Chlorophyll-related Indices Research Articles

Field Plant Monitoring from Macro to Micro Scale: Feasibility and Validation of Combined Field Monitoring Approaches from Remote to in Vivo to Cope with Drought Stress in Tomato.

Monitoring plant growth and development during cultivation to optimize resource use efficiency is crucial to achieve an increased sustainability of agriculture systems and ensure food security. In this study, we compared field monitoring approaches from the macro to micro scale with the aim of developing novel in vivo tools for field phenotyping and advancing the efficiency of drought stress detection at the field level. To this end, we tested different methodologies in the monitoring of tomato growth under different water regimes: (i) micro-scale (inserted in the plant stem) real-time monitoring with an organic electrochemical transistor (OECT)-based sensor, namely a bioristor, that enables continuous monitoring of the plant; (ii) medium-scale (<1 m from the canopy) monitoring through red-green-blue (RGB) low-cost imaging; (iii) macro-scale multispectral and thermal monitoring using an unmanned aerial vehicle (UAV). High correlations between aerial and proximal remote sensing were found with chlorophyll-related indices, although at specific time points (NDVI and NDRE with GGA and SPAD). The ion concentration and allocation monitored by the index R of the bioristor during the drought defense response were highly correlated with the water use indices (Crop Water Stress Index (CSWI), relative water content (RWC), vapor pressure deficit (VPD)). A high negative correlation was observed with the CWSI and, in turn, with the RWC. Although proximal remote sensing measurements correlated well with water stress indices, vegetation indices provide information about the crop's status at a specific moment. Meanwhile, the bioristor continuously monitors the ion movements and the correlated water use during plant growth and development, making this tool a promising device for field monitoring.

Leaf morphology, optical characteristics and phytochemical traits of butterhead lettuce affected by increasing the far-red photon flux.

Light and its spectral characteristics are crucial for plant growth and development. The far-red photon flux mediates many plant processes through the action of phytochrome and also accelerates the photosynthetic electron transfer rate. In this study, we assessed the effects of far-red addition on butterhead lettuce morphology, light use efficiency, optical properties, and phytochemical characteristics. Three-week-old lettuce plants (Lactuca sativa L. cv. Alyssa) were grown for up to 28 days under a 10% blue and 90% red light spectrum (200 µmol m-2 s-1, 16h photoperiod) to which five different intensities of far-red light (peak at 735 nm) were added (0-9-18-36-72 µmol m-2 s-1). White light-emitting diodes were included as a proxy for sunlight. Increasing supplemental far-red photon flux from zero to 21% increased the light use efficiency (g per mol) by 37% on day 14; 43% on day 21; and 39% on day 28. Measurements of projected head area suggest that this was associated with an increase in leaf expansion and photon capture and not necessarily a direct effect on photosynthesis. Moreover, vegetation indices based on leaf reflectance showed a decrease in chlorophyll-related indices under a high far-red photon flux. This decrease in pigment content was confirmed by chemical analyses, suggesting that the plants may not reach their full potential in terms of photon capture, limiting the overall photosynthetic performance. Furthermore, the stress-related Carter 1 index increased in plants grown under a high far-red photon flux, indicating early plant stress. Far-red tended to decrease the content of total phenolics and increase soluble sugars. The higher sugar levels can be attributed to an improved photochemical efficiency due to photosystem I excitation by far-red wavelengths, also known as the Emerson Enhancement effect. Despite these higher sugar levels, no effect on foliar nitrate content was observed. Our results show that far-red supplementation has the potential to enhance light interception at the early growth stages, although higher intensities of far-red may cause plant stress.

Proximal fluorescence sensing of potassium responsive crops to develop improved predictions of biomass, yield and grain quality of wheat and barley

Precision nutrient management requires accurate assessment of crop nutrient status. This is common for assessing N status, but much less so for other nutrients. Because fluorescence can indicate crop stress, the robustness of different fluorescence indices was assessed to predict crop nutrient status (K, Mg and Ca). The hypothesis was that crop nutrition limitations, especially K, can be detected using fluorescence proximal sensing to quantify crop response with a high degree of spatial resolution. A factorial experiment was imposed with four treatment factors: crop, K fertilizer rate, lime and row management. The soil at the experimental site was K deficient and the crop variables showed significant treatment effects (e.g. yield, protein). Fluorescence sensing identified a significant positive K response for three chlorophyll related indices (SFR_G, SFR_R and CHL), but not for FLAV; while wheat was significantly different from barley. Using a k-fold cross-validation method promising predictive relationships were found. The strongest predictions were for SFR_R to predict crop biomass, for SFR_G to predict crop K content of inter-row wheat, for CHL to predict crop Ca content of inter-row wheat and for FLAV with barley grain protein in the windrow treatment. The fluorescence indices produced more significant crop variable predictions than measuring NDVI using an active sensor. This study illustrates the utility of fluorescence sensing to measure chlorophyll related signals for capturing the nutritional status of barley and wheat crops. These results show encouraging potential to rapidly detect crop nutrient status for non-N nutrients using fluorescence sensing.

Effects of Cu on the content of chlorophylls and secondary metabolites in the Cu-hyperaccumulator lichen Stereocaulon japonicum

Understanding the relationship between Cu and Cu-hyperaccumulator lichens is important for their application in monitoring and assessing heavy metal pollution. We investigated the Cu-hyperaccumulator lichen Stereocaulon japonicum at several Cu-polluted and control sites in Japan, and found the lichen to be widely distributed. Its concentrations of Cu, chlorophylls, and secondary metabolites, chlorophyll-related indices, and absorption spectra were measured, and we observed negative effects of Cu on these concentrations and indices. For highly Cu-polluted samples (>100ppm dry weight), however, we found significant linear correlations between Cu and chlorophyll concentrations. This can be considered as the response of the photobiont in S. japonicum to Cu stress. In highly Cu-polluted samples the chlorophyll-related indices and concentration of total secondary metabolites were almost constant regardless of Cu concentration. This suggests that the increase in chlorophyll concentration with the increase in Cu concentration enhances photosynthetic productivity per unit biomass, which will allow the production of extra structure and energy for maintaining the chlorophyll-related indices under Cu stress. The relationship between the increase in chlorophyll concentration of S. japonicum and the decrease in secondary metabolite concentration of the lichen can be explained by considering the balance of carbohydrates in the lichen. We found that a spectral index A372−A394 can be a useful index of the concentrations of Cu and total secondary metabolites in S. japonicum. These findings show the adjustment of the content of chlorophylls and secondary metabolites in S. japonicum to Cu stress, and provide a better understanding of the relationship between Cu and the Cu-hyperaccumulator lichen.

Monitoring of carbon dioxide fluxes in a subalpine grassland ecosystem of the Italian Alps using a multispectral sensor

Abstract. The study investigates the potential of a commercially available proximal sensing system – based on a 16-band multispectral sensor – for monitoring mean midday gross ecosystem production (GEPm) in a subalpine grassland of the Italian Alps equipped with an eddy covariance flux tower. Reflectance observations were collected for 5 consecutive years, characterized by different climatic conditions, together with turbulent carbon dioxide fluxes and their meteorological drivers. Different models based on linear regression (vegetation indices approach) and on multiple regression (reflectance approach) were tested to estimateGEPm from optical data. The overall performance of this relatively low-cost system was positive. Chlorophyll-related indices including the red-edge part of the spectrum in their formulation (red-edge normalized difference vegetation index, NDVIred-edge; chlorophyll index, CIred-edge) were the best predictors of GEPm, explaining most of its variability during the observation period. The use of the reflectance approach did not lead to considerably improved results in estimating GEPm: the adjusted R2 (adjR2) of the model based on linear regression – including all the 5 years – was 0.74, while the adjR2 for the multiple regression model was 0.79. Incorporating mean midday photosynthetically active radiation (PARm) into the model resulted in a general decrease in the accuracy of estimates, highlighting the complexity of the GEPm response to incident radiation. In fact, significantly higher photosynthesis rates were observed under diffuse as regards direct radiation conditions. The models which were observed to perform best were then used to test the potential of optical data for GEPm gap filling. Artificial gaps of three different lengths (1, 3 and 5 observation days) were introduced in the GEPm time series. The values of adjR2 for the three gap-filling scenarios showed that the accuracy of the gap filling slightly decreased with gap length. However, on average, the GEPm gaps were filled with an accuracy of 73% with the model fed with NDVIred-edge, and of 76% with the model using reflectance at 681, 720 and 781 nm and PARm data.

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.

A near-infrared narrow-waveband ratio to determine Leaf Area Index in orchards

An innovative index for LAI estimation over orchard canopies based on the combination of two near-infrared narrow wavebands (1050 nm and 1250 nm) is presented. The index is insensitive to physiological changes on the leaf level even under changing pheneological developmental stage. In the meantime, the index is highly sensitive for variations at the canopy level. Values of LAI estimated by means of this newly developed index, called sLAIDI (standardized LAI Determining Index), resulted in excellent agreements with in-situ measured LAI values of citrus trees, with determination coefficients ( R 2) of 0.83. Based on in-situ measured data and simulated data (PROSPECT and ACRM), the newly developed sLAIDI was also found to perform outstanding at high LAI values in contrary to well-known LAI related indices such as the Normalized Difference Vegetation Index (NDVI) and the Modified Chlorophyll Ratio Index 2 (MCARI2) that saturate at LAI values of 5 and 8 respectively. Moreover, the sLAIDI index was independent of changing pigment contents. Consequently, this index was regarded a valuable tool to more accurately extract biochemical parameters after normalizing the considered spectral data for LAI. By implementing sLAIDI in existing chlorophyll-related indices (e.g., GM index), the performance to estimate the leaf chlorophyll content from spectral citrus and peach canopy level data is increased ( R 2 increases from 0.68 to 0.77). Moreover, a correction factor was proposed to enable the application of sLAIDI on fields with strong variations of water content.