Unraveling plant phenotype to genotype associations with daily hyperspectral traits in Populus trichocarpa.

Variations in crop variables within wheat canopies and responses of canopy spectral characteristics and derived vegetation indices to different vertical leaf layers and spikes

Canopy chlorophyll content (CCC) indicates the photosynthetic functioning of a crop, which is essential for the growth and development and yield increasing. Accurate estimation of CCC from remote-sensing data benefits from including information on leaf chlorophyll and canopy structures. However, conventional nadir reflectance is usually subject to the lack of an adequate expression on the geometric structures and shaded parts of vegetation canopy, and the derived vegetation indices (VIs) are prone to be saturated at high CCC level. Using 3-year field experiments with different wheat cultivars, leaf colors, structural types, and growth stages, and integrated with PROSPECT+SAILh model simulation, we studied the potential of multi-angle reflectance data for the improved estimation of CCC. The characteristics of angular anisotropy in spectral reflectance were investigated. Analyses based on both simulated and experimental multi-angle hyperspectral data were carried out to compare performances of 20 existing VIs at different viewing angles, and to propose an algorithm to develop novel biangular-combined vegetation indices (BCVIs) for tracking CCC dynamics in wheat. The results indicated that spectral reflectance values, as well as the coefficient of determination (R2) between mono-angular VIs and CCC, at back-scattering directions, were mostly higher than those at forward-scattering directions. Mono-angular VIs at +30° angle, were closest to the hot-spot position in our case, achieved the highest R2 among 13 viewing angles including the nadir observation. The general formulation for the newly developed BCVIs was BCVIVI = f × VI(θ1) − (1 − f) × VI(θ2), in which the VI was used to characterize chlorophyll status, while the subtraction of VI at θ1 and θ2 viewing angles in a proportion was used to highlight the canopy structural information. From our result, the values of the θ1 and θ2 around hot-spot and dark-spot positions, and the f of 0.6 or 0.7 were found as the optimized values. Through comparisons revealed that large improvements on CCC modeling could be obtained by the BCVIs, especially for the experimental data, indicated by the increase in R2 by 25.1–51.4%, as compared to the corresponding mono-angular VIs at +30° angle. The BCVIMCARI[705,750] was proved to greatly undermine the saturation effect of mono-angular MCARI[705,750], expressing the best linearity and the most sensitive to CCC, with R2 of 0.98 and 0.72 for simulated and experimental data, respectively. Our study will eventually have extensive prospects in monitoring crop phenotype dynamics in for example large breeding trials.

The use of the National Oceanic and Atmospheric Administration (NOAA) satellites, and the conventional Normalised Difference Vegetation Index (NDVI) model have shown promise as a large scale monitoring tool to understand the vegetation dynamics of the sparsely vegetated Sahelian grasslands. One of the assumptions of the NDVI model is that the soil background is spectrally homogeneous, which is not the case. Twelve sites, within two Système Probatoire d'Observation de Terre (SPOT) satellite imageries, corresponding to NOAA Advanced Very High Resolution Radiometer (AVHRR) Local Area Coverage (LAC) pixel resolution, were assigned representative soil NDVI values for both dry and wet conditions. These soil NDVI values, together with herbaceous above-ground biomass production estimates, were used in a multiple correlation and regression analysis to assess statistically the soil impact on integrated NDVI values, i.e. values supposed only to express the total amount of vegetation in the end of the rainy season. The analysis showed that soil influence varied significantly with different soil types and moisture content, and should therefore not be ignored in satellite based vegetation monitoring.

During the Saharan Mineral Dust Experiment (SAMUM) conducted in summer 2006 in southeast Morocco, the complex refractive index of desert dust was determined from airborne measurements of particle size distributions and aerosol absorption coefficients at three different wavelengths in the blue (467 nm), green (530 nm) and red (660 nm) spectral regions. The vertical structure of the dust layers was analysed by an airborne high spectral resolution lidar (HSRL). The origin of the investigated dust layers was estimated from trajectory analyses, combined with Meteosat 2nd Generation (MSG) scenes and wind field data analyses. The real part n of the dust refractive index was found almost constant with values between 1.55 and 1.56, independent of the wavelength. The values of the imaginary part k varied between the blue and red spectral regions by a factor of three to ten depending on the dust source region. Absolute values of k ranged from 3.1 × 10−3 to 5.2 × 10−3 at 450 nm and from 0.3 × 10−3 to 2.5 × 10−3 at 700 nm. Groupings of k values could be attributed to different source regions.

19 - Prediction of Soil Organic Carbon and its Mapping Using Regression Analyses and Remote Sensing Data in GIS and R

Visual assessments of disease severity are currently the industry standard for quantification of the necrosis caused by Cercospora beticola on sugar beet (Beta vulgaris) leaves. We compared the precision, reproducibility, and sensitivity of a multispectral radiometer to visual disease assessments. Individual wavebands from the radiometer, as well as vegetative indices calculated from the individual wavebands, were compared with visual disease estimates from two raters at each of two research sites. Visual assessments and radiometric measurements were partially repeated immediately after the first assessment at each site. Precision, as measured by reduced coefficients of variation, was improved for all vegetative indices and individual waveband radiometric measures compared with visual assessments. Visual assessments, near-infrared singlewaveband reflectance values, and four of the six vegetative indices had high treatment F values, suggesting greater sensitivity at discriminating disease severity levels. Reproducibility, as measured by a test-retest method, was high for visual assessments, single-waveband reflectance at 810 nm, and several of the vegetative indices. The use of radiometric methods has the potential to increase the precision of assessments of Cercospora leaf spot foliar symptoms of sugar beet while eliminating potential bias. We recommend this method be used in conjunction with visual disease assessments to improve precision of assessments and guard against potential bias in evaluations.

Unmanned aerial vehicle (UAV) provides an alternative way to collect data conveniently, when corresponding sensor (s) is (are) equipped onboard. In this paper, two multispectral cameras were compared mainly based on simulated channel reflectance, which covered visible (i.e., Blue, Green, and Red), near infrared (NIR), and red-edge (RE) regions. The preliminary investigation shows the between-sensor differences in sensor settings, the related differences in channel reflectance, and in the derived vegetation indices. The between-sensor differences in channel reflectance vary with channels. Compared with the Green and NIR channels, the RE and Red channels show more obvious between-sensor difference, which contributes significantly to the differences in the derived vegetation indices. The between-sensor differences in the RE channel are required to be eliminated, because the RE reflectance and the derived indices correspondingly are important in modeling biophysical parameters. Case study shows that the between-sensor differences of vegetation indices were generally more obvious than the reflectance differences of individual channels. Damage assessment for plant over flooded areas was readily done with the help of vegetation indices. Accordingly, the between-sensor differences are likely significant in practical applications. In conclusion, the importance to make consistency among data collections by different sensors (i.e., onboard UAV) is highlighted, and an effective way for between-sensor transformation is required in further investigations.

Above-ground biomass estimation models of mangrove forests based on remote sensing and field-surveyed data: Implications for C-PFES implementation in Quang Ninh Province, Vietnam

HighlightsThis study addresses two computational challenges in high-throughput phenotyping: scalability and efficiency.Specifically, we focus on extracting crop images and deriving vegetation indices using unmanned aerial systems.To this end, we outline a data processing pipeline, featuring a crop localization algorithm and trie data structure.We demonstrate the efficacy of our approach by computing large-scale and high-precision vegetation indices in a soybean breeding experiment, where we evaluate soybean growth under water inundation and temporal change.Abstract. In agronomy, high-throughput phenotyping (HTP) can provide key information for agronomists in genomic selection as well as farmers in yield prediction. Recently, HTP using unmanned aerial systems (UAS) has shown advantages in both cost and efficiency. However, scalability and efficiency have not been well studied when processing images in complex contexts, such as using multispectral cameras, and when images are collected during early and late growth stages. These challenges hamper further analysis to quantify phenotypic traits for large-scale and high-precision applications in plant breeding. To solve these challenges, our research team previously built a three-step data processing pipeline, which is highly modular. For this project, we present improvements to the previous pipeline to improve canopy segmentation and crop plot localization, leading to improved accuracy in crop image extraction. Furthermore, we propose a novel workflow based on a trie data structure to compute vegetation indices efficiently and with greater flexibility. For each of our proposed changes, we evaluate the advantages by comparison with previous models in the literature or by comparing processing results using both the original and improved pipelines. The improved pipeline is implemented as two MATLAB programs: Crop Image Extraction version 2 (CIE 2.0) and Vegetation Index Derivation version 1 (VID 1.0). Using CIE 2.0 and VID 1.0, we compute canopy coverage and normalized difference vegetation indices (NDVIs) for a soybean phenotyping experiment. We use canopy coverage to investigate excess water stress and NDVIs to evaluate temporal patterns across the soybean growth stages. Both experimental results compare favorably with previous studies, especially for approximation of soybean reproductive stage. Overall, the proposed methodology and implemented experiments provide a scalable and efficient paradigm for applying HTP with UAS to general plant breeding. Keywords: Data processing pipeline, High-throughput phenotyping, Image processing, Soybean breeding, Unmanned aerial systems, Vegetation indices.

Eucalyptus species play an important role in the global carbon cycle, especially in reducing the greenhouse effect as well as storing atmospheric CO₂. Thus, assessing the amount of CO₂ released by the soil in forest areas can generate important information for environmental monitoring. This study aims to verify the relation between soil carbon dioxide (CO₂) flux (FCO₂), spectral bands, and vegetation indices (VIs) derived from a UAV-based multispectral camera over an area of eucalyptus species. Multispectral imageries (green, red-edge, and near-infrared) from the Parrot Sequoia sensor, derived vegetation indices, and the FCO₂ data from a LI-COR 8100 analyzer, combined with soil moisture and temperature data, were collected and related. The vegetation indices ATSAVI (Adjusted Transformed Soil-Adjusted VI), GSAVI (Green Soil Adjusted Vegetation Index), and SAVI (Soil-Adjusted Vegetation Index), which use soil correction factors, exhibited a strong negative correlation with FCO₂ for the species E. camaldulensis, E. saligna, and E. urophylla species. A Multivariate Analysis of Variance showed significance (p < 0.01) for the species factor, which indicates that there are differences when considering all variables simultaneously. The results achieved in this study show a specific correlation between the data of soil CO₂ emission and the eucalypt species, providing a distinction of values between the species in the statistical data.

Reliable and up-to-date urban land cover information is valuable in urban planning and policy development. Due to the increasing demand for reliable land cover information there has been a growing need for robust methods and datasets to improve the classification accuracy from remotely sensed imagery. This study sought to assess the potential of the newly launched Landsat 8 sensor’s thermal bands and derived vegetation indices in improving land cover classification in a complex urban landscape using the support vector machine classifier. This study compared the individual and combined performance of Landsat 8’s reflective, thermal bands and vegetation indices in classifying urban land use-land cover. The integration of Landsat 8 reflective bands, derived vegetation indices and thermal bands overall produced significantly higher accuracy classification results than using traditional bands as standalone (i.e. overall, user and producer accuracies). An overall accuracy above 89.33% and a kappa index of 0.86, significantly higher than the one obtained with the use of the traditional reflective bands as a standalone data-set and other analysis stages. On average, the results also indicate high producer and user accuracies (i.e. above 80%) for most of the classes with a McNemar’s Z score of 9.00 at 95% confidence interval showing significant improvement compared with classification using reflective bands as standalone. Overall, the results of this study indicate that the integration of the Landsat 8’s OLI and TIR data presents an invaluable potential for accurate and robust land cover classification in a complex urban landscape, especially in areas where the availability of high resolution datasets remains a challenge.

Moderate-resolution imaging spectroradiometer (MODIS)-derived vegetation indices (VIs)-the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Land Surface Water Index (LSWI)-are evaluated in terms of their sensitivity to the seasonal greenness pattern and moisture regime of the tropical forests of Hawaii. The annual mean NDVI and EVI signals most rapidly saturated as total annual rainfall increased to a mesic condition, but LSWI was responsive to a much wetter environment. Ecoregional analyses of biweekly VI time series revealed that all three VIs followed the typical pattern of summer-low and winter-high rainfall for dry forests and shrublands. However, NDVI and EVI did not show any significant seasonality of wet forests while LSWI represented a summer-high and winter-low greenness pattern. The three VIs did not respond to the Leaf Area Index (LAI) very well as LAI reached 4, but they sensitively responded to the fraction of photosynthetically active radiation (fPAR) and leaf moisture content. Especially, LSWI responded most sensitively to fPAR and leaf water content in the wet environment, where fPAR and leaf water content were >0.6 and >40%. Greenness seasonality was more strongly represented by LSWI than by NDVI and EVI for all ecoregions considered in the study. In short, it is believed that LSWI is more appropriate for a canopy phenology study than the other two VIs in wet forests of the tropical environment.

Differentiating stress induced by greenbugs and Russian wheat aphids in wheat using remote sensing

Various sensors have been used to obtain the canopy spectral reflectance for monitoring above-ground plant nitrogen (N) uptake in winter wheat. Comparison and intercalibration of spectral reflectance and vegetation indices derived from different sensors are important for multi-sensor data fusion and utilization. In this study, the spectral reflectance and its derived vegetation indices from three ground-based sensors (ASD Field Spec Pro spectrometer, CropScan MSR 16 and GreenSeeker RT 100) in six winter wheat field experiments were compared. Then, the best sensor (ASD) and its normalized difference vegetation index (NDVI (807, 736)) for estimating above-ground plant N uptake were determined (R2 of 0.885 and RMSE of 1.440 g·N·m−2 for model calibration). In order to better utilize the spectral reflectance from the three sensors, intercalibration models for vegetation indices based on different sensors were developed. The results indicated that the vegetation indices from different sensors could be intercalibrated, which should promote application of data fusion and make monitoring of above-ground plant N uptake more precise and accurate.

Advantages of a two band EVI calculated from solar and photosynthetically active radiation fluxes