Joint analysis of remotely sensed soil moisture and water storage variations from satellite gravimetry

2021 ◽  
Author(s):  
Daniel Blank ◽  
Annette Eicker ◽  
Laura Jensen ◽  
Andreas Güntner
Keyword(s):  
Soil Moisture ◽  
Water Storage ◽  
Microwave Remote Sensing ◽  
Time Shift ◽  
Joint Analysis ◽  
Data Sets ◽  
Time Lags ◽  
Data Set ◽  
Satellite Gravimetry ◽  
Storage Change

<p>Information on water storage changes in the soil can be obtained on a global scale from different types of satellite observations. While active or passive microwave remote sensing is limited to investigating the upper few centimeters of the soil, satellite gravimetry is sensitive to variations in the full column of terrestrial water storage (TWS) but cannot distinguish between storage variations occurring in different soil depths. Jointly analyzing both data types promises interesting insights into the underlying hydrological dynamics and may enable a better process understanding of water storage change in the subsurface.</p><p>In this study, we aim at investigating the global relationship of (1) several satellite soil moisture (SM) products and (2) non-standard daily TWS data from the GRACE and GRACE-FO satellite gravimetry missions on different time scales. We decompose the data sets into different temporal frequencies from seasonal to sub-monthly signals and carry out the comparison with respect to spatial patterns and temporal variability. Level-3 (Surface SM up to 5 cm depth) and Level-4 (Root-Zone SM up to 1 m depth) data sets of the SMOS and SMAP missions as well as the ESA CCI data set are used in this investigation.<br>Since a direct comparison of the absolute values is not possible due to the different integration depths of the two data sets (SM and TWS), we will analyze their relationship using Pearson’s pairwise correlation coefficient. Furthermore, a time-shift analysis is carried out by means of cross-correlation to identify time lags between SM and TWS data sets that indicate differences in the temporal dynamics of SM storage change in varying depth layers.</p>

Fully automated near-surface analysis by surface-consistent refraction method

Geophysics ◽  
2016 ◽  
Vol 81 (4) ◽  
pp. U39-U49 ◽  
Author(s):  
Daniele Colombo ◽  
Federico Miorelli ◽  
Ernesto Sandoval ◽  
Kevin Erickson
Keyword(s):  
Surface Analysis ◽  
Seismic Data ◽  
Linear Equations ◽  
Time Shift ◽  
Data Sets ◽  
Time Lags ◽  
Data Set ◽  
Near Surface ◽  
Seismic Acquisition ◽  
Key Aspects

Industry practices for near-surface analysis indicate difficulties in coping with the increased number of channels in seismic acquisition systems, and new approaches are needed to fully exploit the resolution embedded in modern seismic data sets. To achieve this goal, we have developed a novel surface-consistent refraction analysis method for low-relief geology to automatically derive near-surface corrections for seismic data processing. The method uses concepts from surface-consistent analysis applied to refracted arrivals. The key aspects of the method consist of the use of common midpoint (CMP)-offset-azimuth binning, evaluation of mean traveltime and standard deviation for each bin, rejection of anomalous first-break (FB) picks, derivation of CMP-based traveltime-offset functions, conversion to velocity-depth functions, evaluation of long-wavelength statics, and calculation of surface-consistent residual statics through waveform crosscorrelation. Residual time lags are evaluated in multiple CMP-offset-azimuth bins by crosscorrelating a pilot trace with all the other traces in the gather in which the correlation window is centered at the refracted arrival. The residuals are then used to build a system of linear equations that is simultaneously inverted for surface-consistent shot and receiver time shift corrections plus a possible subsurface residual term. All the steps are completely automated and require a fraction of the time needed for conventional near-surface analysis. The developed methodology was successfully performed on a complex 3D land data set from Central Saudi Arabia where it was benchmarked against a conventional tomographic work flow. The results indicate that the new surface-consistent refraction statics method enhances seismic imaging especially in portions of the survey dominated by noise.

scholarly journals Estimating annual water storage variations in medium-scale (2000–10 000 km<sup>2</sup>) basins using microwave-based soil moisture retrievals

2017 ◽  
Vol 21 (3) ◽  
pp. 1849-1862 ◽  
Author(s):  
Wade T. Crow ◽  
Eunjin Han ◽  
Dongryeol Ryu ◽  
Christopher R. Hain ◽  
Martha C. Anderson
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Water Budget ◽  
Surface Soil ◽  
Water Storage ◽  
Microwave Remote Sensing ◽  
The United States ◽  
Surface Soil Moisture ◽  
Medium Scale ◽  
Annual Water

Abstract. Due to their shallow vertical support, remotely sensed surface soil moisture retrievals are commonly regarded as being of limited value for water budget applications requiring the characterization of temporal variations in total terrestrial water storage (dS ∕ dt). However, advances in our ability to estimate evapotranspiration remotely now allow for the direct evaluation of approaches for quantifying dS ∕ dt via water budget closure considerations. By applying an annual water budget analysis within a series of medium-scale (2000–10 000 km2) basins within the United States, we demonstrate that, despite their clear theoretical limitations, surface soil moisture retrievals derived from passive microwave remote sensing contain statistically significant information concerning dS ∕ dt. This suggests the possibility of using (relatively) higher-resolution microwave remote sensing products to enhance the spatial resolution of dS ∕ dt estimates acquired from gravity remote sensing.

The Ongoing Central European Drought Observed with GRACE Follow-On

2020 ◽  
Author(s):  
Eva Boergens ◽  
Andreas Güntner ◽  
Henryk Dobslaw ◽  
Christoph Dahle
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Surface Water ◽  
Central Europe ◽  
Drought Index ◽  
Water Mass ◽  
Water Storage ◽  
Drought Indices ◽  
Severe Drought ◽  
Data Set

&lt;p class=&quot;western&quot;&gt;In the last three years Central Europe experienced an ongoing severe drought. With the data of the GRACE Follow-On (GRACE-FO) mission we are able to quantify the water deficit of these years. Since May 2018 GRACE-FO continues the observations of GRACE (2002-2017) allowing to compare the most recent drought with earlier droughts in 2003 and 2015.&lt;/p&gt; &lt;p class=&quot;western&quot;&gt;In July 2019 the water mass deficit in Central Europe amounted to -154 Gt, which has been the largest deficit in the whole GRACE and GRACE-FO time series. In November 2018 the deficit reached -138 Gt and in June 2020 -147 Gt. Comparing these deficits to the mean annual water storage variation of 162 Gt shows the severity of the ongoing drought. With such a water mass deficit, a fast recovery within one year cannot be expected. In comparison to this, the droughts of 2003 with a deficit of -55 Gt and of 2015 with a deficit of -111 Gt were less severe.&lt;/p&gt; &lt;p class=&quot;western&quot;&gt;The GRACE and GRACE-FO total water storage data set also allows for analysing spatio-temporal drought patterns. In 2018 the drought was centred in in the South-West of Germany and neighbouring countries while parts of Poland were hardly affected by the drought. In 2018 the drought reached its largest extent only in late autumn. However, the exact onset of drought is not determinable due to missing data between July and October. Both in 2019 and 2020 the centre of the drought is located further East and the months with the largest deficit were July and June, respectively. Also in the later years, the drought was more evenly spread out over the whole of Central Europe.&lt;/p&gt; &lt;p class=&quot;western&quot;&gt;Additionally, we compared the GRACE and GRACE-FO data to an external soil moisture index and to surface water drought indices for Lake Constance and Lake M&amp;#252;ritz. To this end, we derive a drought index from the GRACE and GRACE-FO mass anomalies. For the whole time series, the GRACE drought index shows a high congruency to the soil moisture drought index. Overall, the surface water drought index also fits well together with the GRACE drought index. However, the comparison reveals the influence of regional effects on surface waters not observable with GRACE and GRACE-FO.&lt;/p&gt;

scholarly journals GLEAM v3: satellite-based land evaporation and root-zone soil moisture

2016 ◽  
Author(s):  
Brecht Martens ◽  
Diego G. Miralles ◽  
Hans Lievens ◽  
Robin van der Schalie ◽  
Richard A. M. de Jeu ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Eddy Covariance ◽  
Optical Depth ◽  
Root Zone ◽  
European Space Agency ◽  
Data Sets ◽  
Data Set ◽  
Previous Version ◽  
The One

Abstract. The Global Land Evaporation Amsterdam Model (GLEAM) is a set of algorithms dedicated to the estimation of terrestrial evaporation and root-zone soil moisture from satellite data. Ever since its development in 2011, the model has been regularly revised aiming at the optimal incorporation of new satellite-observed geophysical variables, and improving the representation of physical processes. In this study, the next version of this model (v3) is presented. Key changes relative to the previous version include: (1) a revised formulation of the evaporative stress, (2) an optimized drainage algorithm, and (3) a new soil moisture data assimilation system. GLEAM v3 is used to produce three new data sets of terrestrial evaporation and root-zone soil moisture, including a 35-year data set spanning the period 1980–2014 (v3.0a, based on satellite-observed soil moisture, vegetation optical depth and snow water equivalents, reanalysis air temperature and radiation, and a multi-source precipitation product), and two fully satellite-based data sets. The latter two share most of their forcing, except for the vegetation optical depth and soil moisture products, which are based on observations from different passive and active C- and L-band microwave sensors (European Space Agency Climate Change Initiative data sets) for the first data set (v3.0b, spanning the period 2003–2015) and observations from the Soil Moisture and Ocean Salinity satellite in the second data set (v3.0c, spanning the period 2011–2015). These three data sets are described in detail, compared against analogous data sets generated using the previous version of GLEAM (v2), and validated against measurements from 64 eddy-covariance towers and 2338 soil moisture sensors across a broad range of ecosystems. Results indicate that the quality of the v3 soil moisture is consistently better than the one from v2: average correlations against in situ surface soil moisture measurements increase from 0.61 to 0.64 in case of the v3.0a data set and the representation of soil moisture in the second layer improves as well, with correlations increasing from 0.47 to 0.53. Similar improvements are observed for the two fully satellite-based data sets. Despite regional differences, the quality of the evaporation fluxes remains overall similar as the one obtained using the previous version of GLEAM, with average correlations against eddy-covariance measurements between 0.78 and 0.80 for the three different data sets. These global data sets of terrestrial evaporation and root-zone soil moisture are now openly available at http://GLEAM.eu and may be used for large-scale hydrological applications, climate studies and research on land-atmosphere feedbacks.

scholarly journals Global joint assimilation of GRACE and SMOS for improved estimation of root-zone soil moisture and vegetation response

2018 ◽  
Author(s):  
Siyuan Tian ◽  
Luigi J. Renzullo ◽  
Albert I. J. M. van Dijk ◽  
Paul Tregoning ◽  
Jeffrey P. Walker
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Soil Water ◽  
Root Zone ◽  
Water Storage ◽  
Microwave Remote Sensing ◽  
Open Loop ◽  
Total Water ◽  
Vegetation Response ◽  
Vegetation Greenness

Abstract. The lack of direct measurement of root-zone soil moisture poses a challenge to the large-scale prediction of ecosystem response to variation in soil water. Microwave remote sensing capability is limited to measuring moisture content in the uppermost few centimetres of soil. In contrast, GRACE (Gravity Recovery and Climate Experiment) mission detected the variability in storage within the total water column, which is often dominated by groundwater variation. However, not all vegetation communities can access groundwater. In this study, satellite-derived water content from GRACE and SMOS were jointly assimilated into an ecohydrological model to better predict the impact of changes in root-zone soil moisture on vegetation vigour. Overall, the accuracy of root-zone soil moisture prediction though the joint assimilation of surface soil moisture and total water storage retrievals showed improved consistency with ground-based soil moisture measurements and satellite-observed greenness when compared to open-loop estimates (i.e. without assimilation). For example, the correlation between modelled and in-situ measurements of root-zone moisture increased by 0.1 on average over grasslands and croplands. Improved correlations were found between vegetation greenness and soil water storage derived from the joint assimilation with an increase up to 0.47 over grassland compared to open-loop estimates. Joint assimilation results show a more severe deficit in soil water in eastern Australia, western North America and eastern Brazil over the period of 2010 to 2015 than the open-loop, consistent with the satellite-observed vegetation greenness. The assimilation of satellite-observed water content contributes to more accurate knowledge of soil water availability, providing new insights for monitoring hidden water stress and vegetation response.

scholarly journals Two-stage Linked Component Analysis for Joint Decomposition of Multiple Biologically Related Data Sets

2021 ◽  
Author(s):  
By Huan Chen ◽  
Brian Caffo ◽  
Genevieve Stein-O’Brien ◽  
Jinrui Liu ◽  
Ben Langmead ◽  
...  
Keyword(s):  
R Package ◽  
Component Analysis ◽  
Biological Data ◽  
Joint Analysis ◽  
Data Sets ◽  
Biological Processes ◽  
Two Stage ◽  
Data Set ◽  
Multiple Data ◽  
Multiple Data Sets

SummaryIntegrative analysis of multiple data sets has the potential of fully leveraging the vast amount of high throughput biological data being generated. In particular such analysis will be powerful in making inference from publicly available collections of genetic, transcriptomic and epigenetic data sets which are designed to study shared biological processes, but which vary in their target measurements, biological variation, unwanted noise, and batch variation. Thus, methods that enable the joint analysis of multiple data sets are needed to gain insights into shared biological processes that would otherwise be hidden by unwanted intra-data set variation. Here, we propose a method called two-stage linked component analysis (2s-LCA) to jointly decompose multiple biologically related experimental data sets with biological and technological relationships that can be structured into the decomposition. The consistency of the proposed method is established and its empirical performance is evaluated via simulation studies. We apply 2s-LCA to jointly analyze four data sets focused on human brain development and identify meaningful patterns of gene expression in human neurogenesis that have shared structure across these data sets. The code to conduct 2s-LCA has been complied into an R package “PJD”, which is available at https://github.com/CHuanSite/PJD.

scholarly journals The International Soil Moisture Network: serving Earth system science for over a decade

2021 ◽  
Author(s):  
Wouter Dorigo ◽  
Irene Himmelbauer ◽  
Daniel Aberer ◽  
Lukas Schremmer ◽  
Ivana Petrakovic ◽  
...  
Keyword(s):  
Quality Control ◽  
Soil Moisture ◽  
European Space Agency ◽  
Data Repository ◽  
Reference Database ◽  
Data Sets ◽  
Scientific Publications ◽  
Data Set ◽  
Space Agency

Abstract. In 2009, the International Soil Moisture Network (ISMN) was initiated as a community effort, funded by the European Space Agency, to serve as a centralised data hosting facility for globally available in situ soil moisture measurements (Dorigo et al., 2011a, b). The ISMN brings together in situ soil moisture measurements collected and freely shared by a multitude of organisations, harmonizes them in terms of units and sampling rates, applies advanced quality control, and stores them in a database. Users can freely retrieve the data from this database through an online web portal (https://ismn.earth). Meanwhile, the ISMN has evolved into the primary in situ soil moisture reference database worldwide, as evidenced by more than 3000 active users and over 1000 scientific publications referencing the data sets provided by the network. As of December 2020, the ISMN now contains data of 65 networks and 2678 stations located all over the globe, with a time period spanning from 1952 to present.The number of networks and stations covered by the ISMN is still growing and many of the data sets contained in the database continue to be updated. The main scope of this paper is to inform readers about the evolution of the ISMN over the past decade,including a description of network and data set updates and quality control procedures. A comprehensive review of existing literature making use of ISMN data is also provided in order to identify current limitations in functionality and data usage, and to shape priorities for the next decade of operations of this unique community-based data repository.

scholarly journals Global terrestrial water storage connectivity revealed using complex climate network analyses

2015 ◽  
Vol 22 (4) ◽  
pp. 433-446 ◽  
Author(s):  
A. Y. Sun ◽  
J. Chen ◽  
J. Donges
Keyword(s):  
Spatial Patterns ◽  
Land Surface ◽  
Water Storage ◽  
Data Sets ◽  
Terrestrial Water Storage ◽  
Land Surface Models ◽  
Data Set ◽  
Surface Models ◽  
Global Land ◽  
Terrestrial Water

Abstract. Terrestrial water storage (TWS) exerts a key control in global water, energy, and biogeochemical cycles. Although certain causal relationship exists between precipitation and TWS, the latter quantity also reflects impacts of anthropogenic activities. Thus, quantification of the spatial patterns of TWS will not only help to understand feedbacks between climate dynamics and the hydrologic cycle, but also provide new insights and model calibration constraints for improving the current land surface models. This work is the first attempt to quantify the spatial connectivity of TWS using the complex network theory, which has received broad attention in the climate modeling community in recent years. Complex networks of TWS anomalies are built using two global TWS data sets, a remote sensing product that is obtained from the Gravity Recovery and Climate Experiment (GRACE) satellite mission, and a model-generated data set from the global land data assimilation system's NOAH model (GLDAS-NOAH). Both data sets have 1° × 1° grid resolutions and cover most global land areas except for permafrost regions. TWS networks are built by first quantifying pairwise correlation among all valid TWS anomaly time series, and then applying a cutoff threshold derived from the edge-density function to retain only the most important features in the network. Basinwise network connectivity maps are used to illuminate connectivity of individual river basins with other regions. The constructed network degree centrality maps show the TWS anomaly hotspots around the globe and the patterns are consistent with recent GRACE studies. Parallel analyses of networks constructed using the two data sets reveal that the GLDAS-NOAH model captures many of the spatial patterns shown by GRACE, although significant discrepancies exist in some regions. Thus, our results provide further measures for constraining the current land surface models, especially in data sparse regions.

Time-lapse acoustic impedance variations during CO2 injection in Weyburn oilfield, Canada

Geophysics ◽  
2019 ◽  
Vol 85 (1) ◽  
pp. M1-M13 ◽  
Author(s):  
Yichuan Wang ◽  
Igor B. Morozov
Keyword(s):  
Oil Recovery ◽  
Seismic Data ◽  
Acoustic Impedance ◽  
Time Lapse ◽  
Time Shift ◽  
Co2 Injection ◽  
Inversion Method ◽  
Data Sets ◽  
Storage Site ◽  
Data Set

For seismic monitoring injected fluids during enhanced oil recovery or geologic [Formula: see text] sequestration, it is useful to measure time-lapse (TL) variations of acoustic impedance (AI). AI gives direct connections to the mechanical and fluid-related properties of the reservoir or [Formula: see text] storage site; however, evaluation of its subtle TL variations is complicated by the low-frequency and scaling uncertainties of this attribute. We have developed three enhancements of TL AI analysis to resolve these issues. First, following waveform calibration (cross-equalization) of the monitor seismic data sets to the baseline one, the reflectivity difference was evaluated from the attributes measured during the calibration. Second, a robust approach to AI inversion was applied to the baseline data set, based on calibration of the records by using the well-log data and spatially variant stacking and interval velocities derived during seismic data processing. This inversion method is straightforward and does not require subjective selections of parameterization and regularization schemes. Unlike joint or statistical inverse approaches, this method does not require prior models and produces accurate fitting of the observed reflectivity. Third, the TL AI difference is obtained directly from the baseline AI and reflectivity difference but without the uncertainty-prone subtraction of AI volumes from different seismic vintages. The above approaches are applied to TL data sets from the Weyburn [Formula: see text] sequestration project in southern Saskatchewan, Canada. High-quality baseline and TL AI-difference volumes are obtained. TL variations within the reservoir zone are observed in the calibration time-shift, reflectivity-difference, and AI-difference images, which are interpreted as being related to the [Formula: see text] injection.

The detection of irrigation through remote sensing soil moisture and a land surface model: a case study in Spain

2020 ◽  
Author(s):  
Jacopo Dari ◽  
Pere Quintana-Seguí ◽  
María José Escorihuela ◽  
Luca Brocca ◽  
Renato Morbidelli ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Land Surface ◽  
Hydrological Cycle ◽  
Land Surface Model ◽  
Surface Model ◽  
Data Sets ◽  
Data Set ◽  
Agricultural Areas ◽  
Irrigation Practices

&lt;p&gt;Irrigation practices introduce imbalances in the natural hydrological cycle at different spatial scales and put pressure on water resources, especially under climate changing and population increasing scenarios. Despite the implications of irrigation on food production and on the rational management of the available freshwater, detailed information about the areas where irrigation actually occurs is still lacking. For this reason, the comprehensive knowledge of the dynamics of the hydrological cycle over agricultural areas is often tricky.&lt;/p&gt;&lt;p&gt;The first aim of this study is to evaluate the capability of five remote sensing soil moisture data sets to detect the irrigation signal over an intensely irrigated area located within the Ebro river basin, in the North of Spain, during the biennium 2016-2017. As a second objective, a methodology to map the irrigated areas through the K-means clustering algorithm is proposed. The remotely sensed soil moisture products used in this study are: SMOS (Soil Moisture and Ocean Salinity) at 1 km, SMAP (Soil Moisture Active Passive) at 1 km and 9 km, Sentinel-1 at 1 km and ASCAT (Advanced SCATterometer) at 12.5 km. The 1 km versions of SMOS and SMAP are DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) downscaled versions of the corresponding coarser resolution products. An additional data set of soil moisture simulated by the SURFEX-ISBA (&lt;em&gt;Surface Externalis&amp;#233;e - Interaction Sol Biosph&amp;#232;re Atmosph&amp;#232;re&lt;/em&gt;) land surface model is used as a support for the performed analyses.&lt;/p&gt;&lt;p&gt;The capability of soil moisture products to detect irrigation has been investigated by exploiting indices representing the spatial and temporal dynamics of soil moisture. The L-band passive microwave downscaled products, especially SMAP at 1 km, result the best performing ones in detecting the irrigation signal over the pilot area; on the basis of these data sets, the K-means algorithm has been employed to classify three kinds of surfaces within the study area: the dryland, the forest or natural areas, and the actually irrigated areas. The resulting maps have been validated by exploiting maps of crops in Catalonia as ground truth data set. The percentage of irrigated areas well classified by the proposed method reaches the value of 78%; this result is obtained for the period May - September 2017. In addition, the method performs well in distinguishing the irrigated areas from rainfed agricultural areas, which are dry during summer, thus representing a useful tool to obtain explicit spatial information about where irrigation practices actually occur over agricultural areas equipped for this purpose.&lt;/p&gt;

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