scholarly journals Impact of Soil Moisture Data Characteristics on the Sensitivity to Crop Yields Under Drought and Excess Moisture Conditions

2019 ◽  
Vol 11 (4) ◽  
pp. 372 ◽  
Author(s):  
Catherine Champagne ◽  
Jenelle White ◽  
Aaron Berg ◽  
Stephane Belair ◽  
Marco Carrera
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Crop Yields ◽  
Agricultural Drought ◽  
Data Sets ◽  
Surface Soil Moisture ◽  
Short Baseline ◽  
Data Set ◽  
Soil Moisture Data ◽  
The Relationship

Soil moisture is often considered a direct way of quantifying agricultural drought since it is a measure of the availability of water to support crop growth. Measurements of soil moisture at regional scales have traditionally been sparse, but advances in land surface modelling and the development of satellite technology to indirectly measure surface soil moisture has led to the emergence of a number of national and global soil moisture data sets that can provide insight into the dynamics of agricultural drought. Droughts are often defined by normal conditions for a given time and place; as a result, data sets used to quantify drought need a representative baseline of conditions in order to accurately establish a normal. This presents a challenge when working with earth observation data sets which often have very short baselines for a single instrument. This study assessed three soil moisture data sets: a surface satellite soil moisture data set from the Soil Moisture and Ocean Salinity (SMOS) mission operating since 2010; a blended surface satellite soil moisture data set from the European Space Agency Climate Change Initiative (ESA-CCI) that has a long history and a surface and root zone soil moisture data set from the Canadian Meteorology Centre (CMC)’s Regional Deterministic Prediction System (RDPS). An iterative chi-squared statistical routine was used to evaluate each data set’s sensitivity to canola yields in Saskatchewan, Canada. The surface soil moisture from all three data sets showed a similar temporal trend related to crop yields, showing a negative impact on canola yields when soil moisture exceeded a threshold in May and June. The strength and timing of this relationship varied with the accuracy and statistical properties of the data set, with the SMOS data set showing the strongest relationship (peak X2 = 170 for Day of Year 145), followed by the ESA-CCI (peak X2 = 89 on Day of Year 129) and then the RDPS (peak X2 = 65 on Day of Year 129). Using short baseline soil moisture data sets can produce consistent results compared to using a longer data set, but the characteristics of the years used for the baseline are important. Soil moisture baselines of 18–20 years or more are needed to reliably estimate the relationship between high soil moisture and high yielding years. For the relationship between low soil moisture and low yielding years, a shorter baseline can be used, with reliable results obtained when 10–15 years of data are available, but with reasonably consistent results obtained with as few as 7 years of data. This suggests that the negative impacts of drought on agriculture may be reliably estimated with a relatively short baseline of data.

scholarly journals From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model simulations

2008 ◽  
Vol 12 (6) ◽  
pp. 1323-1337 ◽  
Author(s):  
C. Albergel ◽  
C. Rüdiger ◽  
T. Pellarin ◽  
J.-C. Calvet ◽  
N. Fritz ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Surface Soil ◽  
Root Zone ◽  
Synthetic Data ◽  
Surface Soil Moisture ◽  
Data Set ◽  
Water Index ◽  
In Situ Observations

Abstract. A long term data acquisition effort of profile soil moisture is under way in southwestern France at 13 automated weather stations. This ground network was developed in order to validate remote sensing and model soil moisture estimates. In this paper, both those in situ observations and a synthetic data set covering continental France are used to test a simple method to retrieve root zone soil moisture from a time series of surface soil moisture information. A recursive exponential filter equation using a time constant, T, is used to compute a soil water index. The Nash and Sutcliff coefficient is used as a criterion to optimise the T parameter for each ground station and for each model pixel of the synthetic data set. In general, the soil water indices derived from the surface soil moisture observations and simulations agree well with the reference root-zone soil moisture. Overall, the results show the potential of the exponential filter equation and of its recursive formulation to derive a soil water index from surface soil moisture estimates. This paper further investigates the correlation of the time scale parameter T with soil properties and climate conditions. While no significant relationship could be determined between T and the main soil properties (clay and sand fractions, bulk density and organic matter content), the modelled spatial variability and the observed inter-annual variability of T suggest that a weak climate effect may exist.

scholarly journals An Evaluation of the EnKF vs. EnOI and the Assimilation of SMAP, SMOS and ESA CCI Soil Moisture Data over the Contiguous US

2019 ◽  
Vol 11 (5) ◽  
pp. 478 ◽  
Author(s):  
Jostein Blyverket ◽  
Paul Hamer ◽  
Laurent Bertino ◽  
Clément Albergel ◽  
David Fairbairn ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Open Loop ◽  
Surface Zone ◽  
Surface Soil Moisture ◽  
Atmospheric Forcing ◽  
Significance Level ◽  
Soil Moisture Data ◽  
In Situ Observations

A number of studies have shown that assimilation of satellite derived soil moisture using the ensemble Kalman Filter (EnKF) can improve soil moisture estimates, particularly for the surface zone. However, the EnKF is computationally expensive since an ensemble of model integrations have to be propagated forward in time. Here, assimilating satellite soil moisture data from the Soil Moisture Active Passive (SMAP) mission, we compare the EnKF with the computationally cheaper ensemble Optimal Interpolation (EnOI) method over the contiguous United States (CONUS). The background error–covariance in the EnOI is sampled in two ways: (i) by using the stochastic spread from an ensemble open-loop run, and (ii) sampling from the model spinup climatology. Our results indicate that the EnKF is only marginally superior to one version of the EnOI. Furthermore, the assimilation of SMAP data using the EnKF and EnOI is found to improve the surface zone correlation with in situ observations at a 95 % significance level. The EnKF assimilation of SMAP data is also found to improve root-zone correlation with independent in situ data at the same significance level; however this improvement is dependent on which in situ network we are validating against. We evaluate how the quality of the atmospheric forcing affects the analysis results by prescribing the land surface data assimilation system with either observation corrected or model derived precipitation. Surface zone correlation skill increases for the analysis using both the corrected and model derived precipitation, but only the latter shows an improvement at the 95 % significance level. The study also suggests that assimilation of satellite derived surface soil moisture using the EnOI can correct random errors in the atmospheric forcing and give an analysed surface soil moisture close to that of an open-loop run using observation derived precipitation. Importantly, this shows that estimates of soil moisture could be improved using a combination of assimilating SMAP using the computationally cheap EnOI while using model derived precipitation as forcing. Finally, we assimilate three different Level-2 satellite derived soil moisture products from the European Space Agency Climate Change Initiative (ESA CCI), SMAP and SMOS (Soil Moisture and Ocean Salinity) using the EnOI, and then compare the relative performance of the three resulting analyses against in situ soil moisture observations. In this comparison, we find that all three analyses offer improvements over an open-loop run when comparing to in situ observations. The assimilation of SMAP data is found to perform marginally better than the assimilation of SMOS data, while assimilation of the ESA CCI data shows the smallest improvement of the three analysis products.

Estimating soil moisture from COSMO-SkyMed data at an active landslide site in North Yorkshire, UK

2020 ◽  
Author(s):  
Clare Bliss ◽  
John Wainwright ◽  
Danny Donoghue ◽  
Colm Jordan
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Early Warning Systems ◽  
Slope Instability ◽  
Surface Soil Moisture ◽  
High Soil Moisture ◽  
Active Landslide ◽  
Landslide Early Warning ◽  
The Relationship ◽  
Important Measurement

<p>Surface soil moisture is recognised as an important measurement for use in the assessment of potential slope instability in hydraulically driven landslides.  In this poster we present a nine month time series of surface soil moisture estimates derived from ESA’s Cosmo SkyMed Synthetic Aperture RADAR (SAR) product at the Hollin Hill Landslide Observatory in North Yorkshire, UK.   </p><p>We show the relationship between these SAR-derived SM values and ground-truthed surface soil moisture data, explore spatial relationships between areas of high soil moisture and landslide activity and briefly discuss the potential of SAR data as an input for Landslide Early Warning systems.</p>

scholarly journals Estimating root zone soil moisture from surface soil moisture data and soil-vegetation-atmosphere transfer modeling

1999 ◽  
Vol 35 (12) ◽  
pp. 3735-3745 ◽  
Author(s):  
Jean-Pierre Wigneron ◽  
Albert Olioso ◽  
Jean-Christophe Calvet ◽  
Patrick Bertuzzi
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Root Zone ◽  
Surface Soil Moisture ◽  
Soil Moisture Data
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