Effects of soil composition and mineralogy on remote sensing of crop residue cover

The management of crop residues (non-photosynthetic vegetation) in agricultural fields influences soil erosion and soil carbon sequestration. Remote sensing methods can efficiently assess crop residue cover and related tillage intensity over many fields in a region. Although the reflectance spectra of soils and crop residues are often similar in the visible, near infrared, and the lower part of the shortwave infrared (400-1900 nm) wavelength region, specific diagnostic chemical absorption features are evident in the upper shortwave infrared (1900-2500 nm) region. Two reflectance band height indices used for estimating residue cover are the Cellulose Absorption Index (CAI) and the Lignin-Cellulose Absorption (LCA) index, both of which use reflectances in the upper shortwave infrared (SWIR). Soil mineralogy and composition will affect soil spectral properties and may limit the usefulness of these spectral indices in certain areas. Our objectives were to (1) identify minerals and soil components with absorption features in the 2000 nm to 2400 nm wavelength region that would affect CAI and LCA and (2) assess their potential impact on remote sensing estimates of crop residue cover. Most common soil minerals had CAI values ≤ 0.5, whereas crop residues were always > 0.5, allowing for good contrast between soils and residues. However, a number of common soil minerals had LCA values > 0.5, and, in some cases, the mineral LCA values were greater than those of the crop residues, which could limit the effectiveness of LCA for residue cover estimation. The LCA of some dry residues and live corn canopies were similar in value, unlike CAI. Thus, the Normalized Difference Vegetation Index (NDVI) or similar method should be used to separate out green vegetation pixels. Mineral groups, such as garnets and chlorites, often have wide ranges of CAI and LCA values, and thus, mineralogical analyses often do not identify individual mineral species required for precise CAI estimation. However, these methods are still useful for identifying mineral soils requiring additional scrutiny. Future advanced multi- and hyperspectral remote sensing platforms should include CAI bands to allow for crop residue cover estimation.     

Mentoring students and professionals with disabilities.

Mentoring can have a significant positive impact on the lives of individuals. People with disabilities seeking to locate mentors face a variety of potential challenges and benefits. This article addresses mentoring-related issues faced by professionals with disabilities. The article presents a model of mentoring and discusses the difficulties faced by individuals with disabilities in locating mentors. It describes the negative impact of the lack of available mentors upon the educational and vocational development of people with disabilities. It offers effective strategies aimed at enhancing mentoring relationships with people with disabilities, including macrofocused strategies capable of enhancing the effectiveness of disability-related mentoring programs and microfocused strategies useful in developing quality mentoring relationships. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Quantifying arsenic-induced morphological changes in spinach leaves: implications for remote sensing

Arsenic (As) is a common soil contaminant that can be accumulated into plant parts. The ability to detect As in contaminated plants is an important tool to minimize As-induced health risks in humans. Near-infrared (NIR) spectra are strongly affected by leaf structural characteristics. Therefore, quantitative analyses of structural changes in the arrangement of mesophyll cells caused by As will help to explain spectral responses to As. The objectives of this study were to use stereological methods to quantify internal structural changes in leaves with As treatment in spinach plants, and to relate these changes to leaf spectral properties in NIR spectra. Hydroponically grown spinach was treated with 0, 5, 10 and 20 μmol l^?1 for four weeks in a growth chamber. Spectral properties of leaves were obtained for visible and infrared frequencies. Leaf structural properties, such as mesophyll thickness and mesophyll surface area, were measured using stereological methods. Quantitative analysis of leaf structure showed that total leaf thickness and intercellular spaces in spongy mesophyll cells decreased with increasing As treatment. Changes in leaf reflectance in NIR wavelengths were strongly correlated with leaf As concentration and leaf structural changes. Multiple linear regression of leaf reflectance values at the highest correlated wavelengths (1048, 1098 and 1080 nm) generated an R ² value of 0.69. Results from this study support the hypothesis that relationships between leaf structure and reflectance may be useful in the interpretation of spectral data to detect plant leaf As concentration.     

Experience of depression in college students: A concept map.

Discusses previous depression-related research, which has emphasized conceptual and measurement issues; consequent lack of understanding of the experience of depression is suggested. Concept mapping, an alternative methodological approach combining qualitative and quantitative strategies, was used to clarify the scope and interrelations among elements of the experience of depression in 78 college students. Whereas participants' experience of depression included affective and somatic symptoms consistent with generally accepted diagnostic criteria, the experience of depression was not limited to these domains. Findings are discussed as they relate to depression research and counseling practice. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Tailoring Encodable Lanthanide‐Binding Tags as MRI Contrast Agents

Lanthanide‐binding tags (LBTs), peptide‐based coexpression tags with high affinity for lanthanide ions, have previously been applied as luminescent probes to provide phasing for structure determination in X‐ray crystallography and to provide restraints for structural refinement and distance information in NMR. The native affinity of LBTs for Gd³⁺ indicates their potential as the basis for engineering of peptide‐based MRI agents. However, the lanthanide coordination state that enhances luminescence and affords tightest binding would not be ideal for applications of LBTs as contrast agents, due to the exclusion of water from the inner coordination sphere. Herein, we use structurally defined LBTs as the starting point for re‐engineering the first coordination shell of the lanthanide ion to provide for high contrast through direct coordination of water to Gd³⁺ (resulting in the single LBT peptide, m‐sLBT). The effectiveness of LBTs as MRI contrast agents was examined in vitro through measurement of binding affinity and proton relaxivity. For imaging applications that require targeted observation, fusion to specific protein partners is desirable. However, a fusion protein comprising a concatenated double LBT (dLBT) as an N‐terminal tag for the model protein ubiquitin had reduced relaxivity compared with the free dLBT peptide. This limitation was overcome by the use of a construct based on the m‐sLBT sequence (q‐dLBT–ubiquitin). The structural basis for the enhanced contrast was examined by comparison of the X‐ray crystal structure of xq‐dLBT–ubiquitin (wherein two tryptophan residues are replaced with serine), to that of dLBT‐ubiquitin. The structure shows that the backbone conformational dynamics of the MRI variant may allow enhanced water exchange. This engineered LBT represents a first step in expanding the current base of specificity‐targeted agents available.     

Feasibility of estimating leaf water content using spectral indices from WorldView-3’s near-infrared and shortwave infrared bands

A recently-launched high-resolution commercial satellite, DigitalGlobe’s WorldView-3, has 8 bands in the shortwave infrared (SWIR) wavelength region, which may be capable of estimating canopy water content at 3.7-m spatial resolution. WorldView-3 also has 8 multispectral bands at 1.24-m resolution with two bands in the near-infrared (NIR). The relative spectral response functions for WorldView-3 were provided by DigitalGlobe, Inc., and band reflectances were determined for reflectance spectra of PROSPECT model simulations and leaf data from maize, trees, grasses, and broadleaf herbaceous eudicots. For laboratory measurements, the range of leaf water contents was extended by including drying leaves and leaf stacks of corn, soybean, oaks, and maples. Correlations between leaf water content and spectral indices from model simulations suggested that indices using SWIR band 1 (center wavelength 1210 nm) had low variability with respect to leaf water content, but also low sensitivity. Other indices using SWIR band 5 (2165 nm) had the highest sensitivity, but also had high variability caused by different values of the leaf structure parameter in PROSPECT. Indices using SWIR bands 2, 3 and 4 (1570, 1660, and 1730 nm, respectively) had high correlations and intermediate variability from the leaf structure parameter. Spectral indices calculated from the leaf data had the same overall patterns as the simulations for variation and sensitivity; however, indices using SWIR band 1 had low correlations, and the best correlations were from indices that used SWIR bands 2, 3 and 4. Spectral indices for maize, grasses, and herbaceous crops and weeds had similar responses to leaf water content; tree leaves had higher index values and saturated at lower leaf water contents. The specified width of NIR band 2 (860–1040 nm) overlaps the water absorption feature at 970 nm wavelength; however, the normalized difference of NIR band 1 and 2 was insensitive to water content because NIR band 2’s spectral response was most heavily weighted to wavelengths less than 930 nm. The high spatial resolution of the WorldView-3 SWIR data will help analyze how variation among plant species and functional groups affects spectral responses to differences in canopy water content.     

Utility of an image-based canopy reflectance modeling tool for remote estimation of LAI and leaf chlorophyll content at the field scale

This paper presents a physically-based approach for estimating critical variables describing land surface vegetation canopies, relying on remotely sensed data that can be acquired from operational satellite sensors. The REGularized canopy reFLECtance (REGFLEC) modeling tool couples leaf optics (PROSPECT), canopy reflectance (ACRM), and atmospheric radiative transfer (6SV1) model components, facilitating the direct use of at-sensor radiances in green, red and near-infrared wavelengths for the inverse retrieval of leaf chlorophyll content (C_ab) and total one-sided leaf area per unit ground area (LAI). The inversion of the canopy reflectance model is constrained by assuming limited variability of leaf structure, vegetation clumping, and leaf inclination angle within a given crop field and by exploiting the added radiometric information content of pixels belonging to the same field. A look-up-table with a suite of pre-computed spectral reflectance relationships, each a function of canopy characteristics, soil background effects and external conditions, is accessed for fast pixel-wise biophysical parameter retrievals. Using 1 m resolution aircraft and 10 m resolution SPOT-5 imagery, REGFLEC effectuated robust biophysical parameter retrievals for a corn field characterized by a wide range in leaf chlorophyll levels and intermixed green and senescent leaf material. Validation against in-situ observations yielded relative root-mean-square deviations (RMSD) on the order of 10% for the 1 m resolution LAI (RMSD = 0.25) and C_ab (RMSD = 4.4 μg cm^− 2) estimates, due in part to an efficient correction for background influences. LAI and C_ab retrieval accuracies at the SPOT 10 m resolution were characterized by relative RMSDs of 13% (0.3) and 17% (7.1 μg cm^− 2), respectively, and the overall intra-field pattern in LAI and C_ab was well established at this resolution. The developed method has utility in agricultural fields characterized by widely varying distributions of model variables and holds promise as a valuable operational tool for precision crop management. Work is currently in progress to extend REGFLEC to regional scales.     

Sun-view angle effects on reflectance factors of corn canopies

The bidirectional reflectance characteristics of vegetation canopies vary with time of day and through the growing season. In this study the effects of sun and view angles on bidirectional reflectance factors from corn (Zea mays L.) canopies ranging in development from the six leaf stage to harvest maturity were examined. For nadir-acquired reflectance factors there was a strong solar angle dependence in all spectral bands for canopies with low leaf area index (LAI). A decrease in contrast between bare soil and vegetation due to shadows at larger solar zenith angles appeared to be the cause of this dependence. Sun angle dependence was least for well-developed canopies with higher LAI. However, for higher LAI canopies a moderate increase in reflectance factor was observed at larger solar zenith angles and was attributed to the presence of specular reflectance. Trends of off-nadir reflectance factors with respect to sun angle at different view azimuth angles indicated that the position of the sensor relative to the sun was an important factor for determining the angular reflectance characteristics of corn canopies. Reflectance factors were maximized for coincident sun and view angles and minimized when the sensor view direction was towards the sun. View direction relative to row orientation also contributed to the variation in reflectance factors.     

Narrowband to broadband conversions of land surface albedo: II. Validation

In the first paper of this series, we developed narrowband to broadband albedo conversion formulae for a series of sensors. These formulae were determined based on extensive radiative transfer simulations under different surface and atmospheric conditions. However, it is important to validate the simulation results using independent measurement data. In this paper, the validation results for three broadband albedos (total-shortwave, -visible and -near-IR albedos) using ground measurement of several cover types on five different days at Beltsville, MD are presented. Results show that the conversion formulae in the previous paper are very accurate and the average residual standard errors of the resulting broadband albedos for most sensors are around 0.02, which meets the required accuracy for land surface modeling.