Hyperspectral Reflectance Measurements Research Articles

An Unmanned Aerial System (UAS) for concurrent measurements of solar-induced chlorophyll fluorescence and hyperspectral reflectance toward improving crop monitoring

Unmanned aerial system (UAS)-based remote sensing can provide high spatial- and temporal-resolution crop monitoring for precision management. Existing crop monitoring UAS primarily use conventional techniques such as multi-spectral broadband vegetation indices (VIs) to track canopy structure/biomass. Recently developed lightweight hyperspectral sensors can track crop physiology and performance via complementary signals such as solar-induced chlorophyll fluorescence (SIF) and photochemical reflectance index (PRI) that can be derived from hyperspectral reflectance. However, few existing UAS can acquire high-quality SIF and hyperspectral reflectance. We designed a novel UAS that simultaneously captures far-red SIF and hyperspectral reflectance, using a single bifurcated fiber and motorized arm to measure downwelling and upwelling irradiance. The UAS was tested over (1) a heterogeneous corn field with spatially varying crop yield potential and (2) a set of soybean and corn plots under differing nutrient treatments. Our UAS can maintain stability under low-to-moderate winds (2–3 m/s) with <0.4 m geolocation accuracy, <0.1 m altitude drift, and <1.5° error of the fiber optic from nadir position during scanning. Weekly seasonal campaigns reveal that SIF outperforms conventional VIs such as the normalized difference vegetation index (NDVI) for distinguishing plots with different crop yield potential, especially after canopy closure when NDVI tends to saturate. The UAS can capture the diurnal dynamics of SIF and PRI for both corn and soybean. At the seasonal scale, SIF acquired from the UAS correlates well with that from a fixed SIF tower system (R2 = 0.81), which measured the same target albeit with different footprints, demonstrating the capability of UAS in characterizing the seasonal progression of crop activity. In conclusion, our newly developed UAS provides high-quality SIF and hyperspectral reflectance, facilitating mechanistic understanding of the physiological control on photosynthesis dynamics. Our findings also imply that this UAS could enable extension of fixed tower-based systems to monitor multiple targets from diurnal to seasonal scales and improve monitoring of heterogeneous fields.

Hyperspectral discrimination of tea plant varieties using machine learning, and spectral matching methods

Remote sensing-based discrimination and mapping of tea (Camellia sinensis) plantations are valuable for efficient management of inventory and optimization of resources by the tea production industry. Apart from the diverse tea plant varieties, growth of natural plant species is a common scenario in tea plantations. The objective of this research is spectral discrimination of nine popular tea plant varieties in the presence of six natural plant species in Munnar, Western Ghats of India. Canopy level hyperspectral reflectance measurements acquired for tea and natural plant species were analyzed using several statistical, and machine learning methods namely, k-nearest neighbourhood classifier (k-NN), linear discriminant analysis (LDA), support vector machines (SVM), normalized spectral similarity score (NS3), maximum likelihood classifier (MLC), and artificial neural networks (ANNs). In addition, the existence and statistical significance of the spectral separability among 15 tea and natural plant species was assessed by non-parametric MANOVA. Results indicate that six out of nine tea plant varieties could be discriminated with accuracies between 75% and 80%. The presence of natural plant species has decreased the inter-species spectral variability for a few tea plant varieties. However, there has been enhanced spectral variability for a few other tea plant varieties. The presence of natural plant species does not need to be disadvantageous to the spectral discrimination of tea species.

Assessing Hyperspectral Vegetation Indices Responses of Six Pigweed Species

Pigweeds (Amaranthus species), negatively impact crop production systems throughout the world. They are distinguished from each other using manual methods that are tedious and time-consuming to complete. Hyperspectral light reflectance properties of plant leaves and canopies have shown promise for detecting and mapping weeds in crop production systems. Vegetation indices derived from hyperspectral reflectance data enhance differences between plants, leading to better detection of them from other targets. The objective was to evaluate the biomass and structural index, the biochemical index, the red edge index, the water and moisture index, the light-use efficiency index, and the lignin cellulose index for measuring differences among six pigweed species: Amaranthus albus (L), A. hybridus (L), A. palmeri (S. Wats.), A. retroflexus (L), A. spinosus (L), and A. tuberculatus [(Moq.) Sauer]. Two experiments were conducted under greenhouse conditions. Hyperspectral reflectance measurements were collected from the plant canopies on two dates for each experiment. Analysis of variance (ANOVA) and Tukey’s honest significant difference (HSD) test were used to determine if statistical differences (P ≤ 0.05) existed among the pigweed species canopies and to identify which species were statistically different for a vegetation index, respectively. The ANOVA analysis detected statistical differences among the canopy vegetation index values. Tukey’s HSD showed that the biochemical index and the red edge index detected differences between two to three pigweeds species on all dates of data collection. However, the differences were date-specific. Furthermore, statistical differences were not observed for all six species for any vegetation index. On the data collection dates, A. albus and A. tuberculatus index values were statistically different from other pigweed species for one or more of the vegetation indices. Future research should focus on using the vegetation indices in combination with each other to measure differences between the pigweed species and between them and other weeds and crops.

Frost damage to maize in northeast India: assessment and estimated loss of yield by hyperspectral proximal remote sensing

Frost in the uplands of northeast India frequently damages maize, leading to poor yields or even complete crop failure. We studied the feasibility of using reflectance spectra from a handheld radiometer and a camera on board a drone at 80 m above the crop to assess physiological changes, differential responses of crop to nutrient managements (N, P, and NPK) and also predicted the likely losses of grain yield caused by one night’s frost damage in 90-day-old maize crop in Meghalaya, northeast India. A portable single-beam field spectroradiometer (model SVC-HR-1024) was used to measure radiation in the range of 350 to 2500 nm with varying bandwidth at 1 m above the crop. All measurements (both before and after frost) were made strictly between 11:00 and 13:00 h, when the sun was almost overhead. Similarly, a Parrot Sequoia multispectral camera sensing in four wavebands was mounted underneath a drone (DJI Matrix 600 model). We compared the measured spectra with those recorded on the same crop before the frost and also measured several biochemical constituents in the leaves on the two occasions for comparison. The frost damage increased the reflectance in the photosynthetically active visible and infrared regions with a strong peak at 2100 nm while it caused a sharp decline in the near-infrared (between 720 and 1350 nm) and shift in the red edge. It caused a decrease in the normalized difference vegetation index as measured by the radiometer from an average of 0.36 to 0.13 and a decrease from 0.5 to 0.31 as measured from the drone. The yield in frosted year was on average 870 kg ha^{− 1} less than in the previous frost-free year on the same plots. Frost injury reduced leaf pigments (chlorophyll and carotenoids), nitrogen, and grain yield by a greater magnitude in P-stressed and NPK-deficient maize crops over N-stressed and stress-free (with NPK) plants. Spectral reflectance and vegetation indices also varied significantly. We could also assess quantitatively the crop’s loss of photosynthetic potential from the hyperspectral reflectance measurements and from the low-flying drone. The latter technology should enable farmers to monitor and assess frost damage immediately and to predict the likely losses of yield.

Mapping within-field leaf chlorophyll content in agricultural crops for nitrogen management using Landsat-8 imagery

Spatial information on crop nutrient status is central for monitoring vegetation health, plant productivity and managing nutrient optimization programs in agricultural systems. This study maps the spatial variability of leaf chlorophyll content within fields with differing quantities of nitrogen fertilizer application, using multispectral Landsat-8 OLI data (30 m). Leaf chlorophyll content and leaf area index measurements were collected at 15 wheat (Triticum aestivum) sites and 13 corn (Zea mays) sites approximately every 10 days during the growing season between May and September 2013 near Stratford, Ontario. Of the 28 sites, 9 sites were within controlled areas of zero nitrogen fertilizer application. Hyperspectral leaf reflectance measurements were also sampled using an Analytical Spectral Devices FieldSpecPro spectroradiometer (400–2500 nm). A two-step inversion process was developed to estimate leaf chlorophyll content from Landsat-8 satellite data at the sub-field scale, using linked canopy and leaf radiative transfer models. Firstly, at the leaf-level, leaf chlorophyll content was modelled using the PROSPECT model, using both hyperspectral and simulated mulitspectral Landsat-8 bands from the same leaf sample. Hyperspectral and multispectral validation results were both strong (R2 = 0.79, RMSE = 13.62 μg/cm2 and R2 = 0.81, RMSE = 9.45 μg/cm2, respectively). Secondly, leaf chlorophyll content was estimated from Landsat-8 satellite imagery for 7 dates within the growing season, using PROSPECT linked to the 4-Scale canopy model. The Landsat-8 derived estimates of leaf chlorophyll content demonstrated a strong relationship with measured leaf chlorophyll values (R2 = 0.64, RMSE = 16.18 μg/cm2), and compared favourably to correlations between leaf chlorophyll and the best performing tested spectral vegetation index (Green Normalised Difference Vegetation Index, GNDVI; R2 = 0.59). This research provides an operational basis for modelling within-field variations in leaf chlorophyll content as an indicator of plant nitrogen stress, using a physically-based modelling approach, and opens up the possibility of exploiting a wealth of multispectral satellite data and UAV-mounted multispectral imaging systems.

Distinguishing Photosynthetic and Non-Photosynthetic Vegetation: How Do Traditional Observations and Spectral Classification Compare?

Remotely sensed ground cover maps are routinely validated using field data collected by observers who classify ground cover into defined categories such as photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), bare soil (BS), and rock. There is an element of subjectivity to the classification of PV and NPV, and classifications may differ between observers. An alternative is to estimate ground cover based on in situ hyperspectral reflectance measurements (HRM). This study examines observer consistency when classifying vegetation samples of wheat (Triticum aestivum var. Gladius) covering the full range of photosynthetic activity, from completely senesced (0% PV) to completely green (100% PV), as photosynthetic or non-photosynthetic. We also examine how the classification of spectra of the same vegetation samples compares to the observer results. We collected HRM and photographs, over two months, to capture the transition of wheat leaves from 100% PV to 100% NPV. To simulate typical field methodology, observers viewed the photographs and classified each leaf as either PV or NPV, while spectral unmixing was used to decompose the HRM of the leaves into proportions of PV and NPV. The results showed that when a leaf was ≤25% or ≥75% PV observers tended to agree, and assign the leaf to the expected category. However, as leaves transitioned from PV to NPV (i.e., PV ≥ 25% but ≤ 75%) observers’ decisions differed more widely and their classifications showed little agreement with the spectral proportions of PV and NPV. This has significant implications for the reliability of data collected using binary methods in areas containing a significant proportion of vegetation in this intermediate range such as the over/underestimation of PV and NPV vegetation and how reliably this data can then be used to validate remotely sensed products.

Semi-empirical model for upscaling leaf spectra (SEMULS): a novel approach for modeling canopy spectra from in situ leaf reflectance spectra

The use of in situ hyperspectral reflectance and bio-physical measurements has been increasing in forestry. Due to limited physical accessibility in a forest environment, most of the reflectance measurements of trees are acquired at a leaf or bunch of leaves level. A few radiative transfer models are available for upscaling leaf spectra to canopy level. While these models are sophisticated, they retrieve canopy spectra based on certain assumptions. We propose ‘semi-empirical model for upscaling leaf spectra (SEMULS)’ which upscales in situ leaf spectra to canopy level based on the relationship between leaf spectra and its bio-physical parameters. The performance of the model has been quantitatively validated by comparing the upscaled canopy spectra with spectra from – CHRIS hyperspectral imagery acquired concurrently and from the PROSAIL model. Results indicate that the SEMULS retrievals are comparable with image spectra and PROSAIL with additional advantages of not requiring scene-dependent geometric-radiometric parameters and assumptions.

Hyperspectral analysis of objects under shadow conditions based on field reflectance measurements.

Shaded objects in remote sensing data are usually neglected because of their low reflectance. The common treatment for detecting shadows consists of simple identification and straightforward removal; however, this approach is often criticized owing to the complexity and difficulty of its application and the incompleteness of the treated data. Recent efforts with hyperspectral field reflectance measurements have provided alternatives to scrutinize the issues of shadows. To demonstrate our proposed framework, we first gathered basic hyperspectral data with a spectrometer at Peking University, Beijing, China. Subsequently, we classified the spectral reflectances into three types: sunlight reflectance, shadow reflectance, and simulated reflectance. The spectral characteristics of shadows can provide pertinent classification information despite their low reflectance. Under full shadow conditions, the simulated reflectance was close to the sunlight reflectance because the same reduction ratio was used for the spectralon panel during the measurement process. In addition, the ratio of the shadow reflectance to the sunlight reflectance seemed to be related to the characteristics of the obstruction. The findings in this paper can contribute to a comprehensive understanding of the influence of shadows in remote sensing research, demonstrating the potential of fully utilizing typically neglected shadow information.

A Random Forest Machine Learning Approach for the Retrieval of Leaf Chlorophyll Content in Wheat

Developing rapid and non-destructive methods for chlorophyll estimation over large spatial areas is a topic of much interest, as it would provide an indirect measure of plant photosynthetic response, be useful in monitoring soil nitrogen content, and offer the capacity to assess vegetation structural and functional dynamics. Traditional methods of direct tissue analysis or the use of handheld meters, are not able to capture chlorophyll variability at anything beyond point scales, so are not particularly useful for informing decisions on plant health and status at the field scale. Examining the spectral response of plants via remote sensing has shown much promise as a means to capture variations in vegetation properties, while offering a non-destructive and scalable approach to monitoring. However, determining the optimum combination of spectra or spectral indices to inform plant response remains an active area of investigation. Here, we explore the use of a machine learning approach to enhance the estimation of leaf chlorophyll (Chlt), defined as the sum of chlorophyll a and b, from spectral reflectance data. Using an ASD FieldSpec 4 Hi-Res spectroradiometer, 2700 individual leaf hyperspectral reflectance measurements were acquired from wheat plants grown across a gradient of soil salinity and nutrient levels in a greenhouse experiment. The extractable Chlt was determined from laboratory analysis of 270 collocated samples, each composed of three leaf discs. A random forest regression algorithm was trained against these data, with input predictors based upon (1) reflectance values from 2102 bands across the 400–2500 nm spectral range; and (2) 45 established vegetation indices. As a benchmark, a standard univariate regression analysis was performed to model the relationship between measured Chlt and the selected vegetation indices. Results show that the root mean square error (RMSE) was significantly reduced when using the machine learning approach compared to standard linear regression. When exploiting the entire spectral range of individual bands as input variables, the random forest estimated Chlt with an RMSE of 5.49 µg·cm−2 and an R2 of 0.89. Model accuracy was improved when using vegetation indices as input variables, producing an RMSE ranging from 3.62 to 3.91 µg·cm−2, depending on the particular combination of indices selected. In further analysis, input predictors were ranked according to their importance level, and a step-wise reduction in the number of input features (from 45 down to 7) was performed. Implementing this resulted in no significant effect on the RMSE, and showed that much the same prediction accuracy could be obtained by a smaller subset of indices. Importantly, the random forest regression approach identified many important variables that were not good predictors according to their linear regression statistics. Overall, the research illustrates the promise in using established vegetation indices as input variables in a machine learning approach for the enhanced estimation of Chlt from hyperspectral data.

Hyperspectral Measurements, Parameterizations, and Atmospheric Correction of Whitecaps and Foam From Visible to Shortwave Infrared for Ocean Color Remote Sensing

Breaking waves are highly reflective features on the sea surface that change the spectral properties of the ocean surface in both magnitude and spectral shape. Here, hyperspectral reflectance measurements of whitecaps from 400 to 2,500 nm were taken in Long Island Sound, USA of natural and manufactured breaking waves to explore new methods to estimate whitecap contributions to ocean color imagery. Whitecap reflectance was on average ~40% in visible wavelengths and decreased significantly into the near infrared and shortwave infrared following published trends. The spectral shape was well-characterized by a third order polynomial function of liquid water absorption that can be incorporated into coupled ocean-atmospheric models and spectral optimization routines. Localized troughs in whitecap reflectance correspond to peaks in liquid water absorption and depths of the troughs are correlated to the amount and intensity of the breaking waves. Specifically, baseline-corrected band depths at 980 and 1,200 nm explained 77 and 90% of the whitecap-enhanced reflectance on a logarithmic scale, respectively. Including these wavebands into future ocean color sensors could potentially provide new tools to estimate whitecap contributions to reflectance more accurately than with wind speed. An effective whitecap factor was defined as the optical enhancements within a pixel due to whitecaps and foam independent of spatial scale. A simple mixed-pixel model of whitecap and background reflectance explained as much of the variability in measured reflectance as more complex models incorporating semi-transparent layers of foam. Using an example atmosphere, enhanced radiance from whitecaps was detectable at the top of the atmosphere and a multiple regression of at-sensor radiance at 880, 1,038, 1,250, and 1,615 nm explained 99% of the variability in whitecap factor. A proposed model of whitecap-free reflectance includes contributions from water-leaving radiance, glint, and diffuse reflected skylight. The epsilon ratio at 753 and 869 nm commonly used for aerosol model selection is nearly invariant with whitecap factor compared to the ratio at shortwave infrared bands. While more validation data is needed, this research suggests several promising avenues to retrieve estimates of the whitecap reflectance and to use ocean color to further elucidate the physics of wave breaking and gas exchange.

Canopy Hyperspectral Reflectance of Redroot Pigweed versus Okra and Super Okra Leaf Cotton

Redroot pigweed (Amaranthus retroflexus L.) is a nuisance weed that affects cotton (Gossypium hirsutum L.) growth and yield worldwide. Being able to distinguish redroot pigweed from cotton would help producers and crop consultants better implement strategies used to suppress and control it. Hyperspectral reflectance properties of weed and crop canopies have been used to differentiate between them. Currently, no information is available on the application of hyperspectral data to distinguish redroot pigweed from cotton with different leaf shapes. Positive results will further support the exploration of remote sensing technology for distinguishing redroot pigweed from cotton. The objectives were to compare canopy hyperspectral reflectance of redroot pigweed to canopy hyperspectral reflectance of okra and super okra leaf cotton and to identify regions of the spectrum in which differences exist in their reflectance properties. Hyperspectral reflectance measurements of redroot pigweed and cotton were obtained with a spectroradimeter on May 6 and June 27, 2019. Plants grown in a greenhouse were used for this study. One-hundred and sixty-two 10-nm bands (400 - 2350 nm spectral range) were evaluated with analysis of variance (p ≤ 0.05) and Dunnett’s test (p ≤ 0.05) to determine the wavebands that were useful for separating redroot pigweed from okra leaf and super okra leaf cotton. The following bands were consistent in distinguishing redroot pigweed and okra leaf cotton on both dates: 420 nm, 510 - 650 nm, 690 - 740 nm, and 2000 - 2010 nm; whereas, 400 - 500 nm, 1480 - 1780 nm, and 1990 - 2350 nm were identified for both dates for separating redroot pigweed from super okra leaf cotton. Commercial imaging systems used on ground-based or airborne platforms can be easily tuned into the spectral bands listed in this study, thus providing managers with a tool to use for identifying redroot pigweed in cotton production systems.

Remote Estimation of Nitrogen Vertical Distribution by Consideration of Maize Geometry Characteristics

The vertical leaf nitrogen (N) distribution in the crop canopy is considered to be an important adaptive response of crop growth and production. Remote sensing has been widely applied for the determination of a crop’s N status. Some studies have also focused on estimating the vertical leaf N distribution in the crop canopy, but these analyses have rarely considered the plant geometry and its influences on the remote estimation of the N vertical distribution in the crop canopy. In this study, field experiments with three types of maize (Zea mays L.) plant geometry (i.e., horizontal type, intermediate type, and upright type) were conducted to demonstrate how the maize plant geometry influences the remote estimation of N distribution in the vertical canopy (i.e., upper layer, middle layer, and bottom layer) at different growth stages. The results revealed that there were significant differences among the three maize plant geometry types in terms of canopy architecture, vertical distribution of leaf N density (LND, g m−2), and the LND estimates in the leaves of different layers based on canopy hyperspectral reflectance measurements. The upright leaf variety had the highest correlation between the lower-layer LND (R2 = 0.52) and the best simple ratio (SR) index (736, 812), and this index performed well for estimating the upper (R2 = 0.50) and middle (R2 = 0.60) layer LND. However, for the intermediate leaf variety, only 25% of the variation in the lower-layer LND was explained by the best SR index (721, 935). The horizontal leaf variety showed little spectral sensitivity to the lower-layer LND. In addition, the growth stages also affected the remote detection of the lower leaf N status of the canopy, because the canopy reflectance was dominated by the biomass before the 12th leaf stage and by the plant N after this stage. Therefore, we can conclude that a more accurate estimation of the N vertical distribution in the canopy is obtained by canopy hyperspectral reflectance when the maize plants have more upright leaves.

Analysis of Different Hyperspectral Variables for Diagnosing Leaf Nitrogen Accumulation in Wheat.

Hyperspectral remote sensing is a rapid non-destructive method for diagnosing nitrogen status in wheat crops. In this study, a quantitative correlation was associated with following parameters: leaf nitrogen accumulation (LNA), raw hyperspectral reflectance, first-order differential hyperspectra, and hyperspectral characteristics of wheat. In this study, integrated linear regression of LNA was obtained with raw hyperspectral reflectance (measurement wavelength = 790.4 nm). Furthermore, an exponential regression of LNA was obtained with first-order differential hyperspectra (measurement wavelength = 831.7 nm). Coefficients (R2) were 0.813 and 0.847; root mean squared errors (RMSE) were 2.02 g·m−2 and 1.72 g·m−2; and relative errors (RE) were 25.97% and 20.85%, respectively. Both the techniques were considered as optimal in the diagnoses of wheat LNA. Nevertheless, the better one was the new normalized variable (SDr − SDb)/(SDr + SDb), which was based on vegetation indices of R2 = 0.935, RMSE = 0.98, and RE = 11.25%. In addition, (SDr − SDb)/(SDr + SDb) was reliable in the application of a different cultivar or even wheat grown elsewhere. This indicated a superior fit and better performance for (SDr − SDb)/(SDr + SDb). For diagnosing LNA in wheat, the newly normalized variable (SDr − SDb)/(SDr + SDb) was more effective than the previously reported data of raw hyperspectral reflectance, first-order differential hyperspectra, and red-edge parameters.

ENABLING SEARCHES ON WAVELENGTHS IN A HYPERSPECTRAL INDICES DATABASE

Abstract. Spectral indices derived from hyperspectral reflectance measurements are powerful tools to estimate physical parameters in a non-destructive and precise way for several fields of applications, among others vegetation health analysis, coastal and deep water constituents, geology, and atmosphere composition. In the last years, several micro-hyperspectral sensors have appeared, with both full-frame and push-broom acquisition technologies, while in the near future several hyperspectral spaceborne missions are planned to be launched. This is fostering the use of hyperspectral data in basic and applied research causing a large number of spectral indices to be defined and used in various applications. Ad hoc search engines are therefore needed to retrieve the most appropriate indices for a given application. In traditional systems, query input parameters are limited to alphanumeric strings, while characteristics such as spectral range/ bandwidth are not used in any existing search engine. Such information would be relevant, as it enables an inverse type of search: given the spectral capabilities of a given sensor or a specific spectral band, find all indices which can be derived from it. This paper describes a tool which enables a search as described above, by using the central wavelength or spectral range used by a given index as a search parameter. This offers the ability to manage numeric wavelength ranges in order to select indices which work at best in a given set of wavelengths or wavelength ranges.

Optical types of inland and coastal waters

AbstractInland and coastal waterbodies are critical components of the global biosphere. Timely monitoring is necessary to enhance our understanding of their functions, the drivers impacting on these functions and to deliver more effective management. The ability to observe waterbodies from space has led to Earth observation (EO) becoming established as an important source of information on water quality and ecosystem condition. However, progress toward a globally valid EO approach is still largely hampered by inconsistences over temporally and spatially variable in‐water optical conditions. In this study, a comprehensive dataset from more than 250 aquatic systems, representing a wide range of conditions, was analyzed in order to develop a typology of optical water types (OWTs) for inland and coastal waters. We introduce a novel approach for clustering in situ hyperspectral water reflectance measurements (n = 4045) from multiple sources based on a functional data analysis. The resulting classification algorithm identified 13 spectrally distinct clusters of measurements in inland waters, and a further nine clusters from the marine environment. The distinction and characterization of OWTs was supported by the availability of a wide range of coincident data on biogeochemical and inherent optical properties from inland waters. Phylogenetic trees based on the shapes of cluster means were constructed to identify similarities among the derived clusters with respect to spectral diversity. This typification provides a valuable framework for a globally applicable EO scheme and the design of future EO missions.

Mangrove Species Discrimination in Southern Vietnam Based on in-situ Measured Hyperspectral Reflectance

The advances in hyperspectral imaging sensors offer unprecedented levels of detail to detect subtle reflectance differences between targets of study. This study assesses the spectral separability of canopies of common mangrove species in Southern Vietnam using a large sample-set of field measurements of hyperspectral reflectance. Although data collection has been conducted under consistent calibration and stable geometric set-ups, field spectroscopy deals with significant and multitudinous environmental noises compared to laboratory spectroscopic setups. Field measurements of canopies are however believed to hold greater relevancy for UAV, airborne and satellite remote sensing due a closer resemblance of real-world intra-specific variation and environmental determinants. The present study shows that the large sample-set of hyperspectral data recorded in the field provides a sound base for discrimination common mangrove species in Southern Vietnam – identifying the visible and red-edge as a key regions for species discrimination. This brings us a step further to a comprehensive spectral library for larger scale remote sensing applications of vegetation mapping and the monitoring of species distributions.

Evaluating Hyperspectral Vegetation Indices for Leaf Area Index Estimation of Oryza sativa L. at Diverse Phenological Stages.

Hyperspectral reflectance derived vegetation indices (VIs) are used for non-destructive leaf area index (LAI) monitoring for precise and efficient N nutrition management. This study tested the hypothesis that there is potential for using various hyperspectral VIs for estimating LAI at different growth stages of rice under varying N rates. Hyperspectral reflectance and crop canopy LAI measurements were carried out over 2 years (2015 and 2016) in Meichuan, Hubei, China. Different N fertilization, 0, 45, 82, 127, 165, 210, 247, and 292 kg ha-1, were applied to generate various scales of VIs and LAI values. Regression models were used to perform quantitative analyses between spectral VIs and LAI measured under different phenological stages. In addition, the coefficient of determination and RMSE were employed to evaluate these models. Among the nine VIs, the ratio vegetation index, normalized difference vegetation index (NDVI), modified soil-adjusted vegetation index (MSAVI), modified triangular vegetation index (MTVI2) and exhibited strong and significant relationships with the LAI estimation at different phenological stages. The enhanced vegetation index performed moderately. However, the green normalized vegetation index and blue normalized vegetation index confirmed that there is potential for crop LAI estimation at early phenological stages; the soil-adjusted vegetation index and optimized soil-adjusted vegetation index were more related to the soil optical properties, which were predicted to be the least accurate for LAI estimation. The noise equivalent accounted for the sensitivity of the VIs and MSAVI, MTVI2, and NDVI for the LAI estimation at phenological stages. The results note that LAI at different crop phenological stages has a significant influence on the potential of hyperspectral derived VIs under different N management practices.

Non-invasive spectral detection of the beneficial effects of Bradyrhizobium spp. and plant growth-promoting rhizobacteria under different levels of nitrogen application on the biomass, nitrogen status, and yield of peanut cultivars

ABSTRACT High-throughput phenotyping using spectral reflectance measurements offers the potential to provide more information for making better-informed management decisions at the crop canopy level in real time. The aim of this study was to investigate the suitability of hyperspectral reflectance measurements of the crop canopy for the assessment of biomass, nitrogen concentration, nitrogen uptake, relative chlorophyll contents, and yield in 2 peanut cultivars, Giza 5 and Giza 6. Peanuts were grown under field conditions and subjected to 3 doses of nitrogen fertilizer with or without the application of 2 bio-fertilizers, Bradyrhizobium spp. or plant growth-promoting rhizobacteria. Simple linear regression of normalized difference spectral indices and partial least square regression (PLSR) were employed to develop predictive models to estimate the measured parameters. The tested spectral reflectance indices were significantly related to all measured parameters with R2 of up to 0.89. The spectral reflectance index values differed at the same level of nitrogen fertilizer, as well as among the 3 levels of nitrogen fertilizer application for inoculation with Bradyrhizobium and co-inoculation with Bradyrhizobium and plant growth-promoting rhizobacteria. The calibration models of PLSR data analysis further improved the results, with R2 values reaching 0.95. The overall results of this study indicate that hyperspectral reflectance measurements monitoring peanut plants enable rapid and non-destructive assessment of biomass, nitrogen status, and yield parameters of peanut cultivars subjected to various agronomic treatments.

Spectral Signatures of Immature Lucilia sericata (Meigen) (Diptera: Calliphoridae).

Hyperspectral remote sensing is an innovative technology with applications in many sciences and is a non-destructive method that may offer more precise aging within development stages. Hyperspectral reflectance measurements from the anterior, midsection, and posterior of Lucilia sericata (Meigen) larvae and pupae were conducted daily from samples of the developing insects beginning at second instar. Only midsection measurements were conducted on second instar larvae due to their size, to ensure that the measurement was not of reflective surroundings. Once measured, all insects were washed with deionized water, blotted with filter paper, and re-measured. Daily age prediction during the post-feeding stage was not impacted by the unwashed insect measurements and was best predicted based on posterior measurements. The second and third instar larvae, which move about their food source, had different contributing coefficients to the functional regression model for the hyperspectral measurements of the washed compared with unwashed specimens. Although washing did not affect the daily prediction within these stages, it is still encouraged in order to decrease the effect of food source on spectral reflectance. Days within the intra-puparial period were best predicted based on anterior measurements and were not well distinguished from one another in the first few days based on midsection and posterior measurements.

Hyperspectral remote sensing to assess the water status, biomass, and yield of maize cultivars under salinity and water stress

Spectral remote sensing offers the potential to provide more information for making better-informed management decisions at the crop canopy level in real time. In contrast, the traditional methods for irrigation management are generally time-consuming, and numerous observations are required to characterize them. The aim of this study was to investigate the suitability of hyperspectral reflectance measurements of remote sensing technique for salinity and water stress condition. For this, the spectral indices of 5 maize cultivars were tested to assess canopy water content (CWC), canopy water mass (CWM), biomass fresh weight (BFW), biomass dry weight (BDW), cob yield (CY), and grain yield (GY) under full irrigation, full irrigation with salinity levels, and the interaction between full irrigation with salinity levels and water stress treatments. The results showed that the 3 water spectral indices (R970 − R900)/(R970 + R900), (R970 − R880)/(R970 + R880), and (R970 − R920)/(R970 + R920) showed close and highly significant associations with the mentioned measured parameters, and coefficients of determination reached up to R2 = 0.73*** in 2013. The model of spectral reflectance index (R970 − R900)/(R970 + R900) of the hyperspectral passive reflectance sensor presented good performance to predict the CY, GY, and CWC compared to CWM, BFW, and BDW under full irrigation with salinity levels and the interaction between full irrigation with salinity levels and water stress treatments. In conclusion, the use of spectral remote sensing may open an avenue in irrigation management for fast, high-throughput assessments of water status, biomass, and yield of maize cultivars under salinity and water stress conditions.