Precipitation retrieval from space has seen great advances thanks to the improving quality of PMW measurements, the refinement of precipitation retrieval techniques, and the increasing number of microwave radiometers on board LEO satellites orbiting around the Earth. With the recent advent of the Global Precipitation Measurement (GPM) mission the constellation of cross-track and conically scanning microwave radiometers with precipitation-sensing capabilities currently ensures 1- to 3-hourly coverage at mid/high latitudes. Traditionally passive microwave (PMW) retrieval algorithms are based on the principle that surface precipitation can be estimated from the mul- tichannel brightness temperature (TB) measurements because these are affected (in different ways depending on channel frequency, viewing geometry, spatial resolution, and surface background conditions) by the microphysical properties and 3-D distribution of liquid and frozen hydrometeors within the precipitating cloud, and, therefore, can be related to surface precipitation. These approaches can be categorized as top-down approaches and they provide instantaneous precipitation rate estimate at the surface at the time of the satellite observation. Recently a new perspective for surface precipitation estimate has been proposed, the bottom-up approach, based on the principle that the soil moisture can be considered as a "natural raingauge" and can be employed for "measuring" rainfall. The algorithm, called SM2RAIN, allows estimating rainfall directly from soil moisture retrieved from spaceborne sensors (i.e. ASCAT). Several recent studies have demonstrated that the approach is very effective for precipitation estimation from the daily to 5-daily scale, even though not applicable in regions where soil moisture retrieval is not feasible (i.e. highly vegetated areas, frozen surfaces, oceans). This study shows that the precipitation estimates obtained by PMW observations using the two approaches (top-down and bottom-up) can be combined to provide accurate estimates of daily rainfall. For the top-down approach two different precipitation retrieval algorithms are used_ the Cloud Dynamics Radiation Database algorithm (CDRD), a physically ¬based Bayesian algorithm applied to the conically scanning radiometer SSMIS, and the Passive microwave Neural network Precipitation Retrieval (PNPR) algorithm for cross-track scanning radiometers AMSU/MHS. Four-year (2011-2014) assessment over Italy (complex territory with orography, sea-land interface, vegetation, frozen soils) is carried out for the daily rainfall estimates obtained from SM2RAIN, combined CDRD-PNPR, and merged SM2RAIN-CDRD-PNPR products, all regridded at 0.25ox0.25o spatial resolution. Rainfall measurements from the Italian raingauge network are used as reference. Results show that both SM2RAIN and CDRD-PNPR rainfall products are as accurate as state-of-the-art products, e.g., the TRMM product 3B42RT. The CDRD-PNPR product shows sensitivity to the number and frequency of SSMIS and AMSU/MHS overpasses available, and evidences worse results along the coast, while SM2RAIN shows criticalities mostly related to soil moisture retrieval feasibility. The merged SM2RAIN-CDRD-PNPR product shows better results in terms of correlation (R=0.7), accuracy (RMSE=6.6 mm), and rainfall detection capability (POD=0.75, FAR=0.32, TS=0.5 for 1 mm threshold). The study demonstrates that daily rainfall estimate over a complex territory such as Italy can benefit from the exploitation of multiple PMW radiometers, and from the integration of the top-down and bottom-up approaches.

Geological slope failure processes have been observed on the Moon surface for decades, nevertheless a detailed and exhaustive lunar landslide inventory has not been produced yet. For a preliminary survey, WAC images and DEM maps from LROC at 100 m/pixels have been exploited in combination with the criteria applied by Brunetti et al. (2015) to detect the landslides. These criteria are based on the visual analysis of optical images to recognize mass wasting features. In the literature, Chebyshev polynomials have been applied to interpolate crater cross-sections in order to obtain a parametric characterization useful for classification into different morphological shapes. Here a new implementation of Chebyshev polynomial approximation is proposed, taking into account some statistical testing of the results obtained during Least-squares estimation. The presence of landslides in lunar craters is then investigated by analyzing the absolute values off odd coefficients of estimated Chebyshev polynomials. A case study on the Cassini A crater has demonstrated the key-points of the proposed methodology and outlined the required future development to carry out.

The research project 'Moon Mapping' has been established in 2014 between the Italian and Chinese Governments to promote cooperation and exchange between undergraduate students from both countries. The operational phase of the project started in early 2015, and will end in 2017, for a total length of three years. The main aim is to train new scholars to be able to work on different kinds of remotely-sensed data collected over the Moon surface by the Chinese space missions Chang'E-1/2. The project coordination has been assigned to the Italian Space Agency for the Italian side and to the Center of Space Exploration, China Ministry of Education, for the Chinese side. Several Chinese universities and Italian national research institutes and universities have been officially involved in this project. Six main research topics have been identified_ (1) map of the solar wind ion; (2) geomorphological map of the Moon; (3) data preprocessing of Chang'E-1 mission; (4) map of element distribution; (5) establishment of 3D digital visualization system; and (6) compilation and publication of a tutorial on joint lunar mapping.