Application of machine learning techniques to simulate the evaporative fraction and its relationship with environmental variables in corn crops

Abstract Background The evaporative fraction (EF) represents an important biophysical parameter reflecting the distribution of surface available energy. In this study, we investigated the daily and seasonal patterns of EF in a multi-year corn cultivation located in southern Italy and evaluated the performance of five machine learning (ML) classes of algorithms: the linear regression (LR), regression tree (RT), support vector machine (SVM), ensembles of tree (ETs) and Gaussian process regression (GPR) to predict the EF at daily time step. The adopted methodology consisted of three main steps that include: (i) selection of the EF predictors; (ii) comparison of the different classes of ML; (iii) application, cross-validation of the selected ML algorithms and comparison with the observed data. Results Our results indicate that SVM and GPR were the best classes of ML at predicting the EF, with a total of four different algorithms: cubic SVM, medium Gaussian SVM, the Matern 5/2 GPR, and the rational quadratic GPR. The comparison between observed and predicted EF in all four algorithms, during the training phase, were within the 95% confidence interval: the R2 value between observed and predicted EF was 0.76 (RMSE 0.05) for the medium Gaussian SVM, 0.99 (RMSE 0.01) for the rational quadratic GPR, 0.94 (RMSE 0.02) for the Matern 5/2 GPR, and 0.83 (RMSE 0.05) for the cubic SVM algorithms. Similar results were obtained during the testing phase. The results of the cross-validation analysis indicate that the R2 values obtained between all iterations for each of the four adopted ML algorithms were basically constant, confirming the ability of ML as a tool to predict EF. Conclusion ML algorithms represent a valid alternative able to predict the EF especially when remote sensing data are not available, or the sky conditions are not suitable. The application to different geographical areas, or crops, requires further development of the model based on different data sources of soils, climate, and cropping systems

Modeling Gross Primary Production of Agro-Forestry Ecosystems by Assimilation of Satellite-Derived Information in a Process-Based Model

In this paper we present results obtained in the framework of a regional-scale analysis of the carbon budget of poplar plantations in Northern Italy. We explored the ability of the process-based model BIOME-BGC to estimate the gross primary production (GPP) using an inverse modeling approach exploiting eddy covariance and satellite data. We firstly present a version of BIOME-BGC coupled with the radiative transfer models PROSPECT and SAILH (named PROSAILH-BGC) with the aims of i) improving the BIOME-BGC description of the radiative transfer regime within the canopy and ii) allowing the assimilation of remotely-sensed vegetation index time series, such as MODIS NDVI, into the model. Secondly, we present a two-step model inversion for optimization of model parameters. In the first step, some key ecophysiological parameters were optimized against data collected by an eddy covariance flux tower. In the second step, important information about phenological dates and about standing biomass were optimized against MODIS NDVI. Results obtained showed that the PROSAILH-BGC allowed simulation of MODIS NDVI with good accuracy and that we described better the canopy radiation regime. The inverse modeling approach was demonstrated to be useful for the optimization of ecophysiological model parameters, phenological dates and parameters related to the standing biomass, allowing good accuracy of daily and annual GPP predictions. In summary, this study showed that assimilation of eddy covariance and remote sensing data in a process model may provide important information for modeling gross primary production at regional scale

Neural Network Analysis to Evaluate Ozone Damage to Vegetation Under Different Climatic Conditions

Tropospheric ozone (O-3) is probably the air pollutant most damaging to vegetation. Understanding how plants respond to O(3)pollution under different climate conditions is of central importance for predicting the interactions between climate change, ozone impact and vegetation. This work analyses the effect of O(3)fluxes on net ecosystem productivity (NEP), measured directly at the ecosystem level with the eddy covariance (EC) technique. The relationship was explored with artificial neural networks (ANNs), which were used to model NEP using environmental and phenological variables as inputs in addition to stomatal O(3)uptake in Spring and Summer, when O(3)pollution is expected to be highest. A sensitivity analysis allowed us to isolate the effect of O-3, visualize the shape of the O-3-NEP functional relationship and explore how climatic variables affect NEP response to O-3. This approach has been applied to eleven ecosystems covering a range of climatic areas. The analysis highlighted that O(3)effects over NEP are highly non-linear and site-specific. A significant but small NEP reduction was found during Spring in a Scottish shrubland (-0.67%), in two Italian forests (up to -1.37%) and during Summer in a Californian orange orchard (-1.25%). Although the overall seasonal effect of O(3)on NEP was not found to be negative for the other sites, with episodic O(3)detrimental effect still identified. These episodes were correlated with meteorological variables showing that O(3)damage depends on weather conditions. By identifying O(3)damage under field conditions and the environmental factors influencing to that damage, this work provides an insight into O(3)pollution, climate and weather conditions.Peer reviewe

The fourth phase of the radiative transfer model intercomparison (RAMI) exercise : Actual canopy scenarios and conformity testing

The RAdiative transfer Model Intercomparison (RAMI) activity focuses on the benchmarking of canopy radiative transfer (RT) models. For the current fourth phase of RAMI, six highly realistic virtual plant environments were constructed on the basis of intensive field data collected from (both deciduous and coniferous) forest stands as well as test sites in Europe and South Africa. Twelve RT modelling groups provided simulations of canopy scale (directional and hemispherically integrated) radiative quantities, as well as a series of binary hemispherical photographs acquired from different locations within the virtual canopies. The simulation results showed much greater variance than those recently analysed for the abstract canopy scenarios of RAMI-IV. Canopy complexity is among the most likely drivers behind operator induced errors that gave rise to the discrepancies. Conformity testing was introduced to separate the simulation results into acceptable and non-acceptable contributions. More specifically, a shared risk approach is used to evaluate the compliance of RI model simulations on the basis of reference data generated with the weighted ensemble averaging technique from ISO-13528. However, using concepts from legal metrology, the uncertainty of this reference solution will be shown to prevent a confident assessment of model performance with respect to the selected tolerance intervals. As an alternative, guarded risk decision rules will be presented to account explicitly for the uncertainty associated with the reference and candidate methods. Both guarded acceptance and guarded rejection approaches are used to make confident statements about the acceptance and/or rejection of RT model simulations with respect to the predefined tolerance intervals. (C) 2015 The Authors. Published by Elsevier Inc.Peer reviewe

Determinazione della Produzione Ecosistemica Netta in un Impianto Short Rotation Forestry di Pioppo: Confronto fra Misure Biometriche e la Tecnica Eddy Covariance - Measuring Terrestrial CO2 Uptake in a Short Rotation Forestry of Poplar for Bioenergy Production: Comparison between Biometric and Micrometeorological Technique

Short rotation forestry (SRF) for bioenergy production along with other woody biomass feedstock will play a significant role in a more secure and sustainable energy resource for the European community. SRF for energy purposes is rapidly expanding in Europe, because of several environmental benefits. In this study we estimated the carbon dioxide (CO2) sequestration in a Short Rotation Forestry of poplar located in the basin of Ticino River, Lombardy, Italy (45° 19' 00" N 008° 51' 00" E). The SRF plantation (P generosa X P. nigra clone Pegaso) occupies an area of 80 ha: trees were planted in March 2004 using 1-year-old seedlings in a double row design with a spacing of 2.8 x 0.75 x 0.45 m corresponding to a density of 12.500 plants ha-1.We used two independent techniques: eddy correlation (EC) provide measurements of net ecosystem CO2 exchange (NEE) based on the fluxes generated by the photosynthetic and respiration activity, and a traditional approach, based on biomass increment, for estimate the CO2 gain by the vegetation. The heterotrophic respiration of the soil was measure using trench technique in order to quantify the amount of carbon losses by microbial activity. The two approaches allow to determine the Net Ecosystem Production (NEP) of the ecosystem that represent the net amount of CO2 gained by the ecosystem. Obtained data was used to estimate the differences between the two techniques proposed. The cumulated NEP measure by EC was 757 g C m-2** for the year 2005 (corresponding to 27.7 t of CO2) instead the NEP obtained by biometrical techniques was 630 g C m-2 (corresponding to 23. t of CO2.The comparison between the NEP estimates using eddy covariance technique and biometrical measurements show a constant overestimation of the value obtained whit the EC for both years of the investigation.JRC.H.2-Climate chang

Estimating heterotrophic and autotrophic soil respiration in a semi-natural forest of Lombardy, Italy

We studied an unmanaged alluvial forest in Northern Italy in order to better understand the soil carbon cycle and in particular the partitioning of soil respiration between autotrophic and heterotrophic respiration. We measured annual fluxes of soil respiration with a mobile chamber system at 16 per-manent collars and sampled soil organic carbon and root density at each collar in order to apply the indirect regression method for partitioning (Rodegheiro and Cescatti, 2006). The soil pool of organic carbon was very high down the 45 cm profile. The annual respiration rates ranged from 0.6 to 6.9 µmol carbon dioxide (CO2) m-2s-1 with an average value of 3.4 (±2.3) µmol CO2 m-2 s-1, and a cumulative flux of 1.1 kg C m-2y-1. The correlation between total soil respiration and soil temperature was high (R2=0.85), whereas the correlation with soil water content was weaker (R2=0.46). The heterotrophic component accounted for 66% of annual CO2 efflux. Soil temperature largely controlled the heterotrophic respiration (R2=0.93), while the autotrophic component followed irradiation. The indirect regression method enabled us to partition the seasonal course of undisturbed autotroph-ic and heterotrophic respiration. The role of photosynthesis in modulating soil respiration needs to be considered in terrestrial carbon cycle models.JRC.H.7-Climate Risk Managemen