RZWQM2 simulated management practices to mitigate climate change impacts on nitrogen losses and corn production

Gaseous and aqueous nitrogen (N) losses from corn (maize) production systems are concerns under projected climate change. In the present study, the Root Zone Water Quality Model (RZWQM2) was used to test the ability of agricultural management practices (N application rate, corn cultivar, planting date, tillage and controlled drainage) to mitigate future climate change effects on N losses and corn yield in a subsurface drained field in Iowa, USA. Under a future downscaled climate scenario, the simulated non-constant N₂O emission factor (EF), yield-scaled global warming potential and N loss through drainage increased with increasing fertilization above an optimal N rate of 120 kg N ha⁻¹. This rate represents the optimal tradeoff point between environmental issues and economic returns. While yields of the cultivar IB1068 DEKALB declined with climate change, yields of cultivar, IB 0090 GL 482 in the future climate were greater than historical yields of IB 1068 DEKALB.     

冬小麦冠层光谱红边参数的变化及其与氮素含量的相关分析

通过对不同氮肥条件下的小麦植株由上而下进行器官疏剪,分析了不同处理下冠层光谱反射率及其红边参数的变化。结果表明,冠层光谱反射率因不同肥力、不同疏剪处理而有较大的差异,表现出不同程度的红边的“红移”和“蓝移”现象。各处理的红边曲线形状均出现双峰现象,表现为第二个峰值高于第一个峰值,并且均为N1>N2>N0。相关分析表明,随着由上而下的疏剪处理,不同叶位叶片光谱反射率对冠层光谱的贡献增加,并且其红边参数与相应的叶片全氮含量的相关系数也增加,部分达到显著或极显著相关水平。该结果为利用下部缺素敏感叶片的光谱特征进行小麦养分的及时补充提供了可靠的理论依据。     

Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture

A growing number of studies have focused on evaluating spectral indices in terms of their sensitivity to vegetation biophysical parameters, as well as to external factors affecting canopy reflectance. In this context, leaf and canopy radiative transfer models are valuable for modeling and understanding the behavior of such indices. In the present work, PROSPECT and SAILH models have been used to simulate a wide range of crop canopy reflectances in an attempt to study the sensitivity of a set of vegetation indices to green leaf area index (LAI), and to modify some of them in order to enhance their responsivity to LAI variations. The aim of the paper was to present a method for minimizing the effect of leaf chlorophyll content on the prediction of green LAI, and to develop new algorithms that adequately predict the LAI of crop canopies. Analyses based on both simulated and real hyperspectral data were carried out to compare performances of existing vegetation indices (Normalized Difference Vegetation Index [NDVI], Renormalized Difference Vegetation Index [RDVI], Modified Simple Ratio [MSR], Soil-Adjusted Vegetation Index [SAVI], Soil and Atmospherically Resistant Vegetation Index [SARVI], MSAVI, Triangular Vegetation Index [TVI], and Modified Chlorophyll Absorption Ratio Index [MCARI]) and to design new ones (MTVI1, MCARI1, MTVI2, and MCARI2) that are both less sensitive to chlorophyll content variations and linearly related to green LAI. Thorough analyses showed that the above existing vegetation indices were either sensitive to chlorophyll concentration changes or affected by saturation at high LAI levels. Conversely, two of the spectral indices developed as a part of this study, a modified triangular vegetation index (MTVI2) and a modified chlorophyll absorption ratio index (MCARI2), proved to be the best predictors of green LAI. Related predictive algorithms were tested on CASI (Compact Airborne Spectrographic Imager) hyperspectral images and, then, validated using ground truth measurements. The latter were collected simultaneously with image acquisition for different crop types (soybean, corn, and wheat), at different growth stages, and under various fertilization treatments. Prediction power analysis of proposed algorithms based on MCARI2 and MTVI2 resulted in agreements between modeled and ground measurement of non-destructive LAI, with coefficients of determination (r²) being 0.98 for soybean, 0.89 for corn, and 0.74 for wheat. The corresponding RMSE for LAI were estimated at 0.28, 0.46, and 0.85, respectively.     

Impacts of spectral band difference effects on radiometric cross-calibration between satellite sensors in the solar-reflective spectral domain

In order for quantitative applications to make full use of the ever-increasing number of Earth observation satellite systems, data from the various imaging sensors involved must be on a consistent radiometric scale. This paper reports on an investigation of radiometric calibration errors due to differences in spectral response functions between satellite sensors when attempting cross-calibration based on near-simultaneous imaging of common ground targets in analogous spectral bands, a commonly used post-launch calibration methodology. Twenty Earth observation imaging sensors (including coarser and higher spatial resolution sensors) were considered, using the Landsat solar reflective spectral domain as a framework. Scene content was simulated using spectra for four ground target types (Railroad Valley Playa, snow, sand and rangeland), together with various combinations of atmospheric states and illumination geometries. Results were obtained as a function of ground target type, satellite sensor comparison, spectral region, and scene content. Overall, if spectral band difference effects (SBDEs) are not taken into account, the Railroad Valley Playa site is a “good” ground target for cross calibration between most but not all satellite sensors in most but not all spectral regions investigated. “Good” is defined as SBDEs within ± 3%. The other three ground target types considered (snow, sand and rangeland) proved to be more sensitive to uncorrected SBDEs than the RVPN site overall. The spectral characteristics of the scene content (solar irradiance, surface reflectance and atmosphere) are examined in detail to clarify why spectral difference effects arise and why they can be significant when comparing different imaging sensor systems. Atmospheric gas absorption features are identified as being the main source of spectral variability in most spectral regions. The paper concludes with recommendations on spectral data and tools that would facilitate cross-calibration between multiple satellite sensors.     

Ground and space spectral measurements for assessing the semi-arid ecosystem phenology related to CO2 fluxes of biological soil crusts

This paper reports on ranges of carbon dioxide (CO₂) activity in biological soil crusts (BSC) correlated with different ranges of the BSC's spectral reflectance throughout the phenological cycle of the year. Methodology is based on surface CO₂ exchange measurements, ground spectral measurements, and satellite images interpretation. Thirty-nine field campaigns, each of duration of 3 days, were conducted over the course of 2 years at a sand dunes and a loess environment of the northwestern Negev desert in Israel, in order to relate the CO₂ fluxes and the spectral signals to the seasonal phenology. The Normalized Difference Vegetation Index (NDVI) was derived from ground measurements of the BSC's reflectance and correlated with their CO₂ exchange data. A linear mixture model, incorporating the different contributions of the sites' ground features, was calculated and compared with SPOT-HRV data. From the ground measurements, fairly good correlations were found between the NDVI and the CO₂ fluxes on a seasonal scale. Hence, the NDVI successfully indicates the potential magnitude and capacity of the BSC's assimilation activity. The linear mixture model successfully describes the phenological cycles of the BSC, annual, and perennial plants and corresponds well to the satellite data. Moreover, the model enables annual changes of the phenology cycle and the growing season length to be distinguished. Both the linear mixture model and the derived NDVI values recorded the recovery of the BSC at the beginning of the wet season before annuals had germinated. Finally, it is concluded that a combination of CO₂ exchange measurements, linear mixture model, and NDVI values is suitable for monitoring BSC's productivity in arid regions.     

氮素形态对不同磷效率基因型小麦生长和氮素吸收及基因型差异的影响

对2种不同磷效率基因型小麦幼苗水培结果表明,NO3-N和NH4NO3-N对小麦植株地上部生长的影响无明显差异,但是对根系生长的影响明显不同。NH4-N对小麦幼苗的生长有明显的抑制作用,且对根系生长的抑制程度显著大于对地上部;对磷低效基因型Jing411的抑制程度明显大于对磷高效基因型Xiaoyan54。NH4NO3-N处理有利于提高植株地上部氮含量和植株的氮吸收效率。Xiaoyan54的植株吸氮量在NH4NO3-N处理中最高,Jing411在NO3-N处理中最高。不同处理对营养液pH值的影响明显不同。NH4NO3-N和NH4-N处理导致营养液pH值降低,NO3-N处理使营养液pH值升高,不同磷效率基因型小麦使营养液pH值降低或升高的程度不同。小麦磷效率基因型差异的表现与否和氮素形态有关,以植株地上部干重为磷效率指标的基因型差异在供应NO3-N时不表现。磷高效基因型Xiaoyan54的生长显著优于磷低效基因型Jing411。     

Fluorescence sensing systems: In vivo detection of biophysical variations in field corn due to nitrogen supply

Leaf and canopy fluorescence properties of field corn (Zea mays L.) grown under varying levels of nitrogen (N) fertilization were characterized to provide an improved N sensing capability which may assist growers in site-specific N management decisions. In vivo fluorescence emissions can occur in the wavelength region from 300 to 800 nm and are dependent on the wavelength of illumination. These light emissions have been grouped into five primary bands with maxima most frequently received from corn at 320 nm (UV), 450 nm (blue), 530 nm (green), 685 nm (red), and 740 nm (far-red). Two active fluorescence sensing systems have been custom developed; a leaf level Fluorescence Imaging System (FIS), and a canopy level Laser Induced Fluorescence Imaging System (LIFIS). FIS sequentially acquires high-resolution images of fluorescence emission bands under darkened laboratory conditions, while LIFIS simultaneously acquires four band images of plant canopies ≥1 m² under ambient sunlit conditions. Fluorescence emissions induced by these systems along with additional biophysical measures of crop condition; namely, chlorophyll content, N/C ratio, leaf area index (LAI), and grain yield, exhibited similar curvilinear responses to levels of supplied N. A number of significant linear correlations were found among band emissions and several band ratios versus measures of crop condition. Significant differences were obtained for several fluorescence band ratios with respect to the level of supplied N. Leaf adaxial versus abaxial surface emissions exhibited opposing trends with respect to the level of supplied N. Evidence supports that this confounding effect could be removed in part by the green/blue and green/red ratio images. The FIS and LIFIS active fluorescence sensor systems yielded results which support the underlying hypothesis that leaf and canopy fluorescence emissions are associated with other biophysical attributes of crop growth and this information could potentially assist in the site-specific management of variable-rate N fertilization programs.     

Optimizing spectral indices and chemometric analysis of leaf chemical properties using radiative transfer modeling

We used synthetic reflectance spectra generated by a radiative transfer model, PROSPECT-5, to develop statistical relationships between leaf optical and chemical properties, which were applied to experimental data without any readjustment. Four distinct synthetic datasets were tested: two unrealistic, uniform distributions and two normal distributions based on statistical properties drawn from a comprehensive experimental database. Two methods used in remote sensing to retrieve vegetation chemical composition, spectral indices and Partial Least Squares (PLS) regression, were trained both on the synthetic and experimental datasets, and validated against observations. Results are compared to a cross-validation process and model inversion applied to the same observations. They show that synthetic datasets based on normal distributions of actual leaf chemical and structural properties can be used to optimize remotely sensed spectral indices or other retrieval methods for analysis of leaf chemical constituents. This study concludes with the definition of several polynomial relationships to retrieve leaf chlorophyll content, carotenoid content, equivalent water thickness and leaf mass per area using spectral indices, derived from synthetic data and validated on a large variety of leaf types. The straightforward method described here brings the possibility to apply or adapt statistical relationships to any type of leaf.     

Causality analysis of neural connectivity: New tool and limitations of spectral Granger causality

Granger causality (GC) is one of the most popular measures to reveal causality influence of time series based on the estimated linear regression model and has been widely applied in economics and neuroscience due to its simplicity, understandability and easy implementation. Especially, its counterpart in frequency domain, spectral GC, has recently received growing attention to study causal interactions of neurophysiological data in different frequency ranges. In this paper, on the one hand, for one equality in the linear regression model (frequency domain) we point out that all items at the right-hand side of the equality make contributions (thus have causal influence) to the unique item at the left-hand side of the equality, and thus a reasonable definition for causality from one variable to another variable (i.e., the unique item) should be able to describe what percentage the variable occupies among all these contributions. Along this line, we propose a new spectral causality definition. On the other hand, we point out that spectral GC has its inherent limitations because of the use of the transfer function of the linear regression model and as a result may not reveal real causality at all and lead to misinterpretation result. By one example we demonstrate that the results of spectral GC analysis are misleading but the results from our definition are much reasonable. So, our new tool may have wide potential applications in neuroscience.     

北京麦蚜虫害的光谱测量与分析

通过测定小麦生育期内叶绿素含量变化及分析叶绿素含量与麦蚜量间的动态关系,提出小麦蚜虫灾害遥感监测的植物生理学依据。通过1995、1996和1998年的地面光谱测量,绘制出蚜量同小麦光谱的相关性曲线,证明利用小麦的反射光谱植被指数RVI值监测麦蚜的可行性,并给出了确定小麦蚜虫防治点即百株蚜量500头左右的RVI值方法。为确定麦蚜虫害最佳防治时间提供依据。     

Effects of spatial and spectral resolution in estimating ecosystem α-diversity by satellite imagery

Remote sensing represents a powerful tool to derive quantitative and qualitative information about ecosystem biodiversity. In particular, since plant species richness is a fundamental indicator of biodiversity at the community and regional scales, attempts were made to predict species richness (spatial heterogeneity) by means of spectral heterogeneity. The possibility of using spectral variance of satellite images for predicting species richness is known as Spectral Variation Hypothesis. However, when using remotely sensed data, researchers are limited to specific scales of investigation. This paper aims to investigate the effects of scale (both as spatial and spectral resolution) when searching for a relation between spectral and spatial (related to plant species richness) heterogeneity, by using satellite data with different spatial and spectral resolution. Species composition was sampled within square plots of 100 m² nested in macroplots of 10,000 m². Spectral heterogeneity of each macroplot was calculated using satellite images with different spatial and spectral resolution: a Quickbird multispectral image (4 bands, spatial resolution of 3 m), an Aster multispectral image (first 9 bands used, spatial resolution of 15 m for bands 1 to 3 and 30 m for bands 4 to 9), an ortho-Landsat ETM+ multispectral image (bands 1 to 5 and band 7 used; spatial resolution, 30 m), a resampled 60 m Landsat ETM+ image.Quickbird image heterogeneity showed a statistically highly significant correlation with species richness (r = 0.69) while coarse resolution images showed contrasting results (r = 0.43, r = 0.67, and r = 0.69 considering the Aster, Landsat ETM+, and the resampled 60 m Landsat ETM+ images respectively). It should be stressed that spectral variability is scene and sensor dependent. Considering coarser spatial resolution images, in such a case even using SWIR Aster bands (i.e. the additional spectral information with respect to Quickbird image) such an image showed a very low power in catching spectral and thus spatial variability with respect to Landsat ETM+ imagery. Obviously coarser resolution data tend to have mixed pixel problems and hence less sensitive to spatial complexity. Thus, one might argue that using a finer pixel dimension should inevitably result in a higher level of detail. On the other hand, the spectral response from different land-cover features (and thus different species) in images with higher spectral resolution should exhibit higher complexity.Spectral Variation Hypothesis could be a basis for improving sampling designs and strategies for species inventory fieldwork. However, researchers must be aware on scale effects when measuring spectral (and thus spatial) heterogeneity and relating it to field data, hence considering the concept of scale not only related to a spatial framework but even to a spectral one.     

Continuous wavelets for the improved use of spectral libraries and hyperspectral data

Spectral libraries are commonly established as a means to archive representative signatures of natural materials. Such signatures can then be used to train feature extraction and classification algorithms applied to imagery, for comparison with unlabeled spectra. A number of spectral libraries are publicly available and widely used in the community. Disparities in viewing and illumination measurement configurations between libraries generally preclude the direct comparison of spectra for the same materials. Within libraries, measurements may be reported for varying sample properties, such as grain size in the case of powdered minerals or leaf or canopy structure in the case of vegetation. In such instances, use of the library and the selection of representative spectra to identify an unknown material may require a priori knowledge or an educated guess of the physical properties of the unknown material to conduct the comparison.This study demonstrates that continuous wavelet analysis can provide a new and useful representation of spectral libraries and minimize these disparities amongst libraries. In the context of spectral mixture analysis we suggest that the selection of representative endmember spectra from spectral libraries can be more readily defined in the wavelet domain than using reflectance data. In the context of sensing target compositional variability, for example changes in the chemistry of a given mineral, spectral differences due to distinct sample composition are more readily identified using wavelets. The examples provided in this paper are mainly for powdered mineral spectra because there are a number of widely known public spectral libraries of powdered minerals that have been in common use in the hyperspectral community but the principles apply to a range of natural materials including vegetation.     

On the condition number of some spectral collocation operators and their finite element preconditioning

Spectral collocation approximations based on Legendre-Gauss-Lobatto nodes is considered. The collocation method is settled in a variational form, starting from the weak formulation of the differential problem. Numerical approximations to first and second order operators are introduced and the behavior of their discrete eigenvalues is studied. A finite element preconditioner for second order problems is proposed. Several numerical results concerning the condition number of the preconditioned spectral matrices and the application to conjugate gradient iterations are reported.     

Estimating the irradiance spectrum from measurements in a limited number of spectral bands

Accurate measurement and characterisation of fluctuations in the irradiance environment is important for many areas of optical remote sensing. This paper describes a method of estimating spectral irradiance over the region 400-1000 nm from the radiance of a calibrated reference panel, measured in four narrow spectral bands (FWHM approx. 10 nm). The reproducibility of the method was found to have an average root-mean-squared error of approximately 30 mW m^− 2 nm^− 1 over the region 400-1000 nm when applied to spectra covering a range of clear-sky conditions typical of mid-latitude temperate regions. This was approximately twice as precise as the sequential method, even when the interval between target and reference panel measurements was very short (median interval 23 s). The method provides an alternative to linear interpolation between successive reference panel measurements and is particularly appropriate for conditions when irradiance is varying in a non-systematic way, for example, during the passage of sub-visual clouds.     

Improved similarity assessment and spectral clustering for unsupervised linking of data extracted from bridge inspection reports

Textual bridge inspection reports are important data sources for supporting data-driven bridge deterioration prediction and maintenance decision making. Information extraction methods are available to extract data/information from these reports to support data-driven analytics. However, directly using the extracted data/information in data analytics is still challenging because, even within the same report, there exist multiple data records that describe the same entity, which increases the dimensionality of the data and adversely affects the performance of the analytics. The first step to address this problem is to link the multiple records that describe the same entity and same type of instances (e.g., all cracks on a specific bridge deck), so that they can be subsequently fused into a single unified representation for dimensionality reduction without information loss. To address this need, this paper proposes a spectral clustering-based method for unsupervised data linking. The method includes: (1) a concept similarity assessment method, which allows for assessing concept similarity even when corpus or semantic information is not available for the application at hand; (2) a record similarity assessment method, which captures and uses similarity assessment dependencies to reduce the number of falsely-linked records; and (3) an improved spectral clustering method, which uses iterative bi-partitioning to better link records in an unsupervised way and to address the transitive closure problem. The proposed data linking method was evaluated in linking records extracted from ten bridge inspection reports. It achieved an average precision, recall, and F-1 measure of 96.2%, 88.3%, and 92.1%, respectively.     

A classification-based assessment of the optimal spectral and spatial resolutions for Great Lakes coastal wetland imagery

We analyzed hyperspectral airborne imagery (CASI 2 with 46 contiguous VIS/NIR bands) that was acquired over a Lake Huron coastal wetland. To support detailed Great Lakes coastal wetland mapping, the optimal spatial resolution of imagery was determined to be less than 2 m. There was a 23% change in classification resiliency using the SAM classifier upon resampling the original 1-meter, 18-band imagery to 2-meter pixels, and further classifications with larger pixels (4 and 8 m) increased overall classification change to 35% and 50%, respectively.We performed a series of image classification experiments incorporating three independent band selection methodologies (derivative magnitude, fixed interval and derivative histogram), in order to explore the effects of spectral resampling on classification resiliency. This research verified that a minimum of seven, strategically located bands in the VIS-NIR wavelength region (425.4 nm, 514.9 nm, 560.1 nm, 685.5 nm, 731.5 nm, 812.3 nm and 916.7 nm) are necessary to maintain a classification resiliency above the 85% threshold. Significantly, these seven bands produced the highest classification resiliency using the fewest number of bands of any of the 63 band-reduction strategies that were tested.Analyzing only derivative magnitudes proved to be an unreliable tool to identify optimal bands. The fixed interval method was adversely influenced by the starting band location, making its implementation problematic. The combined use of derivative magnitude and frequency of occurrence appears to be the best method to determine the “optimal” bands for a wetland mapping hyperspectral application.     

Remote estimation of gross primary production in maize and support for a new paradigm based on total crop chlorophyll content

The accurate quantification of gross primary production (GPP) in crops is important for regional and global studies of carbon budgets. Because of the observed close relationship between GPP and total canopy chlorophyll content in crops, vegetation indices related to chlorophyll can be used as a proxy of GPP. In this study, we justified the approach, tested the performance of several widely used chlorophyll-related vegetation indices in estimating total chlorophyll content and GPP in maize based on spectral data collected at a close range, 6 meters above the top of the canopy, over a period of eight years (2001 to 2008). The results show that GPP can be accurately estimated with chlorophyll-related indices that use near infra-red and either green or the red edge range of the spectrum. These indices provide the best approximation of the widely variable GPP in maize under both irrigated and rainfed conditions.