Water Band Index Research Articles

Detection of Emerging Stress in Trees Using Hyperspectral Indices as Classification Features

This research presents a classification methodology for the detection of new or emerging stress in trees using indices derived from hyperspectral data and tests whether existing hyperspectral indices are effective when used as the classification features for this problem. We tested six existing indices—Water Band Index (WBI), Gitelson–Merzlyak B Index (GMb), Normalized Phaeophytization Index (NPQI), Combined Carotenoid/Chlorophyll Ratio Index (CCRI), Photochemical Reflectance Index (PRI), and Red-Edge Chlorophyll Index (CIre)—along with a seventh Test Index—generated as a composite of PRI and Cire—as classification features. Analysis was conducted using data collected from trees with and without emerald ash borer (EAB) infestation to develop a methodology that could be adapted to measure emerging stress from other pathogens or invasive pests in other tree species. Previous work has focused specifically on the identification of damage or stress symptoms caused by a specific pathogen. In this study, we adapted that work to develop a system of classification that can be applied to the identification of stress symptoms from a range of sources, measurable in trees based on spectral response and, in some cases, detectable prior to the onset of visible symptoms that can be measured through human observation. Our data indicate that existing indices derived from hyperspectral data are effective as classification features when measuring spectral responses indicative of emerging stress in trees.

Vegetation Indices for Early Grey Mould Detection in Lettuce Grown under Different Lighting Conditions.

Early detection of pathogenic fungi in controlled environment areas can prevent major food production losses. Grey mould caused by Botrytis cinerea is often detected as an infection on lettuce. This paper explores the use of vegetation indices for early detection and monitoring of grey mould on lettuce under different lighting conditions in controlled environment chambers. The aim was focused on the potential of using vegetation indices for the early detection of grey mould and on evaluating their changes during disease development in lettuce grown under different lighting conditions. The experiment took place in controlled environment chambers, where day/night temperatures were 21 ± 2/17 ± 2 °C, a 16 h photoperiod was established, and relative humidity was 70 ± 10% under different lighting conditions: high-pressure sodium (HPS) and light-emitting diode (LED) lamps. Lettuces were inoculated by 7-day-old fungus Botrytis cinerea isolate at the BBCH 21. As a control, non-inoculated lettuces were grown under HPS and LEDs (non-inoculated). Then, the following were evaluated: Anthocyanin Reflectance Index 2 (ARI2); Carotenoid Reflectance Index 2 (CRI2); Structure Intensive Pigment Index (SIPI); Flavanol Reflectance Index (FRI); Greenness (G); Greenness 2 (G2); Redness (R); Blue (B); Blue Green Index 2 (BGI2); Browning Index 2 (BRI2); Lichtenthaler Index 1 (LIC1); Pigment Specific Simple Ratio (PSSRa and PSSRb); Gitelson and Merzlyak (GM1 and GM2); Zarco Tejada-Miller Index (ZMI); Normalized Difference Vegetation Index (NDVI); Simple Ratio (SR); Red-Eye Vegetation Stress Index (RVSI); Photochemical Reflectance Index (PRI); Photochemical Reflectance Index 515 (PRI515); Water Band Index (WBI); specific disease index for individual study (fD); Healthy Index (HI); Plant Senescence Reflectance (PSRI); Vogelmann Red Edge Index (VREI1); Red Edge Normalized Difference Vegetation Index (RENDVI); and Modified Red Edge Simple Ratio (MRESRI). Our results showed that the PSRI and fD vegetation indices significantly detected grey mould on lettuce grown under both lighting systems (HPS and LEDs) the day after inoculation. The results conclusively affirmed that NDVI, PSRI, HI, fD, WBI, RVSI, PRI, PRI515, CRI2, SIPI, chlorophyll index PSSRb, and coloration index B were identified as the best indicators for Botrytis cinerea infection on green-leaf lettuce (Lactuca sativa L. cv Little Gem) at the early stage of inoculated lettuce's antioxidative response against grey mould with a significant increase in chlorophyll indices.

Detection of Fungal Diseases in Lettuce by VIR-NIR Spectroscopy in Aquaponics.

One of the main challenges facing the development of aquaponics is disease control, due on one hand to the fact that plants cannot be treated with chemicals because they can lead to mortality in cultured fish. The aim of this study was to apply the visible-near-infrared spectroscopy and vegetation index approach to test aquaponically cultivated lettuce (Lactuca sativa L.) infected with different fungal pathogens (Aspergillus niger, Fusarium oxysporum, and Alternaria alternata). The lettuces on the third leaf formation were placed in tanks (with dimensions 1 m/0.50 m/0.35 m) filled up with water from the aquaponics system every second day. In this study, we included reference fungal strains Aspergillus niger NBIMCC 3252, Fusarium oxysporum NBIMCC 125, and Alternaria alternata NBIMCC 109. Diffuse reflectance spectra of the leaves of lettuce were measured directly on the plants using a USB4000 spectrometer in the 450-1100 nm wavelength range. In near-infrared spectral range, the reflectance values of infected leaves are lower than those of the control, which indicates that some changes in cell structures occurred as a result of the fungal infection. All three investigated pathogens had a statistically significant effect on leaf water content and water band index. Vegetative indices such as Chlorophyll Absorption in Reflectance Index (CARI), Modified chlorophyll absorption in reflectance index (MCARI), Plant Senescence Reflectance Index (PSRI), Red Edge Index (REI2), Red Edge Index (REI3), and Water band index (WBI) were found to be effective in distinguishing infected plants from healthy ones, with WBI demonstrating the greatest reliability.

Spectral indices for characterizing lentil accessions in the dryland of Pacific Northwest

Lentil is a cool season legume crop suffering terminal stress in different parts of the world, especially in North America, Australia, Southwest Asia and North Africa. Drought stress, which is often linked with high temperatures, is very damaging to grain legumes in practically all agricultural environments. In this experiment, 49 accessions were sown with a check variety in an augmented design in Whitlow farm 2015, central ferry farm 2016 and Spillman farm 2016, Washington state university. The three different locations were in the dryland of southeastern Washington. The collected 49 accessions were screened, where 32 accession were taken from the United States Department of Agriculture's single plant-derived core collection and 16 from lentil reference population of ICARDA and one check variety was Avondale. Secondary traits such as green Normalized difference vegetation index (GNDVI), red Normalized difference vegetation index (RNDVI), photochemical reflectance index (PRI), red Normalized difference vegetation index (RNDVI), water band index (WBI), and normalized water index (NWI) were collected using a multi spectral reflectance device and agronomic metrics such as days to flowering (DF), biomass yield per plant (BYP) and seed yield per plant (SYP) data were also collected as well. Multi trait genotype ideotype distance (MGIDI) addresses the restrictions of AMMI and GGE analysis by using BLUP and ideotype distance, which can properly select genotypes considering higher yields as well as consider other desirable parameters GNDVI, RNDVI, PRI, WBI, NWI. The accessions Avondale, PI 368647, ILL7090, PI33690, PI 5187, ILL 86, ILL595, PI 533690, PI 518734, ILL 1649, ILL 4781 were selected as the ten best genotypes and Avondale, ILL7090, PI533690 were less affected by G x E interaction and selected for further evaluation based on AMMI, GGE and MGIDI index.

Response of crop water indices to soil wetness and vegetation water content

Hyperspectral sensors offer a unique opportunity to monitor crop stress through the multiple narrow bands from visible to short wave infrared wavelengths. It provides prominent water absorption bands in near and shortwave regions centered at 970 nm, 1200 nm, 1440 nm, 1664 nm, 1950 nm and 2250 nm, which are unavailable with conventional multispectral sensors. It gives bands which otherwise are not available with traditional multispectral sensors. However, the spectral regions of 1350–1480, 1800–2000 and 2350–2500 nm, has strong absorption of light due to water vapour and hence not useful. This study investigates the indices, namely the Water Band Index (WBI), Normalized Difference Water Index (NDWI), Normalized Difference Infrared Index (NDII) and Land Surface Water Index (LSWI), derived from the narrow bands of the AVIRIS-NG sensor for its response to varying degree of soil wetness and vegetation water content. The analyses showed that the WBI and NDWI could discern the saturated soil. At the same time, The NDII and LSWI displayed a better dynamic range because of the stronger absorption due to soil/vegetation liquid water content with reference to the base wavelength of 862 nm, which enables the depiction of the different degrees of soil wetness and vegetation water content. Since the water indices have varied responses to the same soil wetness and vegetation water content, a Water Indices vs Vegetation Index triangle space was used to derive the Water Condition Index (WCI). The analyses of the WCI’s indicate that the values of the water indices are normalized and have similar values under different soil wetness and vegetation water content. However, the WCIWBI had lower values among WCIs, and WCILSWI could demonstrate the variation in the soil wetness and vegetation water content owing to its better dynamic range.

Evaluating automated endmember extraction for classifying hyperspectral data and deriving spectral parameters for monitoring forest vegetation health.

The hyperspectral remote sensing datasets possess high capability in differentiating the spectrally similar features, and thus, they are immensely important in various forestry activities, especially vegetation classifications. But extracting endmembers for data training is a challenging task. The present study is focused on the use of automated endmember extraction technique for deriving endmembers during the unavailability of ground spectra. We used the Sequential Maximum Angle Convex Cone (SMACC) method on EO-1 Hyperion data for endmember extraction in the Barkot forest range of Dehradun district, Uttarakhand which were used for classification of the study area using support vector machine (SVM). Further, we estimated the vegetation health of the region by assigning the threshold weights for various derived environmental variables such as NDVI (Normalised Difference Vegetation Index), CRI (Carotenoid Reflectance Index), Anthocyanin Reflectance Index (ARI), Modified Simple Ratio (MSR), Modified Chlorophyll Absorption Ratio Index (MCARI) and WBI (Water Band Index). Then, to further validate the health of the forest types, we correlated it with the Land Surface Temperature (LST) from LANDSAT 5 ETM + data. The results showed a high classification accuracy of 89.13%. The healthy vegetation area coverage of the area was about 78.6% with most healthy class as Tectona grandis and Shorea robusta and its correlation with LST showed lower temperature range in healthy vegetation areas and vice versa. The study was useful in determining the superiority of SMACC automated endmember extraction and estimating the vegetation health.

Hyperspectral Vegetation Indices to Assess Water and Nitrogen Status of Sweet Maize Crop

The deployment of novel technologies in the field of precision farming has risen to the top of global agendas in response to the impact of climate change and the possible shortage of resources such as water and fertilizers. The present research addresses the performance of water and nitrogen-sensitive narrow-band vegetation indices to evaluate the response of sweet maize (Zea mays var. saccharata L.) to different irrigation and nitrogen regimes. The experiment was carried out in Valenzano, Bari (Southern Italy), during the 2020 growing season. Three irrigation regimes (full irrigation, deficit irrigation, and rainfed) and two nitrogen levels (300 and 50 kg ha−1) were tested. During the growing season, a Field Spec Handheld 2 spectroradiometer operating in the range of 325–1075 nm was utilized to capture spectral data regularly. In addition, soil water content, biometric parameters, and physiological parameters were measured. The DATT index, based on near-infrared and red-edge wavelengths, performed better than other indices in explaining the variation in chlorophyll content, whereas the double difference index (DD) showed the greatest correlation with the leaf–gas exchange. The modified normalized difference vegetation index (NNDVI) and the ratio of water band index to normalized difference vegetation index (WBI/NDVI) showed the highest capacity to distinguish the interaction of irrigation x nitrogen, while the best discriminating capability of these indices was under a low nitrogen level. Moreover, red-edge-based indices had higher sensitivity to nitrogen levels compared to the structural and water band indices. Our study highlighted that it is critical to choose proper narrow-band vegetation indices to monitor the plant eco-physiological response to water and nitrogen stresses.

Spectral Indices of Vegetation Condition and Soil Water Content Reflect Controls on CH4 and CO2 Exchange in Sphagnum‐Dominated Northern Peatlands

AbstractNorthern peatlands play an important role in the global C cycle due to their large C stocks and high potential methane (CH4) emissions. The CH4 and CO2 cycles of these systems are closely linked to hydrology, with water table level regulating the balance of oxic and anoxic conditions and the water content of Sphagnum mosses that dominate primary production. Previous work has demonstrated that hyperspectral indices well‐suited to the detection of altered hydrology in Sphagnum peatlands are also highly correlated with GPP. However, little work has been done to extend these findings to CH4 effluxes. In this study, we evaluate the utility of four hyperspectral indices, two reflecting vegetation photosynthetic function (chlorophyll index (CI); normalized difference vegetation index) and two reflecting water content (wetness index (WI); floating water band index), for detecting effects of altered water table, precipitation, and vegetation community on CH4 and CO2 exchange in two peatland mesocosm studies. We found that CI is a good predictor of net CO2 exchange, and that it captured both drought and vegetation effects consistently across a broad range of vegetation treatments. Further, we demonstrate for the first time that WI combined with CI explained a significant percentage of CH4 efflux (R2 = 0.32–0.57). Our results indicate that CI and WI together may be effective tools for detecting effects of altered hydrology and vegetation on northern Sphagnum‐peatland CH4 and CO2 emissions, with implications for detecting and modeling changes in emissions of greenhouse gases at scales ranging from the ecosystem to the Earth system.

Application of an innovative narrow band vegetation index to corn leaves to explore the characteristics of corn spectra under Cu2+ stress

ABSTRACT In this study, an innovative narrow band vegetation index (INBVI) was proposed to explore the sensitive regions of corn leaves spectra under copper stress. In 2016, the spectra and Cu2+ contents of corn leaves under different copper stress concentrations were measured by designing corn stress experiments with different copper gradients. Firstly, we divided the spectra of corn leaves into 11 subintervals, and chose the spectral reflectance which corresponded to the middle wavelength of each subinterval to build INBVI. Then, the Pearson correlation coefficient (r) and Root Mean Square Error (RMSE) were calculated, and we analysed correlation of the INBVI and Cu2+ contents in corn leaves. What’s more, we compared it with the three conventional vegetation indexes of Water Band Index (WBI), Modified Chlorophyll Absorption Ratio Index (MCARI) and Normalized Difference Water Index (NDWI). Finally, the spectra of corn leaves obtained under the same experimental conditions in other year were used to verify the stability and effectiveness of INBVI. The results showed that the four subintervals of Green peak, Red edge, Near valley and Near peak A were sensitive intervals, because the absolute value of r exceeded .9, and the RMSE values were lower than other subintervals and the three conventional vegetation indexes. We verified the above results and found the sensitive intervals were Green peak, Red edge, Near valley and Near peak A, which were consistent with the sensitive subintervals obtained from the experiment data in 2016, and confirmed the effectiveness and stability of INBVI. The INBVI proposed in this paper can be used as a method to monitor copper pollution in corn leaves, and provide some theoretical support for the research of heavy metal pollution in other crops.

Leaf Pigments, Surface Wax and Spectral Vegetation Indices for Heat Stress Resistance in Pea

Pea is a grain legume crop commonly grown in semi-arid temperate regions. Pea is susceptible to heat stress that affects development and reduces yield. Leaf pigments and surface wax in a crop canopy make the primary interaction with the environment and can impact plant response to environmental stress. Vegetation indices can be used to indirectly assess canopy performance in regard to pigment, biomass, and water content to indicate overall plant stress. Our objectives were to investigate the contribution of leaf pigments and surface wax to heat avoidance in pea canopies, and their associations with spectral vegetation indices. Canopies represented by 24 pea cultivars varying in leaf traits were tested in field trials across six environments with three stress levels in western Canada. Compared with the control non-stress environments, heat stress reduced leaf lamina and petiole chlorophyll a, chlorophyll b, and carotenoid concentrations by 18–35%, and increased leaf lamina chlorophyll a/b ratio, anthocyanin and wax concentrations by 24–28%. Generally, greater leaf pigment and wax concentrations were associated with cooler canopy temperature and high heat tolerance index (HTI) values. Upright cultivars had higher HTI values, whereas the lowest HTI was associated with normal leafed vining cultivars. Vegetation indices, including photochemical reflectance index (PRI), green normalized vegetation index (GNDVI), normalized pigment chlorophyll ratio index (NPCI), and water band index (WBI), had strong correlations with HTI and with heat avoidance traits. This study highlights the contribution of pigments and wax as heat avoidance traits in crop canopies, and the potential application of spectral measurements for selecting genotypes with more heat resistant vegetation.

Utilizing spectral vegetation indices for yield assessment of tomato genotypes grown in arid conditions

Tomato is among important vegetable crops cultivated in different climates; however, heat stress can greatly affect fruit quality and overall yield. Crop reflectance measurements based on ground reflectance sensor data are reliable indicators of crop tolerance to abiotic stresses. Here, we report on using non-destructive spectral vegetation indices to monitor yield traits of 10 tomato genotypes transplanted on three different dates (Aug. 2, Sept. 3 and Oct. 1) during 2019 growing season in the Riyadh region. The ten genotypes comprised six commercial cultivars–(Pearson Improved, Strain B, Valentine, Marmande VF, Super Strain B, and Pearson early) ––and four local Saudi cultivars (Al-Ahsa, Al-Qatif, Hail and Najran). Spectral reflectance data were utilized using a FieldSpec 3 spectroradiometer in the range of 350–2500 nm to calculate nine vegetation indices (VIs): Normalized Water Band Index (NWBI), Difference Water Index (NDWI), Photochemical Reflectance Index (PRI), Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Red Edge Normalized Difference Vegetation Index, Soil Adjusted Vegetation Index (SAVI), Red Edge Normalized Difference Vegetation Index (RENDVI), Renormalized Difference Vegetation Index (RDVI), and Normalized Difference Nitrogen Index (NDNI). VIs and yield parameters (total fruit yield, harvest index) revealed that second transplanting date was optimal for all the genotypes. Valentine showed the best growth performance followed by Najran, Hail, Super Strain B and finally Pearson early. For all the three transplanting dates, Valentine recorded the highest total fruit yield. Additionally, some genotypes had no significant differences in the VIs values or the total fruit yield between the second and third transplanting dates. This study indicated that yield parameters could be linked to rapid, non-destructive hyperspectral reflectance data to predict tomato production under heat stress.

Spectral Diffractive Lenses for Measuring a Modified Red Edge Simple Ratio Index and a Water Band Index.

We propose a novel type of spectral diffractive lenses that operate in the ±1-st diffraction orders. Such spectral lenses generate a sharp image of the wavelengths of interest in the +1-st and –1-st diffraction orders. The spectral lenses are convenient to use for obtaining remotely sensed vegetation index images instead of full-fledged hyperspectral images. We discuss the design and fabrication of spectral diffractive lenses for measuring vegetation indices, which include a Modified Red Edge Simple Ratio Index and a Water Band Index. We report synthesizing diffractive lenses with a microrelief thickness of 4 µm using the direct laser writing in a photoresist. The use of the fabricated spectral lenses in a prototype scheme of an imaging sensor for index measurements is discussed. Distributions of the aforesaid spectral indices are obtained by the linear scanning of vegetation specimens. Using a linear scanning of vegetation samples, distributions of the above-said water band index were experimentally measured.

Reflectance spectroscopy to characterize the response of Corymbia calophylla to Phytophthora root rot and waterlogging stress

Abstract The health of Corymbia calophylla (marri), a keystone tree species in the native forests of southwest Western Australia, has been in decline for the past few decades. Phytophthora root disease and waterlogging have often been cited as contributing to this decline. Traditional methods (i.e., field surveys and sampling) of mapping Phytophthora root infection in the field are time-consuming and expensive; thus, the potential of reflectance spectroscopy to characterize marri response to Phytophthora and waterlogging stress was investigated. Twelve-month old marri plants were infected with either P. cinnamomi or P. multivora in two glasshouse trials and waterlogged for 24 h each fortnight. Spectral measurements with a portable high-resolution spectroradiometer were taken weekly. Plant biophysical measurements were taken at harvest time. Normalized difference spectral index (NDSI) was calculated for every combination of reflectance values between 400 and 2500 nm for all time points, correlated with the treatment effects and displayed as heat maps. Narrowband vegetation indices (VIs), utilizing different wavelengths of the electromagnetic spectrum, were also calculated from the spectral data. The Phytophthora treatments did not cause significant differences with the biophysical measurements in both trials. In the second trial, the waterlogging treatment significantly lowered plant top dry weight (P = 0.016) and diameter (P = 0.044). Reflectance values plotted against wavelength displayed differences between treatments as well as a seasonal trend. The NDSI heat maps indicated that the Phytophthora and waterlogging treatment effects were strongest correlated with bandwidths in the visible and near-infrared portions of the electromagnetic spectrum (538–558 nm and 701–709 nm). Six of the VIs (normalized difference nitrogen index 2, anthocyanin reflectance index 1, photochemical reflectance index, Carter index 1, Vogelman index 3 and water band index) were able to track the biochemical changes in the leaves over the 10 weeks, confirming the seasonal trend. The interaction effect between P. cinnamomi, waterlogging and elapsed time in the first trial was significant for water band index (P = 0.010). This study demonstrates that reflectance spectroscopy holds promise for characterizing marri response but more work needs to be done to identify the optimum wavelengths for identifying Phytophthora and waterlogging stress with marri.

Improving Soil Moisture Assessment of Turfgrass Systems Utilizing Field Radiometry

The need for water conservation continues to increase as global freshwater resources dwindle. Turfgrass mangers are adapting to these concerns by implementing new tools to reduce water consumption. Time-domain reflectometer (TDR) soil moisture sensors can decrease water usage when scheduling irrigation, but nonuniformity across unsampled locations creates irrigation inefficiencies. Remote sensing data have been used to estimate soil moisture stress in turfgrass systems through the normalized difference vegetation index (NDVI). However, numerous stressors other than moisture constraints impact NDVI values. The water band index (WBI) is an alternative index that uses narrowband, near-infrared light reflectance to estimate moisture limitations within the plant canopy. The green-to-red ratio index (GRI) is a vegetation index that has been proposed as a cheaper alternative to WBI as it can be measured using digital values of visible light instead of relying on more costly hyperspectral reflectance measurements. A replicated 2 × 3 factorial experimental design was used to repeatedly measure turf canopy reflectance and soil moisture over time as soils dried. Pots of ‘007’ creeping bentgrass (CBG) and ‘Latitude 36’ hybrid bermudagrass (HBG) were grown on three soil textures: United States Golf Association (USGA) 90:10 sand, loam, and clay. Reflectance data were collected hourly between 07:00 and 19:00 using a hyperspectral radiometer and volumetric water content (VWC) data were collected continuously using an embedded soil moisture sensor from soil saturation until complete turf necrosis by drought stress. The WBI had the strongest relationship to VWC (r = 0.62) compared to GRI (r = 0.56) and NDVI (r = 0.47). The WBI and GRI identified significant moisture stress approximately 28 h earlier than NDVI (p = 0.0010). Those metrics also predicted moisture stress prior to fifty percent visual estimation of wilt (p = 0.0317), with lead times of 12 h (WBI) and 9 h (GRI). By contrast, NDVI provided 2 h of prediction time. Nonlinear regression analysis showed that WBI and GRI can be useful for predicting moisture stress of CBG and HBG grown on three different soil textures in a controlled environment.

Ecophysiological Variability of Alnus viridis (Chaix) DC. Green Alder Leaves in the Bieszczady Mountains (Poland).

Alnus viridis (Chaix) DC., green alder, is a fast-growing shrub that grows expansively in the European mountainside. In Poland, A. viridis naturally occurs only in the Bieszczady Mountains (south-eastern part of the country), above the upper forest border. In this study, we assessed the potential of green alder to expand in post-farming areas in the Bieszczady Mountains. We investigated the effects of topographical, climatic, and edaphic characteristics of four various study sites on the physiological and morphological properties of A. viridis leaves in order to answer the question whether the growth of plants in lower positions improves their physiological condition to such an extent that it increases the species invasiveness. This is the first comprehensive ecophysiological study of this species to be carried out in this part of Europe. The photochemical efficiency of PSII, the chlorophyll content, and leaf 13C and 15N discrimination were analyzed. On the basis of leaf radiation reflection, coefficients such as reflectance indices of anthocyanins, carotenoids, flavonoids (ARI2, CRI1, FRI), photochemical index of reflection (PRI), and the water band index (WBI) were calculated. We observed favorable physiological effects in A. viridis plants growing in locations below the upper forest border compared to plants growing in higher locations. As a result, A. viridis may become an invasive species and disturb the phytocoenotic balance of plant communities of the altitudinal zones in the Polish Western Carpathians.

Аналіз характеристик та обґрунтування індексів рослинності

In this article there has been given the analyzing of non-fiction sources in order to find out the popular indexes of the vegetable control parameters. There are such parameters as chlorophyll presence, water containing, the efficiency of a light use, nitrogen and carbon presence, foliage pigmentation.  The chlorophyll presence control is conducted by such indexes as the difference vegetation index, the global environmental monitoring index, the infrared percentage vegetation index, the modified simple ratio, the modified Soil adjusted vegetation index 2, the non-linear index, the normalized difference vegetation index, the optimized soil adjusted vegetation index, the renormalized difference vegetation index, the soil adjusted vegetation index, the simple ratio etc. The control of water content can be based on the moisture stress index, the normalized difference infrared index, the normalized difference water index, the normalized multi-band drought index, the water band index. There is the photochemical reflectance index, the structure insensitive pigment index, the red green ratio index for the efficiency control of the light use. The nitrogen presence is controlled by the normalized differential nitrogen index. The carbon presence can be detected by the implementing of cellulose absorption index, the lignin cellulose absorption index, the normalized difference lignin index, the plant senescence reflectance index. The foliage pigmentations can be controlled by the means of the anthocyanin reflectance index 1, the anthocyanin reflectance index 2, the carotenoid reflectance index 1, and the carotenoid reflectance index 2. The specificity of the presented indexes is described; the calculation formulas and its main straits are proposed.

Using Spectral Indices to Estimate Water Content and GPP inSphagnumMoss and Other Peatland Vegetation

Peatlands provide important ecosystem services including carbon storage and biodiversity conservation. Remote sensing shows potential for monitoring peatlands, but most off-the-shelf data products are developed for unsaturated environments and it is unclear how well they can perform in peatland ecosystems. Sphagnum moss is an important peatland genus with specific characteristics which can affect spectral reflectance, and we hypothesized that the prevalence of Sphagnum in a peatland could affect the spectral signature of the area. This article combines results from both laboratory and field experiments to assess the relationship between spectral indices and the moisture content and gross primary productivity (GPP) of peatland (blanket bog) vegetation species. The aim was to consider how well the selected indices perform under a range of conditions, and whether Sphagnum has a significant impact on the relationships tested. We found that both water indices tested [normalized difference water index (NDWI) and floating water band index (fWBI)] were sensitive to the water content changes in Sphagnum moss in the laboratory, and there was little difference between them. Most of the vegetation indices tested [the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), structure insensitive pigment index (SIPI), and chlorophyll index (CIm)] were found to have a strong relationship with GPP both in the laboratory and in the field. The NDVI and EVI are useful for large-scale estimation of GPP, but are sensitive to the proportion of Sphagnum present. The CIm is less affected by different species proportions and might therefore be the best to use in areas where vegetation species cover is unknown. The photochemical reflectance index (PRI) is shown to be best suited to small-scale studies of single species.

Deep Phenotyping of Yield-Related Traits in Wheat

The complex formation of grain yield (GY) is related to multiple dry matter (DM) traits; however, due to their time-consuming determination, they are not readily accessible. In winter wheat (Triticum aestivum L.), both agronomic treatments and genotypic variation influence GY in interaction with the environment. Spectral proximal sensing is promising for high-throughput non-destructive phenotyping but was rarely evaluated systematically for dissecting yield-related variation in DM traits. Aiming at a temporal, spectral and organ-level optimization, 48 vegetation indices were evaluated in a high-yielding environment in 10 growth stages for the estimation of 31 previously compared traits related to GY formation—influenced by sowing time, fungicide, N fertilization, and cultivar. A quantitative index ranking was evaluated to assess the stage-independent index suitability. GY showed close linear relationships with spectral vegetation indices across and within agronomic treatments (R2 = 0.47–0.67 ***). Water band indices, followed by red edge-based indices, best used at milk or early dough ripeness, were better suited than the widely used normalized difference vegetation index (NDVI). Index rankings for many organ-level DM traits were comparable, but the relationships were often less close. Among yield components, grain number per spike (R2 = 0.24–0.34 ***) and spike density (R2 = 0.23–0.46 ***) were moderately estimated. GY was mainly estimated by detecting total DM rather than the harvest index. Across agronomic treatments and cultivars, seasonal index rankings were the most stable for GY and total DM, whereas traits related to DM allocation and translocation demanded specific index selection. The results suggest using indices with water bands, near infrared/red edge and visible light bands to increase the accuracy of in-season spectral phenotyping for GY, contributing organ-level traits, and yield components, respectively.

High-Throughput Field Phenotyping Traits of Grain Yield Formation and Nitrogen Use Efficiency: Optimizing the Selection of Vegetation Indices and Growth Stages.

High-throughput, non-invasive phenotyping is promising for evaluating crop nitrogen (N) use efficiency (NUE) and grain yield (GY) formation under field conditions, but its application for genotypes differing in morphology and phenology is still rarely addressed. This study therefore evaluates the spectral estimation of various dry matter (DM) and N traits, related to GY and grain N uptake (Nup) in high-yielding winter wheat breeding lines. From 2015 to 2017, hyperspectral canopy measurements were acquired on 26 measurement dates during vegetative and reproductive growth, and 48 vegetation indices from the visible (VIS), red edge (RE) and near-infrared (NIR) spectrum were tested in linear regression for assessing the influence of measurement stage and index selection. For most traits including GY and grain Nup, measurements at milk ripeness were the most reliable. Coefficients of determination (R²) were generally higher for traits related to maturity than for those related to anthesis canopy status. For GY (R² = 0.26–0.51 in the three years, p < 0.001), and most DM traits, indices related to the water absorption band at 970 nm provided better relationships than the NIR/VIS indices, including the normalized difference vegetation index (NDVI), and the VIS indices. In addition, most indices including RE bands, notably NIR/RE combinations, ranked above the NIR/VIS group. Due to index saturation, the index differentiation was most apparent in the highest-yielding year. For grain Nup and total Nup, the RE/VIS index MSR_705_445 and the simple ratio R780_R740 ranked highest, followed by other RE indices. Among the vegetative organs, R² values were mostly highest and lowest for leaf and spike traits, respectively. For each trait, index and partial least squares regression (PLSR) models were validated across years at milk ripeness, confirming the suitability of optimized index selection. PLSR improved the prediction errors of some traits but not consistently the R² values. The results suggest the use of sensor-based phenotyping as a useful support tool for screening of yield potential and NUE and for identifying contributing plant traits—which, due to their expensive and cumbersome destructive determination are otherwise not readily available. Water band and RE indices should be preferred over NIR/VIS indices for DM traits and N-related traits, respectively, and milk ripeness is suggested as the most reliable stage.

The Monitoring of the Pollution Degree of Maize Under Copper Stress

This study proposed a new vegetation index ADVI to detect the pollution degree of different varieties of maize under copper stress, which provides a new idea for the detection of heavy metal pollution in vegetation. In order to ensure the outdoor growth environment of maize, we put all maize into outdoor greenhouse. The spectral reflectance interval of 450–850 nm of maize leaves was processed by the absorbance conversion (A) and the first-order differential (D), and the absorbance differential (AD) spectral curve was obtained. The Pearson correlation coefficient (r) was used to analyze the AD data and the biochemical data and select characteristic bands that are sensitive to heavy metal copper (Cu). The calculated Pearson correlation coefficients suggested that the AD value at 547–553 nm and 672–674 nm presented a linear positive correlation close to 1 with the Cu2+ contents in soil and leaves, and a linear negative correlation close to − 1 was present in the range of 496–506 nm and 677–679 nm. We selected the AD value of wavelengths 501 nm, 550 nm, 673 nm, and 678 nm to establish ADVI and compared it with conventional vegetation indices (VIs) by calculating Pearson correlation coefficient between them and Cu contents in soil and leaves, and vegetation indices include Water Band Index (WBI), Photochemical Reflectance Index (PRI), MERIS Terrestrial Chlorophyll Index (MTCI), Modified Red Edge Simple Ratio Index (mSR), Modified Chlorophyll Absorption Ratio Index (MCARI), Water Index (WI), Normalized Difference Water Index (NDWI). Maize leaf spectral data obtained from experiments in 2017 were used for verification, and ADVI was also compared with WBI, PRI, MTCI, mSR, MCARI, WI, and NDWI. The calculated Pearson correlation coefficient between ADVI and Cu2+ contents in soil and leaves is 0.9816 and 0.9460 (experimental data of 2016). The calculated Pearson correlation coefficient between ADVI and Cu2+ contents in soil and leaves is 0.9109 and 0.9639 (experimental data of 2017). The results suggested that ADVI showed a significant correlation with Cu2+ stress concentration, and the correlation of ADVI was much stronger than that of other vegetation indices. The proposed ADVI detects the pollution degree of maize with different varieties and in different periods under copper stress has advantages of straightforward calculation, robustness, and high effectiveness. This study focused on the laboratory leaf scale, so it is expected that future work extends it to a wide range of field scale and image scale.