In this work we study the problem of mapping soil moisture by means of Synthetic Aperture Radar (SAR) images. A test site has been set in Companhia das Lezirias, close to Lisbon, Portugal. The main advantage of using SAR images is their capability to map soil moisture at a very high spatial resolution. This opens interesting perspectives for agricultural applications, where soil moisture can abruptly change across field boundaries depending on the agricultural practices. The study area is characterized by flat topography, large agricultural areas and sparse vegetation. Five sensors have been deployed in a test area to measure soil moisture with a sampling time of one hour for a period of seven months. In-situ measurements are compared with the results obtained by processing 33 C-band Sentinel-1 images using the SAR interferometry technique. The aim of the study is to analyze the relation between the interferometric phase and time varying soil moisture. The main advantage of SAR interferometry with respect to the use of radar cross-section is that the information about soil moisture can be recovered using a reduced number of in-situ measurements. In particular, we combine three interferograms obtained from three SAR images, acquired over the same area at different times, to derive maps of bi-coherence and phase triplet. This last quantity allows to disentangle the phase contribution due to soil moisture from those related to microwave propagation in atmosphere and terrain displacements. Results are compared to those obtained using the interferometric phase and coherence to emphasize the importance to split the effects due to propagation (e.g. atmosphere) from those related to volume scattering.
In this study, an experiment aimed to integrate Global Navigation Satellite System (GNSS) atmospheric data with meteorological data into a neural network system is performed. Precipitable Water Vapor (PWV) estimates derived from GNSS are combined with surface pressure, surface temperature and relative humidity obtained continuously from ground-based meteorological stations. The work aims to develop a methodology to forecast short-term intense rainfall. Hence, all the data is sampled at one hour interval. A continuous time series of 3 years of GNSS data from one station in Lisbon, Portugal, is processed. Meteorological data from a nearby meteorological station are collected. Remote sensing
data of cloud top from SEVIRI is used, providing collocated data also on an hourly basis. A 3 year time series of hourly accumulated precipitation data are also available for evaluation of the neural network results. In previous studies, it was found that time varying PWV is correlated with rainfall, with a strong increase of PWV peaking just before intense rainfall, and with a strong decrease afterwards. However, a significant amount of false positives was found, meaning that the evolution of PWV does not contain enough information to infer future rain. In this work a multilayer fitting network is used to process the GNSS and meteorological data inputs in order to estimate the target outputs, given by the hourly
precipitation. It is found that the combination of GNSS data and meteorological variables processed by neural network improves the detection of heavy rainfall events and reduces the number of false positives.
Observing the water vapor distribution on the troposphere remains a challenge for the weather forecast. Radiosondes provide precise water vapor profiles of the troposphere, but lack geographical and temporal coverage, while satellite meteorological maps have good spatial resolution but even poorer temporal resolution. GPS has proved its capacity to measure the integrated water vapor in all weather conditions with high temporal sampling frequency. However these measurements lack a vertical water vapor discretization. Reconstruction of the slant path GPS observation to the satellite allows oblique water vapor measurements. Implementation of a 3D grid of voxels along the troposphere over an area where GPS stations are available enables the observation ray tracing. A relation between the water vapor density and the distanced traveled inside the voxels is established, defining GPS tomography. An inverse problem formulation is needed to obtain a water vapor solution. The combination of precipitable water vapor (PWV) maps obtained from MODIS satellite data with the GPS tomography is performed in this work. The MODIS PWV maps can have 1 or 5 km pixel resolution, being obtained 2 times per day in the same location at most. The inclusion of MODIS PWV maps provides an enhanced horizontal resolution for the tomographic solution and benefits the stability of the inversion problem. A 3D tomographic grid was adjusted over a regional area covering Lisbon, Portugal, where a GNSS network of 9 receivers is available. Radiosonde measurements in the area are used to evaluate the 3D water vapor tomography maps.
The electromagnetic signal transmitted by the global navigation and positioning systems (GNSS) suffers a delay which is
mainly caused by the water vapor in the atmosphere. Estimating the delay affecting the signal propagation, it is possible
to estimate the water vapor column on the troposphere above each station. The aim of this study is to characterize the
water vapor field on the troposphere over time by GNSS techniques. It is expected that can also come to assist in the
Nowcasting particularly in the prediction of severe meteorological phenomena. Several events of strong, intense and
short precipitation, observed in the Lisbon region throughout 2012 were analyzed. The choice of these events was based
on the analysis of hourly precipitation given by a meteorological station located on Lisbon center. This region is
monitored by a network of 15 GNSS stations covering about 100 square kilometers. The relationship between the GPS
precipitable water vapor (PWV) and the hourly accumulated precipitation was evaluated over time (1D closest GPSmeteorological
station plots) and spatially (2D maps) interpolated over the GNSS and meteorological stations. It was
verified that there were a high and sudden increment of the GPS PWV prior to severe precipitation events. The PWV
increment starts 6 to 10 hours before the rain and the value has increased between 57% and 75% relatively to the PWV
value observed previously. In this study is shown that GPS data has good potential for forecasting severe rain events and
high moisture flux situations.