A method is presented for integrating the information available in a limited area (corresponding to Tuscany in Italy) coming from satellite sensors, point measurement stations and ground-based radars. The objective is the exploitation of the complementary information provided by the variety of methods and instruments nowadays existing for measuring precipitation or precipitation-related parameters, in order to upgrade the capability of reconstructing weather phenomena of main interest. Ground- and satellite-based measurements, working locally or remotely, are jointly analyzed to evaluate how heterogeneous data can amplify the effectiveness of the measurements, when synergically analyzed, and this holds also when some of the available instruments essentially give just qualitative information. A way to synthesize the different information provided by various instruments is presented, assessing the quality of all the available observations. Namely, steps are described for the achievement of a mosaic of qualitative weather radars, and it is shown how the joint analysis of satellite, rain gauge and lightning observations can support a correct interpretation of precipitation phenomena. Finally, a logical scheme for data integration is presented and discussed.
Global standard ocean colour algorithms may be inefficient to estimate the concentration of seawater constituents in the
Mediterranean Sea. Local overestimation or underestimation of chlorophyll, suspended sediments and yellow substance
are in fact quite common. To avoid this problem, our research group works on the local calibration of empirical or semi-analytical
algorithms through comparison to in situ measured data. The spectral features of chlorophyll, suspended
sediments and yellow substance were found for a number of samples near the coast of Tuscany (Italy). An
unconventional algorithm was then developed and applied to satellite data (MODIS) for the retrieval of water constituent
concentrations. This inversion algorithm is based on the minimization of the spectral angle between simulated and
measured remote sensing reflectances. The estimated concentrations show a lower error with respect to that obtained by
a standard error minimization criterion. Monthly maps of seawater constituent concentrations obtained by applying the
proposed algorithm to numerous satellite images confirm the oligotrophic nature of the Tuscany Sea, where high values
of these concentrations can be found only in early spring near the mouths of the main rivers.
Several methods have been proposed for the extraction of latent information from multispectral remotely sensed scenes based on the definition of indices and rotational transformations. A common drawback of these techniques is that they are ultimately based only on statistical relationships among pixel values rather than on physical characteristics of the scenes. Linear pixel unmixing is an alternative method which assumes that the pixel signal is the linear combination of some basic spectral components the fractions of which can be retrieved with good approximation. The method is straightforward and produces results which can be easily interpreted, but presents the problem of the identification of suitable end-members, which generally requires some external knowledge. In order to overcome this problem, in the present research a statistical method is developed for the automatic identification of end-members. This methodology is composed by several steps, that are describe and then applied to a case study with a Landsat 5 TM scene from Central Ethiopia (Africa). The results, evaluated in comparison with those of a more usual principal component transformation, indicate the good performance of the new procedure.