Spectral Reflectance Values Research Articles

Use of Hyperpectral Data For Chlorophyll Estimation Based on Leaf Area Indices (LAI) in Wheat

Spectral Indexes are frequently used in the estimation of green parts of plants, usually developed to reduce the spectral effects of external factors such as atmosphere and soil. The aim of this study was to evaluate the ability of different spectral indices to estimate chlorophyll in wheat according to phenological developmental stages and to calculate their optimal band combinations. In this study, chlorophyll-pigment related indices primarily used in chlorophyll estimation, as well as structural and red edge indices were used. Spectral reflectance values obtained for different phenological periods were correlated with SPAD (Minolta-502) values and Partial Least Square (PLS) model was used to calculate the prominent hyperspectral indices and their optimal band combinations. Hyperspectral multispectral data were used to calculate the amount of chlorophyll in wheat according to phenological periods, and the correlation (R²), standard error (RMSE), relative error (% RE) values obtained by evaluating the old and newly developed spectral indices together to calculate the amount of chlorophyll according to phenological periods. Many vegetation indices are strongly influenced by the unfavourable reflection properties of the soil at low LAI. As the Leaf Area Index (LAI) increases, the predictive power of the spectral index increases as the saturation effect decreases. In this study, the responses and sensitivities of different spectral indices for chlorophyll estimation against LAI change in phenological periods were investigated. As a result, the indices that were least and most affected by saturation changes were revealed. Thus, the power of the indices to predict the chlorophyll content of the canopy was demonstrated. Leaf Area Index values obtained from the experimental area in Haymanada between 2013-2014 varied between 1.08-2.81 in the early period (13 May 2014). In chlorophyll estimation, NDVI (705,750) was the least affected by the saturation change due to the increasing LAI value in the early period and showed a high correlation (LAI= 2.63, R²= 0.554). This was followed by Red Edge (740-720), (LAI= 2.63,1.722), NDVI (550,780), (LAI= 2.63,0.733), SRPI (430,680) (LAI= 2.63,0.661), LCCI (705,750) (LAI= 2.63,0.554), and NPCI (430,680) (LAI= 2.63, 0.203). These indices, which showed high correlation in the early period, were in the range of R2=0.836-0.761. In Haymana in the late period between 2013-2014 (26 May,04-12-24 June 2014) LAI values vary between 0.63-3.38 and correlation values are between R2 =0.892-0.862. MSR (705,750) was the least affected by the saturation change due to the increasing LAI value in the late period and showed high correlation (LAI=1.904, 0.906). This was followed by NDVI670 (LAI=1.904,0.703), NDVI550 (LAI=1.904,0.651) and LCCI (LAI=1.904,0.448).

Change of bioactive properties, spectral reflectance, and color characteristics of European cranberry (Viburnum opulus L.) juice as affected by foam mat drying technique

The European cranberry bush, known for its health benefits, can only be consumed through fermentation. This study aimed to develop a fruit leather made from European cranberry bush using quince seed gel and the foam drying method. For this purpose, quince seed gel was added to European cranberry juice to increase consistency. Then, European cranberry fruit leather was obtained by drying at 70, 80, and 90 °C air temperatures using foam mat drying technology. Spectral reflectance, color, drying kinetics, anthocyanin, ascorbic acid, and total phenolic content, antiradical activity, and macro-micronutrient concentrations of the resulting fruit pulp were investigated. The foam mat drying process at 90 °C had the greatest values of ascorbic acid (0.996 mg g− 1), anthocyanin (275.9 mg kg− 1), DPPH (47.77%), and ABTS.+ (68.76 µg TE g− 1). In addition, the highest value of total phenolic content (37.75 mg g− 1) was obtained in the foam mat drying process at 80 °C. The highest concentration of P, Na, Mg, K, Ca, and Mn in fruit leather was obtained at 70 °C, and the highest concentration of S, Cu, and Zn was obtained at 90 °C. The lowest spectral reflectance values were measured at 90 °C. In conclusion, the present study explored the fact that adding quince seed gel, extremely rich in biochemical content, significantly enhanced the bioactivity properties of European cranberry bush fruit leather.

Effect of Atmospheric Mixing on Spectral Reflectivity in Sentinel Images of Baghdad Province

The lowest layer of the atmosphere is called the atmospheric mixed layer, characterized by small-scale, irregular air motions defined by winds that change in speed and direction. Aerosol radiative effects impact the atmospheric boundary layer (ABL), which holds most aerosols in the lower atmosphere. Aerosol absorption and scattering both lower the quantity of solar energy that reaches the ground, which has an impact on the spectral signature of the land coverings. In this study, 51 locations in downtown Baghdad were chosen for four different types of land cover (water bodies, farms, open areas, and residential areas) for Sentinel 2 satellite imagery, and the time the pictures were taken was 8:00 am ( 22 March, 22 June, 20 September, and 22 December) 2021. Their spectral reflectance was calculated at the NIR band using a mathematical equation in the ArcGIS program for Sentinel 2 satellite images that had been processed and analyzed. Also, atmospheric boundary layer height and solar radiation values were downloaded for the same date as the satellite images and compared with the spectral reflectivity values of the land covers(agriculture area, residential area, open area, and river) and knowing the effect of the mixing layer and solar radiation on the spectral reflectance values. The highest value of spectral reflectivity, mixing layer height, and solar radiation was in June at (0.065, 1965.524629, and 897.7088) respectively. The spectral reflectivity of plants at near-infrared (NIR) was higher than the rest of the earth’s features because plants reflect near-infrared radiation and absorb the red and blue parts of the spectrum.

Hyperspectral Reflectance Assessment for Preliminary Identification of Degraded Soil Zones in Industrial Sites, India

The study explores the potential of next-generation satellite hyperspectral imaging systems for screening and predicting surface‐soil contamination and degradation by exploiting various spectral indices and signature‐matching techniques at a heavily industrialized area in India. The soil moisture content, desertification and salinity status, clay or fine material content, heavy metal content, vegetation health status, and stress levels were assessed from continuum-removed spectral reflectance values. Results indicated the presence of water in two tailings ponds, high salinity, and desertification values in most of the tailings ponds and dump sites, clay boundary liner along four ponds, high heavy metal indices along three ponds and all dump sites, highly stressed vegetation near all tailings ponds and coal dump sites, and pollutants in nearby water channels. The results suggest a strategy for the initial identification of priority areas for ground-based investigations and an alternative rapid methodology to monitor large industrial hubs in India.

Mapping tree species of wetlands using multispectral images of UAVs and machine learning: A case study of the Dong Rui Commune

Wetlands provide resources, regulate the environment, and stabilize shorelines; however, they are among the most vulnerable ecosystems in the world. The classification of mangrove species allows the determination of the habitat of each species, thereby serving as a basis for determining protection solutions and planning plans for mangrove conservation and restoration according to each environmental condition. We used Phantom 4 multispectral unmanned aerial vehicles (UAVs) to collect data from wetland areas in the Dong Rui Commune, which is one of the most diverse and valuable wetland ecosystems in northern Vietnam. A tree-species classification map was constructed through a combination of the object-based image analysis method and spectral reflectance values of each plant species, and the characteristic distributions of mangrove plants, including Bruguiera gymnorrhiza, Rhizophora stylosa, and Kandelia obovata, were determined with an overall accuracy of 91.11 % and a kappa coefficient (K) of 0.87. The overall accuracy for Rhizophora stylosa was the highest (94.23 %), followed by Kandelia obovata (93.61 %) and Bruguiera gymnorrhiza (85.50 %). An experiment was conducted to map plant taxonomy in the same area based only on a graph of spectral reflectance values at five single-spectral bands, and normalized difference vegetation index values were constructed, resulting in an overall accuracy of 78.22 % and a K of 0.67. The constructed map is useful for classifying, monitoring, and evaluating the structure of each group of mangroves, thereby serving as a basis for determining the distribution of each mangrove species according to natural conditions and contributing to the formulation of policies for afforestation and mangrove conservation in Dong Rui commune.

Nondestructive Identification of Wheat Seed Variety and Geographical Origin Using Near‐Infrared Hyperspectral Imagery and Deep Learning

ABSTRACTSeed purity assurance is an important aspect of maintaining the quality standards of wheat seeds. It relies significantly on quality parameters, like varietal classification and geographical origin identification. Hyperspectral imaging (HSI) has emerged as an advanced nondestructive technique to determine various quality parameters. In recent years, several studies have utilized HSI for varietal classification, although a limited number of varieties were considered. Additionally, no attention has been paid to determining the geographical origin of wheat seeds. To address these gaps, two separate experiments were performed for varietal classification and geographical origin identification. The seeds from 96 varieties grown across 5 different agricultural regions in India were collected. Hyperspectral images of wheat seeds were acquired in the wavelength ranging 900–1700 nm. The spectral reflectance values were obtained from the region of interest (ROI) corresponding to each seed. Subsequently, the deep learning models (convolutional neural networks [CNNs]) were established and compared with two conventional algorithms, including support vector machines (SVMs) and K‐nearest neighbors (KNNs). The experimental results indicated that the proposed CNN models outperformed the SVM and KNN models, achieving an overall accuracy of 94.88% and 99.02% for varietal classification and geographical origin identification, respectively. These results demonstrate that HSI combined with deep learning has the potential to accurately classify a large number of wheat varieties. Moreover, HSI can be used to precisely identify the geographical origins of wheat seeds. This study provides an accurate and nondestructive method that can assist in breeding, quality evaluation, and the development of high‐quality wheat seeds.

Optimization of Reservoir Water Quality Parameters Retrieval and Treatment Using Remote Sensing and Artificial Neural Networks

Inland water bodies are critical ecosystems that serve several functions including the provision of freshwater, regulation of climate and hydrological flows, and pollution control. Therefore, effective monitoring and management of these water resources is critical for sustainable water supply systems. This study evaluated the possible use of satellite data to estimate water quality parameters (WQPs) in an inland water body. The study also used artificial neural network (ANN) models in addition to the satellite data to determine the optimum coagulant dose for water treatment. The use of earth observations and machine learning methods has not been done extensively in developing countries, specifically, in water quality monitoring and management. The study utilized empirical multivariate regression modelling (EMRM) of the spectral reflectances from satellite data for the retrieval of Chla-a, Turbidity, and total suspended solids (TSS) concentrations in an inland water body. Using MLP-ANN modelling, the extracted spectral reflectance values from the selected sampling points in the reservoir were used as model inputs for the prediction of treated WQPs. A second MLP-ANN model was developed to predict the optimum coagulant dose required for raw water treatment. The R2 values achieved with AN model 1 were 0.81, 0.76, and 0.81 respectively for TSS, turbidity, and Chl-a, and 0.99 for the optimum coagulant dose. The study concluded that spectral reflectance from medium resolution satellite data products can be used to estimate WQPs from inland water bodies. Further, the ANN models demonstrate that extracted water quality data from satellite images can be used to inform ANN models for water quality predictions, and for the optimization of water treatment plant operations.

Estimating turbidity concentrations in highly dynamic rivers using Sentinel-2 imagery in Google Earth Engine: Case study of the Godavari River, India.

Turbidity is an essential biogeochemical parameter for water quality management because it shapes the physical landscape and regulates ecological systems. It varies spatially and temporally across large water bodies, but monitoring based on point-source field observations remains a difficult task in developing countries due to the need for logistics and costs. In this study, we present a novel semi-analytical approach for estimating turbidity from remote sensing reflectance in moderate to highly turbid waters in the lower part of the Godavari River (i.e., locations near Rajahmundry). The proposed method includes two sub-algorithms-Normalized Difference Turbidity Index (NDTI) and semi-empirical single-band turbidity ( ) algorithm-to retrieve spectral reflectance information corresponding to the study locations for turbidity modeling. Sentinel-2 Multi-Spectral Imager data have been used to quantify the turbidity in the Google Earth Engine (GEE) platform. The correlation analysis was observed between spectral reflectance values and in situ turbidity data using cubic polynomial regression equations. The results indicated that the , which uses the only red-edge wavelength, identified turbidity as the most accurate across all locations (highest R2 = 0.91, lowest RMSE = 0.003), followed by NDTI (highest R2 = 0.85, lowest RMSE = 0.05), respectively. The remote sensing data application provides a better way to monitor turbidity at large spatio-temporal scales in attaining the water quality standards of the Godavari River.

Estimating Soil Erodible Fraction Using Multivariate Regression and Proximal Sensing Data in Arid Lands, South Egypt

Estimating soil erodible fraction based on basic soil properties in arid lands is a valuable research topic in the field of soil science and land management. The Proximal Sensing (PS) technique offers a non-destructive and efficient method to assess wind erosion potential in arid regions. By using Partial Least Squares Regression (PLSR) and Support Vector Machine (SVM) models and combining soil texture and chemical properties, determined through Visible-Near Infrared (vis-NIR) spectroscopy in 96 soil samples, this study aims to predict soil erodibility, soil organic matter (SOM), and calcium carbonate equivalent (CaCO3) in arid lands located in Elkobaneyya Valley, Aswan Governorate, Egypt. Results showed that the soil erodibility fraction (EF-Factor) had the highest values and possessed a strong relationship between slope and SOM of 0.01% in determining soil erodibility. The PLSR model performed better than SVM for estimating SOM, CaCO3, and EF-Factor. Furthermore, the results showed that the spectral responses of CaCO3 were observed in separate places in the wavelengths of 570, 649, 802, 1161, 1421, 1854, and 2362 nm, and the wavelengths with SOM parameter were 496, 658, 779, 1089, 1417, 1871, and 2423 nm. The EF-factor shows the highest significant correlation with spectral reflectance values at 526, 688, 744, 1418, 1442, 2292, and 2374 nm. The accuracy and performance of the PLSR model in estimating the EF-Factor using spectral reflectance data and the distribution of data points for both the calibration and validation data-sets indicate a good accuracy of the PLSR model, with RMSE values of 0.0921 and 0.0836 Mg h MJ−1 mm−1, coefficient of determination (R2) values of 0.931 and 0.76, and RPD values of 2.168 and 2.147, respectively.

Early detection of bark beetle infestation using UAV-borne multispectral imagery: a case study on the spruce forest in the Czech Republic

Over the last decade, biotic disturbances caused by bark beetles have represented a serious environmental and economic issue in Central Europe. Great efforts are expended on the early detection and management of bark beetle infestation. Our study analyses a time series of UAV-borne multispectral imagery of a 250-ha forest in the Vysočina region in the Czech Republic. The study site represents a typical European spruce forest with routine silvicultural management. UAV-borne data was acquired three times during the vegetation period, specifically (a) before swarming, (b) at the early stage of infestation, and (c) in the post-abandon phase, i.e., after most bark beetle offspring left the trees. The spectral reflectance values and vegetation indices calculated from orthorectified and radiometrically calibrated imageries were statistically analyzed by quadratic discriminant analysis (QDA). The study shows that healthy and infested trees could be distinguished at the early stage of infestation, especially using NIR-related vegetation indices (NDVI and BNDVI in our case). Detecting infested trees is more significant by vegetation indices than spectral bands and increases with the increasing time after infestation. The study verified the usability of UAV-borne multispectral imageries for early detection of bark beetle infestation at the level of individual trees. Thus, these methods can contribute to precise and effective forest management on a local level.

Rapid non-destructive evaluation of texture properties changes in crispy tilapia during crispiness using hyperspectral imaging and data fusion

In this study, two distinct hyperspectral imaging systems, the Visible and Near-Infrared Spectroscopy (VNIR) and the Short-Wave Infrared Spectroscopy (SWIR), were employed in combine with chemometric techniques to examine the alterations in the texture properties (hardness, resilience, chewiness, and cohesiveness) of tilapia during the crispy process. Partial least squares regression (PLSR) was used to establish a linear relationship between the spectral reflectance values and the different texture parameters of tilapia, and various preprocessing, variable selection, and data fusion (Low-Level Fusion (LLF) and Mid-Level Fusion (MLF)) methods were considered to optimize the calibration model. For hardness, with RP, RMSEP, %RMSEP and RPD values of 0.63, 5.84, 21.65% and 1.26 respectively. For resilience, the LLF presented superior performance, with RP, RMSEP, %RMSEP and RPD values of 0.92, 0.03, 6.8% and 2.52 respectively. For chewiness, the MLF presented superior performance, with RP, RMSEP, %RMSEP and RPD values of 0.68, 4.03, 23.05% and 1.36. For cohesiveness, the MLF exhibited the best performance, with RP, RMSEP, %RMSEP and RPD values of 0.82, 0.03, 4.85% and 1.65 respectively. It was demonstrated that the fusion of both hyperspectral datasets provides a promising method for non-destructive texture measurement of tilapia at different stages of crispiness.

Quantifying salinity in calcareous soils through advanced spectroscopic models: A comparative study of random forests and regression techniques across diverse land use systems.

Precise prediction of soil salinity using visible, and near-infrared (vis-NIR) spectroscopy is crucial for ensuring food security and effective environmental management. This paper focuses on the precise prediction of soil salinity utilizing visible and near-infrared (vis-NIR) spectroscopy, a critical factor for food security and effective environmental management. The objective is to utilize vis-NIR spectra alongside a multiple regression model (MLR) and a random forest (RF) modeling approach to predict soil salinity across various land use types, such as farmlands, bare lands, and rangelands accurately. To this end, we selected 150 sampling points representatives of these diverse land uses. At each point, we collected soil samples to measure the soil salinity (ECe) and employed a portable spectrometer to capture the spectral reflectance across the full wavelength range of 400 to 2400 nm. The methodology involved using both individual spectral reflectance values and combinations of reflectance values from different wavelengths as input variables for developing the MLR and RF models. The results indicated that the RF model (RMSE = 4.85 dS m-1, R2 = 0.87, and RPD = 3.15), utilizing combined factors as input variables, outperformed others. Furthermore, our analysis across different land uses revealed that models incorporating combined input variables yielded significantly better results, particularly for farmlands and rangelands. This study underscores the potential of combining vis-NIR spectroscopy with advanced modeling techniques to enhance the accuracy of soil salinity predictions, thereby supporting more informed agricultural and environmental management decisions.

Photometric correction of images obtained from Chandrayaan-2 Imaging Infra-Red Spectrometer (IIRS) data

India’s second mission to the Moon, Chandrayaan-2, carried one of the most advanced imaging spectrometers ever sent to image a planetary surface – the IIRS (Imaging Infra-Red Spectrometer) – a 250 band spectrometer developed at the Space Applications Centre (SAC), ISRO, India. In this study, IIRS data between 800 and 2000 nm have been used to derive photometrically corrected reflectance from calibrated radiance data supplied by ISRO’s online archive (Pradan) of Chandrayaan-2 data. Additionally, the photometric properties of three IIRS bands (950, 1500, and 1700 nm) have been studied in detail. Since the images obtained by IIRS are at different viewing geometry orientations, the purpose of performing a photometric correction on IIRS images is to re-project each pixel to a standard viewing geometry of incidence angle, i = 30°, emission angle, e = 0° and, phase angle, α = 30°. In order to correct the lunar limb darkening effect, a combination of the Lommel-Seeliger function and McEwen’s model was used. In addition, topographic data from the merged Digital Elevation Model of Lunar Reconnaissance Orbiter – Lunar Orbiter Laser Altimeter (LRO-LOLA) and SELENE Terrain Camera (TC) were utilized to remove effects from local topography. A photometric function was then applied on four images captured by IIRS which were included in the first public release of data in August 2021. Radiance and reflectance (obtained after implementing photometric correction on IIRS images) values were compared with L1 and L2 M3 (Moon Mineralogy Mapper) data from Chandrayaan-1. It was found that although IIRS images are much darker than M3, corrected IIRS reflectance images had a much smaller variation in terms of reflectance values with M3. Moreover, the associated spectral reddening and reflectance values are within range when compared with data from other lunar missions. In addition, the reflectance spectra derived from the corrected IIRS images have been plotted for each image. However, the spectra exhibit significantly deeper absorptions than their corresponding M3 data for the same pixel. Phase functions derived from three bands have a good correlation with the reflectance values and phase angles. Consequently, this function can be applied to all IIRS images as and when more data are released to the public.

Using Remote Sensing Vegetation Indices for the Discrimination and Monitoring of Agricultural Crops: A Critical Review

The agricultural sector is currently confronting multifaceted challenges such as an increased food demand, slow adoption of sustainable farming, a need for climate-resilient food systems, resource inequity, and the protection of small-scale farmers’ practices. These issues are integral to food security and environmental health. Remote sensing technologies can assist precision agriculture in effectively addressing these complex problems by providing farmers with high-resolution lenses. The use of vegetation indices (VIs) is an essential component of remote sensing, which combines the variability of spectral reflectance value (derived from remote sensing data) with the growth stage of crops. A wide array of VIs can be used to classify the crops and evaluate their state and health. However, precisely this high number leads to difficulty in selecting the best VI and their combination for specific objectives. Without thorough documentation and analysis of appropriate VIs, users might find it difficult to use remote sensing data or obtain results with very low accuracy. Thus, the objective of this review is to conduct a critical analysis of the existing state of the art on the effective use of VIs for the discrimination and monitoring of several important agricultural crops (wheat, corn, sunflower, soybean, rape, potatoes, and forage crops), grasslands and meadows. This data could be highly useful for all the stakeholders involved in agricultural activities. The current review has shown that VIs appear to be suitable for mapping and monitoring agricultural crops, forage crops, meadows and pastures. Sentinel-1 and Sentinel-2 data were the most utilized sources, while some of the frequently used VIs were EVI, LAI, NDVI, GNDVI, PSRI, and SAVI. In most of the studies, an array of VIs needed to be employed to achieve a good discrimination of crops or prediction of yields. The main challenges in using VIs are related to the variation of the spectral characteristics during the vegetation period and to the similarities of the spectral signatures of various crops and semi-natural meadows. Thus, further studies are needed to establish appropriate models for the use of satellite data that would prove to have greater accuracy and provide more relevant information for the efficient monitoring of agricultural crops.

Application of remote sensing methods for statistical estimation of organic matter in soils

The availability of reliable information on the physicochemical properties of soils is a necessary tool for maintaining and improving fertility and effective optimization of agricultural land management in many countries. However, ground-based research methods require significant financial and time resources. It has been established that methods based on remote sensing data are an efficient, accurate, and less costly solution for studying different types of soil cover parameters. This work aims to determine the predicted indicator of humus content in soils in selected regions of the Kyiv region (Ukraine) with the corresponding soil types. For this, the spectral properties of chernozem soils were investigated based on Landsat 8 OLI satellite images. A mosaic of the mean spectral reflectance values for the study period (2013-2015) was created using the Google Earth Engine. The vegetation indices NDSI, NDWI, NDBI, MSAVI, and NDVI were used to identify bare soils. Using multiple linear regression, an optimal F-Comparing Nested Model was created for predicting humus content in soils, including seven parameters. The model's accuracy was estimated with such indicators R=0.95, R2= 0.90, σy = 0.16 %. The approach based on the proposed model can be used to support the adoption of the necessary management decisions to improve soil fertility and maintain balanced land use.

Instrumental and visual evaluation of the color adjustment potential of a recently introduced single‑shade composite resin versus multishade composite resins

Single-shade composite resins simplify the process of shade selection by providing a narrow range of color but the ability to simulate all shades. However, evidence is limited for the color shifting ability of a newly developed single‑shade composite resin. The purpose of this in vitro study was to evaluate the instrumental color adjustment potential (CAP-I) and visual color adjustment potential (CAP-V) of a recently introduced single-shade composite resin compared with conventional multishade composite resins against different background colors. Four multishade composite resins (Spectrum TPH3, Beautifil II, Clearfil AP-X, and Gradia Direct) and a single-shade composite resin (Charisma Diamond One) were tested. Four base shades (A1, A2, A3, and A3.5) of the same composite resin (Filtek Z350XT) were selected as different background colors. Dual specimens (an outer base material with an inner hole filled with inner test material) and single specimens of all test and base materials were prepared (n=6 per group). Spectral reflectance values were obtained by using a spectroradiometer. The color difference (ΔE00) and relative translucency parameter (RTP) were calculated by using the CIEDE2000 and RTPCIEDE2000 formula. Visual scoring of color matching was performed by independent observers. The CAP-I and CAP-V values were calculated according to ΔE00 and visual scoring. Analysis of variance (ANOVA) was used for statistical analysis (α=.05). CAP-I and CAP-V were significantly affected by composite resin type, background color, and their interaction (P<.001). Positive CAP-I and CAP-V values were found for the majority of test materials. Charisma Diamond One exhibited the highest CAP-I value under all background colors, the highest CAP-V value under most background colors, and the significantly highest RTP value (P<.001). The color adjustment potential was dependent on the material type and background color. Charisma Diamond One exhibited the highest color adjustment potential and the most pronounced color shifting ability.

Robust NIRS Models for Non-Destructive Prediction of Physicochemical Properties and ageing of Basmati Rice

Aim: To determine physicochemical properties and age of rice by non-destructive technique.&#x0D; Place and Duration of Study: Study was conducted at Division of Food Science and Postharvest Technology, Indian Agricultural Research Institute, New Delhi during 2020 to 2021. &#x0D; Methodology: Rice were kept for accelerated aging at 42.6°C temperature &amp; 71% RH for a duration of 30 days. Changes in four physicochemical properties namely amylose content, volume expansion ratio (VER), water absorption ratio (WAR), and kernel elongation ratio (KER) were evaluated destructively (by spectrophotometer and cooking method) and non-destructively (by spectroradiometer) at every alternate day, during 30 days storage.&#x0D; Results: The physicochemical parameters of rice showed a good correlation with spectral signatures. Subsequently, Principal component Analysis (PCA), Partial Least Square Regression (PLSR), and Multiple Linear Regression (MLR) were used to model the physicochemical changes occurring during the process of accelerated aging using spectral reflectance values. Based on values of Coefficient of determination (R²) and Root mean square error (RMSE) accuracy of models was determined. Predictions with the MLR model resulted in a coefficient of determination (R2) of 0.82, 0.87, 0.9,7, 0.83 and 0.82 with root mean square error (RMSE) of 0.18, 0.13, 0.21, 0.124 and 4.2 for amylose content, VER, WAR, KER, and ageing process respectively for calibration.&#x0D; Conclusion: The study demonstrated the potential of NIRS in non-destructively predicting the physiochemical parameters of rice.

Study calcareous soils behavior in different climatic conditions based on their spectral signature

Lime and gypsum are among the compounds that give clear indicators when monitored by remote sensing devices and techniques, and since they are present in agricultural soils significantly in arid and semi-arid regions, lime was considered one of the indicators that can be monitored in the soil. Accordingly, four areas were chosen to study the spectral reflectivity of their surface soils under somewhat different climatic conditions, which are the northeast of Dohuk, Al Sherekhan, the village of Balak Qout, and the University of Mosul. They represent the soils of agricultural areas. Representative samples of the soil were taken for each study area, and the value of calcium carbonate was measured, and three levels of soil particles were made for each sample, which are grinding soil in the form of powder, and aggregates of soil particles with a diameter of 2 mm, and aggregates of soil particles with a diameter of 4 mm. After that, the reflectivity of the flat surfaces of these samples was measured using a spectrometer. The results showed a superiority in the spectral reflectivity values between the sizes of the diameters of the soils, as the highest reflectivity of the soil was of soft sizes, then the largest size, then the largest, and that the carbonate content had a clear effect in raising the spectral reflectivity ratios in all locations with the overlap in the amount of reflected rays due to the dispersion that occurs, due to the surface roughness of the samples with larger diameters.

Monitoring Changes in the Spectral Reflectivity of Baghdad City and the Impact of the Atmospheric Elements on it using R.S and GIS

Abstract Climate change in recent years has greatly affected the distribution of ground covers. Monitoring these changes has become very easy due to the development of remote sensitivity science and the use of satellites to monitor these changes. The aim of this research is to monitor changes in the spectral reflectivity of the Baghdad governorate center for the month (March, June, September, December) of the year 2021 using remote sensing and satellite images Sentinel 2 and knowing the climate imact on them. Fifty-one samples were selected for four types of ground cover (agricultural land, water, buildings and open space) and their spectral reflectivity was calculated using satellite images. Sentinel 2, where it was processed and analyzed, and then the spectral reflectivity values were calculated using the ArcGIS program. The effect of climate on the spectral reflectivity comes through its impact on the ground covers and thus changing the spectral signature of them. The relationship between temperature, spectral reflectivity, humidity, spectral reflectivity, and knowledge of the type of the relationship between them by means of mathematical equations. The results showed that the spectral reflection of the plant at the near-infrared beam has the most change than the rest of the ground covers.

Early detection of the herbicidal effect of glyphosate and glufosinate by using hyperspectral imaging

AbstractEarly detection of non‐optimal weed control is now a priority to ensure herbicide efficacy. This study aimed to evaluate the potential of hyperspectral imaging (HSI) for early detection of the effects of glyphosate and glufosinate on weeds. Specific features (bands and vegetation indices [VIs]) were extracted as indicators for glyphosate and glufosinate efficacy. Black nightshade (Solanum nigrum L.) was used as the model weed and treated with glyphosate or glufosinate at the fourth‐leaf stage. Plants were imaged in the laboratory at 6, 24, 48, 72, and 96 hours after treatment (HAT) with a 204‐wavelength (400–1000 nm) hyperspectral camera. The impact of the herbicide treatments on the spectral reflectance values was analyzed using the two‐sided Mann–Whitney U test, followed by classification with a machine learning (ML) model applied on the full spectrum and on 12 VIs. In addition, the contribution of the different wavelengths (features) to classification accuracy was assessed using a feature selection process. For glufosinate, 95% classification accuracy was observed as early as 6 HAT, with four features from the green region required. For glyphosate, four features from the red, red‐edge, and green regions were used to achieve 88% classification accuracy at 24 HAT. The accuracy of VIs‐based classification was generally lower than that of the full spectrum classification accuracy. Above 85% classification accuracy was achieved only at later imaging campaigns, starting at 48 HAT. This study thus demonstrates that non‐optimal application of glyphosate and glufosinate can indeed be detected using spectral imaging.