Estimation of surface soil moisture in alpine areas based on medium spatial resolution SAR time-series and upscaled in-situ measurements

2014 ◽  
Author(s):  
F. Greifeneder ◽  
C. Notarnicola ◽  
G. Cuozzo ◽  
G. Bertoldi ◽  
S. Della Chiesa ◽  
...  
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Spatial Resolution ◽  
Surface Soil ◽  
In Situ Measurements ◽  
Surface Soil Moisture

Exploring the use of machine-learning techniques to integrate ground- and remote sensing-based observations for efficient near-surface soil moisture mapping

2020 ◽  
Author(s):  
Sarah Schönbrodt-Stitt ◽  
Paolo Nasta ◽  
Nima Ahmadian ◽  
Markus Kurtenbach ◽  
Christopher Conrad ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Spatial Resolution ◽  
Surface Soil ◽  
Bare Soil ◽  
Small Scale ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
Campania Region

<p>Mapping near-surface soil moisture (<em>θ</em>) is of tremendous relevance for a broad range of environment-related disciplines and meteorological, ecological, hydrological and agricultural applications. Globally available products offer the opportunity to address <em>θ</em> in large-scale modelling with coarse spatial resolution such as at the landscape level. However, <em>θ</em> estimation at higher spatial resolution is of vital importance for many small-scale applications. Therefore, we focus our study on a small-scale catchment (MFC2) belonging to the “Alento” hydrological observatory, located in southern Italy (Campania Region). The goal of this study is to develop new machine-learning approaches to estimate high grid-resolution (about 17 m cell size) <em>θ</em> maps from mainly backscatter measurements retrieved from C-band Synthetic Aperture Radar (SAR) based on Sentinel-1 (S1) images and from gridded terrain attributes. Thus, a workflow comprising a total of 48 SAR-based <em>θ</em> patterns estimated for 24 satellite overpass dates (revisit time of 6 days) each with ascendant and descendent orbits will be presented. To enable for the mapping, SAR-based <em>θ</em> data was calibrated with in-situ measurements carried out with a portable device during eight measurement campaigns at time of satellite overpasses (four overpass days in total with each ascendant and descendent satellite overpasses per day in November 2018). After the calibration procedure, data validation was executed from November 10, 2018 till March 28, 2019 by using two stationary sensors monitoring <em>θ</em> at high-temporal (1-min recording time). The specific sensor locations reflected two contrasting field conditions, one bare soil plot (frequently kept clear, without disturbance of vegetation cover) and one non-bare soil plot (real-world condition). Point-scale ground observations of <em>θ</em> were compared to pixel-scale (17 m × 17 m), SAR-based <em>θ</em> estimated for those pixels corresponding to the specific positions of the stationary sensors. Mapping performance was estimated through the root mean squared error (RMSE). For a short-term time series of <em>θ</em> (Nov 2018) integrating 136 in situ, sensor-based <em>θ</em> (<em>θ</em><sub>insitu</sub>) and 74 gravimetric-based <em>θ</em> (<em>θ</em><sub>gravimetric</sub>) measurements during a total of eight S1 overpasses, mapping performance already proved to be satisfactory with RMSE=0.039 m³m<sup>-</sup>³ and R²=0.92, respectively with RMSE=0.041 m³m<sup>-</sup>³ and R²=0.91. First results further reveal that estimated satellite-based <em>θ</em> patterns respond to the evolution of rainfall. With our workflow developed and results, we intend to contribute to improved environmental risk assessment by assimilating the results into hydrological models (e.g., HydroGeoSphere), and to support future studies on combined ground-based and SAR-based <em>θ</em> retrieval for forested land (future missions operating at larger wavelengths e.g. NISARL-band, Biomass P-band sensors).</p>

scholarly journals New Downscaling Approach Using ESA CCI SM Products for Obtaining High Resolution Surface Soil Moisture

2020 ◽  
Vol 12 (7) ◽  
pp. 1119 ◽  
Author(s):  
Jovan Kovačević ◽  
Željko Cvijetinović ◽  
Nikola Stančić ◽  
Nenad Brodić ◽  
Dragan Mihajlović
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Random Forest ◽  
Spatial Resolution ◽  
Surface Soil ◽  
Initial Step ◽  
Surface Soil Moisture ◽  
In Situ Data ◽  
Random Forest Models

ESA CCI SM products have provided remotely-sensed surface soil moisture (SSM) content with the best spatial and temporal coverage thus far, although its output spatial resolution of 25 km is too coarse for many regional and local applications. The downscaling methodology presented in this paper improves ESA CCI SM spatial resolution to 1 km using two-step approach. The first step is used as a data engineering tool and its output is used as an input for the Random forest model in the second step. In addition to improvements in terms of spatial resolution, the approach also considers the problem of data gaps. The filling of these gaps is the initial step of the procedure, which in the end produces a continuous product in both temporal and spatial domains. The methodology uses combined active and passive ESA CCI SM products in addition to in situ soil moisture observations and the set of auxiliary downscaling predictors. The research tested several variants of Random forest models to determine the best combination of ESA CCI SM products. The conclusion is that synergic use of all ESA CCI SM products together with the auxiliary datasets in the downscaling procedure provides better results than using just one type of ESA CCI SM product alone. The methodology was applied for obtaining SSM maps for the area of California, USA during 2016. The accuracy of tested models was validated using five-fold cross-validation against in situ data and the best variation of model achieved RMSE, R2 and MAE of 0.0518 m3/m3, 0.7312 and 0.0374 m3/m3, respectively. The methodology proved to be useful for generating high-resolution SSM products, although additional improvements are necessary.

scholarly journals Thermal Inertia Approach Using a Heat Budget Model to Estimate the Spatial Distribution of Surface Soil Moisture over a Semiarid Grassland in Central Mongolia

2018 ◽  
Vol 19 (1) ◽  
pp. 245-265 ◽  
Author(s):  
Dai Matsushima ◽  
Jun Asanuma ◽  
Ichirow Kaihotsu
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Surface Soil ◽  
Heat Budget ◽  
Atmospheric Stability ◽  
Thermal Inertia ◽  
In Situ Observation ◽  
Surface Soil Moisture ◽  
Budget Model

Abstract Thermal inertia is a physical parameter that evaluates soil thermal properties with an emphasis on the stability of the temperature when the soil is affected by heating/cooling. Thermal inertia can be retrieved from a heat budget formulation as a parameter when the time series of Earth surface temperature and forcing variables, such as insolation and air temperature, are given. In this study, a two-layer, linearized heat budget model was employed for the retrieval of thermal inertia over a grassland in a semiarid region. Application of different formulations to the aerodynamic conductance with respect to atmospheric stability significantly improved the accuracy of the thermal inertia retrieval. The retrieved values of thermal inertia were well correlated with in situ surface soil moisture at multiple ground stations. The daily time series of thermal inertia–derived soil moisture qualitatively agreed well with in situ soil moisture after antecedent rainfalls, which was found after fitting the time series to an exponentially decaying function. On the contrary, AMSR2 soil moisture mostly did not agree with in situ soil moisture. The results of the estimation showed high accuracy: the root-mean-square error was 0.038 m3 m−3 compared to the in situ data and was applied to an area of 2° × 2° in which the in situ observation locations were included. The spatiotemporal distribution of surface soil moisture was mapped at a 0.03° × 0.03° spatial resolution in the study area as 10- or 11-day averages over a vegetation growth period of 2012.

scholarly journals Detection of irrigation events on maize plots using sentinel-1 soil moisture products

2020 ◽  
Author(s):  
Michel Le Page ◽  
Lionel Jarlan ◽  
Aaron Boone ◽  
Mohammad El Hajj ◽  
Nicolas Baghdadi ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Surface Soil ◽  
Regional Scale ◽  
Absolute Error ◽  
In Situ Measurements ◽  
Irrigation Event ◽  
Balance Model ◽  
Surface Soil Moisture

<p>An accurate knowledge of irrigation timing and rate is essential to compute the water balance of irrigated plots. However, at the plot scale irrigation is a data essentially known by the irrigator. These data do not go up to higher management scales, thus limiting both the management of water resources on a regional scale and the development of irrigation decision support tools at the farm scale. The study focuses on 6 experimental plots in the south-west of France. The new method consists in assessing surface soil moisture (SSM) change between observations and a water balance model. The approach was tested using both in situ measurements and surface soil moisture (SSM) maps derived from Sentinel-1 radar data. The score is obtained by assessing if the irrigation event is detected within +/- three days. The use of in situ SSM showed that: (1) the best revisit time between two SSM observations is 3 days; short gaps is subject to uncertainties while longer gap miss possible SSM variations; (2) in general, higher rates (>20mm) of irrigation are well identified while it is very difficult to identify irrigation event when it is raining or when irrigation rates are small (<10mm). When using the SSM microwave product, the performances are degraded but are still acceptable given the discontinuity of irrigation events: 34% of absolute error and a bias of 5% for the whole season. Although high vegetation cover degrades the SSM absolute estimates, the dynamic appeared to be in accordance with in-situ measurements.</p>

Optimal Spatial Resolution of Sentinel-1 Surface Soil Moisture Evaluated Using Intensive in Situ Observations

2021 ◽  
Author(s):  
Theresa C. Van Hateren ◽  
Marco Chini ◽  
Patrick Matgen ◽  
Luca Pulvirenti ◽  
Nazzareno Pierdicca ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Surface Soil ◽  
Surface Soil Moisture ◽  
In Situ Observations

scholarly journals Operational Soil Moisture from ASCAT in Support of Water Resources Management

2019 ◽  
Vol 11 (5) ◽  
pp. 579 ◽  
Author(s):  
Khidir Deng ◽  
Salim Lamine ◽  
Andrew Pavlides ◽  
George Petropoulos ◽  
Prashant Srivastava ◽  
...  
Keyword(s):  
Soil Moisture ◽  
United States Of America ◽  
Surface Soil ◽  
Comprehensive Evaluation ◽  
Statistical Significance ◽  
In Situ Measurements ◽  
Surface Soil Moisture ◽  
Resources Management ◽  
Practical Applications

This study provides the results of an extensive investigation of the Advanced Scaterometter (ASCAT) surface soil moisture global operational product accuracy across three continents (United States of America (USA), Europe, and Australia). ASCAT predictions of surface soil moisture were compared against near concurrent in situ measurements from the FLUXNET observational network. A total of nine experimental sites were used to assess the accuracy of ASCAT Surface Soil Moisture (ASCAT SSM) predictions for two complete years of observations (2010, 2011). Results showed a generally reasonable agreement between the ASCAT product and the in situ soil moisture measurements in the 0–5 cm soil moisture layer. The Root Mean Square Error (RMSE) was below 0.135 m3 m−3 at all of the sites. With a few exceptions, Pearson’s correlation coefficient was above 45%. Grassland, shrublands, and woody savanna land cover types exhibited satisfactory agreement in all the sites analyzed (RMSE ranging from 0.05 to 0.13 m3 m−3). Seasonal performance was tested, but no definite conclusion can be made with statistical significance at this time, as the seasonal results varied from continent to continent and from year to year. However, the satellite and in situ measurements for Needleleaf forests were practically uncorrelated (R = −0.11 and −0.04). ASCAT predictions overestimated the observed values at all of the sites in Australia. A positive bias of approximately 0.05 m3 m−3 was found with respect to the observed values that were in the range 0–0.3 m3 m−3. Better agreement was observed for the grassland sites in most cases (RMSE ranging from 0.09 to 0.10 m3 m−3 and R from 0.46 to 0.90). Our results provide supportive evidence regarding the potential value of the ASCAT global operational product for meso-scale studies and the relevant practical applications. A key contribution of this study is a comprehensive evaluation of ASCAT product soil moisture estimates at different sites around the globe. These sites represent a variety of climatic, environmental, biome, and topographical conditions.

scholarly journals Error Propagation from Remotely Sensed Surface Soil Moisture Into Soil Water Index Using an Exponential Filter

10.29007/kvhb ◽  
2018 ◽  
Author(s):  
Domenico De Santis ◽  
Daniela Biondi
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Error Propagation ◽  
Surface Soil ◽  
Root Zone ◽  
In Situ Measurements ◽  
Surface Soil Moisture ◽  
Water Index ◽  
Study Sites

In this study an error propagation (EP) scheme was introduced in parallel to exponential filter computation for soil water index (SWI) estimation. A preliminarily assessment of the computed uncertainties was carried out comparing satellite-derived SWI and reference root-zone in situ measurements. The EP scheme has shown skills in detecting potentially less reliable SWI values in the study sites, as well as a better understanding of the exponential filter shortcomings. The proposed approach shows a potential for SWI evaluation, providing simultaneous estimates of time-variant uncertainty.

scholarly journals Calibration of a large-scale hydrological model using satellite-based soil moisture and evapotranspiration products

2017 ◽  
Author(s):  
Patricia Lopez Lopez ◽  
Edwin Sutanudjaja ◽  
Jaap Schellekens ◽  
Geert Sterk ◽  
Marc Bierkens
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Large Scale ◽  
Hydrological Model ◽  
Surface Soil ◽  
River Basins ◽  
Model Parameters ◽  
Surface Soil Moisture ◽  
Earth Observations

Abstract. A considerable number of river basins around the world lack sufficient ground observations of hydro-meteorological data for effective water resources assessment and management. Several approaches can be developed to increase the quality and availability of data in these poorly gauged or ungauged river basins, and among those, the use of earth observations products has recently become promising. Earth observations of various environmental variables can be used potentially to increase the knowledge about the hydrological processes in the basin and to improve streamflow model estimates, via assimilation or calibration. The present study aims to calibrate the large-scale hydrological model PCR-GLOBWB using satellite-based products of evapotranspiration and soil moisture for the Moroccan Oum Er Rbia basin. Daily simulations at a spatial resolution of 5 arcmin × 5 arcmin are performed with varying parameters values for the 32-year period 1979–2010. Five different calibration scenarios are inter-compared: (i) reference scenario using the hydrological model with the standard parameterization, (ii) calibration using in-situ observed discharge time series, (iii) calibration using GLEAM actual evapotranspiration time series, (iv) calibration using ESA CCI surface soil moisture time series and (v) step-wise calibration using GLEAM actual evapotranspiration and ESA CCI surface soil moisture time series. The impact on discharge estimates of precipitation in comparison with model parameters calibration is investigated using three global precipitation products, including EI, WFDEI and MSWEP. Results show that GLEAM evapotranspiration and ESA CCI soil moisture may be used for model calibration resulting in reasonable discharge estimates (NSE values from 0.5 to 0.75), although better model performance is achieved when the model is calibrated with in-situ streamflow observations. Independent calibration based on only evapotranspiration or soil moisture observations improves model predictions to a lesser extent. Precipitation input affects to discharge estimates more than calibrating model parameters. The use of WFDEI precipitation leads to the lowest model performances. Apart from the in-situ discharge calibration scenario, the highest discharge improvement is obtained when EI and MSWEP precipitation products are used in combination with a step-wise calibration approach based on evapotranspiration and soil moisture observations. This study opens up the possibility to use globally available earth observations and reanalysis products of precipitation, evapotranspiration and soil moisture into large-scale hydrological models to estimate discharge at a river basin scale.

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