Changes In Spectral Reflectance Research Articles

Changes in Spectral Reflectance, Photosynthetic Performance, Chlorophyll Fluorescence, and Growth of Mini Green Romaine Lettuce According to Various Light Qualities in Indoor Cultivation

Light quality can be stated to be the identity of an artificial light source, and although the response of light quality may vary depending on the crop, the effect is clearly visible and can produce various results depending on the combination of an artificial light source. This study investigated the spectral reflectance, photosynthetic performance, and chlorophyll fluorescence of mini green romaine lettuce based on light quality. Most parameters related to spectral reflectance showed the best results under blue light, and photosynthetic performance was more effective with mixed light than with single-colored light, among which blue + red (BR)-LED was the most suitable. Red light was ineffective, showing mostly low results in parameters of spectral reflectance and photosynthetic performance. In the case of chlorophyll fluorescence, the light quality influenced photomorphogenesis, resulting in increased leaf length and width with R- and quantum dot (QD)-LED, which expanded the leaf area and allowed for more external light to be captured (ABS/RC and TRo/RC). However, the ratio of electronized energy (ETo/RC) was low, and the amount of energy dissipated as heat (DIo/RC) was high. Consequently, the degree of electron acceptor reduction and overall photosynthetic performance (PIABS and PItotal) were lower compared to other light qualities. Additionally, the contrasting results of QD-LED and BR-LED were attributed to the form of red light and the presence or absence of far-red light when comparing spectra. Principal component analysis also clearly distinguished light qualities for photosynthesis and growth. Growth was increased by red (R)- and QD-LED, while photosynthetic performance was increased by BR- and blue (B)-LED.

Comparisons of aerosol types and optical characters over Shouxian Area China observed from ground- and space-based systems

This study evaluates the performance of moderate-resolution Imaging spectroradiometer (MODIS) in aerosol optical depth(AOD) and Ångström exponent(AE) retrievals under high aerosol loading conditions across various aerosol types, utilizing ground-based and space-borne aerosol measurements in Shouxian, China. The intercomparison reveals cloud-aerosol LiDAR with orthogonal polarization's (CALIOP) efficacy in detecting significant aerosol layers and the refinement of sunphotometer-based aerosol type classification through CALIPSO, achieving approximately 80% accuracy. Analysis of 2016-2017 data indicates substantial aerosol presence in Shouxian, with monthly mean AODs ranging from 0.35 to 0.72 at 550 nm, significantly above the global average. The predominant aerosol types were mixed-type (54.8%), desert dust (21.2%), urban/industrial(15.5%), biomass-burning aerosol (6.4%), and continental aerosol (12.1%), with frequent observations of elevated long-range transported aerosol layers. MODIS AOD retrievals generally align with sunphotometer measurements but exhibit higher biases, especially with increasing AOD magnitudes. However, there is a notable difference between MODIS and sunphotometer aerosol AE measurements, with MODIS accurately assessing BBA but showing varied performance across other aerosol types. The combination of AOD and AE of the DD aerosol type is the most accurate. Further analysis showed that MODIS AOD biases and AE biases are negatively correlated, these negative bias correlations show strong aerosol type sensitivities. Monthly analysis of MODIS and sunphotometer comparisons highlights varying performance, particularly during normalized difference vegetation index (NDVI) transitions, suggesting that local vegetation cycles and associated surface spectral reflectance changes significantly impact MODIS aerosol retrieval accuracy under high aerosol loading conditions.

Assessing Interactions between Nitrogen Supply and Leaf Blast in Rice by Hyperspectral Imaging

Mineral nitrogen (N) supply reportedly increases rice susceptibility to the fungal pathogen Magnaporthe oryzae causing blast disease. These biotic and abiotic factors cause changes in spectral reflectance of leaves; however, the effects of N × pathogen interactions on spectral characteristics of rice have not been studied. In this study, hyperspectral imaging was used to assess the effect of N supply on symptoms of rice leaf blast under greenhouse conditions. Three rice genotypes differing in blast susceptibility grown at low, medium, and high N supply were inoculated at the four-leaf stage with three M. oryzae isolates differing in virulence. The reflectance spectra (400 to 1000 nm) of healthy and symptomatic leaves were analyzed using the spectral angle mapper algorithm for supervised classification. Mineral N supply increased the contents of chlorophyll and total N. The number and area of lesions and total blast severity varied depending on rice genotype—M. oryzae isolate interactions and the amount of mineral N applied. The reflectance spectra of healthy tissue and of blast symptom subareas differed with N supply; rice genotypes differed in the response to N supply. Infected plants at high mineral N supply could be distinguished from those at low N supply due to higher differences in the spectra of symptom subareas. Results reveal the potential (and limitations) of hyperspectral imaging for quantifying N effects on rice leaves, disease severity, and symptom expression. The impact of these findings on plant phenotyping and remote sensing under field conditions is discussed.

Non-Destructive Monitoring of Foliar Fungicide Efficacy with Hyperspectral Sensing in Grapevine.

Frequent fungicide applications are required to manage grapevine powdery mildew (Erysiphe necator). However, this practice is costly and has led to widespread fungicide resistance. A method of monitoring in-field fungicide efficacy could help growers maximize spray-interval length, thereby reducing costs and the rate of fungicide resistance emergence. The goal of this study was to evaluate if hyperspectral sensing in the visible to shortwave infrared range (400 to 2,400 nm) can quantify foliar fungicide efficacy on grape leaves. Commercial formulations of metrafenone, Bacillus mycoides isolate J (BmJ), and sulfur were applied on Chardonnay grapevines in vineyard or greenhouse settings. Foliar reflectance was measured with handheld hyperspectral spectroradiometers at multiple days post-application. Fungicide efficacy was estimated as a proxy for fungicide residue and disease control measured with the Blackbird microscopy imaging robot. Treatments could be differentiated from the untreated control with an accuracy of 73.06% for metrafenone, 67.76% for BmJ, and 94.10% for sulfur. The change in spectral reflectance was moderately correlated with the cube root of the area under the disease progress curve for metrafenone- and sulfur-treated samples (R2 = 0.38 and 0.36, respectively) and with sulfur residue (R2 = 0.42). BmJ treatment impacted foliar physiology by enhancing the leaf mass/area and reducing the nitrogen and total phenolic content as estimated from spectral reflectance. The results suggest that hyperspectral sensing can be used to monitor in-situ fungicide efficacy, and the prediction accuracy depends on the fungicide and the time point measured. The ability to monitor in-situ fungicide efficacy could facilitate more strategic fungicide applications and promote sustainable grapevine protection. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Identificating vegetation stress under natural gas micro-leakage based on leaf scale temporal hyperspectrum

ABSTRACT Timely detection of underground natural gas leakage is significant for the safe production of gas storage and the protection of ecological environment. Direct detection makes it difficult to identify natural gas micro-leakage accurately, while hyperspectral remote sensing can identify micro-leakage based on spectral changes of vegetation near the leakage area. However, the spectral morphological characteristics of vegetation leaves under natural gas micro-leakage will undergo complex changes over time, making it difficult to identify vegetation stress. Temporal spectral characteristics improve the capability of capturing spectral differences of stressed vegetation at different times, allowing for indirect evaluation of leaked information. This study simulates the stress of underground natural gas storage micro leaks on bean growth through field experiments. Based on the changes in spectral reflectance and chlorophyll content of leaves, temporal features were constructed based on multi-temporal reflectance data, and feature selection methods were used to obtain bands sensitive to natural gas stress. This paper proposed a leaf scale natural gas micro-leakage identification index based on temporal spectral features (TNII) to identify bean stress under natural gas leakage. The results are summarized as follows: (1) With the leakage of underground natural gas, the spectral reflectance of leaves increased in the visible and near-infrared range, and the chlorophyll content decreased. (2) The TNII ( R 676 − R 526 ) Tn − ( R 676 − R 526 ) T 1 ( R 676 − R 526 ) Tn + ( R 676 − R 526 ) T 1 constructed based on sensitive wavebands (676 and 526 nm) can be employed to identify bean stress under natural gas after 17 days of leakage. The JM (Jefferies Matusita) distance test indicates that TNII could timely and stably identify bean stress under natural gas micro-leakage (JM > 1.8). This study provides the theoretical basis and technology support for micro-leakage detection of underground gas storage or pipelines.

In vivo spectroscopy and machine learning for the early detection and classification of different stresses in apple trees

The use of in vivo spectroscopy to detect plant stress in its early stages has the potential to enhance food safety and reduce the need for plant protection products. However, differentiating between various stress types before symptoms appear remains poorly studied. In this study, we investigated the potential of Vis–NIR spectroscopy to differentiate between stress types in apple trees (Malus x domestica Borkh.) exposed to apple scab, waterlogging, and herbicides in a greenhouse. Using a spectroradiometer, we collected spectral signatures of leaves still attached to the tree and utilized machine learning techniques to develop predictive models for detecting stress presence and classifying stress type as early as 1–5 days after exposure. Our findings suggest that changes in spectral reflectance at multiple regions accurately differentiate various types of plant stress on apple trees. Our models were highly accurate (accuracies between 0.94 and 1) when detecting the general presence of stress at an early stage. The wavelengths important for classification relate to photosynthesis via pigment functioning (684 nm) and leaf water (~ 1800–1900 nm), which may be associated with altered gas exchange as a short-term stress response. Overall, our study demonstrates the potential of spectral technology and machine learning for early diagnosis of plant stress, which could lead to reduced environmental burden through optimizing resource utilization in agriculture.

Spectral reflectance and fluorescence is a rapid, non-destructive tool for drought tolerance monitoring in Withania somnifera (L.) Dunal.

Withania somnifera plants were exposed to drought stress for 23days. Relative water content (RWC), gaseous exchange, fluorescence parameters, and spectral reflectance changes were monitored under drought stress. Assimilation rate and RWC decreased by 81% and 65%, respectively, during drought exposure of 23days. Photosynthetic reflectance index (PRI) and water index (WI) showed a decreasing pattern under drought stress and correlated with Amax and RWC. Anthocyanin reflectance index and anthocyanin content increased with drought stress. Similarly, rational among R727, R696, R770, and R731 reflects chlorophyll content and Chl a/b ratio and copes with actual chlorophyll content. Fluorescence changes showed the opening and closing of PSII reaction centers, while absorbance change at 830/875nm showed activity and energy balance of PSI. Non-photochemical quenching increased under drought, which showed depoxydation of xanthine cycle pigment. Energy balance at the acceptor and donor side of PSI adjusted under drought stress by increasing electron carrying limitation at donor side. Energy balance between PSI and PSII is maintained by increasing cyclic electron flux under mild drought stress. Both protective mechanism depoxydation of xanthine cycle pigment and enhancement of cyclic electron flux reduced or diminished under severe drought stress. Decrease in leaf area and stomatal closure may cause a reduction in transpiration that results into loss of RWC and altered physiological processes. Since fluorescence, absorbance change and spectral reflectance are non-invasive measurements that may be used as indicators for assessing drought tolerance in medicinal plants.

Comparison of Individual Sensors in the Electronic Nose for Stress Detection in Forest Stands.

Forests are increasingly exposed to natural disturbances, including drought, wildfires, pest outbreaks, and windthrow events. Due to prolonged droughts in the last years in Europe, European forest stands significantly lost vitality, and their health condition deteriorated, leading to high mortality rates, especially, but not limited to, Norway spruce. This phenomenon is growing, and new regions are being affected; thus, it is necessary to identify stress in the early stages when actions can be taken to protect the forest and living trees. Current detection methods are based on field walks by forest workers or deploying remote sensing methods for coverage of the larger territory. These methods are based on changes in spectral reflectance that can detect attacks only at an advanced stage after the significant changes in the canopy. An innovative approach appears to be a method based on odor mapping, specifically detecting chemical substances which are present in the forest stands and indicate triggering of constitutive defense of stressed trees. The bark beetle attacking a tree, for example, produces a several times higher amount of defense-related volatile organic compounds. At the same time, the bark beetle has an aggregation pheromone to attract conspecifics to overcome the tree defense by mass attack. These substances can be detected using conventional chemical methods (solid-phase microextraction fibers and cartridges), and it is proven that they are detectable by dogs. The disadvantage of classic chemical analysis methods is the long sampling time in the forest, and at the same time, the results must be analyzed in the laboratory using a gas chromatograph. A potential alternative novel device appears to be an electronic nose, which is designed to detect chemical substances online (for example, dangerous gas leaks or measure concentrations above landfills, volcanic activity, etc.). We tested the possibility of early-stage stress detection in the forest stands using an electronic nose Sniffer4D and compared the individual sensors in it for detecting the presence of attacked and dead trees. Our results indicate the promising applicability of the electronic nose for stress mapping in the forest ecosystem, and more data collection could prove this approach.

Temporal Variation in Surface Bidirectional Reflectance of the Railroad Valley Vicarious Calibration Test Site in Nevada

Spectral reflectance-based vicarious calibration (VicCal) requires accurate characterization of the bidirectional reflectance distribution function (BRDF) of the ground-based target. Railroad Valley (RRV) Playa, Nevada, has been used as a VicCal test site since 1995 as it is large, appears stable over time, and has a reasonably consistent surface. This study presents the results of a diurnal measurement cycle that closely replicated illumination geometries for Earth Observing (EO) satellites over a year. By measuring the rate of change of the BRDF with respect to time, we recorded the range of BRDF effects while holding the surface constant with respect to moisture and surface condition variation. The rate of spectral reflectance change increased rapidly with view angle in the backscatter direction, reaching rates of change that are 2.3 and 10.5 times greater in the backscatter than in the forward scatter direction for view angles of 20° and 40°, respectively. The results show that larger off-nadir viewing angles in the backscatter direction are particularly sensitive to changes in solar/view geometries.

Monitoring PHB production in Synechocystis sp. with hyperspectral images.

Microalgae wastewater treatment systems have the potential for producing added-value products. More specifically, cyanobacteria are able to accumulate polyhydroxybutyrates (PHBs), which can be extracted and used for bioplastics production. Nonetheless, PHB production requires proper culture conditions and continue monitoring, challenging the state-of-the-art technologies. The aim of this study was to investigate the application of hyperspectral technologies to monitor cyanobacteria population growth and PHB production. We have established a ground-breaking measurement method able to discern spectral reflectance changes from light emitted to cyanobacteria in different phases. All in all, enabling to distinguish between cyanobacteria growth phase and PHB accumulation phase. Furthermore, first tests of classification algorithms used for machine learning and image recognition technologies had been applied to automatically recognize the different cyanobacteria species from a complex microbial community containing cyanobacteria and microalgae cultivated in pilot-scale photobioreactors (PBRs). We have defined three main indicators for monitoring PHB production: (i) cyanobacteria specific-strain density, (ii) differentiate between growth and PHB-accumulation and (iii) chlorosis progression. The results presented in this study represent an interesting alternative for traditional measurements in cyanobacteria PHB production and its application in pilot-scale PBRs. Although not directly determining the amount of PHB production, they would give insights on the undergoing processes.

Early Detection of Dendroctonus valens Infestation with Machine Learning Algorithms Based on Hyperspectral Reflectance

The red turpentine beetle (Dendroctonus valens LeConte) has caused severe ecological and economic losses since its invasion into China. It gradually spreads northeast, resulting in many Chinese pine (Pinus tabuliformis Carr.) deaths. Early detection of D. valens infestation (i.e., at the green attack stage) is the basis of control measures to prevent its outbreak and spread. This study examined the changes in spectral reflectance after initial attacking of D. valens. We also explored the possibility of detecting early D. valens infestation based on spectral vegetation indices and machine learning algorithms. The spectral reflectance of infested trees was significantly different from healthy trees (p < 0.05), and there was an obvious decrease in the near-infrared region (760–1386 nm; p < 0.01). Spectral vegetation indices were input into three machine learning classifiers; the classification accuracy was 72.5–80%, while the sensitivity was 65–85%. Several spectral vegetation indices (DID, CUR, TBSI, DDn2, D735, SR1, NSMI, RNIR•CRI550 and RVSI) were sensitive indicators for the early detection of D. valens damage. Our results demonstrate that remote sensing technology could be successfully applied to early detect D. valens infestation and clarify the sensitive spectral regions and vegetation indices, which has important implications for early detection based on unmanned airborne vehicle and satellite data.

Monitoring rice lodging grade via Sentinel-2A images based on change vector analysis

ABSTRACT Lodging stress influences the yield, quality, and mechanical harvesting ability of rice (Oryza sativa L.). It is of great significance for the quantitative and objective evaluation of rice yield loss to obtain the rice lodging distribution and grade information rapidly and nondestructively. The purpose of this paper was to establish a remote sensing monitoring method for the grade of rice lodging based on change vector analysis via Sentinel-2A images before and after lodging. Taking the lodging ratio (LR) of rice in the parcel as the characterization index, the changes in spectral reflectance and vegetation index of the rice canopy under different lodging grade stresses were analyzed. A 7-dimensional space vector was composed of a canopy spectral reflectance and vegetation index. A new angle vector was formed by the angle between each vector object and the axis of the canopy spectrum (vegetation index). Based on the difference in feature vectors before and after lodging, a lodging grade monitoring model based on rice parcels was constructed, and the accuracy of the model was verified using field observations. The results showed that the spectral reflectance of the rice canopy after lodging increased with the increase in lodging stress. The difference in vegetation index before and after lodging increased with the increase in lodging severity. Based on the change in the magnitude of the canopy spectral reflectance and its angle vector, and the vegetation index and its angle vector, the R 2 of the rice lodging model was 0.66, 0.66, 0.60, and 0.59, respectively, and the RMSE values were 21.03%, 20.37%, 22.13%, and 22.84%, respectively. Therefore, the accuracy of the model constructed using the vegetation index was the highest. The canopy spectral reflectance and difference in the vegetation index of rice changed regularly with lodging grade stress. The model based on the change magnitude of the vegetation index could effectively realize remote sensing monitoring of the grade of rice lodging in Wuchang.

Assessment of Palm Jumeirah Island’s Construction Effects on the Surrounding Water Quality and Surface Temperatures during 2001–2020

Climate change stressors like rising and warmer seas, increased storms and droughts, and acidifying oceans are rapidly threatening coastal zones, which are the world’s most densely inhabited places. This research assesses the effects of Palm Jumeirah Island (PJI) construction on its surrounding water quality and temperature, using Landsat-7 and 8 spectral and thermal bands for the years 2001, 2014, 2016, 2019, and 2020. To aid in this goal, the changes in water spectral reflectance was observed and interpreted, based on previous research and measurements, to discover the correlation between water quality and its spectral reflectance. Then, the sea surface temperature (SST) was calculated for the years under review and changes in water temperature were evaluated. Finally, the Green Normalized Difference Vegetation Index (GNDVI) and the Normalized Difference Turbidity Index (NDTI) were calculated to estimate water chlorophyll levels and water turbidity, respectively, and changes were observed and interpreted for the time period under review. The present study showed that the PJI construction not only increased the water reflectance in the 0.5–0.8 µm of wavelength, which can be considered to be the increase of suspended sediments and chlorophyll but the water temperature also increased by 7.5 °C during the 19 years. In addition, a gradual increase in the values of GNDVI (by 0.097–0.129) and NDTI (by 0.118~0.172) were observed. A drop in chlorophyll and suspended sediment spectral reflectance and GNDVI and NDTI values were also observed in 2020 compared to 2019 which can be attributed to the 63 to 82% decrease in tourists in Dubai in 2020 as a result of the COVID-19 pandemic. This study aims to draw attention to environmental issues by clarifying the effect of creating artificial islands in the sea and our analysis and results are a suitable reference for specialized hydrological and environmental studies based on spectral information and distance measurements, as presented in this paper.

Early detection of a tree pathogen using airborne remote sensing.

Native forests of Hawai'i Island are experiencing an ecological crisis in the form of Rapid 'Ōhi'a Death (ROD), a recently characterized disease caused by two fungal pathogens in the genus Ceratocystis. Since approximately 2010, this disease has caused extensive mortality of Hawai'i's keystone endemic tree, known as 'ōhi'a (Metrosideros polymorpha). Visible symptoms of ROD include rapid browning of canopy leaves, followed by death of the tree within weeks. This quick progression leading to tree mortality makes early detection critical to understanding where the disease will move at a timescale feasible for controlling the disease. We used repeat laser-guided imaging spectroscopy (LGIS) of forests on Hawai'i Island collected by the Global Airborne Observatory (GAO) in 2018 and 2019 to derive maps of foliar trait indices previously found to be important in distinguishing between ROD-infected and healthy 'ōhi'a canopies. Data from these maps were used to develop a prognostic indicator of tree stress prior to the visible onset of browning. We identified canopies that were green in 2018, but became brown in 2019 (defined as "to become brown"; TBB), and a corresponding set of canopies that remained green. The data mapped in 2018 showed separability of foliar trait indices between TBB and green 'ōhi'a, indicating early detection of canopy stress prior to the onset of ROD. Overall, a combination of linear and non-linear analyses revealed canopy water content (CWC), foliar tannins, leaf mass per area (LMA), phenols, cellulose, and non-structural carbohydrates (NSC) are primary drivers of the prognostic spectral capability which collectively result in strong consistent changes in leaf spectral reflectance in the near-infrared (700-1300 nm) and shortwave-infrared regions (1300-2500 nm). Results provide insight into the underlying foliar traits that are indicative of physiological responses of M.polymorpha trees infected with Ceratocycstis and suggest that imaging spectroscopy is an effective tool for identifying trees likely to succumb to ROD prior to the onset of visible symptoms.

Canopy Temperature Is Regulated by Ecosystem Structural Traits and Captures the Ecohydrologic Dynamics of a Semiarid Mixed Conifer Forest Site

AbstractPlant canopy temperature (Tc) is partly regulated by evaporation and transpiration from the canopy surface and can be used to infer changes in stomatal regulation and vegetation water stress. In this study, we used a thermal Unmanned Aircraft Systems in conjunction with eddy covariance, sap flow, and spectral reflectance data to assess the diurnal characteristics of Tc and water stress status over a semiarid mixed conifer forest in Arizona, USA. Diurnal Tc dynamics were closely related to tree sap flow and changes in spectral reflectance associated with stomatal regulation. Consistent with previously reported deviations, we found that on average Tc was 1.8°C lower than the above canopy air temperature (Ta). However, the relationship between Tc and Ta varied significantly according to tree density and tree height classes, with taller and denser trees exhibiting relatively low |Tc‐Ta| (2.4 and 2.1°C cooler canopies, respectively) compared to shorter and less‐dense tree stands (1.7 and 1.5°C cooler canopies, respectively). We used these data to evaluate space‐borne diurnal measurements of Tc and water stress from the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission. We found that ECOSTRESS observations of Tc accurately tracked seasonal shifts in diurnal surface temperatures and vegetation water stress, and that site‐level observations of heterogeneity in forest composition and structure could be applied to separate the processes of canopy transpiration and soil evaporation within the ECOSTRESS footprint. This study demonstrates how proximal and satellite remote sensing approaches can be combined to reveal the diurnal and seasonally dynamic nature of Tc and water stress.

Analysing the Changes in Water Quality of River Ganga Passing Through Urban Cities with Remote Sensing and GIS Support

River Ganga in India has tremendous self-purification capacity due to its dynamic, turbulent and meandering characteristics. In addition due to the presence of anti-bacterial agents such as Bacteriophages virus killing bacteria and its medicinal properties, the refinement capacity increases. In the present work, water quality samples of river Ganga at Kanpur, Prayagraj, Varanasi, Patna and Bhagalpur were collected and analysed for the years 2017-2019 to assess the change in water quality of the river Ganga in terms of total suspended solids (TSS) and turbidity through remote sensing data and ground observations. The change in spectral reflectance of water along the river in the visible region has been analysed using the Landsat-8 multispectral remote sensing data and water quality samples have been collected from all the sites on the date of pass of Landsat-8 satellite. The results obtained shows that the satellite based remote sensing approach can be effectively used to make qualitative and quantitative estimates of total suspended solids and turbidity using nonlinear equations with high accuracy, even in the absence of field observations.

Pulsed Electrophoretic Deposition of Carbon Nanotube Based Low-Reflective Coatings for Space Applications

Space observatory missions requires the development of low-reflectivity surfaces/coatings for space-borne instruments, such as seeker telescopes, optical sensors, etc., to minimize stray and reflected light across the visible and infrared wavebands for facilitating the direct exoplanet detection and characterization. All space application materials require lightweight, good thermo-structural properties, and durability when exposed to harsh environments, such as UV, atomic oxygen, vacuum, etc. The excellent mechanical, thermal, optical, and electrical properties of carbon nanotubes (CNTs) make them as ideal coating materials for obtaining low reflectivity surfaces for space applications.In this presentation, Faraday Technology Inc. will discuss an innovative electrophoretic deposition (EPD) manufacturing process, based on the use of pulsed electric fields, for controlled, reproducible, scalable application of low reflective CNT based coatings. The low-reflectivity CNT coatings have been successfully deposited on a variety of geometries: flat coupons, curved surfaces, sharp edges, and internal and external surface of square tubes using the FARADAYIC® EPD process with different anode-cathode configurations (Figure 1 A). The CNT coatings show the total hemispherical reflection of ~ 1% across visible to near infrared (NIR) wavebands, which is 5 times better than the reflectance of the typically used Z306 black paint (Figure 1 B). Most importantly, the CNT coating shows no measurably discernible spectral reflectance changes after 2500 equivalent solar hours (ESH) UV/VUV illumination (Figure 1B). As a comparison, Z306 black paint shows more than 2% reflectance increases with same ESH UV/VUV illumination. These CNT coatings also withstood simulated launch conditions vibrational tests, and demonstrated no weight loss or optical degradation. Furthermore, the CNT coatings were evaluated for atomic oxygen erosion resistance in a simulated low earth orbit (LEO) environment, and showed enhanced resilience when compared to Kapton and HOPG.In summary, a scalable manufacturing process for fabricating CNT based coatings with low reflectivity across visible to near infrared wavebands has been demonstrated and shown great potential in facilitating space observatory missions by minimizing stray and reflected light on targeted telescope and baffle materials and construction. The CNT based coatings will also be beneficial for optoelectronic fields for minimizing unwanted surface reflections for solar thermal power generation and storage, solar-driven steam generation for sanitization and water purification, and so on. Acknowledgements: The financial support of NASA SBIR program through contracts No. 80NNSC18P2062 & 80NSSC19C0177 is acknowledged. The authors acknowledge Alan Hopkins, Peter Fuqua, Amber Hennessy, Aura C Labatete-Goeppinger, Martin Ciofalo, and Lee Wizda from the Aerospace Corporation for simulated launch conditions vibration tests, optical characterization, and UV radiation effect tests. David Oakes and Alan Hoskinson from Physical Sciences Inc. are acknowledged for the atomic oxygen erosion tests on the CNT based coatings. Figure 1

Protective Effects of Salicylic Acid and Calcium Chloride on Sage Plants (Salvia officinalis L. and Salviaelegans Vahl) under High-Temperature Stress.

High-temperature stress is a major risk to fresh-market Salvia production, and heat intolerance is a major constraint in sage cultivation, particularly during the hot summer season. Previously, we investigated heat tolerance in five common-market cultivars of sage plants using leaf relative injury (RI) values and found that S. elegans Vahl (SE) and S. officinalis L. (SO) were the most and least heat-tolerant species, respectively. The exogenous applications of salicylic acid (SA) and calcium chloride (CaCl2) to alleviate heat stress in various species have been extensively studied, but reports of the effects of SA and CaCl2 treatments on the heat tolerance of sage plants are scarce. The objective of this study was to investigate how SA and CaCl2 affect the physiology and morphology of SE and SO plants under high-temperature conditions. Potted plants were pretreated with SA (0, 100, 200, 400, and 800 μM) and CaCl2 (0, 5, 10, and 15 mM), alone and combined, exposed to 55 °C and 80% humidity for 30 min, then placed in an environment-controlled chamber at 30 °C for three days and evaluated for changes in phenotypic appearance, RI, spectral reflectance, and chlorophyll fluorescence indices at different time intervals. Plants watered without chemical solutions were used as controls. Our results show that the growth of SO plants pretreated with SA and CaCl2 was more robust, compared with control plants, which were considerably affected by heat stress, resulting in brown, withered leaves and defoliation. The effects of the combined applications of SA (100 μM) and CaCl2 (5 mM) to SO plants were superior to control plants in increasing values of soil-plant analysis development (SPAD), normalized difference vegetation index (NDVI), and the maximal quantum yield of photosystemII photochemistry (Fv/Fm), while reducing RI%. Furthermore, SO plants exhibited higher SPAD and Fv/Fm values and lower RI% than SE plants in combined treatments at all time intervals after heat stress, implying that different genotypes displayed variations in their SPAD, Fv/Fm, and RI%. Thus, a combined treatment of 100 μM of SA and 5 mM of CaCl2 is effective and beneficial to plant appearance and ability to ameliorate heat stress. These indices can be used as indicators to characterize the physiology of these plants and applied on a commercial scale for informing the development of rapid and precise management practices on bedded sage plants grown in plant factories to achieve maximum market benefit.

Monitoring of Wheat Powdery Mildew under Different Nitrogen Input Levels Using Hyperspectral Remote Sensing

Both wheat powdery mildew severities and nitrogen input levels can lead to changes in spectral reflectance, but they have been rarely studied simultaneously for their effect on spectral reflectance. To determine the effects and influences of different nitrogen input levels on monitoring wheat powdery mildew and estimating yield by near-ground hyperspectral remote sensing, Canopy hyperspectral reflectance data acquired at Feekes growth stage (GS) 10.5.3, 10.5.4, and 11.1 were used to monitor wheat powdery mildew and estimate grain yield under different nitrogen input levels during the 2016–2017, 2017–2018, 2018–2019 and 2019–2020 seasons. The relationships of powdery mildew and grain yield with vegetation indices (VIs) derived from spectral reflectance data across the visible (VIS) and near-infrared (NIR) regions of the spectrum were studied. The relationships of canopy spectral reflectance or first derivative spectral reflectance with powdery mildew did not differ under different nitrogen input levels. However, the dynamics of VIs differed in their sensitivities to nitrogen input levels, disease severity, grain yield, The area of the red edge peak (Σdr680–760 nm) was a better overall predictor for both disease severity and grain yield through linear regression models. The slope parameter estimates did not differ between the two nitrogen input levels at each GSs. Hyperspectral indices can be used to monitor wheat powdery mildew and estimate grain yield under different nitrogen input levels, but such models are dependent on GS and year, further research is needed to consider how to incorporate the growth stage and year-to-year variation into future applications.

Assessment of water quality in a tropical ramsar wetland of southern India in the wake of COVID-19

The novel SARS-CoV-2 virus influenced the world severely in the first half of 2020 caused shut down of all kind of human activities. It is reported that a word-wide ecological improvement in terms of air quality and water quality during this lock down period. In the present study, an attempt has been made to study the progression in water quality through examining suspended particulate matter using remote sensing data in a tropical Ramsar site viz, Asthamudi Lake in Southern India. The change in spectral reflectance of water along the study area were analyzed and suspended particulate matter (SPM) is estimated from Landsat 8 OLI images. A comparison analysis of pre and co lockdown periods reveal that the concentration of SPM values during lockdown (mean SPM 8.01 mg/l) is lower than that of pre-lockdown (10.03 mg/l). The time series analysis of last five-year data from 2015 to 2020 also shows an average decrease of 43% in SPM concentration during lockdown period compared to the last five-year average value of 9.1 mg/l. The reasons for improvement of SPM in water quality during the lockdown period in April–May 2020 was discussed, in terms of the role of anthropogenic activities and strategies for the sustainable management of coastal ecosystems and water resources in the Asthamudi Lake were also presented.