We propose a Global Navigation Satellite System-Reflectometry (GNSS-R) interference pattern technique method to estimate the temporal variations of the soil moisture content of the ground surrounding a single geodetic antenna. Three parameters can be inverted from GNSS signal-to-noise ratio (SNR) acquisitions: amplitude/phase of the multipath contribution to SNR and effective antenna height. Our method is applied to determine the surface moisture of a bare soil at Lamasquère, France, from February 5 to March 15, 2014. First, only data from low satellite elevation angles (<; 30°) are taken into consideration and are compared with independent 2-cm depth soil moisture records. The combination of the measurements from all GPS satellites, tested for the first time, improves the quality of the results with a correlation coefficient reaching 0.95, with a 10-min sampling rate. Our study shows that it is also possible to take high satellite elevation angles into account, even if the sign of the correlation appears to be reversed w.r.t. data from low satellite elevation angles. The cutoff angle where the sign of the correlation reverses seems to be around 30°. With regard to the effective antenna height, only a very low correlation is observed for high satellite elevation angles. We propose a new inversion method taking the pseudo-dynamic of the surface into account, which increases the correlation from 0.39 to 0.82. By normalizing and inverting the time series obtained from either low or high satellite elevation angles, it is possible to combine them, which enhances the results (correlation = 0.95).

This work aims to estimate soil moisture and vegetation height from Global Navigation Satellite System (GNSS) Signal to Noise Ratio (SNR) data using direct and reflected signals by the land surface surrounding a ground-based antenna. Observations are collected from a rainfed wheat field in southwestern France. Surface soil moisture is retrieved based on SNR phases estimated by the Least Square Estimation method, assuming the relative antenna height is constant. It is found that vegetation growth breaks up the constant relative antenna height assumption. A vegetation-height retrieval algorithm is proposed using the SNR-dominant period (the peak period in the average power spectrum derived from a wavelet analysis of SNR). Soil moisture and vegetation height are retrieved at different time periods (before and after vegetation's significant growth in March). The retrievals are compared with two independent reference data sets: in situ observations of soil moisture and vegetation height, and numerical simulations of soil moisture, vegetation height and above-ground dry biomass from the ISBA (interactions between soil, biosphere and atmosphere) land surface model. Results show that changes in soil moisture mainly affect the multipath phase of the SNR data (assuming the relative antenna height is constant) with little change in the dominant period of the SNR data, whereas changes in vegetation height are more likely to modulate the SNR-dominant period. Surface volumetric soil moisture can be estimated (R² = 0.74, RMSE = 0.009 m³ m⁻³) when the wheat is smaller than one wavelength (∼ 19 cm). The quality of the estimates markedly decreases when the vegetation height increases. This is because the reflected GNSS signal is less affected by the soil. When vegetation replaces soil as the dominant reflecting surface, a wavelet analysis provides an accurate estimation of the wheat crop height (R² = 0.98, RMSE = 6.2 cm). The latter correlates with modeled above-ground dry biomass of the wheat from stem elongation to ripening. It is found that the vegetation height retrievals are sensitive to changes in plant height of at least one wavelength. A simple smoothing of the retrieved plant height allows an excellent matching to in situ observations, and to modeled above-ground dry biomass.

Floods are the most frequent natural hazard globally and incidences have been increasing in recent years as a result of human activity and global warming, making significant impacts on people’s livelihoods and wider socio-economic activities. In terms of the management of the environment and water resources, precise identification is required of areas susceptible to flooding to support planners in implementing effective prevention strategies. The objective of this study is to develop a novel hybrid approach based on Bald Eagle Search (BES), Support Vector Machine (SVM), Random Forest (RF), Bagging (BA) and Multi-Layer Perceptron (MLP) to generate a flood susceptibility map in Thua Thien Hue province, Vietnam. In total, 1621 flood points and 14 predictor variables were used in this study. These data were divided into 60% for model training, 20% for model validation and 20% for testing. In addition, various statistical indices were used to evaluate the performance of the model, such as Root Mean Square Error (RMSE), Receiver Operation Characteristics (ROC), and Mean Absolute Error (MAE). The results show that BES, for the first time, successfully improved the performance of individual models in building a flood susceptibility map in Thua Thien Hue, Vietnam, namely SVM, RF, BA and MLP, with high accuracy (AUC > 0.9). Among the models proposed, BA-BES was most effective with AUC = 0.998, followed by RF-BES (AUC = 0.998), MLP-BES (AUC = 0.998), and SVM-BES (AUC = 0.99). The findings of this research can support the decisions of local and regional authorities in Vietnam and other countries regarding the construction of appropriate strategies to reduce damage to property and human life, particularly in the context of climate change.

In this study, three months of records (January–March 2010) that were acquired by a geodetic Global Navigation Satellite Systems (GNSS) station from the permanent network of RGP (Réseau GNSS Permanent), which was deployed by the French Geographic Institute (IGNF), located in Socoa, in the south of the Bay of Biscay, were used to determine the tide components and identify the signature of storms on the signal to noise ratio (SNR) during winter 2010. The Xynthia storm hit the French Atlantic coast on the 28th of February 2010, causing large floods and damages from the Gironde to the Loire estuaries. Blind separation of the tide components and of the storm signature was achieved while using both a singular spectrum analysis (SSA) and a continuous wavelet transform (CWT). A correlation of 0.98/0.97 and root mean square error (RMSE) of 0.21/0.28 m between the tide gauge records of Socoa and our estimates of the sea surface height (SSH) using the SSA and the CWT, respectively, were found. Correlations of 0.76 and 0.7 were also obtained between one of the modes from the SSA and atmospheric pressure from a meteorological station and a mode of the SSA. Particularly, a correlation reaches to 0.76 when using both the tide residual that is associated to surges and atmospheric pressure variation.

With population growth, water will increase in the following decades tremendously. The optimization of water allocation for agriculture requires accurate soil moisture (SM) monitoring. Recent Global Navigation Satellite System Reflectometry (GNSS-R) studies take advantage of continuously emitted navigation signals by the Global Navigation Satellite System (GNSS) constellations to retrieve spatiotemporal soil moisture changes for soil with high clay content. It presents the advantage of sensing a whole surface around a reference GNSS antenna. This article focuses on sandy SM monitoring in the driest condition observed in the study field of Dahra, (Senegal). The area consists of 95% sand and in situ volumetric soil moisture (VSM) range from ~3% to ~5% durinf the dry to the rainy season. Unfortunately, the GNSS signals’ waves penetrated deep into the soil during the dry period and strongly reduced the accuracy of GNSS reflectometry (GNSS-R) surface moisture measurements. However, we obtain VSM estimate at low/medium penetration depth. The correlation reaches 0.9 with VSM error lower than 0.16% for the 5–10-cm-depth probes and achieves excellent temporal monitoring to benefit from the antenna heights directly correlated to spatial resolution. The SM measurement models in our research are potentially valuable tools that contribute to the planning of sustainable agriculture, especially in countries often affected by drought.

As multipaths still represent a major problem for reaching precise GNSS positioning, the mitigation of their influence has been widely investigated. However, previous studies have lately proposed to use these interferences of GNSS electromagnetic waves to estimate parameters related to the reflecting surface (e.g., antenna heights, rugosity,...). Variations of the nature of the surface is likely to modify the properties of the reflected waves, and consequently lead to variations of amplitude / phase of the signal-to-noise ratio (SNR), e.g. recorded at 1 Hz by a GNSS receiver. By analyzing the time variations of SNR measurements linked to the dielectric constant of the surrounding soil, we use a method to recover the local fluctuations of the soil moisture content. It is simply based on the obvious linear correlation between SNR amplitude / phase and retrieved antenna height time series and independent measurements of humidity probe at 2 and 5 cm depths. This method of combination is applied to determine soil moisture in a corn and soya field at Lamasquère, France, for 21 successive days. Results show a good correlation (e.g. 0.96 with GPS PRN-01 satellite) between SNR inversion and humidity probes for most satellites.