Meta-analysis of the detection of plant pigment concentrations using hyperspectral remotely sensed data

Passive optical hyperspectral remote sensing of plant pigments offers potential for understanding plant ecophysiological processes across a range of spatial scales. Following a number of decades of research in this field, this paper undertakes a systematic meta-analysis of 85 articles to determine whether passive optical hyperspectral remote sensing techniques are sufficiently well developed to quantify individual plant pigments, which operational solutions are available for wider plant science and the areas which now require greater focus. The findings indicate that predictive relationships are strong for all pigments at the leaf scale but these decrease and become more variable across pigment types at the canopy and landscape scales. At leaf scale it is clear that specific sets of optimal wavelengths can be recommended for operational methodologies: total chlorophyll and chlorophyll a quantification is based on reflectance in the green (550–560nm) and red edge (680–750nm) regions; chlorophyll b on the red, (630–660nm), red edge (670–710nm) and the near-infrared (800–810nm); carotenoids on the 500–580nm region; and anthocyanins on the green (550–560nm), red edge (700–710nm) and near-infrared (780–790nm). For total chlorophyll the optimal wavelengths are valid across canopy and landscape scales and there is some evidence that the same applies for chlorophyll a

Sequential application of hyperspectral indices for delineation of stripe rust infection and nitrogen deficiency in wheat

© 2015, Springer Science+Business Media New York. Nitrogen (N) fertilization is crucial for the growth and development of wheat crops, and yet increased use of N can also result in increased stripe rust severity. Stripe rust infection and N deficiency both cause changes in foliar physiological activity and reduction in plant pigments that result in chlorosis. Furthermore, stripe rust produce pustules on the leaf surface which similar to chlorotic regions have a yellow color. Quantifying the severity of each factor is critical for adopting appropriate management practices. Eleven widely-used vegetation indices, based on mathematic combinations of narrow-band optical reflectance measurements in the visible/near infrared wavelength range were evaluated for their ability to discriminate and quantify stripe rust severity and N deficiency in a rust-susceptible wheat variety (H45) under varying conditions of nitrogen status. The physiological reflectance index (PhRI) and leaf and canopy chlorophyll index (LCCI) provided the strongest correlation with levels of rust infection and N-deficiency, respectively. When PhRI and LCCI were used in a sequence, both N deficiency and rust infection levels were correctly classified in 82.5 and 55 % of the plots at Zadoks growth stage 47 and 75, respectively. In misclassified plots, an overestimation of N deficiency was accompanied by an underestimation of the rust infection level or vice versa. In 18 % of the plots, there was a tendency to underestimate the severity of stripe rust infection even though the N-deficiency level was correctly predicted. The contrasting responses of the PhRI and LCCI to stripe rust infection and N deficiency, respectively, and the relative insensitivity of these indices to the other parameter makes their use in combination suitable for quantifying levels of stripe rust infection and N deficiency in wheat crops under field conditions

Reflectance in the Red and Near Infra-Red Ranges of the Spectrum as Tool for Remote Chlorophyll Estimation in Inland Waters: Lake Kinneret Case Study

Signature analysis of reflectance spectra was used for the selection of the most suitable spectral bands for remote sensing of chlorophyll in inland waters. The parameters of the reflectance peak near 700 nm were employed for construction of algorithms for chlorophyll determination. The best model, validated by independent data sets, enabled estimation of chlorophyll concentration with an error \u3c 0.6 mg/m3 for period of low Chl concentration and \u3c 6.5 mg/m3 for period of the phytoplankton bloom. For the purpose of chlorophyll mapping in Lake Kinneret, the use of three relatively narrow spectral bands was sufficient. Radiometric data were also used to simulate radiances in the channels of TM Landsat and to find algorithm for chlorophyll assessment. The ratio (TM2-TM3)/TMl was used to retrieve chlorophyll in the range 3-10 mg/m3 with an error of \u3c 1 mg.m-3; the ratio TM4/TM3 was used to map chlorophyll in the range 10-200 mg/m3 with 10 gradations

Remote Estimation of Crop Health [ABSTRACT]

First sentence of abstract: In this paper we discuss developed techniques to remotely assess the fraction of photosynthetically active radiation absorbed by green vegetation [fAPAR-GREEN=fAPAR*(green LAI/total LAI)], fractional green vegetation cover (FGVC), green leaf area index (GLAI) green leaf biomass (GLB) and net ecosystem carbon dioxide exchange (NEE) in crops

THE SPECTRAL CONTRIBUTION OF CAROTENOIDS TO LIGHT ABSORPTION AND REFLECTANCE IN GREEN LEAVES

Absorbance and reflectance spectra in the visible and near infrared range of the spectrum, acquired for maple (Acer platanoides L.) leaves were studied. Standard deviation of absorbance spectra showed that in yellow to green leaves, with chlorophyll content at least up to 30 nmol/cm2, there is a spectral feature at 520 nm attributable to carotenoids. Reflectance around 520 nm also correlates closely with carotenoids content in yellow to green leaves. Thus, this spectral feature at 520 nm could be used as a measure of carotenoids content in green leaves and plants

Satellite Estimation of Chlorophyll-\u3ci\u3ea\u3c/i\u3e Concentration Using the Red and NIR Bands of MERIS—The Azov Sea Case Study

We present here the results of calibrating and validating a three-band model and, its special case, a two-band model, which use MEdium Resolution Imaging Spectrometer (MERIS) reflectances in the red and near-infrared spectral regions for estimating chlorophyll-a (chl-a) concentration in inland, estuarine, and coastal turbid productive waters. During four data collection campaigns in 2008 and one campaign in 2009 in the Taganrog Bay and the Azov Sea, Russia, water samples were collected, and concentrations of chl-a and total suspended solids were measured in the laboratory. The data collected in 2008 were used for model calibration, and the data collected in 2009 were used for model validation. The models were applied to MERIS images acquired within two days from the date of in situ data collection. Two different atmospheric correction procedures were considered for processing the MERIS images. The results illustrate the high potential of the models to estimate chl-a concentration in turbid productive (Case II) waters in real time from satellite data, which will be of immense value to scientists, natural resource managers, and decision makers involved in managing the inland and coastal aquatic ecosystems

AVHRR-Based Spectral Vegetation Index for Quantitative Assessment of Vegetation State and Productivity: Calibration and Validation

The goal of the work was to estimate, quantitatively, vegetation state and productivity using AVHRR-based Vegetation Condition Index (VCI). The VCI algorithm includes application of post-launch calibration to visible channels, calculation of NDVI from channels’ reflectance, removal of high-frequency noise from NDVI’s annual time series, stratification of ecosystem resources, and separation of ecosystem and weather components in the NDVI value. The weather component was calculated by normalizing the NDVI to the difference of the extreme NDVI fluctuations (maximum and minimum), derived from multi-year data for each week and land pixel. The VCI was compared with wheat density measured in Kazakhstan. Six test fields were located in different climatic (annual precipitation 150 to 700 mm) and ecological (semi-desert to steppe-forest) zones with elevations from 200 to 700 m and a wide range of NDVI variation over space and season from 0.05 to 0.47. Plant density (PD) was measured in wheat fields by calculating the number of stems per unit area. PD deviation from year to year (PDD) was expressed as a deviation from median density calculated from multi-year data. The correlation between PDD and VCI for all stations was positive and quite strong (r2 \u3e 0.75) with the Standard Errors of Estimates (SEE) of PDD less than 16 percent; for individual stations, the SEE was less than 11 percent. The results indicate that VCI is an appropriate index for monitoring weather impact on vegetation and for assessment of pasture and crop productivity in Kazakhstan. Because satellite observations provide better spatial and temporal coverage, the VCI-based system will provide efficient tools for management of water resources and the improvement of agricultural planning. This system will serve as a prototype in the other parts of the world where ground observations are limited or not available

A Belief System's Organization Based on a Computational Model of the Dynamic Context: First Approximation

In this article we present a model of organization of a belief system based on a set of binary recursive functions that characterize the dynamic context that modifies the beliefs. The initial beliefs are modeled by a set of two-bit words that grow, update, and generate other beliefs as the different experiences of the dynamic context appear. Reason is presented as an emergent effect of the experience on the beliefs. The system presents a layered structure that allows a functional organization of the belief system. Our approach seems suitable to model different ways of thinking and to apply to different realistic scenarios such as ideologies

Using AVHRR Data for Quantitative Estimation of Vegetation Conditions: Calibration and Validation

NDVI-derived Vegetation Condition Index (VCI) was compared with vegetation density, biomass and reflectance measured in the fields. The VCI numerically estimates fluctuation of NDVI related to intra-annual weather change only and is a measure of weather impact on vegetation. Test fields were located in different climatic (annual precipitation 150-700 mm) and ecological zones (semi-desert to steppe-forest) with elevation from 200 to 700 m in Kazakhstan. A range of NDVI variation was from 0.05 to 0.47. The determination coefficient between AVHRR-derived vegetation state and actually measured vegetation density of more than 0.76 was achieved. For the first time it was shown that the VCI-derived vegetation condition data can be effectively used for quantitative assessments of both vegetation state and productivity (density and biomass) over large areas