[show abstract][hide abstract] ABSTRACT: In a recent paper, Leroux et al.  compared three satellite soil moisture data sets (SMOS, AMSR-E, and ASCAT) and ECMWF forecast soil moisture data to in situ measurements over four watersheds located in the United States. Their conclusions stated that SMOS soil moisture retrievals represent “an improvement [in RMSE] by a factor of 2- 3 compared with the other products” and that the ASCAT soil moisture data are “very noisy and unstable”. In this clarification, the analysis of Leroux et al. is repeated using a newer version of the ASCAT data and additional metrics are provided. It is shown that the ASCAT retrievals are skillful, although they show some unexpected behavior during summer for two of the watersheds. It is also noted that the improvement of SMOS by a factor of 2-3 mentioned by Leroux et al. is driven by differences in bias and only applies relative to AMSR-E and the ECWMF data in the now obsolete version investigated by Leroux et al.
IEEE Transactions on Geoscience and Remote Sensing 05/2014; 52(3):1901-1906. · 3.47 Impact Factor
[show abstract][hide abstract] ABSTRACT: In this study, the combined surface status and surface soil moisture
products retrieved by the ASCAT sensor within the ESA-DUE Permafrost
project are compared to the hydrological outputs of the land surface
model ORCHIDEE over Northern Eurasia. The objective is to derive broad
conclusions as to the strengths and weaknesses of hydrological modelling
and, to a minor extent, remote sensing of soil moisture over an area
where data is rare and hydrological modelling is though crucial for
climate and ecological applications. The spatial and temporal
resolutions of the ASCAT products make them suitable for comparison with
model outputs. Modelled and remotely-sensed surface frozen
and unfrozen statuses agree reasonably well, which allows for a seasonal
comparison between modelled and observed (liquid) surface soil moisture.
The atmospheric forcing and the snow scheme of the land surface model
are identified as causes of moderate model-to-data divergence in terms
of surface status. For unfrozen soils, the modelled and
remotely-sensed surface soil moisture signals are positively correlated
over most of the study area. The correlation deteriorates in the
North-Eastern Siberian regions, which is consistent with the lack of
accurate model parameters and the scarcity of meteorological data. The
model shows a reduced ability to capture the main seasonal dynamics and
spatial patterns of observed surface soil moisture in Northern Eurasia,
namely a characteristic spring surface moistening resulting from snow
melt and flooding. We hypothesize that these weak performances mainly
originate from the non-representation of flooding and surface ponding in
the model. Further identified limitations proceed from the coarse
treatment of the hydrological specificities of mountainous areas and
spatial inaccuracies in the meteorological forcing in remote,
North-Eastern Siberian areas. Investigations are currently underway to
determine to which extent plausible inaccuracies in the satellite data
could also contribute to the diagnosed model-to-data discrepancies.
Hydrology and Earth System Sciences Discussions 08/2013; 10(8):11241-11291. · 3.59 Impact Factor
[show abstract][hide abstract] ABSTRACT: The natural environment and livelihoods in the Lower Mekong Basin (LMB) are significantly affected by the annual hydrological cycle. Monitoring of soil moisture as a key variable in the hydrological cycle is of great interest in a number of Hydrological and agricultural applications. In this study we evaluated the quality and spatiotemporal variability of the soil moisture product retrieved from C-band scatterometers data across the LMB sub-catchments. The soil moisture retrieval algorithm showed reasonable performance in most areas of the LMB with the exception of a few sub-catchments in the eastern parts of Laos, where the land cover is characterized by dense vegetation. The best performance of the retrieval algorithm was obtained in agricultural regions. Comparison of the available in situ evaporation data in the LMB and the Basin Water Index (BWI), an indicator of the basin soil moisture condition, showed significant negative correlations up to R = −0.85. The inter-annual variation of the calculated BWI was also found corresponding to the reported extreme hydro-meteorological events in the Mekong region. The retrieved soil moisture data show high correlation (up to R = 0.92) with monthly anomalies of precipitation in non-irrigated regions. In general, the seasonal variability of soil moisture in the LMB was well captured by the retrieval method. The results of analysis also showed significant correlation between El Niño events and the monthly BWI anomaly measurements particularly for the month May with the maximum correlation of R = 0.88.
[show abstract][hide abstract] ABSTRACT: The C-band scatterometer data have been demonstrated in many studies [1-9] to be valuable for monitoring of surface soil moisture using the so-called TU Wien change detection method [10-11]. High temporal sampling in all weather conditions, multi-viewing capability and availability of long-term measurements make the European C-band scatterometers excellent observation tools for soil moisture change detection. The observations of the ERS-1/2 scatterometers together with the new series of advanced scatterometers (ASCAT) onboard Metop satellites ensure long-term global observation (from 1991 until at least 2020). Soil moisture is recognized as an important component of the water cycle in hydrological and natural environmental processes. Information on surface and profile soil moisture is demanding for a wide range of applications concerning water supply, agriculture, weather forecasting, climate modeling, and etc.
This study presents an in-depth evaluation of the soil moisture products retrieved from C-band scatterometer data (from 1991-2000 and 2007-2010) in regional scale and demonstrates application examples in lower Mekong Basin in Southeast Asia. The Mekong River is the longest river in Southeast Asia and is one of the ten longest rivers in the world. It rises in the Tibetan highlands and flows through six states and drains an area of 795,000 km². It crosses the southeast Chinese province of Yun-Nan, forming the border between Myanmar and Laos and in the lower reaches of a large part of the border between Laos and Thailand. It flows through Cambodia and into South Vietnam branched into several mouth-arms that make up the vast Mekong delta where it empties into the South China Sea. The climate of the Mekong region is influenced by the Southwest and Northeast monsoons. The tropical monsoonal regime in lower Mekong area generates a distinctly biseasonal pattern of wet and dry periods of more or less equal length. This results in an annual flood pulse and therefore a distinct seasonality in the annual hydrological cycle between a flood season and a low-flow season. The strong seasonal variations in rainfall leads to extreme conditions for the people of the lower Mekong Region: Large-scale and long-lasting floods alternating with periods of drought and water shortage. Floods and droughts can occur anywhere in the basin imposing large economic and social costs on the people . Therefore monitoring of soil moisture conditions in Mekong basin is valuable for many hydrological and agricultural applications.
The study includes a catchment-base noise analysis of soil moisture data in lower Mekong basin to identify the areas where soil moisture retrieval is robust and applicable. In general, the seasonal variability of soil moisture in Mekong basin is well captured by the retrieval method especially in agricultural areas. Comparison of the soil moisture data with topography and land cover classifications showed that the soil moisture noise increases in highlands with complex topography and in areas covered by very dense vegetation. It is found that the quality of soil moisture is strongly degraded during oversaturated soil situations particularly by large flooded events in delta region in Vietnam and Tonle Sap basin in Cambodia during the peak of the wet season. Furthermore, a catchment-base statistical analysis of the soil moisture data has been carried out to evaluate the relation between the Basin Water Index (BWI), an indicator of the basin soil moisture condition, with in-situ hydrometeorological measurements in different months of year. The results of analysis also showed significant correlations between Enso events and monthly BWI anomaly measurements.
IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Remote Sensing for a Dynamic Earth; 07/2012
[show abstract][hide abstract] ABSTRACT: Soil Moisture is an Essential Climate Variable and a key parameter in
hydrology, meteorology and agriculture. Surface Soil Moisture (SSM) can
be estimated from measurements taken by ASCAT onboard Metop-A and have
been successfully validated by several studies (C. Albergel et.al. 2009
and 2012, M.Parrens et.al. 2012). Profile soil moisture, while equally
important, can not be measured directly by remote sensing. The near
real-time Soil Water Index (SWI) product, developed within the framework
of the GMES project geoland2 aims to close this gap. It is produced from
ASCAT SSM estimates using a two-layer water balance model which
describes the relationship between surface and profile soil moisture as
a function of time. It provides daily global data about moisture
conditions for 8 characteristic time lengths representing different
depths. The objective of this work was to assess the quality of the SWI
data for different measurement depths. SWI data from January 1st 2007
until the end of 2010 was compared to in situ soil moisture data from
420 stations belonging to 22 observation networks which are available
through the International Soil Moisture Network. These stations
delivered 1331 station/depth combinations which were compared to the SWI
values. After excluding observations made during frozen conditions the
average significant correlation coefficients were 0.564 (min -0.684, max
0.955) while being greater than 0.3 for 88% of all station/depth
International Journal of Applied Earth Observation and Geoinformation. 04/2012; 30:10189-.
[show abstract][hide abstract] ABSTRACT: Information on soil surface state is valuable for many applications such as climate studies and monitoring of permafrost regions. C-band scatterometer data indicate good potential to deliver information on surface freeze/thaw. Variation in state or amount of water contained in the soil causes significant alteration of dielectric properties of the soil which is markedly observable in scatterometer backscattered signal. A threshold-analysis method is developed to derive a set of parameters to be used in evaluating the normalized backscatter measurements through decision trees and anomaly detection modules for determination of freeze/thaw conditions. The model parameters are extracted from two years (2007–2008) backscatter data from ASCAT scatterometer onboard Metop satellite collocated with ECMWF ReAnalysis (ERA-Interim) soil temperature. Backscatter measurements are flagged as indicator of frozen/unfrozen surface, and snowmelt or existing water on the surface. The output product, so-called surface state flag (SSF), compares well with two modeled soil temperature data sets as well as the air temperature measurements from synoptic meteorological stations across the northern hemisphere. The SSF time series are also validated with soil temperature data available at four in situ observation sites in Siberian and Alaska regions showing the overall accuracy of about 80% to 90%.
IEEE Transactions on Geoscience and Remote Sensing 01/2012; 50(7):2566-2582. · 3.47 Impact Factor
[show abstract][hide abstract] ABSTRACT: The Mekong River is the longest river in Southeast Asia with a drainage basin of 795,000 km². It rises in the Tibetan highlands and flows through six states, the southeast Chinese province of Yun-Nan, Myanmar, Laos, Thailand, Cambodia and Vietnam. The climate of the Mekong region is influenced by the Southwest and Northeast monsoons. The strong seasonal variation in rainfall leads frequently to extreme flood and drought conditions. Occurrences of large-scale and long-lasting floods alternating with periods of drought and water shortage significantly affect the people living in Mekong watershed and cause severe economic and civil damages. Therefore accurate information on the frequency and the extent of extreme events is critical for preparedness, prevention and management of the disaster. Satellite remote sensing has become valuable for monitoring surface parameters related to floods and droughts by providing comprehensive and multi temporal coverage of large areas. The active microwave sensors have proven to be applicable instruments for monitoring of surface water and soil moisture due to high sensitivity of radar signal to water. One of the major advantages of active sensors is that they can acquire imagery regardless of solar illumination during day and night and unimpeded by cloud cover, with the latter being of special importance during rainy periods, when wetlands are often easier to discriminate but mostly clouded. In this study we use the backscatter and soil moisture information extracted from the C-band Advanced SCATterometer (ASCAT) onboard Metop and the Advanced Synthetic Aperture Radar (ASAR) onboard Envisat to demonstrate the capability of active sensors for monitoring flood and drought events in lower Mekong region. The method is based on anomaly detection and threshold analysis of backscatter and its relative noise for detection of extreme dry, wet and inundated conditions. It is shown that the analysis of ASCAT backscatter noise helps to remove ambiguities in low backscatter domain for discrimination between extremely dry soil and inundated surface. The results show the potential of active sensors for change detection of surface water bodies and monitoring of soil moisture dynamics in the Mekong region which has applications in agriculture, hydrology, and disaster management.
ISPRS 2012, XXII International Society for Photogrammetry & Remote Sensing; 01/2012
[show abstract][hide abstract] ABSTRACT: In Central Asia, water is a particularly scarce and valuable good. In many ecosystems of this region, the vegetation development during the growing season is dependent on water provided by rainfall. With climate change, alterations of the seasonal distribution of precipitation patterns and a higher frequency of extreme events are expected. Vegetation dynamics are likely to respond to these changes and thus ecosystem services will be affected. However, there is still a lack in understanding the response of vegetation to precipitation anomalies, especially for dryland regions such as Central Asia. This study aims to contribute to an improved understanding of vegetation sensitivity to precipitation anomalies and corresponding temporal reaction patterns at regional scale. The presented analyses are based on time-series of Normalized Difference Vegetation Index (NDVI) and gridded precipitation datasets (GPCC Full Data Reanalysis) for the years 1982–2006. Time-series correlation analyses show that vegetation development is sensitive to precipitation anomalies for nearly 80% of the Central Asian land surface. Results indicate a particularly strong sensitivity of vegetation in areas with 100–400 mm of annual rainfall. Temporal rainfall–NDVI response patterns show a temporal lag between precipitation anomalies and vegetation activity of 1–3 months. The reaction of vegetation was found to be strongest for precipitation anomalies integrated over periods of 2–4 months. The observed delayed response of vegetation to precipitation anomalies reveals potential for drought prediction in Central Asia. The spatial patterns of vegetation reactions are discussed with focus on the role of precipitation amount and seasonality, land use and land cover.
Global and Planetary Change 01/2012; · 3.16 Impact Factor
[show abstract][hide abstract] ABSTRACT: Due to the high temporal sampling rate of ASAR Global Monitoring (GM) mode, it has a high application potential for analyzing the land surface freeze/thaw process in high latitudes. This study aims to develop effective methods of extracting freeze/thaw transition dates of permafrost areas from ASAR GM data sets. In order to use ASAR GM time-series for analyzing freeze/thaw states, a least square fitting of piecewise step function is introduced. The thawing date can be determined by minimizing the sum of squared residuals between measured backscattering time-series and a pre-defined step function. An experimental result for a Siberian permafrost region illustrates that it can be a promising approach in monitoring permafrost ecosystems.
Remote Sensing of Environment - REMOTE SENS ENVIRON. 12/2011;
[show abstract][hide abstract] ABSTRACT: Knowledge of the spatial and temporal patterns of root zone soil moisture is crucial for agronomical and water resources management. In this study, multiple data sources, including remotely sensed surface soil moisture retrieved from the European Remote Sensing Satellite-1/2 scatterometer (SCAT), soil moisture simulated by the VIP (Vegetation Interface Processes) eco-hydrological dynamical model, and in situ soil profile measurements were employed to assess root zone soil moisture over the Baiyang Lake Basin, north China. Correlation coefficients between the SCAT surface soil moisture dataset and VIP simulation in four seasons varied from 0.47 to 0.66 (p < 0.01). General agreement among remotely sensed retrieval, the eco-hydrological model prediction and in situ measurements shows the potential of the scatterometer for routine acquirement of surface soil moisture patterns in the study area. In addition, the overall agreement between VIP predicted root-layer soil moisture and in situ measurements confirms the reliability of using the VIP model for root-layer soil moisture monitoring at seasonal scales.
J-H01 on GRACE, Remote Sensing and Ground-based Methods in Multi-Scale Hydrology, IUGG2011; 01/2011
[show abstract][hide abstract] ABSTRACT: The role and the importance of soil moisture for meteorological, agricultural and hydrological applications is widely known. Remote sensing offers the unique capability to monitor soil moisture over large areas (catchment scale) with, nowadays, a temporal resolution suitable for hydrological purposes. However, the accuracy of the remotely sensed soil moisture estimates has to be carefully checked. The validation of these estimates with in-situ measurements is not straightforward due the well-known problems related to the spatial mismatch and the measurement accuracy. The analysis of the effects deriving from assimilating remotely sensed soil moisture data into hydrological or meteorological models could represent a more valuable method to test their reliability. In particular, the assimilation of satellite-derived soil moisture estimates into rainfall-runoff models at different scales and over different regions represents an important scientific and operational issue. In this study, the soil wetness index (SWI) product derived from the Advanced SCATterometer (ASCAT) sensor onboard of the Metop satellite was tested. The SWI was firstly compared with the soil moisture temporal pattern derived from a continuous rainfall-runoff model (MISDc) to assess its relationship with modeled data. Then, by using a simple data assimilation technique, the linearly rescaled SWI that matches the range of variability of modelled data (denoted as SWI*) was assimilated into MISDc and the model performance on flood estimation was analyzed. Moreover, three synthetic experiments considering errors on rainfall, model parameters and initial soil wetness conditions were carried out. These experiments allowed to further investigate the SWI potential when uncertain conditions take place. The most significant flood events, which occurred in the period 2000-2009 on five subcatchments of the Upper Tiber River in central Italy, ranging in extension between 100 and 650 km², were used as case studies. Results reveal that the SWI derived from the ASCAT sensor can be conveniently adopted to improve runoff prediction in the study area, mainly if the initial soil wetness conditions are unknown.
Hydrology and Earth System Sciences 10/2010; 14(10):1881-1893. · 3.59 Impact Factor
[show abstract][hide abstract] ABSTRACT: The impact of measurement incidence angle (θ) on the accuracy of radar-based surface soil moisture (Θ<sub>s</sub>) retrievals is largely unknown due to discrepancies in theoretical backscatter models as well as limitations in the availability of sufficientlyextensive ground-based Θ<sub>s</sub> observations for validation. Here, we apply a data assimilation-based evaluation technique for remotely-sensed Θ<sub>s</sub> retrievals that does not require groundbased soil moisture observations to examine the sensitivity of skill in surface Θ<sub>s</sub> retrievals to variations in θ. Application of the evaluation approach to the TU-Wien European Remote Sensing (ERS) scatterometer Θ<sub>s</sub> data set over regional-scale (~10002 km<sup>2</sup>) domains in the Southern Great Plains (SGP) and Southeastern (SE) regions of the United States indicate a relative reduction in correlation-based skill of 23% to 30% for Θ<sub>s</sub> retrievals obtained from far-field (θ > 50°) ERS observations relative to Θ<sub>s</sub> estimates obtained at θ <; 26°. Such relatively modest sensitivity to θ is consistent with Θ<sub>s</sub> retrieval noise predictions made using the TU-Wien ERS Water Retrieval Package 5 (WARP5) backscatter model. However, over moderate vegetation cover in the SE domain, the coupling of a bare soil backscatter model with a "vegetation water cloud" canopy model is shown to overestimate the impact of θ on Θ<sub>s</sub> retrieval skill.
Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International; 08/2010
[show abstract][hide abstract] ABSTRACT: Since December 2008 the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) has been disseminating global 25 km ASCAT surface soil moisture data in near real-time (within 135 minutes after sensing) over its broadcast system EUMETCast. The ASCAT surface soil moisture product is thus the first truly operational satellite soil moisture product that may be used for Numerical Weather Prediction (NWP), flood forecasting and other time-critical applications. In this paper we provide information about the status of the ASCAT Level 2 soil moisture processor, review first published validation and application studies and discuss plans for further improvements.
Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International; 08/2010
[show abstract][hide abstract] ABSTRACT: The impact of measurement incidence angle (θ) on the accuracy of radar-based surface soil-moisture (Θ s ) retrievals is largely unknown due to discrepancies in theoretical backscatter models as well as limitations in the availability of sufficiently extensive ground-based Θ s observations for validation. Here, we apply a data-assimilation-based evaluation technique for remotely sensed Θ s retrievals that does not require ground-based soil-moisture observations to examine the sensitivity of skill in surface Θ s retrievals to variations in θ. Past results with the evaluation approach have shown that it is capable of detecting relative variations in the anomaly correlation coefficient between remotely sensed Θ s retrievals and ground-truth soil-moisture measurements. Application of the evaluation approach to the Vienna University of Technology (TU Wien) European Remote Sensing (ERS) scatterometer Θ s data set over regional-scale ( ~ 1000<sup>2</sup> km<sup>2</sup>) domains in the Southern Great Plains and southeastern (SE) regions of the U.S. indicate a relative reduction in correlation-based skill of 23% to 30% for Θ s retrievals obtained from far-field (θ>50°) ERS observations relative to Θ s estimates obtained at θ <; 26°. Such relatively modest sensitivity to θ is consistent with Θ s retrieval noise predictions made using the TU-Wien ERS Water Retrieval Package 5 backscatter model. However, over moderate vegetation cover in the SE domain, the coupling of a bare soil backscatter model with a “vegetation water cloud” canopy model is shown to overestimate the impact of θ on Θ s retrieval skill.
[show abstract][hide abstract] ABSTRACT: C-band scatterometers have demonstrated to be valuable sensors for large-scale observation of the Earth's surface in a variety of disciplines. High temporal sampling in all weather conditions, multi-viewing capability and availability of long-term measurements make the European C-band scatterometers excellent Earth observation tools. Scatterometer data are used to extract geophysical parameters such as wind speed and direction, surface soil moisture, seasonal dynamics of vegetation, spatial and temporal variability of frozen train in high latitudes, snowmelt and sea ice. Furthermore the scatterometer data are utilized in hydrological modeling, observation of extreme events, flood and drought monitoring, and also used for climate change studies. The observations of the ERS-1/2 scatterometers
[show abstract][hide abstract] ABSTRACT: Understanding the error structures of remotely sensed soil moisture observations is essential for correctly interpreting observed variations and trends in the data or assimilating them in hydrological or numerical weather prediction models. Nevertheless, a spatially coherent assessment of the quality of the various globally available datasets is often hampered by the limited availability over space and time of reliable in-situ measurements. As an alternative, this study explores the triple collocation error estimation technique for assessing the relative quality of several globally available soil moisture products from active (ASCAT) and passive (AMSR-E and SSM/I) microwave sensors. The triple collocation is a powerful statistical tool to estimate the root mean square error while simultaneously solving for systematic differences in the climatologies of a set of three linearly related data sources with independent error structures. Prerequisite for this technique is the availability of a sufficiently large number of timely corresponding observations. In addition to the active and passive satellite-based datasets, we used the ERA-Interim and GLDAS-NOAH reanalysis soil moisture datasets as a third, independent reference. The prime objective is to reveal trends in uncertainty related to different observation principles (passive versus active), the use of different frequencies (C-, X-, and Ku-band) for passive microwave observations, and the choice of the independent reference dataset (ERA-Interim versus GLDAS-NOAH). The results suggest that the triple collocation method provides realistic error estimates. Observed spatial trends agree well with the existing theory and studies on the performance of different observation principles and frequencies with respect to land cover and vegetation density. In addition, if all theoretical prerequisites are fulfilled (e.g. a sufficiently large number of common observations is available and errors of the different datasets are uncorrelated) the errors estimated for the remote sensing products are hardly influenced by the choice of the third independent dataset. The results obtained in this study can help us in developing adequate strategies for the combined use of various scatterometer and radiometer-based soil moisture datasets, e.g. for improved flood forecast modelling or the generation of superior multi-mission long-term soil moisture datasets.