Short-Term and Long-Term Surface Soil Moisture Memory Time Scales Are Spatially Anticorrelated at Global Scales

Abstract Land–atmosphere feedbacks occurring on daily to weekly time scales can magnify the intensity and duration of extreme weather events, such as droughts, heat waves, and convective storms. For such feedbacks to occur, the coupled land–atmosphere system must exhibit sufficient memory of soil moisture anomalies associated with the extreme event. The soil moisture autocorrelation e-folding time scale has been used previously to estimate soil moisture memory. However, the theoretical basis for this metric (i.e., that the land water budget is reasonably approximated by a red noise process) does not apply at finer spatial and temporal resolutions relevant to modern satellite observations and models. In this study, two memory time scale metrics are introduced that are relevant to modern satellite observations and models: the “long-term memory” τL and the “short-term memory” τS. Short- and long-term surface soil moisture (SSM) memory time scales are spatially anticorrelated at global scales in both a model and satellite observations, suggesting hot spots of land–atmosphere coupling will be located in different regions, depending on the time scale of the feedback. Furthermore, the spatial anticorrelation between τS and τL demonstrates the importance of characterizing these memory time scales separately, rather than mixing them as in previous studies.

Download Full-text

Improving Satellite-Based Rainfall Accumulation Estimates Using Spaceborne Surface Soil Moisture Retrievals

Journal of Hydrometeorology ◽

10.1175/2008jhm986.1 ◽

2009 ◽

Vol 10 (1) ◽

pp. 199-212 ◽

Cited By ~ 75

Author(s):

Wade T. Crow ◽

George J. Huffman ◽

Rajat Bindlish ◽

Thomas J. Jackson

Keyword(s):

Soil Moisture ◽

Surface Soil ◽

Rain Gauge ◽

Surface Soil Moisture ◽

Complementary Information ◽

Short Term ◽

Rain Gauge Data ◽

Gauge Data ◽

Cover Density

Abstract Over land, remotely sensed surface soil moisture and rainfall accumulation retrievals contain complementary information that can be exploited for the mutual benefit of both product types. Here, a Kalman filtering–based tool is developed that utilizes a time series of spaceborne surface soil moisture retrievals to enhance short-term (2- to 10-day) satellite-based rainfall accumulation products. Using ground rain gauge data as a validation source, and a soil moisture product derived from the Advanced Microwave Scanning Radiometer aboard the NASA Aqua satellite, the approach is evaluated over the contiguous United States. Results demonstrate that, for areas of low to moderate vegetation cover density, the procedure is capable of improving short-term rainfall accumulation estimates extracted from a variety of satellite-based rainfall products. The approach is especially effective for correcting rainfall accumulation estimates derived without the aid of ground-based rain gauge observations. Special emphasis is placed on demonstrating that the approach can be applied in continental areas lacking ground-based observations and/or long-term satellite data records.

Download Full-text

An evaluation of ASCAT surface soil moisture products with in-situ observations in Southwestern France

Hydrology and Earth System Sciences ◽

10.5194/hess-13-115-2009 ◽

2009 ◽

Vol 13 (2) ◽

pp. 115-124 ◽

Cited By ~ 158

Author(s):

C. Albergel ◽

C. Rüdiger ◽

D. Carrer ◽

J.-C. Calvet ◽

N. Fritz ◽

...

Keyword(s):

Soil Moisture ◽

Surface Soil ◽

Mountainous Area ◽

Surface Soil Moisture ◽

Water Index ◽

In Situ Observations ◽

Weather Stations ◽

Term Data

Abstract. A long term data acquisition effort of profile soil moisture is currently underway at 13 automatic weather stations located in Southwestern France. In this study, the soil moisture measured in-situ at 5 cm is used to evaluate the normalised surface soil moisture (SSM) estimates derived from coarse-resolution (25 km) active microwave data of the ASCAT scatterometer instrument (onboard METOP), issued by EUMETSAT for a period of 6 months (April–September) in 2007. The seasonal trend is removed from the satellite and in-situ time series by considering scaled anomalies. One station (Mouthoumet) of the ground network, located in a mountainous area, is removed from the analysis as very few ASCAT SSM estimates are available. No correlation is found for the station of Narbonne, which is close to the Mediterranean sea. On the other hand, nine stations present significant correlation levels. For two stations, a significant correlation is obtained when considering only part of the ASCAT data. The soil moisture measured in-situ at those stations, at 30 cm, is used to estimate the characteristic time length (T) of an exponential filter applied to the ASCAT product. The best correlation between a soil water index derived from ASCAT and the in-situ soil moisture observations at 30 cm is obtained with a T-value of 14 days.

Download Full-text

An improved global remote-sensing-based surface soil moisture (RSSSM) dataset covering 2003–2018

Earth System Science Data ◽

10.5194/essd-13-1-2021 ◽

2021 ◽

Vol 13 (1) ◽

pp. 1-31

Author(s):

Yongzhe Chen ◽

Xiaoming Feng ◽

Bojie Fu

Keyword(s):

Remote Sensing ◽

Neural Networks ◽

Soil Moisture ◽

Data Fusion ◽

Surface Soil ◽

Microwave Remote Sensing ◽

Satellite Monitoring ◽

Surface Soil Moisture ◽

Spatial Coverage

Abstract. Soil moisture is an important variable linking the atmosphere and terrestrial ecosystems. However, long-term satellite monitoring of surface soil moisture at the global scale needs improvement. In this study, we conducted data calibration and data fusion of 11 well-acknowledged microwave remote-sensing soil moisture products since 2003 through a neural network approach, with Soil Moisture Active Passive (SMAP) soil moisture data applied as the primary training target. The training efficiency was high (R2=0.95) due to the selection of nine quality impact factors of microwave soil moisture products and the complicated organizational structure of multiple neural networks (five rounds of iterative simulations, eight substeps, 67 independent neural networks, and more than 1 million localized subnetworks). Then, we developed the global remote-sensing-based surface soil moisture dataset (RSSSM) covering 2003–2018 at 0.1∘ resolution. The temporal resolution is approximately 10 d, meaning that three data records are obtained within a month, for days 1–10, 11–20, and from the 21st to the last day of that month. RSSSM is proven comparable to the in situ surface soil moisture measurements of the International Soil Moisture Network sites (overall R2 and RMSE values of 0.42 and 0.087 m3 m−3), while the overall R2 and RMSE values for the existing popular similar products are usually within the ranges of 0.31–0.41 and 0.095–0.142 m3 m−3), respectively. RSSSM generally presents advantages over other products in arid and relatively cold areas, which is probably because of the difficulty in simulating the impacts of thawing and transient precipitation on soil moisture, and during the growing seasons. Moreover, the persistent high quality during 2003–2018 as well as the complete spatial coverage ensure the applicability of RSSSM to studies on both the spatial and temporal patterns (e.g. long-term trend). RSSSM data suggest an increase in the global mean surface soil moisture. Moreover, without considering the deserts and rainforests, the surface soil moisture loss on consecutive rainless days is highest in summer over the low latitudes (30∘ S–30∘ N) but mostly in winter over the mid-latitudes (30–60∘ N, 30–60∘ S). Notably, the error propagation is well controlled with the extension of the simulation period to the past, indicating that the data fusion algorithm proposed here will be more meaningful in the future when more advanced microwave sensors become operational. RSSSM data can be accessed at https://doi.org/10.1594/PANGAEA.912597 (Chen, 2020).

Download Full-text

Developing a drought-monitoring index for the contiguous US using SMAP

Hydrology and Earth System Sciences ◽

10.5194/hess-22-6611-2018 ◽

2018 ◽

Vol 22 (12) ◽

pp. 6611-6626 ◽

Cited By ~ 11

Author(s):

Sara Sadri ◽

Eric F. Wood ◽

Ming Pan

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Drought Index ◽

Surface Soil ◽

Root Zone ◽

Surface Model ◽

Surface Soil Moisture ◽

Short Term ◽

Near Surface ◽

Index Maps

Abstract. Since April 2015, NASA's Soil Moisture Active Passive (SMAP) mission has monitored near-surface soil moisture, mapping the globe (between 85.044∘ N/S) using an L-band (1.4 GHz) microwave radiometer in 2–3 days depending on location. Of particular interest to SMAP-based agricultural applications is a monitoring product that assesses the SMAP near-surface soil moisture in terms of probability percentiles for dry and wet conditions. However, the short SMAP record length poses a statistical challenge for meaningful assessment of its indices. This study presents initial insights about using SMAP for monitoring drought and pluvial regions with a first application over the contiguous United States (CONUS). SMAP soil moisture data from April 2015 to December 2017 at both near-surface (5 cm) SPL3SMP, or Level 3, at ∼36 km resolution, and root-zone SPL4SMAU, or Level 4, at ∼9 km resolution, were fitted to beta distributions and were used to construct probability distributions for warm (May–October) and cold (November–April) seasons. To assess the data adequacy and have confidence in using short-term SMAP for a drought index estimate, we analyzed individual grids by defining two filters and a combination of them, which could separate the 5815 grids covering CONUS into passed and failed grids. The two filters were (1) the Kolmogorov–Smirnov (KS) test for beta-fitted long-term and the short-term variable infiltration capacity (VIC) land surface model (LSM) with 95 % confidence and (2) good correlation (≥0.4) between beta-fitted VIC and beta-fitted SPL3SMP. To evaluate which filter is the best, we defined a mean distance (MD) metric, assuming a VIC index at 36 km resolution as the ground truth. For both warm and cold seasons, the union of the filters – which also gives the best coverage of the grids throughout CONUS – was chosen to be the most reliable filter. We visually compared our SMAP-based drought index maps with metrics such as the U.S. Drought Monitor (from D0–D4), 1-month Standard Precipitation Index (SPI) and near-surface VIC from Princeton University. The root-zone drought index maps were shown to be similar to those produced by the root-zone VIC, 3-month SPI, and the Gravity Recovery and Climate Experiment (GRACE). This study is a step forward towards building a national and international soil moisture monitoring system without which quantitative measures of drought and pluvial conditions will remain difficult to judge.

Download Full-text

Estimating surface soil moisture from satellite observations using a generalized regression neural network trained on sparse ground-based measurements in the continental U.S

Journal of Hydrology ◽

10.1016/j.jhydrol.2019.124351 ◽

2020 ◽

Vol 580 ◽

pp. 124351 ◽

Cited By ~ 2

Author(s):

Qiangqiang Yuan ◽

Hongzhang Xu ◽

Tongwen Li ◽

Huanfeng Shen ◽

Liangpei Zhang

Keyword(s):

Neural Network ◽

Soil Moisture ◽

Surface Soil ◽

Satellite Observations ◽

Generalized Regression Neural Network ◽

Surface Soil Moisture ◽

Generalized Regression

Download Full-text

Long Term Global Surface Soil Moisture Fields Using an SMOS-Trained Neural Network Applied to AMSR-E Data

Remote Sensing ◽

10.3390/rs8110959 ◽

2016 ◽

Vol 8 (11) ◽

pp. 959 ◽

Cited By ~ 24

Author(s):

Nemesio Rodríguez-Fernández ◽

Yann Kerr ◽

Robin van der Schalie ◽

Amen Al-Yaari ◽

Jean-Pierre Wigneron ◽

...

Keyword(s):

Neural Network ◽

Soil Moisture ◽

Surface Soil ◽

Surface Soil Moisture ◽

Trained Neural Network ◽

Global Surface

Download Full-text

On the influence of soil moisture on intra-annual peat soil dynamics as observed from SAR amplitude and phase data

10.5194/egusphere-egu2020-18234 ◽

2020 ◽

Author(s):

Falco Bentvelsen ◽

Floris Heuff ◽

Susan Steele-Dunne ◽

Wolfgang Wagner ◽

Raphael Quast ◽

...

Keyword(s):

Time Series ◽

Soil Moisture ◽

Surface Soil ◽

Peat Soil ◽

Soil Dynamics ◽

Surface Elevation ◽

Driving Mechanism ◽

Surface Soil Moisture ◽

Precipitation And Temperature

Polders in the western Netherlands are often covered by pastures. Around 30 percent of the pastures are situated on peat soils, which are artificially drained. Consequently, the exposure to oxygen leads to a decomposition (oxidation) of the material and desiccation leading to shrinking. This results in a decadal subsidence, up to a few centimeters per year, which causes increasingly severe socio-economic impact. However, this long-term subsidence signal has a high spatial variability due to local soil morphology, and possibly high intra-annual temporal variability which is caused by precipitation and evaporation. The problem is that there are currently no geodetic methods that can reliably measure these soil dynamics over wide areas and with high temporal revisits.Here we show how Sentinel-1 SAR interferometry (InSAR) can potentially be used to estimate the surface displacements, given prior information on precipitation and temperature. We observe intra-annual dynamics of surface elevation which seem to be one order of magnitude stronger than the decadal long-term subsidence. InSAR surface elevation measurements show &#160;discontinuities (hysteresis) in late summer and early autumn due to strong vegetation and changes in temperature and precipitation patterns. As soil moisture variability appears to be the main driving mechanism for the observed surface elevation dynamics, we investigate whether we can use the amplitude of the identical SAR acquisitions to estimate the soil moisture directly, to reduce the dependency on external precipitation and temperature data.The analysis is performed on time series of the European Space Agency&#8217;s Sentinel-1 mission. Subsidence and upheaval are estimated using a novel InSAR algorithm, which was specially designed for peat soil dynamics. The surface elevation dynamics are compared to surface soil moisture estimates from Sentinel-1 amplitude &#160;data. Soil moisture is retrieved from backscatter time series using a first-order radiative transfer model (RT1) developed at TU Wien. This model describes the scattering behaviour of both soil- and vegetation by using linear combinations of idealized scattering distribution functions. Clay Soil swelling and subsidence are likely influenced by soil layers much deeper than those associated with the surface soil moisture estimates. Therefore, the subsidence estimates are also compared to Soil Water Index (SWI) derived from the surface soil moisture product. This is considered an indicator of moisture availability in the top 100 cm. These results show that the same complex SAR data acquisitions can be used simultaneously, but independently, for estimating soil moisture and for estimating surface elevation dynamics. An integrated application is proposed and evaluated for further exploration.

Download Full-text