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.

Health risks connected with fine particulate matter (PM_2.5) pollutants are well documented; increased risks of asthma, heart attack and heart failure are a few of the effects associated with PM_2.5. Accurately forecasting PM_2.5 is crucial for state agencies directed to devise State Implementation Plans (SIPS) to deal with National Ambient Air Quality Standards (NAAQS) exceedances. In previous work, we explored the application of multi-temporal data-driven neural networks (NNs) to forecasting PM_2.5. Our work showed that under different input conditions, the NN approach achieves higher forecasting scores for local (12 km) resolution when compared to the other Chemical Transport Model forecast models, such as the Community Multi-Scale Air Quality system (CMAQ). Critical to our approach was the inclusion of prior PM_2.5 concentrations, retrieved from ground monitoring stations, as part of the input dataset for the NN. The NN approach can provide high-level forecasting accuracy; however, because of the dependency on ground monitoring stations, the forecast coverage is sparse. Here, we extend our previous station-specific efforts by forecasting hourly PM_2.5 values that are spatially continuous through the use of a deep neural network (DNN). The DNN approach combines spatial Kriging with additional local source variables to interpolate the measured PM_2.5 concentrations across non-station locations. These interpolated PM_2.5 values are used as inputs in the original forecasting NN. Cross-validation testing, using all New York State AirNow PM_2.5 stations, showed that this forecast approach achieves accurate results, with a regression coefficient (R²) of 0.59, and a root mean square error (RMSE) of 2.22$\frac{\mu g}{m^3}$. Additionally, herein we demonstrate the usefulness of this approach on specific temporal events where significant dynamics of PM_2.5 were observed; particularly, we show that even bias-corrected CMAQ forecasts do not track these transients and our NN method.

This study presents a multi-instrument remote sensing approach to characterize the optical properties and vertical distribution of aerosols within the planetary boundary layer (PBL) over New York City during the COVID-19 pandemic and post-pandemic periods. The analysis focuses on changes in aerosol loading associated with variations in anthropogenic activity and examines the relationship between column-integrated aerosol optical properties, vertically resolved aerosol distributions, and near-surface fine particulate matter (PM2.5). Continuous ceilometer observations are used to characterize profiles of attenuated backscatter and to investigate aerosol vertical structure and variability within the PBL. These measurements are complemented by sun-photometer observations of column-integrated aerosol optical properties, radiosonde profiles of atmospheric thermodynamic structure, and ground-based PM2.5 measurements. The synergistic use of these datasets provides a more comprehensive characterization of aerosol optical and physical properties under varying meteorological and boundary-layer conditions than can be obtained from individual instruments alone. In addition, air-mass back trajectories calculated using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model are employed to investigate the potential influence of local, regional, and long-range aerosol transport on observed aerosol variability over New York City. Comparisons between the pandemic and post-pandemic periods reveal substantial differences in aerosol loading and pollution levels, highlighting the combined influence of changes in anthropogenic emissions, PBL dynamics, meteorological conditions, and transported aerosol on the urban aerosol environment. The results demonstrate the value of integrating vertically resolved active remote sensing with columnar and surface observations and atmospheric transport modeling for assessing temporal changes in urban aerosol distributions and their controlling processes.