Fabio Maselli

Accurate estimation of actual evapotranspiration (ETa) is essential for understanding hydrological processes and managing water resources, especially in regions characterized by intensive agriculture and complex groundwater–surface interactions. This study intercompares three independent satellite-based ETa estimation approaches applied over Northeast Italy. The first two methods correspond to the classical MODIS algorithm (MOD16), which is based on a simplified Penman–Monteith approach, and to the more recent Sen-ET modelling framework, which relies on a surface energy balance principle. The outputs of these methods are compared to those produced by a water balance algorithm, NDVI-Cws, which predicts ETa through the combination of conventional ancillary data and MODIS NDVI imagery. The results obtained show that, while the MODIS algorithm yields ETa estimates which are generally lower than those of Sen-ET and NDVI-Cws, the latter methods produce similar predictions for most cover types examined. The same two methods are potentially capable of providing higher spatial resolution daily ETa estimates depending on the satellite inputs used; out of them, however, only NDVI-Cws can yield spatially complete and temporally continuous datasets. The analysis therefore provides insights into the reliability and usability of different remote sensing approaches for regional-scale water resource monitoring.

In the current context of global warming, quantifying carbon fluxes between biosphere and atmosphere and identifying ecosystems as carbon sources or sinks is essential. The goal of this study is to quantify net carbon fluxes for the main forest types in peninsular Spain and characterize them as carbon sources or sinks. A hybrid methodology is proposed. First, net primary production (NPP) is obtained through machine learning using site properties, time metrics of meteorological series, and forest inventory data as inputs. The most accurate NPP estimates (R² ≥ 0.8 and relative RMSE ≤ 30%) were obtained by kernel ridge regression and gaussian process regression using latitude, elevation, time metrics of air temperature, precipitation and incoming solar radiation, and growing stock volume as inputs. Secondly, net ecosystem production (NEP) is obtained by subtracting heterotrophic respiration simulated by Biome-BGC from the previous NPP. All considered forest types presented small and mostly positive NPP and NEP values (greater for deciduous than for evergreen forests), thus generally acting as carbon sinks during the 2004–2018 period.

A semi-empirical method for estimating actual evapotranspiration (ETa) based on ancillary and NDVI data, named NDVI-Cws, is currently being refined for improved applicability to wetlands. The investigation, in particular, addresses the case of semi-natural ecosystems where the impact of meteorological water stress (WS) is limited by groundwater resources. To adapt to this situation, the application of the NDVI-Cws method is preceded by a calibration phase based on spatially enhanced Land Surface Analysis Satellite Application Facility (LSA SAF) evapotranspiration products. This calibration is currently performed in the main wetlands of Tuscany (Central Italy) identified in the Ramsar Convention. The calibrated NDVI-Cws version is then applied to all regional Ramsar areas, yielding outputs that are first examined all over Tuscany. Next, the model estimates are quantitatively assessed versus ETa observations taken in a forest and a grassland Ramsar site. The results of these independent tests show the improvement achieved by the calibration phase with respect to the original model version. This supports the potential of the refined NDVI-Cws method to yield reasonably accurate daily ETa estimates for wetlands at a spatial resolution that is mainly dependent on the NDVI data used.

A recently proposed modelling strategy predicts the net primary production (NPP) of forest ecosystems by combining the outputs of a NDVI-driven model, Modified C-Fix, and a bio-geochemical model, BIOME-BGC. This combination strategy takes into account the effects of forest disturbances but still assumes the presence of a mixture of differently aged trees. The application of this strategy to even-aged forests, therefore, requires a methodological advancement aimed at properly modifying the modelling of main ecosystem processes. In particular, the adaptation of the method to even-aged forests is based on the use of high-spatial-resolution airborne laser scanning (ALS) datasets, which yields green and woody biomass estimates that regulate the simulation of photosynthetic and respiratory processes, respectively. This approach was experimented in a Mediterranean study area, San Rossore Regional Park (Central Italy), which is covered by even-aged pine stands in different development phases. The modelling strategy is driven by MODIS NDVI images and meteorological data across five years (2011–2015), which are combined with estimates of forest canopy cover and height obtained from ALS data taken in 2015. This allows the production of stand NPP estimates, which, when converted into respective current annual increment (CAI) values, reasonably reproduce the age dependency of the available ground observations. The CAI estimates also show a highly significant correlation with these observations (r = 0.773) and moderate error levels (RMSE = 2.03 m³ ha⁻¹ year⁻¹, MBE = −0.45 m³ ha⁻¹ year⁻¹). These results confirm the potential of the modified simulation method to yield accurate high-spatial-resolution NPP estimates, which can offer valuable insights into C cycling and storage, in even-aged forests.

The current communication presents the application of a consolidated model combination strategy to analyze the medium-term carbon fluxes in two Mediterranean pine wood ecosystems. This strategy is based on the use of a NDVI-driven parametric model, Modified C-Fix, and of a biogeochemical model, BIOME-BGC, the outputs of which are combined taking into account the actual development phase of each ecosystem. The two pine ecosystems examined correspond to an old-growth forest and to a secondary succession after clearcuts, which differently respond to the same climatic condition during a ten-year period (2013–2022). Increasing dryness, in fact, exerts a fundamental role in controlling the gross primary and net ecosystem production of the mature stand, while the effect of forest regeneration is prevalent for the uprising of the same variables in the other stand. In particular, the simulated net carbon exchange fluctuates around 200 g C m⁻² year⁻¹ in the first stand and rises to over 600 g C m⁻² year⁻¹ in the second stand; correspondingly, the accumulation of new biomass is nearly undetectable in the former case while becomes notable in the latter. The study, therefore, supports the potential of the applied strategy for predicting the forest carbon balances consequent on diversified natural and human-induced factors.

The current communication presents the application of a consolidated model combination strategy to analyze the medium-term carbon fluxes in two Mediterranean pine wood ecosystems. The modelling strategy is based on the use of a NDVI-driven parametric model, Modified C-Fix, and of a bio-geochemical model, BIOME-BGC, the outputs of which are combined taking into account the actual development phase of each ecosystem. The two pine ecosystems examined correspond to an old-growth forest and to a secondary succession after clearcuts, which differently respond to the same climatic condition during a ten-year period (2013-2022). Increasing dryness, in fact, exerts a fundamental role in controlling the gross primary and net ecosystem production of the mature stand, while the effect of forest regeneration is prevalent for the uprising of the same variables in the other stand. In particular, the simulated net carbon exchange fluctuates around 200 g C m-2 year-1 in the first stand and rises to over 600 g C m-2 year-1 in the second stand; correspondingly, the accumulation of new biomass is nearly undetectable in the former case while becomes notable in the latter. The study therefore supports the potential of the applied strategy for predicting the forest carbon balances consequent on diversified natural and human-induced factors.

The current study presents the application of a model combination strategy to analyse gross and net production in a Mediterranean pine wood ecosystem. The test site is in the San Rossore Regional Park (Central Italy), an area originally characterised by the presence of Pinus pinaster Ait and Pinus pinea L. Since 2004 the former species was attacked by an insect and was therefore subjected to clearcuts around 2012. The latter species, not affected by the insect, has continued to grow, reaching the senescent stage. The evolution of two large stands (i.e., one per species) in terms of gross and net primary production over the years 2013-2022 is then reconstructed by applying satellite-based and process-based models to different datasets: a daily meteorological one, a set of 16-day MODIS NDVI images at 250 m spatial resolution, a LiDAR image taken in 2015 and digital orthophotos referred to 2021. The study shows how this modelling strategy can be successfully used to analyse the influence of climate and management on this forest ecosystem, and to reconstruct the evolution of two stands in different growing phase. Such assessment is relevant to provide the information required for sustainable forest management, concerning in particular carbon cycle ecosystem services.