and volumetric soil samples were gathered. A nearby setup of permanent soil moisture probes additionally provided continuous measurements of soil moisture at different depths, from 10 to 60 centimetres. Global datasets were obtained from the SMOS satellite constellation, GLDAS, MERRA-2 and ESA CCI.</p><p>Pulvirenti, L., Squicciarino, G., Cenci, L., Boni, G., Pierdicca, N., Chini, M., Versace, P. & Campanella, P. (2018). A surface soil moisture mapping service at national (Italian) scale based on Sentinel-1 data. <em>Environmental Modelling & Software</em>, <em>102</em>, 13-28.</p>

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>

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