FOSS4G 2022 academic track

Sea water turbidity is a measure of the amount of light scattered by particles in water. It is due to the presence of suspended particles, which it is operationally defined as the fraction in water with less than 2 µm in diameter. Plankton can also generate turbidity, but high turbidity events are dominated by high concentrations of inanimate inorganic particles. High levels of suspended sediments in coastal regions can occur as consequence of high sediment load from rivers, from bottom sediment resuspension due to wave actions or due to anthropogenic activities, such as dredging operations or bottom resuspension from ship propellants. The increase of turbidity can determine negative environmental effects both on the biotic and abiotic marine ecosystem. In highly anthropized coastal marine systems, like harbours, sediments represent a sink for contaminants and resuspension can contribute to propagate pollution to unpolluted areas (Lisi et al., 2019).

Many marine water quality monitoring programmes measure turbidity. Traditional methods (e.g., in situ monitoring) offer high accuracy but provide sparse information in space and time. Earth Observation (EO) techniques, on the other hand, have a potential to provide a comprehensive, fast and inexpensive monitoring system to observe the biophysical and biochemical conditions of water bodies (Caballero et al., 2018; Saberioon et al., 2020; Sagan et al., 2020). Hence, some of the authors are developing a semi-empirical model for predicting water turbidity by combining Sentinel-2A data and machine learning methods using samples collected along the North Tyrrhenian Sea (Italy). Field data collected at the study site from April 2015 to December 2020 were made available by ARPAL, even though most of these data refer to low turbidity events.

In the framework of this research activity, Sentinel-2A multispectral optical images, freely available within the EU Copernicus programme, are elaborated. It’s well known that such products are provided at Level-1C (L1C) Top of Atmosphere (TOA) and at Level-2A (L2A) Bottom-Of-Atmosphere (BOA). L2A BOA reflectance products are preferred as they are already corrected for effects of the atmosphere. However, the official L2A data are available for wider Europe from March 2018 onwards.

The necessity to use the complete on-site dataset to calibrate the predicting model, and not only data after March 2018, required the identification of the most appropriate algorithm for atmospheric correction of L1C images relative to study area between 2015 and 2018.

Hence, a comparison between the available L2A BOA product ant the corresponding L1C image corrected in different open source environment was performed. In particular, the free and open source QGIS and GRASS GIS, and the Sentinel Application Platform (SNAP), provided by ESA/ESRIN free of charge to the Earth Observation Community, published under the GPL license and with its sources code available on GitHub, were used.

Both image-based method, i.e. the Dark Object Subtraction (DOS) method in QGIS, and physically-based methods, i.e. the Second Simulation of Satellite Signal in the Solar Spectrum (6S) method in i.atcorr module of GRASS GIS and the Sen2Cor algorithm inside SNAP, were applied (Lantzanakis et al., 2017). The great advantage of the DOS method is that it focuses only on the spectral and radiometric characteristics of the processed image, hence it doesn’t require remote or in-situ atmospheric measurements. But the performed correction doesn’t seem so accurate. Instead, the physically-based approach requires atmospheric measurements and parameters, that are difficult to be identified so to be coherent in space and time with the processed image.

The most complex physical parameter to set is Aerosol Optical Depth (AOD), which is a dimensionless parameter related to the amount of aerosol in the vertical column of the atmosphere over the target station. It usually range from 0 to 1, with values less than 0,1 that corresponds to a clean atmosphere with high visibility, and values higher than 0,4 that corresponds to hazy atmosphere with very less visibility. AOD is spatially and temporally very variable. It can be estimated from AERONET (AErosol RObotic NETwork), a federation of ground-based remote sensing aerosol networks with more than 25 years of data. A station which measured the Aerosol Optical Depth at 500 nm at Level 2 (quality-assured) at the same time as the scene was taken, is not always available nearby the site under study. Hence the evaluation of AOD variability in time and space was analysed for the area and the events of interest, so to identify the proper values. Expecially i.atcorr seems very sensitive to the set values of AOD.

Once the proper method for atmospheric correction was identify, it was applied to the L1C images relative to the collected field data from April 2015 to March 2018. Then, the correlation between the in-site dataset and the individual bands known to be most sensitive to water turbidity, i.e. blue (B2), green (B3), red (B4) and near infrared (B8 and B8A) bands, was analysed, finding good results for the visible bands, and a weak correlation with NIR bands. In addition, indexes defined by the ratio between the three visible bands were checked to see which combination could best highlight the turbidity of the water from the Sentinel-2 images. Preliminary results seem to confirm that the identified EO technique could provide a fast and inexpensive monitoring system to observe sea water turbidity along the Northern Tyrrhenian Sea (Italy).

Slope stability is strongly influenced by soil hydraulic conditions, affected by the meteoric events to which the site is subject. With particular reference to shallow landslides triggered by rainfalls, the stability conditions can be influenced by the propagation of the saturation front inside the unsaturated zone. The soil shear strength varies in the vadose zone depending on the type of soil and the variations of soil moisture. In general, monitoring of the unsaturated zone can be done by measuring suction and/or water content.

The measurement of the volumetric water content can be performed using low-cost instrumentation, such as the Waterscout SM100 capacitive sensors (Spectrum Tec.), distributed over the study areas. Such sensors provide data in near-real time and are relatively easy to install and replace. However, it is essential to perform a site-specific calibration of the instrumentation, since previous work (Bovolenta et al. 2020) has shown that the factory settings lead to a general overestimation of the actual volumetric soil water content. Therefore, following a sampling of the analyzed soil and a specific laboratory procedure, it is necessary to define the calibration curve that allows the transition from raw data, meant as the ratio between sensor output voltage and input voltage, to soil water content.

Then, the knowledge of soil water content allows the estimation of the suction parameter, thanks to a Water Retention Curve (WRC), and consequently the definition of the soil shear strength in partly saturated conditions.

Several methodologies for landslide susceptibility assessment, based on global Limit Equilibrium (LEM) or Finite Element (FEM) methods, need the soil shear strength description in order to evaluate the slope stability conditions. Both in the recent literature (Escobar-Wolf et al. 2020, Moresi et al. 2020) and in the GRASS GIS software (r.shalstab), models are already proposed for shallow landslide susceptibility estimation in GIS, based mainly on LEM. However, these models do not usually consider the unsaturated soil behaviour, but at most take into account the strength contribution provided by the vegetation root systems.

The present contribution describes the implementation of an automatic procedure in GRASS GIS that, starting from monitoring data related to the soil volumetric water content, provides a 3D description of the soil shear strength in the vadose zone, that is essential for the subsequent landslide susceptibility assessment, especially in the case of shallow landslides.

Soil moisture sensors data come from five monitoring networks that were set up between 2019 and 2021 in the framework of the Interreg Alcotra AD-VITAM project. Each network was organized into measurement nodes (from three to five) instrumented with four soil moisture sensors each and communicating via radio with a receiver. The receiver was then connected to a modem for remote data transmission. The four sensors in each node have been placed in the soil at four different depths (-15, -35, -55, -85 cm from the ground level). The monitoring systems allow to obtain data with a minimum frequency of 5 minutes, in .csv format so that can feed a geodatabase.

Starting from a properly storing of data recorded by the monitoring network in a geodatabase, at the moment within GRASS GIS but in the near future in PostGIS, the equation of the site-specific sensor calibration, defined in laboratory, and the equation of the WRC are implemented in a procedure that allows to pass automatically from the raw sensor data to the soil water content, and then to the evaluate the suction parameter. Hence, the soil strength can be estimated for each depth at which a soil moisture sensor is installed. Moreover, since the study area is often in the order of few square kilometers, the information must be spatialized over the entire area of interest, through appropriate techniques of interpolation and extrapolation.

This procedure could be integrated into a LEM or FEM, including the above cited, taking advantage of the soil moisture measurements to improve the evaluation of the stability conditions over time, by analysing the evolution of the saturation front according to the weather conditions.

The authors, in particular, will integrate it into a system called LAMP (LAndslide Monitoring and Predicting), which has been under development for several years through the implementation in a GIS environment of an Integrated Hydrological-Geotechnical (IHG) 3D model for the assessment of landslide risk triggered by measured or forecasted precipitation. The integration of this procedure in LAMP will allow to obtain a simple but effective modelling for the assessment of susceptibility to shallow landslides, too.

Note that the contribution in the landslide risk management of the present procedure could be important even in the days following the rainfall event of interest, providing the technical staff in charge of territorial protection with a useful tool for the landslide susceptibility assessment, especially in the case of shallow landslides.

In order to allow the scientific community to evaluate the usefulness of the proposed procedure and consequently to have the possibility to implement it in the above-mentioned methods (LEM-FEM) improving the assessment of landslide susceptibility, soil moisture data at a specific site, related to significant rainfall events, and the implemented procedure will be openly shared, once the testing phase is completed.