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Magliulo, Vincenzo

Publications and source records attributed to Magliulo, Vincenzo.

10 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Evaluation of the Uncertainty in Satellite-Based Crop State Variable Retrievals Due to Site and Growth Stage Specific Factors and Their Potential in Coupling with Crop Growth Models

Coupling crop growth models and remote sensing provides the potential to improve our understanding of the genotype x environment x management (G × E × M) variability of crop growth on a global scale. Unfortunately, the uncertainty in the relationship between the satellite measurements and the crop state variables across different sites and growth stages makes it difficult to perform the coupling. In this study, we evaluate the effects of this uncertainty with MODIS data at the Mead, Nebraska Ameriflux sites (US-Ne1, US-Ne2, and US-Ne3) and accurate, collocated Hybrid-Maize (HM) simulations of leaf area index (LAI) and canopy light use efficiency (LUE Canopy ). The simulations are used to both explore the sensitivity of the satellite-estimated genotype × management (G × M) parameters to the satellite retrieval regression coefficients and to quantify the amount of uncertainty attributable to site and growth stage specific factors. Additional ground-truth datasets of LAI and LUE Canopy are used to validate the analysis. The results show that uncertainty in the LAI/satellite measurement regression coefficients lead to large uncertainty in the G × M parameters retrievable from satellites. In addition to traditional leave-one-site-out regression analysis, the regression coefficient uncertainty is assessed by evaluating the retrieval performance of the temporal change in LAI and LUE Canopy . The weekly change in LAI is shown to be retrievable with a correlation coefficient absolute value (|r|) of 0.70 and root-mean square error (RMSE) value of 0.4, which is significantly better than the performance expected if the uncertainty was caused by random error rather than secondary effects caused by site and growth stage specific factors (an expected |r| value of 0.36 and RMSE value of 1.46 assuming random error). As a result, this study highlights the importance of accounting for site and growth stage specific factors in remote sensing retrievals for future work developing methods coupling remote sensing with crop growth models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

A temperature threshold to identify the driving climate forces of the respiratory process in terrestrial ecosystems

Terrestrial ecosystem respiration (R e ) is the second largest carbon flux between the biosphere and atmosphere. Therefore, climate-driven changes of Re greatly impact on future atmospheric CO 2 concentration. The aim of this study was to derive an air temperature threshold to identify the driving climate forces of the respiratory process in terrestrial ecosystems within different temperature zones. A global dataset of 647 site-years of ecosystem flux data and related variables were collected at 152 sites. The quantile regression was applied to evaluate relationships between the maximum realizable Re rates and mean annual air temperature (MAT) as well as other micrometeorological factors (i.e., atmospheric CO 2 concentration, atmospheric water content, soil heat flux, sensible heat flux, latent heat flux, precipitation, relative humidity, and soil water content). Our analysis revealed an ecosystem threshold of MAT of 11 ± 2.3 °C. In ecosystems with MATs lower than the threshold, the maximum R e rates were primarily dependent on temperature and respiration was mainly a temperature-driven process. In ecosystems with MATs higher than the threshold, besides MAT, other factors, such as water availability and surface heat flux, became significant driving forces of the maximum Re rates and respiration was a multi-factor-driven process. Temperature played the key role in generation of the maximum R e rates in the terrestrial ecosystems, while other driving forces reduce the maximum R e rates and the temperature sensitivity of the respiratory process. According to a regression tree analysis, MAT was also the most influencing factor of mean R e rates among the climate forces. The information from this study should be useful to predict the respiratory process in terrestrial ecosystems with different temperatures under the climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Impacts of droughts and extreme-temperature events on gross primary production and ecosystem respiration: a systematic assessment across ecosystems and climate zones

Extreme climatic events, such as droughts and heat stress, induce anomalies in ecosystem–atmosphere CO 2 fluxes, such as gross primary production (GPP) and ecosystem respiration (R eco ), and, hence, can change the net ecosystem carbon balance. However, despite our increasing understanding of the underlying mechanisms, the magnitudes of the impacts of different types of extremes on GPP and R eco within and between ecosystems remain poorly predicted. Here we aim to identify the major factors controlling the amplitude of extreme-event impacts on GPP, R eco , and the resulting net ecosystem production (NEP). We focus on the impacts of heat and drought and their combination. We identified hydrometeorological extreme events in consistently downscaled water availability and temperature measurements over a 30-year time period. We then used FLUXNET eddy covariance flux measurements to estimate the CO 2 flux anomalies during these extreme events across dominant vegetation types and climate zones. Overall, our results indicate that short-term heat extremes increased respiration more strongly than they downregulated GPP, resulting in a moderate reduction in the ecosystem's carbon sink potential. In the absence of heat stress, droughts tended to have smaller and similarly dampening effects on both GPP and R eco and, hence, often resulted in neutral NEP responses. The combination of drought and heat typically led to a strong decrease in GPP, whereas heat and drought impacts on respiration partially offset each other. Taken together, compound heat and drought events led to the strongest C sink reduction compared to any single-factor extreme. A key insight of this paper, however, is that duration matters most: for heat stress during droughts, the magnitude of impacts systematically increased with duration, whereas under heat stress without drought, the response of R eco over time turned from an initial increase to a downregulation after about 2 weeks. This confirms earlier theories that not only the magnitude but also the duration of an extreme event determines its impact. Our study corroborates the results of several local site-level case studies but as a novelty generalizes these findings on the global scale. Specifically, we find that the different response functions of the two antipodal land–atmosphere fluxes GPP and R eco can also result in increasing NEP during certain extreme conditions. Apparently counterintuitive findings of this kind bear great potential for scrutinizing the mechanisms implemented in state-of-the-art terrestrial biosphere models and provide a benchmark for future model development and testing.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Upscaling instantaneous to daily evapotranspiration using modelled daily shortwave radiation for remote sensing applications: An artificial neural network approach

Upscaling instantaneous evapotranspiration retrieved at any specific time-of-day (ET i ) to daily evapotranspiration (ET d ) is a key challenge in mapping regional ET using polar orbiting sensors. Various studies have unanimously cited the shortwave incoming radiation ( R S ) to be the most robust reference variable explaining the ratio between ET d and ET i . This study aims to contribute in ET i upscaling for global studies using the ratio between daily and instantaneous incoming shortwave radiation ( R Sd / R Si ) as a factor for converting ET i to ET d . This paper proposes an artificial neural network (ANN) machine-learning algorithm first to predict R Sd from R Si followed by using the R Sd / R Si ratio to convert ET i to ET d across different terrestrial ecosystems. Using R Si and R Sd observations from multiple sub-networks of the FLUXNET database spread across different climates and biomes (to represent inputs that would typically be obtainable from remote sensors during the overpass time) in conjunction with some astronomical variables (e.g. solar zenith angle, day length, exoatmospheric shortwave radiation), we developed the ANN model for reproducing R Sd and further used it to upscale ET i to ET d . The efficiency of the ANN is evaluated for different morning and afternoon times of day, under varying sky conditions, and also at different geographic locations. R S -based upscaled ET d produced a significant linear relation ( R 2 = 0.65 to 0.69), low bias (-0.31 to -0.56 MJ m -2 d -1 ; approx. 4 %), and good agreement (RMSE 1.55 to 1.86 MJ m -2 d -1 ; approx. 10 %) with the observed ET d , although a systematic overestimation of ET d was also noted under persistent cloudy sky conditions. Inclusion of soil moisture and rainfall information in ANN training reduced the systematic overestimation tendency in predominantly overcast days. An intercomparison with existing upscaling method at daily, 8-day, monthly, and yearly temporal resolution revealed a robust performance of the ANN-driven R S -based ET i upscaling method and was found to produce lowest RMSE under cloudy conditions. Sensitivity analysis revealed variable sensitivity of the method to biome selection and high ET d prediction errors in forest ecosystems are primarily associated with greater rainfall and cloudiness. As a result, the overall methodology appears to be promising and has substantial potential for upscaling ET i to ET d for field and regional-scale evapotranspiration mapping studies using polar orbiting satellites.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Assessing the ability of $\mathrm{MODIS}$ $\mathrm{EVI}$ to estimate terrestrial ecosystem gross primary production of multiple land cover types

Terrestrial ecosystem gross primary production (GPP) is the largest component in the global carbon cycle. The enhanced vegetation index (EVI) has been proven to be strongly correlated with annual GPP within several biomes. However, the annual GPP-EVI relationship and associated environmental regulations have not yet been comprehensively investigated across biomes at the global scale. In this report we explored relationships between annual integrated EVI (iEVI) and annual GPP observed at 155 flux sites, where GPP was predicted with a log-log model: 1n(GPP) = a x 1n(iEVI) + b. iEVI was computed from MODIS monthly EVI products following removal of values affected by snow or cold temperature and without calculating growing season duration. Through categorisation of flux sites into 12 land cover types, the ability of iEVI to estimate GPP was considerably improved (R 2 from 0.62 to 0.74, RMSE from 454.7 to 368.2 g C m -2 yr -1 ). The biome-specific GPP-iEVI formulae generally showed a consistent performance in comparison to a global benchmarking dataset (R 2 = 0.79, RMSE = 387.8 g C m -2 yr -1 ). Specifically, iEVI performed better in cropland regions with high productivity but poorer in forests. The ability of iEVI in estimating GPP was better in deciduous biomes (except deciduous broadleaf forest) than in evergreen due to the large seasonal signal in iEVI in deciduous biomes. Likewise, GPP estimated from iEVI was in a closer agreement to global benchmarks at mid and high-latitudes, where deciduous biomes are more common and cloud cover has a smaller effect on remote sensing retrievals. Across biomes, a significant and negative correlation (R 2 = 0.37, p < 0.05) was observed between the strength (R 2 ) of GPP-iEVI relationships and mean annual maximum leaf area index (LAI max ), and the relationship between the strength and mean annual precipitation followed a similar trend. LAI max also revealed a scaling effect on GPP-iEVI relationships. Our results suggest that iEVI provides a very simple but robust approach to estimate spatial patterns of global annual GPP whereas its effect is comparable to various light-use-efficiency and data-driven models. The impact of vegetation structure on accuracy and sensitivity of EVI in estimating spatial GPP provides valuable clues to improve EVI-based models.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2016

How Universal is the Relationship Between Remotely Sensed Vegetation Indices and Crop Leaf Area Index? A Global Assessment

Leaf Area Index (LAI) is a key variable that bridges remote sensing observations to the quantification of agroecosystem processes. In this study, we assessed the universality of the relationships between crop LAI and remotely sensed Vegetation Indices (VIs). We first compiled a global dataset of 1459 in situ quality-controlled crop LAI measurements and collected Landsat satellite images to derive five different VIs including Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI), two versions of the Enhanced Vegetation Index (EVI and EVI2), and Green Chlorophyll Index (CI(sub Green)). Based on this dataset, we developed global LAI-VI relationships for each crop type and VI using symbolic regression and Theil-Sen (TS) robust estimator. Results suggest that the global LAI-VI relationships are statistically significant, crop-specific, and mostly non-linear. These relationships explain more than half of the total variance in ground LAI observations (R2 greater than 0.5), and provide LAI estimates with RMSE below 1.2 m2/m2. Among the five VIs, EVI/EVI2 are the most effective, and the crop-specific LAI-EVI and LAI-EVI2 relationships constructed by TS, are robust when tested by three independent validation datasets of varied spatial scales. While the heterogeneity of agricultural landscapes leads to a diverse set of local LAI-VI relationships, the relationships provided here represent global universality on an average basis, allowing the generation of large-scale spatial-explicit LAI maps. This study contributes to the operationalization of large-area crop modeling and, by extension, has relevance to both fundamental and applied agroecosystem research.

Vegetation Index↗