There are numerous applications that require crop classification as early as possible in the growing season. However, information about land cover from official land cover maps of the United States (cropland data layer [CDL] maps by the National Agricultural Statistics Service) are generally not available until after harvest. In the Upper Midwest, the primary rotating crops are corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] (covering ∼63% of Iowa) with an irregular annual rotation. This study investigated the feasibility of early‐season classification of corn and soybean fields in Iowa by comparing the current and previous years’ 30‐m 16‐d Landsat 8 images during the growing season to produce normalized difference vegetation index (NDVI) maps, along with the last‐updated CDL land cover, to construct “agricultural units.” We assigned a geometric weight to each unit by performing Bayesian discriminant analysis using the concept of a sliding threshold to categorize pixels. An examination of 8 yr of 250‐m 16‐d NDVI measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS) over Iowa showed that late June is the most promising time for categorization. The geometrical model was tested on a 24‐ by 28‐km2 region in southwestern Iowa on 1 July 2014 (Day of the Year 182). There was an 86% agreement with the CDL data set (88 and 83% for corn and soybean, respectively, in the confusion matrix). This demonstrates that in spite of the complexity of crop behavior, a geometrical approach integrating probabilistic methods, previous statistical records, and map disaggregation into agricultural units can be a promising method for early‐season crop classification. Classification of corn and soybeans in Iowa is important early in the season. Bayesian discriminant analysis and field geometry were combined with a sliding threshold. This geometrical approach is a promising method for early‐season crop classification.

High-resolution measurements of streambank retreat (SBR) rates are important for engineering applications, such as infrastructure planning and stream restoration. Conventional methods of measuring SBR, such as erosion pins or total stations, lack sufficient point density to accurately capture the spatial variability of bank retreat over an entire streambank surface. This paper presents a newly developed terrestrial laser scanner (TLS) that is affordable, robust, and simple. The TLS was employed to profile a approximate to 9-m wide portion of a meander bend on Clear Creek in Coralville, Iowa. Bank profiles were acquired in July 2012 and July 2013; the resulting subtraction yielded a mean SBR rate of 2.13m/year, with a maximum of 3.7m at the upper portion of the bank.

We introduce a new eddy-covariance method that uses a spectral decomposition algorithm called empirical mode decomposition. The technique is able to calculate contributions to near-surface fluxes from different periodic components. Unlike traditional Fourier methods, this method allows for non-orthogonal contributions to the total flux, which are shown to be errors due to the undersampling of low-frequency processes. Inspection of the non-orthogonal terms with relation to sampling duration and periodicity reveals that a measured periodic process requires approximately six cycles in order to be sufficiently sampled. This determines the maximum eddy size sufficiently measured given a particular sampling duration.

Empirical mode decomposition (EMD) is a spectral decomposition algorithm, which acts as a dyadic filter in the time-domain when extracting periodic components from turbulent atmospheric data. A new development in the algorithm allows it to work with discontinuous data. This investigation uses the discontinuous form of EMD (DEMD) to develop a new Ogive function, or cumulative flux calculation, which may be used with atmospheric data containing data gaps. The method is simple and effective, and will extend the utility of Ogives. The code is written in Matlab and available for use. Published by Elsevier B.V.

Empirical Mode Decomposition (EMD) is a tool that can decompose and analyze the cyclic components from oscillatory data in the time-domain. When combined with the traditional Hilbert spectral analysis, it is similar to spectral tools such as Fourier analysis, wavelet analysis, and generalized time–frequency analysis. However, the EMD method is specifically designed to analyze nonstationary data from nonlinear processes. Fluctuations of total solar irradiance, global temperature, sunspot number, and CO2 concentration are decomposed into their periodic components using the EMD method. The cyclic components of the data are analyzed and compared in the time-domain. An 11-year oscillation in global mean temperature is found and compared with the Schwabe cycle from sunspot and total solar irradiance proxy data. Also, the relative radiative forcing from different periodic components of total solar irradiance and CO2 concentration are empirically estimated.

Measurements of the aerosol size distribution from 11 nm to 2.5 microns were made in Mexico City in March 2006, during the MILAGRO (Megacity Initiative: Local and Global Research Observations) field campaign. Observations at the urban supersite, referred to as T0, could often be characterized by morning conditions with high particle mass concentrations, low mixing heights, and highly correlated particle number and CO2 concentrations, indicative that particle number is controlled by primary emissions. Average size-resolved and total number- and volume-based emission factors for combustion sources impacting T0 have been determined using a comparison of peak sizes in particle number and CO2 concentration. Peaks are determined by subtracting the measured concentration from a calculated baseline concentration time series. The number emission and volume emission factors for particles from 11 nm to 494 nm are 1.56 × 1015 particles, and 9.48 × 1011 cubic microns per kg of carbon, respectively. The uncertainty of the number emission factor is approximately plus or minus 50 %. The mode of the number emission factor was between 25 and 32 nm, while the mode of the volume factor was between 0.25 and 0.32 microns. These emission factors are reported as log normal model parameters and are compared with multiple emission factors from the literature. In Mexico City in the afternoon, the CO2 concentration drops during ventilation of the polluted layer, and the coupling between CO2 and particle number breaks down, especially during new particle formation events when particle number is no longer controlled by primary emissions. Using measurements of particle number and CO2 taken aboard the NASA DC-8, the determined primary emission factor was applied to the Mexico City Metropolitan Area (MCMA) plume to quantify the degree of secondary particle formation in the plume; the primary emission factor accounts for less than 50 % of the total particle number and the surplus particle count is not correlated with photochemical age. Primary particle volume and number in the size range 0.1–2 μm are similarly too low to explain the observed volume distribution. Contrary to the case for number, the apparent secondary volume increases with photochemical age. The size distribution of the apparent increase, with a mode at ~250 nm, is reported.

On 7 March 2006, a mobile, ground-based, vertical pointing, elastic lidar system made a North-South transect through the Mexico City basin. Column averaged, aerosol size distribution (ASD) measurements were made on the ground concurrently with the lidar measurements. The ASD ground measurements allowed calculation of the column averaged mass extinction efficiency (MEE) for the lidar system (1064 nm). The value of column averaged MEE was combined with spatially resolved lidar extinction coefficients to produce total aerosol mass concentration estimates with the resolution of the lidar (1.5 m vertical spatial and 1 s temporal). Airborne ASD measurements from DOE G-1 aircraft made later in the day on 7 March 2006, allowed the evaluation of the assumptions of constant ASD with height and time used for estimating the column averaged MEE.The results showed that the aerosol loading within the basin is about twice what is observed outside of the basin. The total aerosol base concentrations observed in the basin are of the order of 200 μg/m3 and the base levels outside are of the order of 100 μg/m3. The local heavy traffic events can introduce aerosol levels near the ground as high as 900 μg/m3.The article presents the methodology for estimating aerosol mass concentration from mobile, ground-based lidar measurements in combination with aerosol size distribution measurements. An uncertainty analysis of the methodology is also presented.

In this study, we report results from scaling analysis of 2.5 m spatial and 1 s temporal resolution lidar-rainfall data. The high resolution spatial and temporal data from the same observing system allows us to investigate the variability of rainfall at very small scales ranging from few meters to ~1 km in space and few seconds to ~30 min in time. The results suggest multiscaling behaviour in the lidar-rainfall with the scaling regime extending down to the resolution of the data. The results also indicate the existence of a space-time transformation of the form t~Lz at very small scales, where t is the time lag, L is the spatial averaging scale and z is the dynamic scaling exponent.

The emission and dispersion of particulates and gases from concentrated animal feeding operations (CAFO) at local to regional scales is a current issue in science and society. The transport of particulates, odors and toxic chemical species from the source into the local and eventually regional atmosphere is largely determined by turbulence. Any models that attempt to simulate the dispersion of particles must either specify or assume various statistical properties of the turbulence field. Statistical properties of turbulence are well documented for idealized boundary layers above uniform surfaces. However, an animal production facility is a complex surface with structures that act as bluff bodies that distort the turbulence intensity near the buildings. As a result, the initial release and subsequent dispersion of effluents in the region near a facility will be affected by the complex nature of the surface. Previous Lidar studies of plume dispersion over the facility used in this study indicated that plumes move in complex yet organized patterns that would not be explained by the properties of turbulence generally assumed in models. The objective of this study was to characterize the near-surface turbulence statistics in the flow field around an array of animal confinement buildings. Eddy covariance towers were erected in the upwind, within the building array and downwind regions of the flow field. Substantial changes in turbulence intensity statistics and turbulence-kinetic energy (TKE) were observed as the mean wind flow encountered the building structures. Spectra analysis demonstrated unique distribution of the spectral energy in the vertical profile above the buildings.

One of the fundamental issues with lidar‐derived evapotranspiration estimates is its reliance on tower‐based measurements of Monin–Obukhov similarity variables, specifically the Obukhov length ( L ) and the friction velocity ( u ∗ ). Our study indicates that L can be derived in the atmospheric surface layer directly from lidar range‐height scans by estimating the integral length scale (ILS). Data from both three‐dimensional sonic anemometers mounted on towers and lidar data collected during two subsequent field experiments were analyzed using autocorrelation analysis to estimate the ILS. The ILS values were then transformed into L values using a power‐law similarity model and were compared to coincident tower‐based observations. The comparisons between tower‐based eddy covariance sensors and lidar data show that the lidar‐derived L values are within the expected uncertainty and variability of standard point sensor measured observations. An additional model for estimating the friction velocity from the Obukhov length was also derived, and both L and u ∗ were used to calculate the latent energy flux from lidar without external measurements. The evaporative fluxes from the standard method and the new advanced method were compared with eddy covariance fluxes, and it was found that the advanced method is superior.

Remote sensors are useful tools for making measurements that are not possible with conventional point instruments. We developed methods to obtain spatially resolved latent energy fluxes from Raman water vapor data and the regional virtual potential heat flux from elastic lidar data. The evaporation method is based on Monin–Obukhov similarity theory applied to spatially and temporally averaged data. Latent heat flux estimates were found to be well correlated ( R 2 = 0.84, slope = 0.98) compared with eddy correlation measurements. The standard error of the flux estimates was 36.5 W m −2 (a 14% root mean square [RMS] difference), which is close to the predicted uncertainty of 15%. A vertically staring elastic lidar was used to obtain a continuous record of the boundary layer height and thickness of the entrainment zone between a soybean [ Glycine max (L.) Merr.] and a corn ( Zea mays L.) field. The surface heat flux was calculated using the Batchvarova–Gryning boundary layer model. The virtual potential heat flux estimates were found to be well correlated ( R 2 = 0.79, slope = 0.95) compared with eddy correlation measurements. The standard error of the flux estimates was 21.4 Wm −2 (31% RMS difference between estimates and surface measurements), higher than the predicted uncertainty of 16%. Other parameters such as the Monin–Obukhov length, the stability correction functions, and integral scale can be obtained from lidar data.

The Soil Moisture‐Atmosphere Coupling Experiment (SMACEX) was conducted in the Walnut Creek Watershed near Ames, Iowa, over the period from 15 June to 11 July 2002. A main focus of SMACEX is the investigation of the interactions between the atmospheric boundary layer, surface moisture, and canopy. A vertically staring elastic lidar was used to provide a high time resolution, continuous record of the mixed layer height at the edge between a soybean and a corn field. The height and thickness of the entrainment zone are used to estimate the vertical potential temperature profile in the boundary layer using surface energy measurements in the Batchvarova‐Gryning mixed layer model. Calculated values of potential temperature compared well to radiosonde measurements taken simultaneously with the lidar measurements. The root‐mean‐square difference between the lidar‐derived values and the balloon‐based values is 1.20°C.

Observations of multi-dimensional water vapor structures in the first 75m of the stable boundary layer (SBL) were made using a high resolution scanning Raman lidar in October 2000 during the Vertical Transport and Mixing Experiment (VTMX). Lidar images reveal the intermittent presence of plumes and low-frequency structures contributing to much of the nocturnal surface–atmosphere mass exchange. Furthermore, periodic wave–turbulence interactions between solitary waves aloft and coherent structures at the surface were observed. Results show that when low-level waveguides are present in the SBL, downward transport by pressure fluctuations arising from Kelvin–Helmholz instabilities hundreds of meters above the surface appears to pump energy near the surface thereby supporting the development of coherent structures. These structures have a vertical extent determined by the depth of the low-level waveguide inversion. The present results suggest that, in certain nocturnal conditions, coherent structures can transport more than a third of the mass exchanged between the surface and the lowest region of the stable atmospheric boundary layer.

ELASTIC/EVIEW is a software system that controls an elastic scattering atmospheric Light Detection and Ranging (LIDAR) instrument. It can acquire elastic scattering LIDAR data using this system and produce images of one, two, and three-dimensional atmospheric data on particulates and other atmospheric pollutants. The user interface is a modern menu driven syatem with appropriate support for user configuration and printing files.