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Different models driven by remotely sensed vegetation indexes (VIs) and incident photosynthetically active radiation (PAR) were developed to estimate gross primary production (GPP) in a subalpine grassland equipped with an eddy covariance flux tower. Hyperspectral reflectance was collected using an automatic system designed for high temporal frequency acquisitions for three consecutive years, including one (2011) characterized by a strong reduction of the carbon sequestration rate during the vegetative season. Models based on remotely sensed and meteorological data were used to estimate GPP, and a cross-validation approach was used to compare the predictive capabilities of different model formulations. Vegetation indexes designed to be more sensitive to chlorophyll content explained most of the variability in GPP in the ecosystem investigated, characterized by a strong seasonal dynamic. Model performances improved when including also PAR potential defined as the maximal value of incident PAR under clear sky conditions in model formulations. Best performing models are based entirely on remotely sensed data. This finding could contribute to the development of methods for quantifying the temporal variation of GPP also on a broader scale using current and future satellite sensors. [ABSTRACT FROM AUTHOR]

Snow, as a fundamental reservoir of freshwater, is a crucial natural resource. Specifically, knowledge of snow density spatial and temporal variability could improve modelling of snow water equivalent, which is relevant for managing freshwater resources in context of ongoing climate change. The possibility of estimating snow density from remote sensing has great potential, considering the availability of satellite data and their ability to generate efficient monitoring systems from space. In this study, we present an innovative method that combines meteorological parameters, satellite data and field snow measurements to estimate thermal inertia of snow and snow density at a catchment scale. Thermal inertia represents the responsiveness of a material to variations in temperature and depends on the thermal conductivity, density and specific heat of the medium. By exploiting Landsat 8 data and meteorological modelling, we generated multitemporal thermal inertia maps in mountainous catchments in the Western European Alps (Aosta Valley, Italy), from incoming shortwave radiation, surface temperature and snow albedo. Thermal inertia was then used to develop an empirical regression model to infer snow density, demonstrating the possibility of mapping snow density from optical and thermal observations from space. The model allows for estimation of snow density with R2 CV and RMSE CV of 0.59 and 82 kg m−3, respectively. Thermal inertia and snow density maps are presented in terms of the evolution of snow cover throughout the hydrological season and in terms of their spatial variability in complex topography. This study could be considered a first attempt at using thermal inertia toward improved monitoring of the cryosphere. Limitations of and improvements to the proposed methods are also discussed. This study may also help in defining the scientific requirements for new satellite missions targeting the cryosphere. We believe that a new class of Earth Observation missions with the ability to observe the Earth's surface at high spatial and temporal resolution, with both day and night-time overpasses in both optical and thermal domain, would be beneficial for the monitoring of seasonal snowpacks around the globe. • Snow thermal inertia reflects the status of the snowpack evolution. • An empirical model is presented for mapping snow density from thermal inertia. • This study can pave new perspectives in remote sensing of the cryosphere. [ABSTRACT FROM AUTHOR]

This article presents the structure and results of a simplified model (VISTOCK) for simulating grass growth and water dynamics of grassland systems. The model, based on a process-based approach coupled with proximal (SKR 1800 2-Channel Light Sensor) and remote (Sentinel-2) NDVI-derived data for estimating LAI, simulates aboveground biomass (AGB), net primary production (NPP), evapotranspiration (ET), and the fraction of transpirable water in soil (FTSW). VISTOCK simulated a grassland system with few meteorological data (i.e., minimum and maximum daily temperatures, precipitation, global solar radiation), considering limitations to vegetation growth due to thermal and water stresses. It was calibrated for a natural alpine grassland in Italy (site T) during the most contrasting meteorological seasons of the dataset (2012, 2017, and 2018). It was then evaluated for the remaining years at site T (2013, 2014, 2015, and 2016) and for other two sites in Italy (sites B1, B2 and M) with different soil and climate conditions and diverse management strategies (2020 and 2021). VISTOCK accurately predicted AGB during the growing season (RMSE = 445, 240, 219, 365 kg DM ha-1 for T, M, B1, and B2, respectively) as well as for NPP, ET, and FSTW at site T. Simulation results suggest the ability of the model to simulate grassland in diverse environments with few inputs and parameters to be calibrated. The model's simplified structure, combined with easy-to obtain input data and easy applicability, encourages its wider use for out- and/or upscaling and decision making. • Using NDVI as a proxy for LAI simplified the model's structure. • AGB was predicted well under different environmental and management conditions. • The model showed the potential to reproduce water dynamics of grassland systems. • Simplifying the model's structure increased its applicability to different contexts. [ABSTRACT FROM AUTHOR]

Earth hummocks are small cryogenic mounds, covered by grass, closely spaced in grassland or wetlands. Hummock microtopography establishes specific microclimatic conditions, with small-scale variations in soil thermal properties and water regimes, which influence biogeochemical cycles. These properties, coupled with different litter decomposability, may cause variations on soil physical and chemical properties and pedogenesis, as well as a selective distribution of plant species. The work has been carried out at the LTER site of Tellinod (Torgnon, Aosta Valley, NW, Italy). The site is characterized by a Nardus stricta subalpine hummocky grassland located at 2100 masl, which shows the dominance of Nardus on hummocks and a prevalence of dicotyledons in interhummocks (i.e. the depressions between consecutive hummocks). Such distribution indicates that earth hummock pattern was reflected in soil properties. In order to confirm this hypothesis, we analyzed and compared soil pedogenesis and topsoil characteristics between hummocks and interhummocks. In addition, litter bags were incubated in hummock and interhummock positions to investigate litter decomposition rate as related to microtopography and plant species and its effects on topsoil edaphic properties. The results confirm that hummocky topography significantly influences topsoil properties, pedogenesis and vegetation distribution, with large differences between hummocks and interhummocks. The hummocky soil can be fully classified as Podzol, based on both the morphological and chemical diagnostic properties; however, morphological and chemical evidences indicate that the degree of podzolization differs significantly under hummocks and interhummocks. In addition, the results verify a faster decomposition of dicotyledons in the nutrient-richer interhummock topsoils compared to the podzolized hummocks positions, and an overall slower decomposition rate of Nardus litter. All these factors contribute to the creation and conservation of a unique pedo-environment in this subalpine grassland. [ABSTRACT FROM AUTHOR]

Subsurface storage changes (Δ S) represent a key modulator of drought propagation through the hydrological cycle, but their contribution to the annual water balance, and to drought propagation and recovery has rarely been explicitly assessed across catchments and climates. To expand on previous work on this matter, here we performed a large-sample analysis of precipitation, discharge, actual evapotranspiration (ET), and Δ S for 10 hydrological years and 102 catchments across various hydro-climatological regimes in Italy. We found that Δ S cannot be neglected in the annual water balance. Storage depletion leads to the attenuation of hydrological drought compared to meteorological drought, meaning that subsurface storage actively supports discharge during drought. We also found that storage generally recovers from precipitation deficits over time scales similar to the discharge recovery time, while recovery times for ET are longer. These findings show that subsurface storage drives drought propagation and recovery, regardless of climatic and catchment characteristics, and are thus relevant to properly inform water managers about surface- and ground-water availability in a changing climate. • Large-sample analysis of water balance components to study drought propagation and recovery. • Subsurface storage changes account on average for the 11% of long-term mean annual precipitation across the catchments. • Subsurface storage depletion sustains discharge during drought and the following recovery period. • A water balance approach is needed to better understand drought propagation and recovery. [ABSTRACT FROM AUTHOR]

• Greenness biological meaning and its relation with water fluxes are lacking. • New leaves are responsible for the rapid increase in spring canopy greenness. • Canopy greenness can be represented jointly using leaf spectra and leaf age. • Greenness is important to explain variation of canopy conductance. Remote sensing capabilities to monitor evergreen broadleaved vegetation are limited by the low temporal variability in the greenness signal. With canopy greenness computed from digital repeat photography (PhenoCam), we investigated how canopy greenness related to seasonal changes in leaf age and traits as well as variation of trees' water fluxes (characterized by sap flow and canopy conductance). The results showed that sprouting leaves are mainly responsible for the rapid increase in canopy green chromatic coordinate (GCC) in spring. We found statistically significantly differences in leaf traits and spectral properties among leaves of different leaf ages. Specifically, mean GCC of young leaves was 0.385 ± 0.010 (mean ± SD), while for mature and old leaves was 0.369 ± 0.003, and 0.376 ± 0.004, respectively. Thus, the temporal dynamics of canopy GCC can be explained by changes in leaf spectral properties and leaf age. Sap flow and canopy conductance are both well explained by a combination of environmental drivers and greenness (96% and 87% of the variance explained, respectively). In particular, air temperature and vapor pressure deficit (VPD) explained most of sap flow and canopy conductance variance, respectively. Besides, GCC is an important explanatory variable for variation of canopy conductance may because GCC can represent the leaf ontogeny information. We conclude that PhenoCam GCC can be used to identify the leaf flushing for evergreen broadleaved trees, which carries important information about leaf ontogeny and traits. Thus, it can be helpful for better estimating canopy conductance which constraints water fluxes. [ABSTRACT FROM AUTHOR]

• Three different understory leaf area index (LAI u) retrieval methods are compared. • The three methods show different strengths/weaknesses depending on LAI scale. • Variation in LAI u is significantly related to vegetation diversity. Leaf area index (LAI) is a key ecological indicator for describing the structure of canopies and for modelling energy exchange between atmosphere and biosphere. While LAI of the forest overstory can be accurately assessed over large spatial scales via remote sensing, LAI of the forest understory (LAI u) is still largely ignored in ecological studies and ecosystem modelling due to the fact that it is often too complex to be destructively sampled or approximated by other site parameters. Additionally, so far only few attempts have been made to retrieve understory LAI via remote sensing, because dense canopies with high LAI are often hindering retrieval algorithms to produce meaningful estimates for understory LAI. Consequently, the forest understory still constitutes a poorly investigated research realm impeding ecological studies to properly account for its contribution to the energy absorption capacity of forest stands. This study aims to compare three conceptually different indirect retrieval methodologies for LAI u over a diverse panel of forest understory types distributed across Europe. For this we carried out near-to-surface measurements of understory reflectance spectra as well as digital surface photography over the extended network of Integrated Carbon Observation System (ICOS) forest ecosystem sites. LAI u was assessed by exploiting the empirical relationship between vegetation cover and light absorption (Beer-Lambert- Bouguer law) as well as by utilizing proposed relationships with two prominent vegetation indices: normalized difference vegetation index (NDVI) and simple ratio (SR). Retrievals from the three methods were significantly correlated with each other (r = 0.63–0.99, RMSE = 0.53–0.72), but exhibited also significant bias depending on the LAI scale. The NDVI based retrieval approach most likely overestimates LAI at productive sites when LAI u > 2, while the simple ratio algorithm overestimates LAI u at sites with sparse understory vegetation and presence of litter or bare soil. The purely empirical method based on the Beer-Lambert law of light absorption seems to offer a good compromise, since it provides reasonable LAI u values at both low and higher LAI ranges. Surprisingly, LAI u variation among sites seems to be largely decoupled from differences in climate and light permeability of the overstory, but significantly increased with vegetation diversity (expressed as species richness) and hence proposes new applications of LAI u in ecological modelling. [ABSTRACT FROM AUTHOR]