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Sesto Fiorentino, Italy

Maselli F.,CNR Institute for Biometeorology | Papale D.,University of Tuscia | Chiesi M.,CNR Institute for Biometeorology | Matteucci G.,CNR Institute for Agricultural and Forest Systems In the Mediterranean | And 3 more authors.
Remote Sensing of Environment | Year: 2014

Time-varying crop coefficients (Kc) can be obtained from remotely sensed data and combined with daily potential evapotranspiration estimates for the operational prediction of actual evapotranspiration (ETA). This approach, however, presents relevant limitations when applied in mixed, water stressed ecosystems. The current paper addresses these issues by introducing two innovations. First, fractional vegetation cover (FVC) is derived from NDVI and utilized to split evaporating and transpiring surfaces, whose behavior is simulated under fully watered conditions by the use of generalized Kc. Next, the short term effect of water shortage is taken into account by means of downregulating factors which are based on meteorological observations (potential evapotranspiration and rainfall) and act differently for vegetated and not vegetated surfaces. The new method is tested against latent heat of evaporation (LE) measurements taken by the eddy covariance technique in six sites of Central Italy representative of various forest and herbaceous ecosystems. In this experiment the method is driven by 1-km meteorological data obtained from a pan-European archive and by 250m MODIS NDVI imagery. Satisfactory accuracies are obtained in all experimental situations, which encourages the application of the method for the operational monitoring of ETA on regional scale. © 2014 Elsevier Inc. Source


Maselli F.,CNR Institute for Biometeorology | Argenti G.,University of Florence | Chiesi M.,CNR Institute for Biometeorology | Angeli L.,LAMMA | Papale D.,University of Tuscia
Agriculture, Ecosystems and Environment | Year: 2013

This paper presents the assessment of a NDVI-based parametric model (C-Fix) and a bio-geochemical model (BIOME-BGC) for the simulation of semi-natural grassland primary productivity in Italy. The two models are first calibrated using the gross primary productivity (GPP) data of an eddy covariance flux tower placed over a Mediterranean-temperate hilly area in Central Italy. Next, they are applied to estimate the net primary productivity (NPP) of three independent areas representative of different eco-climatic zones. The first area shows a typical Alpine climate, while the other two are characterized by more or less pronounced Mediterranean features. The accuracy of the NPP estimates is assessed through comparison with destructive dry matter measurements taken in the three areas. The results obtained support the capability of the two models to predict spatial NPP differences across the various grassland sites. The greatest estimation errors are found in the mountain area, mostly due to inaccuracies in the meteorological input data. These errors affect particularly the outputs of the bio-geochemical model and are mitigated by the use of C-Fix, which exploits the remotely sensed information related to the seasonal evolution of green biomass. © 2012 Elsevier B.V. Source


Maselli F.,CNR Institute of Neuroscience | Mari R.,LAMMA | Chiesi M.,CNR Institute of Neuroscience
International Journal of Remote Sensing | Year: 2013

A method has been recently presented to predict the net primary production (NPP) of Mediterranean forests by integrating conventional and remote-sensing data. This method was based on the use of two models, C-Fix and BIOME-BGC, whose outputs are combined with estimates of stem volume and tree age to predict the NPP of the examined ecosystems. This article investigates the possibility of deriving these two forest attributes from airborne high-resolution lidar data. The research was carried out in the San Rossore pine forest, a test site in Central Italy where several investigations have been conducted. First, estimates of stand stem volume and tree age were obtained from lidar data by application of a simplified method based on existing literature and a few ground measurements. The accuracy of these stand attributes was assessed by comparison with the independent ground data derived from a recent forest inventory. Next, the stem volume and tree age estimates were used to drive the NPP modelling strategy, whose outputs were evaluated against the inventory measurements of current annual increment (CAI). The simplified lidar data processing method produces stand stem volume and tree age estimates having moderate accuracy, which are useful to feed the modelling strategy and predict CAI at a stand level. This method's success raises the possibility of integrating ecosystem modelling techniques and lidar data for the simulation of net forest carbon fluxes. © 2013 Copyright Taylor and Francis Group, LLC. Source


Chiesi M.,CNR Institute for Biometeorology | Rapi B.,CNR Institute for Biometeorology | Battista P.,CNR Institute for Biometeorology | Fibbi L.,LAMMA | And 4 more authors.
European Journal of Remote Sensing | Year: 2013

A recent paper of our research group has proposed a simplified "water balance" model which predicts actual evapotranspiration (ETA) based on ground and remotely sensed data. The model combines estimates of potential evapotranspiration (ET0) and of fractional vegetation cover derived from NDVI in order to separately simulate transpirative and evaporative processes. The new method, named NDVI-Cws, was validated against latent heat measurements taken by the eddy covariance technique over various vegetation types in Central Italy. The current paper extends this validation to three other test sites in Tuscany for which reference data are obtained from different sources. In the first two sites (non-irrigated winter wheat and irrigated maize fields) seasonal reference ETA data series are obtained by the WinEtro model. In situ transpiration measurements are instead used as reference data for a deciduous oak forest stand. The ETA and transpiration estimates of the NDVI-Cws method are very similar to the reference data in terms of both annual totals and seasonal evolutions. Examples are finally provided of the model application for operationally monitoring ETA in Tuscany. Source

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