Ratio Spectral Index Research Articles

Assessment of apple soluble sugar content by hyperspectral vegetation indexes

Soluble sugar content in apple fruit is a crucial internal quality factor. Rapid and accurate estimation of soluble sugar content in apple fruit is helpful to predicting maturity. However, few studies have used an approach of hyperspectral vegetation indexes for monitoring apple soluble sugar content from ground-based handheld hyperspectral data. This study systematically analyzed the performance of hyperspectral vegetation indexes of apple in estimating the soluble sugar content. The visible and near infrared spectral of apple samples were measured by using a hyperspectral equipment, with the wavelength of 350–2500 nm. Eleven early-ripening apple varieties were collected from orchards located in five provinces of China, and the sugar content of each apple fruit sample was measured using laboratory methods. Two-band combination method was used to identify the effective spectral index. Results showed that the newly developed two-band ratio spectral index R833/R834 (Rλ is the reflectance value at wavelength λ) was recommended as the optimal index for monitoring early-ripening apple soluble sugar content, generated the coefficients of determination, root mean square error (RMSE) and residual prediction deviation (RPD) values between the measured and predicted values of 0.73%, 0.72%, and 1.38%, respectively. Result indicated that the newly developed two-band index R833/R834 can be used to nondestructively determine apple fruit soluble sugar content. The findings can provide insight into the relationship between the optical properties of apple fruit at different wavelengths and soluble sugar content.

Selection of Spectral Parameters and Optimization of Estimation Models for Soil Total Nitrogen Content during Fertilization Period in Apple Orchards

The rapid and accurate diagnosis of nitrogen content in apple orchard soil is of great significance for the rational application of nitrogen fertilizer in orchards to improve apple yield and quality. An apple orchard in Shuangquan Town, Changqing District, Jinan City, Shandong Province, was taken as the experimental area. The optimal method for extracting spectral characteristic bands and screening spectral characteristic indices (SCIs) of soil total nitrogen (TN) for independent and comprehensive fertilization periods was explored. Independent and comprehensive soil TN content estimation models were constructed and optimized for each and the entire fertilization period, respectively. The results show that compared with the correlation coefficient method, stepwise multiple linear regression (SMLR) performs better in extracting hyperspectral characteristic bands of soil TN content. It helps to achieve a higher modeling accuracy, smaller root mean square error (RMSE), and is more conducive to avoiding the influence of multicollinearity of model variables. The sensitive areas of soil TN content in the SCI do not undergo significant changes due to different fertilization periods. Among them, the ratio spectral indices (RSIs) are in the range of 800–900 nm, 1900–1950 nm, and 2200–2300 nm, while the sensitive areas of the difference spectral index (DI) and Normalized difference spectral index (NDSI) are in the range of 1900–1950 nm and 2200–2300 nm. The combination of SCI and characteristic bands significantly improves the prediction accuracy of soil TN estimation models. The independent and comprehensive estimation models for each fertilization period based on the BP (back propagation) neural network optimized by the Mind Evolution Algorithm (MEA-BPNN) can achieve a more stable and accurate estimation of soil TN. Finally, using soil spectral characteristic bands selected through continuum removal (CR) transformation and SMLR, combined with SCI, the model based on the MEA-BPNN (CR-SCI-MEA-BPNN) has the best prediction performance. The modeling determination coefficients R2 for each fertilization period reached 0.94, 0.95, 0.92, and 0.94, respectively, with RMSE of 0.0032, 0.0024, 0.0035, and 0.0027. The R2 and RMSE of the modeling and validation set of the entire fertilization period comprehensive model are 0.899, 0.0038, and 0.89, 0.0041, respectively. The results of this article provide technical support for promoting the timely monitoring of soil TN content and guiding rational fertilization in apple orchards.

Detection of Aphid-Infested Mustard Crop Using Ground Spectroscopy

Timely detection of pest infestation in agricultural crops plays a pivotal role in the planning and execution of pest management interventions. In this study, a ground measured electromagnetic spectrum through hyperspectral sensing (400–2500 nm) was conducted in healthy and aphid-infested mustard crops in different regions of the Bharatpur district of Rajasthan state, India. The ground measured hyperspectral reflectance and its derivatives during the mustard aphid infestation period were used to identify the sensitive spectral regions in the electromagnetic spectrum concerning Aphid Infestation Severity Grade (AISG) to discriminate Lipaphis-infested mustard crops from the healthy ones. Further Principal Component Analysis (PCA) and Partial Least Square Regression (PLSR) were utilized to identify specific spectral bands to differentiate the healthy from aphid-infested crops. The spectral regions of 493–497 nm (blue), 509–515 nm (green), 690–714 nm (red), 717–721 nm (red edge), and 752–756 nm (NIR) showed high correlation with AISG for reflectance, first and second order derivatives. Further analysis of the spectra using PCA and PLSR indicated that spectral bands of 679 nm, 746 nm, and 979 nm had high sensitivity for discriminating aphid-infested crops from the healthy ones. Average reflectance and various spectral indices such as ratio spectral index (RSI), difference spectral index (DSI), and normalized difference spectral index (NDSI) of identified spectral regions and absolute reflectance of identified specific spectral bands were used for predicting AISG. Several regression models, including PCR and PLSR, were examined to predict the AISG. PLSR was found to better predict infestation grade with RMSE of 0.66 and r2 0.71. Our outcomes counseled that hyperspectral reflectance data have the ability to detect aphid-infested severity in mustard.

On-Site Evaluation of Constituent Content and Functionality of Perilla frutescens var. crispa Using Fluorescence Spectra.

Perilla frutescens leaves are hypothesized to possess antioxidant and amyloid-β (Aβ) aggregation inhibitory properties primarily due to their polyphenol-type compounds. While these bioactivities fluctuate daily, the traditional methods for quantifying constituent contents and functional properties are both laborious and impractical for immediate field assessments. To address this limitation, the present study introduces an expedient approach for on-site analysis, employing fluorescence spectra obtained through excitation light irradiation of perilla leaves. Standard analytical techniques were employed to evaluate various constituent contents (chlorophyl (Chl), total polyphenol content (TPC), total flavonoid content (TFC), and rosmarinic acid (RA)) and functional attributes (DPPH radical scavenging activity, ferric reducing antioxidant power (FRAP), oxygen radical absorbance capacity (ORAC), and Aβ aggregation inhibitory activity). Correlations between the fluorescence spectra and these parameters were examined using normalized difference spectral index (NDSI), ratio spectral index (RSI), and difference spectral index (DSI) analyses. The resulting predictive model exhibited a high coefficient of determination, with R2 values equal to or greater than 0.57 for constituent contents and 0.49 for functional properties. This approach facilitates the convenient, simultaneous, and nondestructive monitoring of both the chemical constituents and the functional capabilities of perilla leaves, thereby simplifying the determination of optimal harvest times. The model derived from this method holds promise for real-time assessments, indicating its potential for the simultaneous evaluation of both constituents and functionalities in perilla leaves.

Evaluation of forage quality in a pea breeding program using a hyperspectral sensing system

Forage quality evaluation is essential in breeding and selecting forage crops to improve livestock health and reduce greenhouse gas emissions. A non-destructive and high-throughput process to assess forage quality can advance cultivar development research and commercial feed analysis. The overall objective of this study was to estimate 12 biomass quality traits of field peas in the 2019 and 2020 field seasons using in-field hyperspectral spectroscopy (350–2500 nm) at the leaf level. Six machine-learning models were utilized to develop the relationships between each quality trait and four feature extraction datasets (first derivative reflectance data, vegetation indices, normalized difference spectral indices, and ratio spectral indices) of leaf reflectance spectra from the hyperspectral data. The high and consistent performance of all quality traits was produced from only ridge regression, elastic net regression, and random forest regression models in first derivative reflectance and vegetation indices datasets. In addition, higher prediction performance (0.81 < R2 < 0. 93; 0.05 < root mean square error (%) < 1.80; 0.03 < mean absolute error (%) < 1.32) was found in the random forest model using normalized difference spectral indices and ratio spectral indices datasets after utilizing a feature selection technique (leave one feature out). The results suggest that the proposed evaluation approach of spectroscopy may be applied to predict field pea quality traits in a timely fashion to assist breeders and farmers in their decision-making.

Post Wildfire Vegetation Response to the Wildland-Urban Interface: A Case Study of the Station Fire

In the past, wildfires served as a method for mother nature to promote biodiversity and to help maintain a functioning ecosystem. However, climate change alters the fire regime, significantly impacting vegetation recovery. Human disturbances and increased land use and land cover heighten vegetation disruption and abundance after a fire. Wildland-urban interface (WUI) – the region where the vegetation intermingles with the roads, houses, and human-made structures – threatens vegetation and the human population. Overall vegetation recovery after the Station Fire of 2009 spread through the San Gabriel Mountains, Los Angeles County was observed using Digital Elevation Model (DEM), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Burn Ratio (nDBR) spectral indices. In addition, Light Detection and Ranging (LiDAR) images were used to measure aboveground biomass (AGB). The study analyzed vegetation biomass recovery by comparing human disturbances and the level of fire severity within the Station Fire perimeter. Low and moderate fire severity were compared in detail against WUI and non-WUI regions by quantifying the amount of biomass in the specified regions. Linear regression model results showed vegetation recovery rates were slower in WUI regions than in non-WUI regions despite having similar regeneration patterns while AGB rebound was similar across both region categories.

Improving water status prediction of winter wheat using multi-source data with machine learning

Water and nitrogen (N) are the most important factors limiting crop productivity. Effective monitoring of the water status of winter wheat under different N treatments is imperative for precision irrigation in improved crop management. Hyperspectral remote sensing is widely used for monitoring the crop water status. However, changes in canopy architecture during ontogeny lead to poor inversion of crop properties and limit the use of spectral indices. This study aimed to improve the water status prediction of winter wheat using multi-source data with machine learning (ML). Two multi-irrigation levels (0, 120, 240, 360 mm) and N rates (75, 225 kg N ha-1) experiments were conducted during the 2019–2021 wheat growing seasons under field conditions using a rainout shelter. Hyperspectral, soil, plant, and climate data were evaluated with two feature selection methods. Selected results were chosen as input variables for prediction models by using three ML algorithms. By constructing the normalized difference spectral index (NDSI), ratio spectral index (RSI), and difference spectral index (DSI), the DSI(2015, 2375), NDSI(2175, 2245), and RSI(720, 1200) showed the strongest correlation with canopy water content (CWC), plant water content (PWC), and canopy equivalent water thickness (CEWT), respectively. The best feature selection method and data were delivered by the Pearson correlation coefficient together with the soil, plant, and climate data. The best performing ML algorithm for CWC and PWC prediction was RF, while SVM was the best ML algorithm for CEWT prediction. The R2 of the optimal models ranged from 0.86 to 0.97. These models with multi-source data significantly improved the prediction accuracy of the water status and can thus assist in precision irrigation management of winter wheat.

Assessing the Spectral Information of Sentinel-1 and Sentinel-2 Satellites for Above-Ground Biomass Retrieval of a Tropical Forest

Earth Observation (EO) spectral indices have been an important tool for quantifying and monitoring forest biomass. Nevertheless, the selection of the bands and their combination is often realized based on preceding studies or generic assumptions. The current study investigates the relationship between satellite spectral information and the Above Ground Biomass (AGB) of a major private forest on the island of Java, Indonesia. Biomass-related traits from a total of 1517 trees were sampled in situ and their AGB were estimated from species-specific allometric models. In parallel, the exhaustive band combinations of the Ratio Spectral Index (RSI) were derived from near-concurrently acquired Sentinel-1 and Sentinel-2 images. By applying scenarios based on the entire dataset, the prevalence and monodominance of acacia, mahogany, and teak tree species were investigated. The best-performing index for the entire dataset yielded R2 = 0.70 (R2 = 0.78 when considering only monodominant plots). An application of eight traditional vegetation indices provided, at best, R2 = 0.65 for EVI, which is considerably lower compared to the RSI best combination. We suggest that an investigation of the complete band combinations as a proxy of retrieving biophysical parameters may provide more accurate results than the blind application of popular spectral indices and that this would take advantage of the amplified information obtained from modern satellite systems.

Retrieving potassium levels in wheat blades using normalised spectra

This work explored potassium nutrient retrieval in wheat blades using reflectance spectra. Spectral data were collected from wheat blades at different growth stages, in different cultivars, and following different fertilisation treatments from 2016 to 2019 using a leaf clip and halogen bulb with an ASD spectrometer. Reflectance data from 350 to 2500 nm were collected, and data of 400 to 2400 nm were used in the retrieval. Using a leaf clip to measure the reflectance of a narrow blade can cause bias, which can be corrected using a normalisation method, i.e. the reflectance of each band was divided by the average reflectance of all bands. Three such methods were employed: vegetation index (VI), partial least squares (PLS), and random forest (RF). The approach yielded leaf potassium content (LKC, %) and leaf potassium per area (LKA, g/m2). The results showed that newly developed VIs outperformed previously published indices. The model using a modified ratio spectral index, mRSI(2275, 1875), yielded LKC with a coefficient of determination (R2) of 0.61 and a root mean square error (RMSE) of 0.57%. Normalisation methods can eliminate multiplicative error in blade spectra, thereby correcting the underestimated reflectance of narrow blades, and improving the accuracy of potassium retrieval models. Among the three methods, PLS achieved the highest accuracy. The retrieval of LKC and LKA based on normalised spectra and the PLS method yielded R2 values of 0.74 and 0.65, respectively, and their corresponding RMSE values were 0.46% and 0.21 g/m2. LKC retrieval models had higher R2 values than LKA models. This comprehensive analysis of different methods revealed the importance of reflectance at 1883 nm and 2305 nm. In conclusion, it is feasible to retrieve wheat leaf potassium levels using spectral data.

MOSEV: a global burn severity database from MODIS (2000–2020)

Abstract. To make advances in the fire discipline, as well as in the study of CO2 emissions, it is of great interest to develop a global database with estimators of the degree of biomass consumed by fire, which is defined as burn severity. In this work we present the first global burn severity database (MOSEV database), which is based on Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance and burned area (BA) products from November 2000 to near real time. To build the database we combined Terra MOD09A1 and Aqua MYD09A1 surface reflectance products to obtain dense time series of the normalized burn ratio (NBR) spectral index, and we used the MCD64A1 product to identify BA and the date of burning. Then, we calculated for each burned pixel the difference of the NBR (dNBR) and its relativized version (RdNBR), as well as the post-burn NBR, which are the most commonly used burn severity spectral indices. The database also includes the pre-burn NBR used for calculations, the date of the pre- and post-burn NBR, and the date of burning. Moreover, in this work we have compared the burn severity metrics included in MOSEV (dNBR, RdNBR and post-burn NBR) with the same ones obtained from Landsat-8 scenes which have an original resolution of 30 m. We calculated the Pearson's correlation coefficients and the significance of the relationships using 13 pairs of Landsat scenes randomly distributed across the globe, with a total BA of 6904 km2 (n=32 163). Results showed that MOSEV and Landsat-8 burn severity indices are highly correlated, particularly the post-burn NBR (R=0.88; P&lt;0.001), and dNBR (R=0.74; P&lt;0.001) showed stronger relationships than RdNBR (R=0.42; P&lt;0.001). Differences between MOSEV and Landsat-8 indices are attributable to variability in reflectance values and to the different temporal resolution of both satellites (MODIS: 1–2 d; Landsat: 16 d). The database is structured according to the MODIS tiling system and is freely downloadable at https://doi.org/10.5281/zenodo.4265209 (Alonso-González and Fernández-García, 2020).

Hyperspectral parameters and prediction model of soil moisture in apple orchards

In this study, the apple orchard of Shuangquan town in mountainous and hilly area of central Shandong Province was taken as the research area. The relationship between the ratio spectral index (RSI), difference spectral index (DI) and normalized difference spectral index (NDSI) and soil moisture content in the range of 400nm-2450nm was explored, and then a quantitative estimation model of soil moisture content was build. The results show that the correlation between soil moisture content and spectral reflectance can be improved by different spectral index calculation. The sensitive modeling areas determined by RSI, DI and NDSI are all near the third water absorption peak, among which RSI (R2304, R2081), RSI (R2306, R2080), RSI (R2306, R2081), RSI (R2307, R2079), RSI (R2307, R2080), RSI (R2307, R2081), RSI (R2308, R2080) and RSI (R2308, R2081) the PLSR regression equation constructed for independent variables has the best estimation effect, the largest determination coefficient (R2 = 0.7918), and the smaller Root Mean Square Error (RMSE = 0.0054). The selection of full band spectral index can better extract effective information, and significantly improve the accuracy and adaptability of soil moisture spectral estimation model.

Monitoring the Foliar Nutrients Status of Mango Using Spectroscopy-Based Spectral Indices and PLSR-Combined Machine Learning Models

Conventional methods of plant nutrient estimation for nutrient management need a huge number of leaf or tissue samples and extensive chemical analysis, which is time-consuming and expensive. Remote sensing is a viable tool to estimate the plant’s nutritional status to determine the appropriate amounts of fertilizer inputs. The aim of the study was to use remote sensing to characterize the foliar nutrient status of mango through the development of spectral indices, multivariate analysis, chemometrics, and machine learning modeling of the spectral data. A spectral database within the 350–1050 nm wavelength range of the leaf samples and leaf nutrients were analyzed for the development of spectral indices and multivariate model development. The normalized difference and ratio spectral indices and multivariate models–partial least square regression (PLSR), principal component regression, and support vector regression (SVR) were ineffective in predicting any of the leaf nutrients. An approach of using PLSR-combined machine learning models was found to be the best to predict most of the nutrients. Based on the independent validation performance and summed ranks, the best performing models were cubist (R2 ≥ 0.91, the ratio of performance to deviation (RPD) ≥ 3.3, and the ratio of performance to interquartile distance (RPIQ) ≥ 3.71) for nitrogen, phosphorus, potassium, and zinc, SVR (R2 ≥ 0.88, RPD ≥ 2.73, RPIQ ≥ 3.31) for calcium, iron, copper, boron, and elastic net (R2 ≥ 0.95, RPD ≥ 4.47, RPIQ ≥ 6.11) for magnesium and sulfur. The results of the study revealed the potential of using hyperspectral remote sensing data for non-destructive estimation of mango leaf macro- and micro-nutrients. The developed approach is suggested to be employed within operational retrieval workflows for precision management of mango orchard nutrients.

Hyperspectral imaging techniques for rapid detection of nutrient content of hydroponically grown lettuce cultivars

The analyses of online quality measurements of four lettuce cultivars (Rex, Tacitus, Black Seeded Simpson, Flandria) using hyperspectral image processing techniques have been studied. Seedlings were planted in Rockwool cubes and fed for 3 weeks using hydroponic nutrient solution containing 0, 50, 100, 150, 200, 250, and 300 ppm nitrogen. The hyperspectral images of the freshly cut lettuce leaves were captured using hyperspectral camera in the wavelength range of 400–1,000 nm. The algorithm to measure nutrient levels of leaf tissues including nitrate (NO3–), calcium (Ca2+), potassium (K+), soluble solid content (SSC), pH, and total chlorophyll concentration (SPAD reading) for lettuce was developed. To locate the optimum wave band combination values, simple linear regression models with two spectral data values, namely reflectance and its first derivative, and two spectral indices, namely ratio spectral index (RSI) and normalized difference spectral index (NDSI), were found. The optimal wave bands were within 506–601 nm and 634–701 nm. The nutrient levels of NO3–, Ca2+, K+, SSC, pH, and SPAD in the leaf samples were estimated using partial least squares regression (PLSR) and principal component analysis (PCA) techniques of the four optimal wave bands. Both PLSR and PCA models produced well correlated outcome in comparison with the laboratory measured freshly cut lettuce leaves in predicting nutrient contents with R2 = 0.784–0.987.

Hyperspectral assessment of soil fertility in farm fields in Fukushima decontaminated after the radioactive fallout

ABSTRACT Farmland in the Fukushima region of Japan experienced unprecedented radioactive contamination as a result of the Fukushima Nuclear Power Plant disaster in 2011. Many fields (4,950 ha; over 30,000 fields to date) have been decontaminated by replacing the top surface soil with non-contaminated soil. However, the fertility of these fields is quite low and within-field heterogeneity is marked. Accordingly, appropriate management of soil and fertilizer is required for recovery of crop productivity in these decontaminated fields. Remote sensing can play a critical role in rapid spatial assessment of soil fertility. This preliminary study investigated the potential of spectral sensing approaches based on hyperspectral reflectance measurements (400–2500 nm) of soil samples from a decontaminated paddy field in the Fukushima region. Spectral index algorithms (the ratio spectral index [RSI] and normalized difference spectral index [NDSI]) and multivariate regression methods (partial least-squares regression [PLSR] and interval PLSR [iPLSR]) were used to identify accurate, robust predictive models. The iPLSR and PLSR showed higher predictive accuracy than the other methods (r2 val = 0.937 and 0.802, respectively). The best spectral indices explored using RSI and NDSI have good potential for assessing the spatial variability of soil carbon content (SC; r2 val = 0.730 ~ 0.844), despite using only two wavebands. The results from the RSI map (or NDSI map) approach provided useful information for creating optimal algorithms for assessing SC values using various sensors, including high-resolution optical satellite sensors. Although we must note that optimal algorithms and their applicability are often site-specific depending on soil type and surface conditions, our results imply that these spectral sensing methods can contribute to the recovery of soil fertility of decontaminated fields in the Fukushima region through careful calibration/validation procedures with sufficient in situ data.

Evaluation of different water absorption bands, indices and multivariate models for water-deficit stress monitoring in rice using visible-near infrared spectroscopy.

Accurate estimation of plant water status is a major factor in the decision-making process regarding general land use, crop water management and drought assessment. Visible-near infrared (VNIR) spectroscopy can provide an effective means for real-time and non-invasive monitoring of leaf water content (LWC) in crop plants. The current study aims to identify water absorption bands, indices and multivariate models for development of non-destructive water-deficit stress phenotyping protocols using VNIR spectroscopy and LWC estimated from 10 different rice genotypes. Existing spectral indices and band depths at water absorption regions were evaluated for LWC estimation. The developed models were found efficient in predicting LWC of the samples kept in the same environment with the ratio of performance to deviation (RPD) values varying from 1.49 to 3.05 and 1.66 to 2.63 for indices and band depths, respectively during validation. For identification of novel indices, ratio spectral indices (RSI) and normalised difference spectral indices (NDSI) were calculated in every possible band combination and correlated with LWC. The best spectral indices for estimating LWC of rice were RSI (R1830, R1834) and NDSI (R1830, R1834) with R2 greater than 0.90 during training and validation, respectively. Among the multivariate models, partial least squares regression (PLSR) provided the best results for prediction of LWC (RPD=6.33 and 4.06 for training and validation, respectively). The approach developed in this study will also be helpful for high-throughput water-deficit stress phenotyping of other crops.

A spectral parameter for the estimation of soil total nitrogen and nitrate nitrogen of winter wheat growth period

AbstractReal‐time monitoring of crop nitrogen and soil nutrient status can provide an important basis for the rational application of nitrogen during fertilization. Two field experiments were conducted in Henan Province, China, using three main wheat cultivars and four nitrogen levels, across two consecutive growing seasons. Canopy spectral reflectance, plant nitrogen and soil nitrogen were synchronously measured during the wheat's main growth stages. Furthermore, the quantitative relationship between the soil nitrogen and plant nitrogen concentrations was analysed, and the relationship with existing spectral parameters and several kinds of hyperspectral indices, including normalized difference spectral indices (NDVI), ratio spectral indices (RVI) and difference spectral indices (DVI), was extracted. All the combinations of two wavebands between 350 and 1 050 nm were calculated, and their quantitative relationships with soil nitrogen content were analysed. The results were used to build a remote monitoring model of soil total nitrogen and nitrate nitrogen (NO3‐‐N) content by using the canopy spectral reflectance. The results showed that the correlation between soil NO3‐‐N and plant nitrogen concentration was superior to the correlation between total nitrogen and plant nitrogen concentrations. After systematically analysing the quantitative relationship between wheat canopy spectra, soil total nitrogen content and NO3‐‐N content, we found that the correlation with the original reflectance in the visible region was superior and that the first derivative spectrum significantly improved the correlation in the near‐infrared region. Furthermore, the soil total nitrogen content, NO3‐‐N content and canopy spectral data were analysed in different growth stages (jointing stage, flowering stage and filling stage). The results showed that NDVI (FD747, FD699) was the best indicator for monitoring soil total nitrogen and NO3‐‐N content modelling, as the modelling decision coefficient and prediction decision coefficient were both above 0.8 for all test cases. Thus, during wheat growth, crop canopy spectroscopy can be used to estimate soil nitrogen nutrition status, and NDVI (FD747, FD699) can be used as an effective spectral parameter for estimating soil total nitrogen and NO3‐‐N content.

Contribution of High-energy GRB Emissions to the Spectrum of the Isotropic Diffuse γ-Ray Background

Abstract High-precision measurement of the isotropic diffuse γ-ray background (IGRB) has been extrapolated to the TeV energy region using the Fermi-LAT experiment. Various kinds of astrophysical sources are candidates for its origin. However, a consensus on the dominant source has been difficult to reach. Recent observations of the γ-ray emission of γ-ray bursts (GRBs), denoted as the synchrotron self-Compton (SSC) component in the afterglow phase, in the sub-TeV energy region by MAGIC and HESS experiments shed new light on this topic. In this work, we revisit the contribution from the SSC component of GRBs to the IGRB. First, a sample set of GRB events is obtained and examined using observations from Fermi-LAT. Second, the SSC component, described by the energy ratio R ext and spectral index β ext, is assigned to every GRB event. We can obtain the total spectrum contribution based on this GRB sample. We find that when R ext and β ext reach ∼20% and −1.6, respectively, the contribution from GRB emission dominates in the energy region of hundreds of GeV. We hope that the LHAASO and CTA experiments under construction can observe a large number of GRBs to fix those parameters in coming years. A surviving tail is expected, which can serve to check our calculations based on future satellite experiments such as HERD and GAMMA400.

Performance Analysis of Deep Convolutional Autoencoders with Different Patch Sizes for Change Detection from Burnt Areas

Fire is one of the primary sources of damages to natural environments globally. Estimates show that approximately 4 million km2 of land burns yearly. Studies have shown that such estimates often underestimate the real extent of burnt land, which highlights the need to find better, state-of-the-art methods to detect and classify these areas. This study aimed to analyze the use of deep convolutional Autoencoders in the classification of burnt areas, considering different sample patch sizes. A simple Autoencoder and the U-Net and ResUnet architectures were evaluated. We collected Landsat 8 OLI+ data from three scenes in four consecutive dates to detect the changes specifically in the form of burnt land. The data were sampled according to four different sampling strategies to evaluate possible performance changes related to sampling window sizes. The training stage used two scenes, while the validation stage used the remaining scene. The ground truth change mask was created using the Normalized Burn Ratio (NBR) spectral index through a thresholding approach. The classifications were evaluated according to the F1 index, Kappa index, and mean Intersection over Union (mIoU) value. Results have shown that the U-Net and ResUnet architectures offered the best classifications with average F1, Kappa, and mIoU values of approximately 0.96, representing excellent classification results. We have also verified that a sampling window size of 256 by 256 pixels offered the best results.

Passive reflectance sensing using optimized two- and three-band spectral indices for quantifying the total nitrogen yield of maize

Precision nitrogen (N) management requires accurate and effective quantification of the total nitrogen yield (TNY) crops. Thus, this study aimed at establishing the robust prediction model to quantify the TNY of maize plants across growth stages, cultivars and years through optimizing two-band spectral indices, i.e. the normalized difference spectral index (NDSI) and the ratio spectral index (RSI), and three-band spectral indices, i.e. the canopy chlorophyll content index (CCCI) by re-determining the central wavelength and bandwidth. Field experiments with three maize cultivars and five N treatments were carried out in the North China Plain during 2011, 2012, and 2013. The results showed that the optimized band ranges of NDSI and RSI were mainly located within 720–760 nm (the red-edge domain) and 750–900 nm (the near-infrared domain). The central wavelengths of the NDSI were 768 and 740 nm, whereas those of the RSI were 756 and 744 nm. The most suitable band domains of the CCCI were 720–760, 500–600 and 740–800 nm, and their central wavelengths were 766, 738 and 548 nm. Our study found that the optimized spectral indices could predict the TNY of maize accurately and robustly compared with existing spectral indices. The relationships between the optimized NDSI and RSI and maize TNY reached a high coefficient of determination (R2 = 0.83). However, the prediction accuracy of TNY using the NDSI and RSI was gradually decreased with an increase in bandwidth, i.e., the bandwidths with central wavelengths of 740 and 768 nm of the NDSI were 13 and 21 nm, respectively. For RSI, the bandwidths with central wavelengths of 744 and 756 nm were 29 and 17 nm, respectively. The results also demonstrated that an optimized narrow and broadband CCCI was significantly linear in relation to the TNY of maize (R2 = 0.85), suggesting an optimized CCCI may be used to quantify TNY of maize plants with higher accuracy by avoiding the saturation effect and improving sensitivity.

Hyperspectral Inversion Model of Chlorophyll Content in Peanut Leaves

The purpose of this study is to determine a method for quickly and accurately estimating the chlorophyll content of peanut plants at different plant densities. This was explored using leaf spectral reflectance to monitor peanut chlorophyll content to detect sensitive spectral bands and the optimum spectral indicators to establish a quantitative model. Peanut plants under different plant density conditions were monitored during three consecutive growth periods; single-photon avalanche diode (SPAD) and hyperspectral data derived from the leaves under the different plant density conditions were recorded. By combining arbitrary bands, indices were constructed across the full spectral range (350–2500 nm) based on blade spectra: the normalized difference spectral index (NDSI), ratio spectral index (RSI), difference spectral index (DSI) and soil-adjusted spectral index (SASI). This enabled the best vegetation index reflecting peanut-leaf SPAD values to be screened out by quantifying correlations with chlorophyll content, and the peanut leaf SPAD estimation models established by regression analysis to be compared and analyzed. The results showed that the chlorophyll content of peanut leaves decreased when plant density was either too high or too low, and that it reached its maximum at the appropriate plant density. In addition, differences in the spectral reflectance of peanut leaves under different chlorophyll content levels were highly obvious. Without considering the influence of cell structure as chlorophyll content increased, leaf spectral reflectance in the visible (350–700 nm): near-infrared (700–1300 nm) ranges also increased. The spectral bands sensitive to chlorophyll content were mainly observed in the visible and near-infrared ranges. The study results showed that the best spectral indicators for determining peanut chlorophyll content were NDSI (R520, R528), RSI (R748, R561), DSI (R758, R602) and SASI (R753, R624). Testing of these regression models showed that coefficient of determination values based on the NDSI, RSI, DSI and SASI estimation models were all greater than 0.65, while root mean square error values were all lower than 2.04. Therefore, the regression model established according to the above spectral indicators was a valid predictor of the chlorophyll content of peanut leaves.