An Approach to Constructing a Homogeneous Time Series of Soil Moisture Using SMOS

One of the limitations of presently available Synthetic Aperture Radar (SAR) surface soil moisture (SSM) products is their moderated temporal resolution (e.g., 3-4 days) that is non optimal for several applications, as most user requirements point to a temporal resolution of 1-2 days or less. A possible path to tackle this issue is to coordinate multi-mission SAR acquisitions with a view to the future Copernicus Sentinel-1 (C&D and Next Generation) and L-band Radar Observation System for Europe (ROSE-L).In this respect, the recent agreement between the Japanese (JAXA) and European (ESA) Space Agencies on the use of SAR Satellites in Earth Science and Applications provides a framework to develop and validate multi-frequency and multi-platform SAR SSM products. In 2019 and 2020, to support insights on the interoperability between C- and L-band SAR observations for SSM retrieval, Sentinel-1 and ALOS-2 systematic acquisitions over the TERENO (Terrestrial Environmental Observatories) Selhausen (Germany) and Apulian Tavoliere (Italy) cal/val sites were gathered. Both sites are well documented and equipped with hydrologic networks.The objective of this study is to investigate the integration of multi-frequency SAR measurements for a consistent and harmonized SSM retrieval throughout the error characterization of a combined C- and L-band SSM product. To this scope, time series of Sentinel-1 IW and ALOS-2 FBD data acquired over the two sites will be analysed. The short time change detection (STCD) algorithm, developed, implemented and recently assessed on Sentinel-1 data [e.g., Balenzano et al., 2020; Mattia et al., 2020], will be tailored to the ALOS-2 data. Then, the time series of SAR SSM maps from each SAR system will be derived separately and aggregated in an interleaved SSM product. Furthermore, it will be compared against in situ SSM data systematically acquired by the ground stations deployed at both sites. The study will assess the interleaved SSM product and evaluate the homogeneous quality of C- and L-band SAR SSM maps.&#160;&#160;ReferencesBalenzano. A., et al., &#8220;Sentinel-1 soil moisture at 1km resolution: a validation study&#8221;, submitted to Remote Sensing of Environment (2020).Mattia, F., A. Balenzano, G. Satalino, F. Lovergine, A. Loew, et al., &#8220;ESA SEOM Land project on Exploitation of Sentinel-1 for Surface Soil Moisture Retrieval at High Resolution,&#8221; final report, contract number 4000118762/16/I-NB, 2020.

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A Prediction Method for Soil Moisture Time Series

Machine Learning for Cyber Security - Lecture Notes in Computer Science ◽

10.1007/978-3-030-62463-7_49 ◽

2020 ◽

pp. 526-537

Author(s):

XiaoXia Yang ◽

Song Jia ◽

Chengming Zhang

Keyword(s):

Time Series ◽

Soil Moisture ◽

Prediction Method

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Teleconnection analysis of runoff and soil moisture over the Pearl River basin in southern China

Hydrology and Earth System Sciences ◽

10.5194/hess-18-1475-2014 ◽

2014 ◽

Vol 18 (4) ◽

pp. 1475-1492 ◽

Cited By ~ 25

Author(s):

J. Niu ◽

J. Chen ◽

B. Sivakumar

Keyword(s):

Time Series ◽

Soil Moisture ◽

River Basin ◽

Southern China ◽

Hydrological Processes ◽

Pearl River ◽

Pearl River Basin ◽

Rank Correlation Method ◽

The Pearl River ◽

The Pearl River Basin

Abstract. This study explores the teleconnection of two climatic patterns, namely the El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD), with hydrological processes over the Pearl River basin in southern China, particularly on a sub-basin-scale basis. The Variable Infiltration Capacity (VIC) model is used to simulate the daily hydrological processes over the basin for the study period 1952–2000, and then, using the simulation results, the time series of the monthly runoff and soil moisture anomalies for its ten sub-basins are aggregated. Wavelet analysis is performed to explore the variability properties of these time series at 49 timescales ranging from 2 months to 9 yr. Use of the wavelet coherence and rank correlation method reveals that the dominant variabilities of the time series of runoff and soil moisture are basically correlated with IOD. The influences of ENSO on the terrestrial hydrological processes are mainly found in the eastern sub-basins. The teleconnections between climatic patterns and hydrological variability also serve as a reference for inferences on the occurrence of extreme hydrological events (e.g., floods and droughts).

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Refining a Polarimetric Decomposition of Multi-Angular UAVSAR Time Series for Soil Moisture Retrieval Over Low and High Vegetated Agricultural Fields

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2019.2909984 ◽

2019 ◽

Vol 12 (5) ◽

pp. 1431-1450 ◽

Cited By ~ 3

Author(s):

Hongquan Wang ◽

Ramata Magagi ◽

Kalifa Goita ◽

Thomas Jagdhuber

Keyword(s):

Time Series ◽

Soil Moisture ◽

Agricultural Fields

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Permafrost Soil Moisture Monitoring Using Multi-Temporal TerraSAR-X Data in Beiluhe of Northern Tibet, China

Remote Sensing ◽

10.3390/rs10101577 ◽

2018 ◽

Vol 10 (10) ◽

pp. 1577 ◽

Cited By ~ 6

Author(s):

Chao Wang ◽

Zhengjia Zhang ◽

Simonetta Paloscia ◽

Hong Zhang ◽

Fan Wu ◽

...

Keyword(s):

Time Series ◽

Soil Moisture ◽

Winter Season ◽

Tibet Plateau ◽

Permafrost Soil ◽

Permafrost Region ◽

Backscattering Coefficient ◽

Sar Images ◽

Northern Tibet ◽

Variation Patterns

Global change has significant impact on permafrost region in the Tibet Plateau. Soil moisture (SM) of permafrost is one of the most important factors influencing the energy flux, ecosystem, and hydrologic process. The objectives of this paper are to retrieve the permafrost SM using time-series SAR images, without the need of auxiliary survey data, and reveal its variation patterns. After analyzing the characteristics of time-series radar backscattering coefficients of different landcover types, a two-component SM retrieval model is proposed. For the alpine meadow area, a linear retrieving model is proposed using the TerraSAR-X time-series images based on the assumption that the lowest backscattering coefficient is measured when the soil moisture is at its wilting point and the highest backscattering coefficient represents the water-saturated soil state. For the alpine desert area, the surface roughness contribution is eliminated using the dual SAR images acquired in the winter season with different incidence angles when retrieving soil moisture from the radar signal. Before the model implementation, landcover types are classified based on their backscattering features. 22 TerraSAR-X images are used to derive the soil moisture in Beiluhe, Northern Tibet with different incidence angles. The results obtained from the proposed method have been validated using in-situ soil moisture measurements, thus obtaining RMSE and Bias of 0.062 cm3/cm3 and 4.7%, respectively. The retrieved time-series SM maps of the study area point out the spatial and temporal SM variation patterns of various landcover types.

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A Time-Varying Causality Formalism Based on the Liang–Kleeman Information Flow for Analyzing Directed Interactions in Nonstationary Climate Systems

Journal of Climate ◽

10.1175/jcli-d-18-0881.1 ◽

2019 ◽

Vol 32 (21) ◽

pp. 7521-7537 ◽

Cited By ~ 4

Author(s):

Daniel Fiifi Tawia Hagan ◽

Guojie Wang ◽

X. San Liang ◽

Han A. J. Dolman

Keyword(s):

Time Series ◽

Soil Moisture ◽

Information Flow ◽

Air Temperature ◽

Land Surface ◽

Full Range ◽

Accurate Estimation ◽

Time Varying ◽

Long Time Series ◽

Long Time

Abstract The interaction between the land surface and the atmosphere is of significant importance in the climate system because it is a key driver of the exchanges of energy and water. Several important relations to heat waves, floods, and droughts exist that are based on the interaction of soil moisture and, for instance, air temperature and humidity. Our ability to separate the elements of this coupling, identify the exact locations where they are strongest, and quantify their strengths is, therefore, of paramount importance to their predictability. A recent rigorous causality formalism based on the Liang–Kleeman (LK) information flow theory has been shown, both theoretically and in real-world applications, to have the necessary asymmetry to infer the directionality and magnitude within geophysical interactions. However, the formalism assumes stationarity in time, whereas the interactions within the land surface and atmosphere are generally nonstationary; furthermore, it requires a sufficiently long time series to ensure statistical sufficiency. In this study, we remedy this difficulty by using the square root Kalman filter to estimate the causality based on the LK formalism to derive a time-varying form. Results show that the new formalism has similar properties compared to its time-invariant form. It is shown that it is also able to capture the time-varying causality structure within soil moisture–air temperature coupling. An advantage is that it does not require very long time series to make an accurate estimation. Applying a wavelet transform to the results also reveals the full range of temporal scales of the interactions.

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Analysis of the Potential Contributors to Common Mode Error in Chuandian Region of China

Remote Sensing ◽

10.3390/rs12050751 ◽

2020 ◽

Vol 12 (5) ◽

pp. 751

Author(s):

Weijie Tan ◽

Junping Chen ◽

Danan Dong ◽

Weijing Qu ◽

Xueqing Xu

Keyword(s):

Time Series ◽

Soil Moisture ◽

Principal Component ◽

Mass Loading ◽

Common Mode ◽

Surface Mass ◽

Common Mode Error ◽

Comparison Results ◽

Wide Range ◽

Gps Time Series

Common mode error (CME) in Chuandian region of China is derived from 6-year continuous GPS time series and is identified by principal component analysis (PCA) method. It is revealed that the temporal behavior of the CME is not purely random, and contains unmodeled signals such as nonseasonal mass loadings. Its spatial distribution is quite uniform for all GPS sites in the region, and the first principal component, uniformly distributed in the region, has a spatial response of more than 70%. To further explore the potential contributors of CME, daily atmospheric mass loading and soil moisture mass loading effects are evaluated. Our results show that ~15% of CME can be explained by these daily surface mass loadings. The power spectral analysis is used to assess the CME. After removing atmospheric and soil moisture loadings from the CME, the power of the CME reduces in a wide range of frequencies. We also investigate the contribution of CME in GPS filtered residuals time series and it shows the Root Mean Squares (RMSs) of GPS time series are reduced by applying of the mass loading corrections in CME. These comparison results demonstrate that daily atmosphere pressure and the soil moisture mass loadings are a part of contributors to the CME in Chuandian region of China.

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Performance analysis of regional landslide early warning based on soil moisture simulations

10.5194/egusphere-egu2020-3362 ◽

2020 ◽

Author(s):

Adrian Wicki ◽

Manfred Stähli

Keyword(s):

Time Series ◽

Soil Moisture ◽

Performance Analysis ◽

Early Warning ◽

Soil Profile ◽

Forecast Skill ◽

Soil Moisture Dynamics ◽

Landslide Early Warning ◽

Moisture Dynamics

In mountainous regions, rainfall-triggered landslides pose a serious risk to people and infrastructure, particularly due to the short time interval between activation and failure and their widespread occurrence. Landslide early warning systems (LEWS) have demonstrated to be a valuable tool to inform decision makers about the imminent landslide danger and to move people or goods at risk to safety. While most operational LEWS are based on empirically derived rainfall exceedance thresholds, recent studies have demonstrated an improvement of the forecast quality after the inclusion of in-situ soil moisture measurements.The use of in-situ soil moisture sensors bears specific limitations, such as the sensitivity to local conditions, the disturbance of the soil profile during installation, and potential data quality issues and inhomogeneity of long-term measurements. Further, the installation and operation of monitoring networks is laborious and costly. In this respect, making use of modelled soil moisture could efficiently increase information density, and it would further allow to forecast soil moisture dynamics. On the other hand, numerical simulations are restricted by assumptions and simplifications related to the soil hydraulic properties and the water transfer in the soil profile. Ultimately, the question arises how reliable and representative landslide early warnings based on soil moisture simulations are compared to warnings based on measurements.To answer this, we applied a state-of-the-art one-dimensional heat and mass transfer model (CoupModel, Jansson 2012) to generate time series of soil water content at 35 sites in Switzerland. The same sites and time period (2008-2018) were used in a previous study to compare the temporal variability of in-situ measured soil moisture to the regional landslide activity (currently under review in Landslides). The same statistical framework for soil moisture dynamics analysis, landslide probability modelling and landslide early warning performance analysis was applied to the modelled and the measured soil moisture time series. This allowed to directly compare the forecast skill of modelling-based with measurements-based landslide early warning.In this contribution, we will highlight three steps of model applications: First, a straight-forward simulation to all 35 sites without site-specific calibration and using reference soil layering only, to assess the forecast skill as if no prior measurements were available. Second, a model simulation after calibration at each site using the existing soil moisture time series and information on the soil texture to assess the benefit of a thorough calibration process on the forecast skill. Finally, an application of the model to additional sites in Switzerland where no soil moisture measurements are available to assess the effect of increasing the soil moisture information density on the overall forecast skill.

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The Ongoing Central European Drought Observed with GRACE Follow-On

10.5194/gstm2020-25 ◽

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

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. 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. 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. 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&#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.

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