Recent studies have demonstrated that the soil water content (SWC) of Mediterranean ecosystems can be simulated by combining ground data and remote sensing observations of Normalized Difference Vegetation Index (NDVI). The application of this approach in heterogeneous and fragmented areas, however, requires the use of spatio-temporal fusion (STF) methods to properly account for the actual NDVI variability of the examined ecosystems. One of these methods, which was specifically developed to produce annual NDVI data series in Mediterranean regions, is currently applied to MODIS and TM/ETM+ images taken over a highly fragmented green urban area in Florence (Central Italy). The performances of this STF method, called SEVIS, are indirectly evaluated by comparing local SWC measurements to simulations driven by the original (MODIS) and fused (MODIS+TM/ETM+) NDVI datasets. The results obtained confirm the critical dependence of the applied SWC simulation strategy on the efficient accounting for the actual NDVI evolution of the observed ecosystem. In particular, the use of the fused NDVI dataset corrects almost completely for the strong SWC underestimation produced by the original MODIS images during the summer dry period, significantly improving all accuracy statistics (r² from 0.564 to 0.855, RMSE from 0.101 to 0.044 cm³ cm⁻³ and MBE from −0.046 to 0.000 cm³ cm⁻³).

This paper presents a new method to predict global solar radiation over irregular terrain, named Estimation of global solar RADiation (ERAD). The method is based on the disaggregation of Satellite Applications Facility on Land Surface Analysis (LSA SAF) data using a digital elevation model and is applied in Italy with a time step of 1 min and a spatial resolution of 200 m. A quantitative assessment of ERAD is performed in comparison with three other standard methods (Mountain Microclimate Simulation Model [MTCLIM], LSA SAF and Copernicus Atmosphere Monitoring Service [CAMS]) using measurements taken in 43 stations located in Italy or in the surrounding countries, in the years 2005–2016. Such assessment concerns the irradiance incoming on a horizontal surface, which is measured by ground radiation sensors and is summarized by means of four accuracy statistics (i.e. mean absolute error [MAE], root mean square error [RMSE], coefficient of determination [R²] and mean bias error [MBE]). Overall, the average daily global solar radiation estimates obtained by ERAD have RMSE and R² about 25 W·m⁻² and 0.943, respectively. These statistics are similar to those of LSA SAF and better than those of CAMS and, above all, MTCLIM. The bias analysis by elevation ranges shows a slight ERAD overestimation over plains and hills and a slight underestimation over mountains. An additional qualitative assessment shows how the ERAD radiation estimates are more spatially detailed than those of the other methods and are redistributed on inclined surfaces consistently with expectations.

Actual evapotranspiration (ETA) is a fundamental component of the land water cycle that can be predicted by the combination of meteorological data and remotely sensed normalized difference vegetation index (NDVI) observations. The proficient application of this approach to the retrospective study of fragmented areas, however, depends on the preliminary use of spatio-temporal fusion (STF) methods capable of integrating different satellite datasets. One of these methods is the Spatial Enhancer of Vegetation Index image Series (SEVIS), which has been recently developed to improve the annual NDVI datasets based on one or a few high spatial resolution images. This STF method is currently applied to moderate resolution imaging spectroradiometer (MODIS) and TM/ETM+/OLI imagery taken over three fragmented areas in Tuscany (Central Italy), representative of different Mediterranean ecosystems, i.e., an urban grassland, a tomato field, and an olive grove. The performance of SEVIS is evaluated by comparing the ETA estimates obtained from the original (MODIS) and synthetic (MODIS plus TM/ETM+/OLI) NDVI datasets to ground ETA observations. The experimental results indicate that the original MODIS NDVI data cannot properly characterize the seasonal vegetation evolutions of the three study sites, which negatively affects the performance of ETA simulation. In contrast, such evolutions are reasonably reproduced by the synthetic NDVI datasets, which improves the accuracy of the ETA estimates both in terms of correlation and errors. The improvements are particularly evident during the summer dry period when the MODIS images are incapable of characterizing the actual vegetation response to water stress.