scholarly journals Accounting for seasonality in a soil moisture change detection algorithm for ASAR Wide Swath time series

2012 ◽  
Vol 16 (3) ◽  
pp. 773-786 ◽  
Author(s):  
J. Van doninck ◽  
J. Peters ◽  
H. Lievens ◽  
B. De Baets ◽  
N. E. C. Verhoest
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Change Detection ◽  
Incidence Angle ◽  
Arable Land ◽  
Correlation Coefficients ◽  
Detection Algorithm ◽  
Model Parameters ◽  
Second Phase ◽  
Wide Swath

Abstract. A change detection algorithm is applied on a three year time series of ASAR Wide Swath images in VV polarization over Calabria, Italy, in order to derive information on temporal soil moisture dynamics. The algorithm, adapted from an algorithm originally developed for ERS scatterometer, was validated using a simple hydrological model incorporating meteorological and pedological data. Strong positive correlations between modelled soil moisture and ASAR soil moisture were observed over arable land, while the correlation became much weaker over more vegetated areas. In a second phase, an attempt was made to incorporate seasonality in the different model parameters. It was observed that seasonally changing surface properties mainly affected the multitemporal incidence angle normalization. When applying a seasonal angular normalization, correlation coefficients between modelled soil moisture and retrieved soil moisture increased overall. Attempts to account for seasonality in the other model parameters did not result in an improved performance.

scholarly journals Accounting for seasonality in a soil moisture change detection algorithm for ASAR Wide Swath time series

2011 ◽  
Vol 8 (6) ◽  
pp. 10333-10367
Author(s):  
J. Van doninck ◽  
J. Peters ◽  
H. Lievens ◽  
B. De Baets ◽  
N. E. C. Verhoest
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Change Detection ◽  
Incidence Angle ◽  
Arable Land ◽  
Correlation Coefficients ◽  
Detection Algorithm ◽  
Model Parameters ◽  
Second Phase ◽  
Wide Swath

Abstract. A change detection algorithm is applied on a three year time series of ASAR Wide Swath images in VV polarization over Calabria, Italy, in order to derive information on temporal soil moisture dynamics. The algorithm, adapted from an algorithm originally developed for ERS Scatterometer, was validated using a simple hydrological model incorporating meteorological and pedological data. Strong positive correlations between modelled soil moisture and ASAR soil moisture were observed over arable land, while the correlation became much weaker over more vegetated areas. In a second phase, an attempt was made to incorporate seasonality in the different model parameters. It was observed that seasonally changing vegetation and soil moisture mainly affected the multitemporal incidence angle normalization. When applying a seasonal angular normalization, correlation coefficients between modelled soil moisture and retrieved soil moisture increased overall. Attempts to account for seasonality in the other model parameters did not result in an improved performance.

Combining time series of Sentinel-1 and -2 with in-situ data for estimating soil moisture at crop field scale

2020 ◽  
Author(s):  
Marc Padilla ◽  
Ana Pérez ◽  
Mirta Pinilla
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Surface Soil ◽  
Correlation Coefficients ◽  
Field Size ◽  
Model Parameters ◽  
Field Scale ◽  
In Situ Data ◽  
Crop Field

<p>Soil moisture is one of the key variables for crop modeling and scheduling farm operations. Current available soil moisture products are generated at global or regional scales and its spatial resolution (~1km<sup>2</sup>) is usually too coarse for common small farms. Within the framework of the EU Horizon2020 funded TWIGA project, we intended to provide improved soil moisture estimates at crop field scale. The advantage of focusing at the scale of a single crop is that the algorithm selection can be based more on the retrieval accuracy rather than on the computing performance.</p><p>Time series of Sentinel-1 SAR backscatter (at VH and VV polarizations) and Sentinel-2 NDVI observations, on each crop field, were assimilated with a semiempirical polarimetric backscattering model for bare soil surfaces (Oh) coupled with a Water Cloud model (WCM). Some of the model parameters are the actual variables of interest to be estimated, in our case the daily surface soil moistures. They were estimated by a Bayesian inversion approach. The key advantage of using WCM, is that the effects of vegetation on backscatter can be taken into account, and therefore soil moisture estimates are available even when vegetation is present. The empirical model parameters (surface roughness, and A and B parameters of WCM) were calibrated with in-situ data from four stations in Ghana, with observations every 30 minutes from May to October 2019 at 10 cm depth. The calibration was based on a hierarchical Bayesian regression, to take into account that model parameter distributions might vary across land cover types and across in-situ stations themselves. The validation was based on the comparison between the soil moisture observations of one in-situ station and estimates from the model couple calibrated with the data from the other three in-situ stations. That procedure was repeated for each station. Correlation coefficients were above 0.64 and root mean square error bellow 0.065 m<sup>3</sup>/m<sup>3</sup> in two out of the four stations. Accuracy tended to be dependent on field size, due to the well known SAR speckle noise. The station with the lowest accuracy was locate on a 30x30m<sup>2</sup> field. Accuracy was additionally affected by likely sudden changes on the surface soil or vegetation during the analysis time windows. Correlation coefficients were higher (~0.85) on the time periods without such sudden changes.</p><p>Given the results of the current study, we would recommend that the location of eventual future in-situ stations should be preferably placed on larger fields, larger than 30x30m<sup>2</sup>. Further research would be needed to improve the model and understand better its limitations for an eventual operational implementation.</p><p> </p>

scholarly journals Identification of Hydrologic Models, Optimized Parameters, and Rainfall Inputs Consistent with In Situ Streamflow and Rainfall and Remotely Sensed Soil Moisture

2018 ◽  
Vol 19 (8) ◽  
pp. 1305-1320 ◽  
Author(s):  
Ashley J. Wright ◽  
Jeffrey P. Walker ◽  
Valentijn R. N. Pauwels
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Input Data ◽  
Remotely Sensed ◽  
Model Parameters ◽  
Rainfall Time Series ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Model Input ◽  
Rainfall Estimates

Abstract An increased understanding of the uncertainties present in rainfall time series can lead to improved confidence in both short- and long-term streamflow forecasts. This study presents an analysis that considers errors arising from model input data, model structure, model parameters, and model states with the objective of finding a self-consistent set that includes hydrological models, model parameters, streamflow, remotely sensed (RS) soil moisture (SM), and rainfall. This methodology can be used by hydrologists to aid model and satellite selection. Taking advantage of model input data reduction and model inversion techniques, this study uses a previously developed methodology to estimate areal rainfall time series for the study catchment of Warwick, Australia, for multiple rainfall–runoff models. RS SM observations from the Soil Moisture Ocean Salinity (SMOS) and Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) satellites were assimilated into three different rainfall–runoff models using an ensemble Kalman filter (EnKF). Innovations resulting from the observed and predicted SM were analyzed for Gaussianity. The findings demonstrate that consistency between hydrological models, model parameters, streamflow, RS SM, and rainfall can be found. Joint estimation of rainfall time series and model parameters consistently improved streamflow simulations. For all models rainfall estimates are less than the observed rainfall, and rainfall estimates obtained using the Sacramento Soil Moisture Accounting (SAC-SMA) model are the most consistent with gauge-based observations. The SAC-SMA model simulates streamflow that is most consistent with observations. EnKF innovations obtained when SMOS RS SM observations were assimilated into the SAC-SMA model demonstrate consistency between SM products.

scholarly journals Multi-constellation GNSS interferometric reflectometry with mass-market sensors as a solution for soil moisture monitoring

2019 ◽  
Author(s):  
Angel Martín ◽  
Sara Ibáñez ◽  
Carlos Baixauli ◽  
Sara Blanc ◽  
Ana B. Anquela
Keyword(s):  
Soil Moisture ◽  
Arable Land ◽  
Correlation Coefficients ◽  
Moisture Determination ◽  
Data Set ◽  
Mass Market ◽  
Innovative Solutions ◽  
Highly Correlated ◽  
Gnss Observations

Abstract. Per capita arable land is decreasing due to rapidly increasing population, and fresh water is becoming scarce and more expensive. Therefore, farmers should continue to use technology and innovative solutions to improve efficiency, save input costs, and optimise environmental resources (such as water). In the case study presented in this manuscript, the GNSS-IR technique was used to monitor soil moisture during 66 days, from December 3, 2018, to February 6, 2019, in the installations of the Cajamar Centre of Experiences, Paiporta, Valencia, Spain. Two main objectives were pursued. The first was the extension of the technique to a multi-constellation solution using GPS, GLONASS, and GALILEO satellites, and the second was to test whether mass-market sensors could be used for this technique. Both objectives were achieved. At the same time the GNSS observations were made, soil samples taken at 5 cm depth were used for soil moisture determination to establish a reference dataset. Based on a comparison with that reference data set, all GNSS solutions, including the three constellations and the two sensors (geodetic and mass-market), were highly correlated, with a correlation coefficients between 70 % and 85 %.

scholarly journals MONITORING OF TIME-SERIES SOIL MOISTURE BASED ON ADVANCED DINSAR

2021 ◽  
Vol V-3-2021 ◽  
pp. 51-55
Author(s):  
C. H. Yang ◽  
A. Müterthies
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Water Content ◽  
Large Scale ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Organic Soil ◽  
Underground Water ◽  
Multi Temporal ◽  
Mean Square Errors

Abstract. Understanding soil moisture is essential for earth and environmental sciences especially in geology, hydrology, and meteorology. Remote sensing techniques are widely applied to large-scale monitoring tasks. Among them, DInSAR using multi-temporal spaceborne SAR images is able to derive surface movement up to mm level over an area. One of the factors inducing the movement is variation of soil moisture. Based on this, a semi-empirical approach can be tailored to retrieve the underground water content. However, the derived movement is often contaminated with other irrelevant noise. Besides, a time-series analysis could not be simply implemented without additional fusion and calibration. In this paper, we propose a novel modelling based on advanced DInSAR to solve these problems. The irrelevant noise will be removed as parts of the modelled elements in the DInSAR processing. A forward model on a scene is built by regressing the measured soil moisture on the DInSAR-derived movement series. We tested our approach using Sentinel-1 images in the grasslands of organic soil within State of Brandenburg, Germany. The Pearson correlation coefficients between the measured soil moistures and the DInSAR-derived movements are up to 0.91. The mean square errors of the predicted soil moistures compared with the measurements reach 3.03 % (volumetric water content) at best. Our study shows a promising new concept to develop a global monitoring of soil moisture in the future.

Estimation of soil moisture from Sentinel data

2020 ◽  
Author(s):  
Stefan Krebs Lange-Willman ◽  
Henning Skriver ◽  
Inge Sandholt
Keyword(s):  
Soil Moisture ◽  
Incidence Angle ◽  
Speckle Noise ◽  
Detection Algorithm ◽  
Retrieval Algorithm ◽  
Innovation Fund ◽  
High Incidence ◽  
Land Cover Maps ◽  
The Impact

<p>The present project presents the technical implementation, testing and validation of a soil moisture retrieval algorithm in Python using C-band Sentinel-1 data at high incidence angle (∼42°). The retrieval algorithm is based on the alpha approximation, first developed by [Balenzano et al. 2011]. The alpha approximation utilizes the dense temporal coverage of the Sentinel-1 mission, assuming that changes in backscatter between subsequent acquisitions are only due to variations in soil moisture, such that vegetation and roughness can be neglected. The area used for testing the algorithm was chosen to be the region surrounding the Foulum test center for agricultural studies in Denmark, due to the availability of time series from 2018 of in situ soil moisture measurements to be used for validation. Masking of too densely vegetated areas have been performed using the cross-polarized component of the SAR backscatter, which have been validated using NDVI maps. </p><p>Auxiliary data, including land cover maps and parcel borders enable the computation of backscatter field means, significantly reducing the impact of speckle noise and thus decreasing uncertainty of the estimated soil moisture. Consequently, the results have field scale resolution (i.e. ∼0.1 km). The permittivity to soil moisture inversion is performed using a polynomial model by [Hallikainen et al. 1985], where a soil texture map provide the information necessary to obtain precise results. </p><p>Further work will aim toward applying a change detection algorithm in order to detect sudden temporal changes in vegetation and surface roughness, as the alpha approximation is inherently sensitive to such sudden changes.</p><p>The study has received partial funding from Innovation Fund Denmark, contract number: 7049-00004B (MOIST).</p>

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