Raw Reflectance Data Research Articles

SPECTROKINETIC INVESTIGATION OF THE PHOTOCHROMIC SYSTEM UNDER CONTINUOUS UV IRRADIANCE USING REFLECTANCE VS. TIME CURVES

The photochromic properties of the dye 5-chloro-1,3,3-trimethylspiro[indoline-2,3'-[3H] naphtho[2,1-b] [1,4]- oxazine] were investigated by exposing it to continuous monochromatic irradiation using 360 nm ultraviolet irradiation using photochromic prints at 293.15  2 K; it undergoes a photocoloration and photodecoloration state, thus following the dynamics of a photochromic cycle of growth and decay phase, respectively. Even if the isolated photo isomeric form of the photochromic dye structure is unavailable, one can quantitatively analyse the photo-induced kinetics of photochromic systems using the raw reflectance data. The quantum colour yield is represented by colour span values [K/S] obtained by converting reflectance data to Kubelka- Munk values. The dynamics of the photoreaction in a photochromic system can be examined by analysing the constants for the rate of colouration and decolouration caused by light. By employing first-order kinetics, the dependent dominant wavelength [max] can be determined by fitting the raw data obtained as a change in reflectance values from the FOTOCHROM3 spectrophotometer. The relationship between the colour span values and the concentration of the photochromic dye employed in the photochromic prints is linear.

Estimation of chlorophyll content of Cinnamomum camphora leaves based on hyperspectral and fractional order differentiation

ABSTRACT Hyperspectral remote sensing combined with data preprocessing techniques and machine learning algorithms is a new way to efficiently estimate plant SPAD. In this study, the raw hyperspectral reflectance data of the dwarf forest of Cinnamomum camphora (Linn.) Presl (C. camphora) were subjected to 0-2nd order differential transformations (a step size of 0.25) using SPAD during the harvesting period of C. camphora. The spectral indices with the highest correlation with SPAD in each order were also selected as input variables for the model, and the measured values of SPAD from simultaneous observations were used as the output variable. Support vector machine (SVM), random forest (RF), and backpropagation neural network (BPNN) were used to construct the SPAD inversion model. The results showed that the correlation between the spectral index and the SPAD of the dwarf forest of C. camphora and the accuracy of the constructed prediction model tended to increase and then decrease with increasing differential order. After a comprehensive comparison, the RF-based SPAD prediction model achieved the highest accuracy with a coefficient of determination (R2) of 0.824, root mean square error (RMSE) of 1.619, and mean relative error (MRE) of 3.207% for the validation set, using spectral indices computed by the 1.5-order differential treatment as the input variables. This study provides a theoretical basis for improving the accuracy of plant leaf SPAD inversion based on hyperspectral remote sensing.

Python Indian Weather Radar Toolkit (pyiwr): An open-source Python library for processing, analyzing and visualizing weather radar data

The Python Indian Weather Radar Toolkit, abbreviated as "pyiwr", is an open-source Python library tailored for the purpose of handling data from the Indian Doppler Weather Radar (DWR). This paper provides a comprehensive overview of the pyiwr, which serves as a toolkit to read, analyze, process, and visualize weather radar data. Apart from this, the toolkit offers a range of robust functions implementing various algorithms covering several aspects of the radar data processing and quality control that facilitate the manipulation and analysis of weather radar data. To demonstrate the practical applicability of pyiwr, various case studies are presented, focusing on processing raw reflectivity data (clutter correction), Quantitative Precipitation Estimation (QPE) using Z-R relationship and time-series analysis of reflectivity and rain intensity, both spatially as well as at a specific location, during various meteorological events. This module enhances the accessibility and compatibility of radar data, enabling researchers, weather forecasters, and hydrologists to efficiently work with DWR data (particularly Indian DWR) that fosters advancements in weather radar research and applications. The open availability of pyiwr's source code on GitHub ensures that researchers and practitioners can not only access the toolkit but also contribute to its ongoing development.

Study of various machine learning approaches for Sentinel-2 derived bathymetry.

In recent years precise and up-to-date information regarding seabed depth has become more and more important for companies and institutions that operate on coastlines. While direct, in-situ measurements are performed regularly, they are expensive, time-consuming and impractical to be performed in short time intervals. At the same time, an ever-increasing amount of satellite imaging data becomes available. With these images, it became possible to develop bathymetry estimation algorithms that can predict seabed depth and utilize them systematically. Since there are a number of theoretical approaches, physical models, and empirical techniques to use satellite observations in order to estimate depth in the coastal zone, the presented article compares the performance and precision of the most common one to modern machine learning algorithms. More specifically, the models based on shallow neural networks, decision trees and Random Forest algorithms have been proposed, investigated and confronted with the performance of pure analytical models. The particular proposed machine learning models differ also in a set of satellite data bands used as an input as well as in applying or not geographical weighting in the learning process. The obtained results point towards the best performance of the regression tree algorithm that incorporated as inputs information about data localization, raw reflectance data from four satellite data bands and a quotient of logarithms of B2 and B3 bands. The study for the paper was performed in relatively optically difficult and spatially variant conditions of the south Baltic coastline starting at Szczecin, Poland on the west (53°26'17'' N, 14°32'32'' E) to Hel peninsula (54°43'04,3774'' N 18°37'56,9175'' E). The reference bathymetry data was acquired from Polish Marine Administration. It was obtained through profile probing with single-beam sonar or direct in-situ probing.

Assessing the Leaf Blade Nutrient Status of Pinot Noir Using Hyperspectral Reflectance and Machine Learning Models

Monitoring grape nutrient status, from flowering to veraison, is important for viticulturists when implementing vineyard management strategies, in order to produce quality wines. However, traditional methods for measuring nutrient elements incur high labour costs. The aim of this study is to explore the potential of predicting grapevine leaf blade nutrient concentration based on hyperspectral data. Leaf blades were collected at two Pinot Noir commercial vineyards at Martinborough, New Zealand. The leaf blade spectral data were obtained with a handheld spectroradiometer, to evaluate surface reflectance and derivative spectra in the spectrum range between 400 and 2400 nm. Afterwards, leaf blades nutrient concentrations (N, P, K, Ca, and Mg) were measured, and their relationships with the hyperspectral data were modelled by machine learning models; partial least squares regression (PLSR), random forest regression (RFR), and support vector regression (SVR) were used. Pearson correlation and recursive feature elimination, based on cross-validation, were used as feature selection methods for RFR and SVR, to improve the model’s performance. The variable importance score of PLSR, and permutation variable importance of RFR and SVR, were used to determine the most sensitive wavelengths, or spectral regions related to each biochemical variable. The results showed that the best predictive performance for leaf blade N concentration was based on PLSR to raw reflectance data (R2 = 0.66; RMSE = 0.15%). The combination of support vector regression with the Pearson correlation selected method and second derivative reflectance provided a high accuracy for K and Ca modelling (R2 = 0.7; RMSE = 0.06%; R2 = 0.62; RMSE = 0.11%, respectively). However, the modelling performance for P and Mg, by different feature groups and variable selection methods, was poor (R2 = 0.15; RMSE = 0.02%; R2 = 0.43; RMSE = 0.43%, respectively). Thus, a larger dataset is needed for improving the prediction of P and Mg. The results indicated that for Pinot Noir leaf blades, raw reflectance data had potential for the prediction of N concentration, while the second-derivative spectra were more suitable to predict K and Ca. This study led to the provision of rapid and non-destructive measurements of grapevine leaf nutrient status.

A baptism by fire: fossil charcoal from eastern Euramerica reveals the earliest (Homerian) terrestrial biota evolved in a flammable world

Charred fossils from the Wenlock (Wales) and Ludlow (Poland) are evidence of the earliest wildfires to date, showing that this phenomenon was contemporaneous with the earliest records of land plant macrofossils. These data indicate that fires began to influence Earth system processes alongside those wrought by the advent of an embryophytic terrestrial flora. By the mid-Silurian, fires affected atmospheric composition, sedimentary systems, carbon and nutrient cycling, landscape diversity, community composition and species interactions. As global heating alters wildfire regimes, greater recognition is being given to fires and their ecosystem impacts, a relationship we now know extends back >430 myr. Here we document the taxonomic composition of charred phytoclasts, evidence of wildfire activity, from two discrete Silurian localities: the Pen-y-lan Mudstone, Rumney, Wales, and the Winnica Formation, Holy Cross Mountains, Poland. Nematophytes dominate each mesofossil assemblage and quantitative reflectance data indicate generally low-temperature fires at both sites, but with locally intense conditions. These and other Silurian assemblages, herein documented as bearing charcoal, are used to evaluate the systematics, fuel load and burn temperatures of these earliest wildfires. We propose a diagrammatic reconstruction to explain the seeming disparity between the diminutive size of the embryophytic biota and the highest temperatures (>700°C) recorded in these charcoals. Supplementary material : Raw mean random reflectance ( R o %) data from Rumney Borehole, Winnica, Ludford Lane and North Brown Clee Hill, sorted by both locality and morphotype, are available at https://doi.org/10.6084/m9.figshare.c.6179309

Classification of rice leaf blast severity using hyperspectral imaging

Rice leaf blast is prevalent worldwide and a serious threat to rice yield and quality. Hyperspectral imaging is an emerging technology used in plant disease research. In this study, we calculated the standard deviation (STD) of the spectral reflectance of whole rice leaves and constructed support vector machine (SVM) and probabilistic neural network (PNN) models to classify the degree of rice leaf blast at different growth stages. Average accuracies at jointing, booting and heading stages under the full-spectrum-based SVM model were 88.89%, 85.26%, and 87.32%, respectively, versus 80%, 83.16%, and 83.41% under the PNN model. Average accuracies at jointing, booting and heading stages under the STD-based SVM model were 97.78%, 92.63%, and 92.20%, respectively, versus 88.89%, 91.58%, and 92.20% under the PNN model. The STD of the spectral reflectance of the whole leaf differed not only within samples with different disease grades, but also among those at the same disease level. Compared with raw spectral reflectance data, STDs performed better in assessing rice leaf blast severity.

Evaluation of Vis-NIR preprocessing combined with PLS regression for estimation soil organic carbon, cation exchange capacity and clay from eastern Croatia

This study aimed to evaluate (i) the potential of visible-near-infrared (Vis-NIR) preprocessing techniques combined with a partial least square (PLS) regression to estimate soil organic carbon (SOC), the cation exchange capacity (CEC), and clay content and (ii) the effects of the wavelengths selection on the predictive capability of the PLS models. The study was conducted on a total of 132 topsoil samples (0–25 cm) collected on arable land in Eastern Croatia. Raw reflectance data and six commonly used spectral preprocessing, namely, multiplicative scatter correction (MSC), standard normal variate (SNV), de-trending (DT), continuum removal (CR) and Savitzky–Golay first and second derivative (SG1.der and SG2.der) were compared to identify the best for predicting selected soil properties. The PLS models calibrated with the SG1.der preprocessing achieved the best predictive performance for all soil properties compared to other methods used. The R2, RPD and RMSE of the SG1.der -PLS model in the validation mode for SOC, CEC and clay were 0.81, 2.12, 2.57 g C kg−1; 0.78, 1.95, 1.80 cmol(+) kg−1 and 0.83, 2.20, 37.2 g kg−1, respectively. The lowest predictability had PLS models that used soil raw reflectance data. These results indicate that the appropriate choice of Vis-NIR preprocessing can optimize the PLS calibration performance for accurate predicting SOC, CEC and clay in arable soils of the study area. The selection of informative wavelengths (IW) obtained using the PLS regression β-coefficient approach resulted in a highly reduced number of wavelengths in the IW-PLS models. Informative wavelengths in the IW-PLS model for SOC lay in the full Vis-NIR spectrum but mainly in the near-infrared (NIR), whereas for the CEC and clay in the NIR spectrum. In addition, IW-PLS models have a lower number of principal components and almost identical prediction capability relative to models based on full spectra (FS–PLS). Our results suggest that the Vis-NIR spectroscopy is a powerful tool to estimate soil properties rapidly, non-destructively and inexpensively applicable in some real situations e.g., monitoring and updating information on soil properties for precise soil management and soil quality assessment.

An impact analysis of pre-processing techniques in spectroscopy data to classify insect-damaged in soybean plants with machine and deep learning methods

Spectroscopy is essential to understand a series of phenomena in multiple fields of study. In remote sensing, vegetation analysis is one of the most prominent fields to explore, aiming to improve a specific task. As a task, modeling insect damage in the plants is essential to establish the correct management of agricultural farmlands. Hyperspectral data, which can be acquired with field spectroscopy at plant or leaf level, is a non-direct, rapid, and trustworthy approach to indicate its health. However, the spectral redundancy inherent is a challenge for the information extraction process, making the pre-processing phase an essential part of the analysis. Currently, artificial intelligence techniques, mostly based on machine and deep learning methods, are a standard application in data processing, being pre-processing techniques an essential part of it. But few studies aimed to measure the impact of such processes in vegetation monitoring, specifically with insect damage and spectral data. Here, we provide an analysis of the impact of pre-processing techniques on machine learning algorithms’ performance over said classification task. For this, we used a field spectroradiometer that operates within the 350–1,000 nm and 1,000–2,500 nm ranges. The dataset was composed of multiple spectral measurements that took place on different days in a controlled environment with soybean plants. As pre-processing techniques, methods like baseline removal, smoothing, first and second-order derivatives, standard normal variate (SNV), multiplicative scatter correction (MSC), and principal components analysis (PCA) were investigated. Several machine learning algorithms and one deep learning method were applied to model the datasets. The impact of the pre-processing techniques was measured within validation metrics relate to its accuracy. Our results indicated that the Extra-Tree (ExT) algorithm was better, mainly when first-order derivative data were extracted from the dataset (accuracy equal to 93.68%). A ranking approach indicated that the most contributive spectral region situates at the near-infrared, between 784 and 911 nm. Our investigation also demonstrates that a deep neural network (DNN) did not return a satisfactory result over raw reflectance data. However, when considering a combination of PCA over the 2nd derivative data, it achieved similar results to the ExT algorithm (accuracy of 91.95%). The implications of such, alongside the ranking approach, are discussed in this paper. We hope that the information presented here serves as a framework for future research when applying pre-processing techniques alongside the machine and deep learning methods over spectral data.

Growth Stage Classification and Harvest Scheduling of Snap Bean Using Hyperspectral Sensing: A Greenhouse Study

The agricultural industry suffers from a significant amount of food waste, some of which originates from an inability to apply site-specific management at the farm-level. Snap bean, a broad-acre crop that covers hundreds of thousands of acres across the USA, is not exempt from this need for informed, within-field, and spatially-explicit management approaches. This study aimed to assess the utility of machine learning algorithms for growth stage and pod maturity classification of snap bean (cv. Huntington), as well as detecting and discriminating spectral and biophysical features that lead to accurate classification results. Four major growth stages and six main sieve size pod maturity levels were evaluated for growth stage and pod maturity classification, respectively. A point-based in situ spectroradiometer in the visible-near-infrared and shortwave-infrared domains (VNIR-SWIR; 400–2500 nm) was used and the radiance values were converted to reflectance to normalize for any illumination change between samples. After preprocessing the raw data, we approached pod maturity assessment with multi-class classification and growth stage determination with binary and multi-class classification methods. Results from the growth stage assessment via the binary method exhibited accuracies ranging from 90–98%, with the best mathematical enhancement method being the continuum-removal approach. The growth stage multi-class classification method used raw reflectance data and identified a pair of wavelengths, 493 nm and 640 nm, in two basic transforms (ratio and normalized difference), yielding high accuracies (~79%). Pod maturity assessment detected narrow-band wavelengths in the VIS and SWIR region, separating between not ready-to-harvest and ready-to-harvest scenarios with classification measures at the ~78% level by using continuum-removed spectra. Our work is a best-case scenario, i.e., we consider it a stepping-stone to understanding snap bean harvest maturity assessment via hyperspectral sensing at a scalable level (i.e., airborne systems). Future work involves transferring the concepts to unmanned aerial system (UAS) field experiments and validating whether or not a simple multispectral camera, mounted on a UAS, could incorporate < 10 spectral bands to meet the need of both growth stage and pod maturity classification in snap bean production.

Multivariate Analysis Models Based on Full Spectra Range and Effective Wavelengths Using Different Transformation Techniques for Rapid Estimation of Leaf Nitrogen Concentration in Winter Wheat.

To develop a stable estimation model and identify effective wavelengths that could explain the variations in leaf nitrogen (N) concentration with different N supplies, growing seasons, ecological locations, growth stages, and wheat cultivars. Four field experiments were performed during two consecutive years (2017–2019) at three sites (Yuanyang, Hebi, and Wenxian) in Henan, China. In situ canopy spectral reflectance data under the aforementioned N supply conditions were obtained over a range of 400–950 nm (visible and near-infrared region). On the basis of the canopy raw spectral reflectance data and their subsequent transformation by two different techniques, first-derivative reflectance (FDR) and continuum removal (CR), four multivariate regression methods were comparatively analyzed and used to develop predictive models for estimating leaf N concentration: multiple linear regression (MLR), principal component regression (PCR), partial least square (PLS), and support vector machine (SVM). Results showed that leaf N concentration and canopy reflectance significantly varied with the levels of N fertilization, and a good correlation was observed for all the spectral techniques. Seven wavelengths with relatively higher r values than the bands of the raw spectra centered at 508, 525, 572, 709, 780, 876, and 925 nm were specified using the FDR technique. Based on the full wavelengths, the FDR-SVM model exhibited a good performance for leaf N concentration estimation, with coefficients of determination (r2val) for the validation datasets and corresponding relative percent deviations (RPDval) values of 0.842 and 2.383, respectively. However, the FDR-PLS yielded a more accurate assessment of the leaf N concentration than did the other methods, with r2val and RPDval values of 0.857 and 2.535, respectively. The variable importance in projection (VIP) scores from the FDR-PLS with the all canopy spectral region were used to screen the effective wavelengths of the spectral data. Therefore, six effective wavelengths centered at 525, 573, 710, 780, 875, and 924 nm were identified for leaf N concentration estimation. The SVM regression method with the effective wavelengths showed excellent performance for leaf N concentration estimation with r2val = 0.823 and RPDval = 2.280. These results demonstrated that the in situ canopy spectral technique is promising for the estimation of leaf N concentration in winter wheat based on the FDR-PLS regression model and the effective wavelengths identified.

Diffuse reflectance spectroscopy for rapid estimation of soil Atterberg limits

Conventional methods for the determination of Atterberg limits based on laboratory tests are time-consuming. Reflectance spectroscopy which is fast and cost-effective has been reported as an alternative method for estimating Atterberg limits. To assess the efficiency of the method, 60 forest soil samples were measured by visible-near infrared reflectance spectroscopy (VNIR) within the range of 350–2500 nm. The Savitzky-Golay (SG) smoothing method for spectral data was used to reduce signal noise and the first derivative reflectance (DR) was applied to accentuate the features and assemble the data for use in the modeling process. In this study, raw reflectance (R) and first derivative reflectance (DR) data were used to estimate Atterberg limits. All two-band combinations of three types of spectra (ratio index, normalized difference index and difference index) as well as PLS and PLS-BPNN were calculated to extract optimum waveband combination and calibrations between Atterberg limits and reflectance spectra. The correlation coefficients between Atterberg limits and reflectance spectra indicated both positive and negative correlation at various wavelengths across the spectrum. The highest correlation for liquid limit (r = −0.86), plastic limit (r = −0.76) and plasticity index (r = −0.7) was found at 1921, 1938 and 1913 nm, respectively. In addition, the results showed the spectral indices technique as a good method for estimating soil plasticity and improving the prediction accuracy of Atterberg limits. In the current study, PLS and PLS–BPNN were also used to develop the calibration models for estimating Atterberg limits using R and DR. The results demonstrated that the prediction accuracy of PLS-BPNN for both R and DR was higher than that of PLS. Correlation coefficients between shrinkage limit (SL) and reflectance spectra showed weak correlation, and overall reflectance had the same correlation. The results represented that this method could be used to estimate soil plasticity properties quickly, non-destructively, and with minimal sample preparation and high accuracy. These advantages may be more important, especially when a huge number of samples should be tested and analyzed within a short period of time.

Study on reflectivity data interpolation and mosaics for multiple Doppler weather radars

Multiple weather radars’ joint detection can enlarge the radar detection range and can improve the detection accuracy, which has become an important method to monitor large-scale weather effectively. In this paper, the methods of interpolating reflectivity volume scan data onto the grids in the Cartesian coordinate system and three-dimensional mosaic for gridded reflectivity data of several radars are studied. In order to retain the physical characteristics of the raw data, the smoothing parameters of adaptive Barnes interpolation are improved which based on the space structure of the raw reflectivity data. Through comparison of constant altitude plan position indication (CAPPI) images obtained by the commonly used interpolation schemes and the improved Adaptive Barnes interpolation, it was found that the latter can provide consecutive reflectivity fields and retain high-resolution structure comparable to the raw data. The mean deviation filter filtering erroneous grid data and the data are fused by various fusion methods. Result shows that Exponential Weighting is an excellent method which can provide continuous three-dimensional reflectivity mosaic data.

Deciphering Color Reflectance Data of a 520‐kyr Sediment Core From the Southern Ocean: Method Application and Paleoenvironmental Implications

AbstractSediment color reflectance contains important information on paleoenvironmental changes. Such data are routinely measured on sediment cores from the Southern Ocean. Their usefulness, however, is undervalued. Here for the first time, color reflectance of an ~520‐kyr sediment core retrieved off Prydz Bay (East Antarctica) was intensively investigated to correlate sediment color changes to changes in sediment compositions. Total carbon, total organic carbon, X‐ray fluorescence‐derived Fe/Ti and Br/Ti ratios, water content and mineral composition of sediment from this core, and color reflectance of another two sediment cores retrieved nearby were analyzed in this study. The raw reflectance data were transformed into their first‐order derivatives and subjected to factor analysis algorithms. A four‐factor model was selected to unmix the data set, explaining >80% of the total data variance. Its robustness is supported by similar factor analysis results from the other two cores. The four factors represent (from Factors 1–4) pulses of sedimentary Mn enrichment and Fe3+/Fe2+ changes in clay minerals, relative content changes of colorless components, hematite and goethite, and water, respectively. Scores of Factor 1 show high spikes at glacial terminations, recording enhanced deglacial ventilation in the abyssal Southern Ocean. Scores of Factor 2 mainly reflect biogenic components and chlorite but are further complicated by refractory terrigenous organic carbon and Mn‐oxides/‐hydroxides. Higher Scores of Factor 3 occur mainly at peak interglacial intervals, associated with enhanced transport of sediment of Antarctic origin by oceanic currents. Water content (Factor 4) in the core sediment was significantly influenced by opal content and sediment grain size compositions.

Estimating the diurnal blue-sky albedo of soils with given roughness using their laboratory reflectance spectra

Various soil properties derived from soil reflectance spectra can effectively provide reliable data for soil monitoring, digital soil mapping, precision agriculture, and environmental modelling. This paper focuses on the improvement of the equations published in the previous paper predicting the diurnal blue-sky albedo of bare soils with given roughness using their reflectance spectra measured in laboratory conditions. The improved equations based on a 33% increase in the number of analysed soil samples and primarily on the quality expansion of the newly tested soil population. The albedo of the soils was measured when they were air-dried under clear sky conditions by albedometers working in a spectral range of 335–2800 nm. The roughness of the cultivated soils was measured using stereo-photographs taken with a digital camera and described by two roughness indices. The laboratory reflectance spectra of the soils were obtained using a spectroradiometer with a Hi-Brite Muglight receptor in the wavelength range of 350–2500 nm. The raw soil reflectance data and their transformations (linearization, normalization and filtering) were used in the equations mentioned above. Transforming spectra to the 2nd derivative using the Savitzky–Golay method gave the strongest accuracy of the prediction expressed by the coefficient of determination equals 0.91 and root mean square error 0.03.

Desert soil clay content estimation using reflectance spectroscopy preprocessed by fractional derivative.

Effective pretreatment of spectral reflectance is vital to model accuracy in soil parameter estimation. However, the classic integer derivative has some disadvantages, including spectral information loss and the introduction of high-frequency noise. In this paper, the fractional order derivative algorithm was applied to the pretreatment and partial least squares regression (PLSR) was used to assess the clay content of desert soils. Overall, 103 soil samples were collected from the Ebinur Lake basin in the Xinjiang Uighur Autonomous Region of China, and used as data sets for calibration and validation. Following laboratory measurements of spectral reflectance and clay content, the raw spectral reflectance and absorbance data were treated using the fractional derivative order from the 0.0 to the 2.0 order (order interval: 0.2). The ratio of performance to deviation (RPD), determinant coefficients of calibration (), root mean square errors of calibration (RMSEC), determinant coefficients of prediction (), and root mean square errors of prediction (RMSEP) were applied to assess the performance of predicting models. The results showed that models built on the fractional derivative order performed better than when using the classic integer derivative. Comparison of the predictive effects of 22 models for estimating clay content, calibrated by PLSR, showed that those models based on the fractional derivative 1.8 order of spectral reflectance ( = 0.907, RMSEC = 0.425%, = 0.916, RMSEP = 0.364%, and RPD = 2.484 ≥ 2.000) and absorbance ( = 0.888, RMSEC = 0.446%, = 0.918, RMSEP = 0.383% and RPD = 2.511 ≥ 2.000) were most effective. Furthermore, they performed well in quantitative estimations of the clay content of soils in the study area.

Spectroscopy-Based Soil Organic Matter Estimation in Brown Forest Soil Areas of the Shandong Peninsula, China

Soil organic matter (SOM) is important for plant growth and production. Conventional analyses of SOM are expensive and time consuming. Hyperspectral remote sensing is an alternative approach for SOM estimation. In this study, the diffuse reflectance spectra of soil samples from Qixia City, the Shandong Peninsula, China, were measured with an ASD FieldSpec 3 portable object spectrometer (Analytical Spectral Devices Inc., Boulder, USA). Raw spectral reflectance data were transformed using four methods: nine points weighted moving average (NWMA), NWMA with first derivative (NWMA + FD), NWMA with standard normal variate (NWMA + SNV), and NWMA with min-max standardization (NWMA + MS). These data were analyzed and correlated with SOM content. The evaluation model was established using support vector machine regression (SVM) with sensitive wavelengths. The results showed that NWMA + FD was the best of the four pretreatment methods. The sensitive wavelengths based on NWMA + FD were 917, 991, 1 007, 1 996, and 2 267 nm. The SVM model established with the above-mentioned five sensitive wavelengths was significant (R2 = 0.875, root mean square error (RMSE) = 0.107 g kg−1 for calibration set; R2 = 0.853, RMSE = 0.097 g kg−1 for validation set). The results indicate that hyperspectral remote sensing can quickly and accurately predict SOM content in the brown forest soil areas of the Shandong Peninsula. This is a novel approach for rapid monitoring and accurate diagnosis of brown forest soil nutrients.

Transient renormalization of the Josephson plasma frequency

Layered superconductors are emerging as compact sources of intense, continuous and coherent terahertz (THz) waves [Welp et al., Nat. Photonics 7, 702 (2013)]. Apart from that, they also represent nonlinear media that can exhibit a host of nonlinear optical effects such as odd harmonic generation, slowing down of THz waves, self-focusing and self-induced transparency [Savel'ev et al., Nat. Phys. 2, 521 (2006)] that further widen their range of applications. In the linear regime, electromagnetic waves can penetrate them only if their frequency is larger than a gap-frequency, the so-called Josephson plasma frequency, whose signature is characterized by an edge in the THz reflectivity spectrum. In this work, transient renormalization of the Josephson plasma frequency in THz-perturbed La1.84Sr0.16CuO4 has been experimentally observed using the time-resolved THz time-domain spectroscopy. As the perturbing THz field evolves in time, the reflectivity edge measured from the raw-reflectivity data is found to exhibit a red shift, thus implying a reduction in the Josephson plasma frequency. This is a nonlinear effect that renders the layered superconductor transparent over a narrow spectral range close to the Josephson plasma frequency—THz waves with frequencies smaller than the unperturbed Josephson plasma frequency can penetrate the sample. The results presented in this paper should stimulate the development of nonlinear physics in layered superconductors and contribute in developing new concepts for well-controllable THz devices.

Mapping Arctic Tundra Vegetation Communities Using Field Spectroscopy and Multispectral Satellite Data in North Alaska, USA

The Arctic is currently undergoing intense changes in climate; vegetation composition and productivity are expected to respond to such changes. To understand the impacts of climate change on the function of Arctic tundra ecosystems within the global carbon cycle, it is crucial to improve the understanding of vegetation distribution and heterogeneity at multiple scales. Information detailing the fine-scale spatial distribution of tundra communities provided by high resolution vegetation mapping, is needed to understand the relative contributions of and relationships between single vegetation community measurements of greenhouse gas fluxes (e.g., ~1 m chamber flux) and those encompassing multiple vegetation communities (e.g., ~300 m eddy covariance measurements). The objectives of this study were: (1) to determine whether dominant Arctic tundra vegetation communities found in different locations are spectrally distinct and distinguishable using field spectroscopy methods; and (2) to test which combination of raw reflectance and vegetation indices retrieved from field and satellite data resulted in accurate vegetation maps and whether these were transferable across locations to develop a systematic method to map dominant vegetation communities within larger eddy covariance tower footprints distributed along a 300 km transect in northern Alaska. We showed vegetation community separability primarily in the 450–510 nm, 630–690 nm and 705–745 nm regions of the spectrum with the field spectroscopy data. This is line with the different traits of these arctic tundra communities, with the drier, often non-vascular plant dominated communities having much higher reflectance in the 450–510 nm and 630–690 nm regions due to the lack of photosynthetic material, whereas the low reflectance values of the vascular plant dominated communities highlight the strong light absorption found here. High classification accuracies of 92% to 96% were achieved using linear discriminant analysis with raw and rescaled spectroscopy reflectance data and derived vegetation indices. However, lower classification accuracies (~70%) resulted when using the coarser 2.0 m WorldView-2 data inputs. The results from this study suggest that tundra vegetation communities are separable using plot-level spectroscopy with hand-held sensors. These results also show that tundra vegetation mapping can be scaled from the plot level (<1 m) to patch level (<500 m) using spectroscopy data rescaled to match the wavebands of the multispectral satellite remote sensing. We find that developing a consistent method for classification of vegetation communities across the flux tower sites is a challenging process, given the spatial variability in vegetation communities and the need for detailed vegetation survey data for training and validating classification algorithms. This study highlights the benefits of using fine-scale field spectroscopy measurements to obtain tundra vegetation classifications for landscape analyses and use in carbon flux scaling studies. Improved understanding of tundra vegetation distributions will also provide necessary insight into the ecological processes driving plant community assemblages in Arctic environments.

On the performance of remote sensing time series reconstruction methods – A spatial comparison

Abstract The satellite observed Normalized Difference Vegetation Index (NDVI) time series, which describe the temporal and spatial variability of global terrestrial vegetation, are inevitably contaminated by clouds, aerosol, snow and ice cover. In general all these conditions yield negative deviations in the time series of NDVI. Many time series reconstruction models have been developed to eliminate effect of the negative deviations and most of them perform differently in different applications and regions. The Harmonic Analysis (HA), Double logistic (DL), Asymmetric Gaussian (AG), Whittaker smoother (WS) and Savitzky–Golay filter (SG) are five of the most widely used time series reconstruction models owing to their simplicity of implementation or the capability to extract phenological metrics from the time series. The performance of these models varies with the NDVI signal and the noise distribution, however, and until now there is no consensus on which method outperforms all others under all situations. Since the NDVI signal and the noise distribution are highly dependent on regional climate and land cover, the reconstruction performance is expected to be spatially variable. Thus this study compared the five reconstruction models at pixel scale to provide practical and biome – specific recommendations for future time series reconstruction applications. Specifically, the 14 years raw daily reflectance data and ancillary Quality Assessment (QA) information from the MODIS sensor were used to generate pixel reference series and noisy series. Then the five candidate models were applied to both reference series and noisy series and three reconstruction performance metrics i.e. Overall Reconstruction Error (ORE), Fitting Related Error (FRE), and Normalized Noise Related Error (NNRE), were calculated. Finally, the performance of the five candidate reconstruction models was evaluated by applying the three metrics. The preliminary results showed that when considering ORE only, the Asymmetric Gaussian model outperforms other models over most areas of high latitude boreal region, while the Savitzky–Golay model gives the best reconstruction performance in tropical and subtropical regions. The FRE and the NNRE helped to reveal the main error sources in the reconstruction in different regions. The comparison method developed and applied in this study led to suggest adaptive selection of the best reconstruction model for specific NDVI signals and noise distribution.