Derivative Of Spectral Reflectance Research Articles

Monitoring of Wheat Powdery Mildew under Different Nitrogen Input Levels Using Hyperspectral Remote Sensing

Both wheat powdery mildew severities and nitrogen input levels can lead to changes in spectral reflectance, but they have been rarely studied simultaneously for their effect on spectral reflectance. To determine the effects and influences of different nitrogen input levels on monitoring wheat powdery mildew and estimating yield by near-ground hyperspectral remote sensing, Canopy hyperspectral reflectance data acquired at Feekes growth stage (GS) 10.5.3, 10.5.4, and 11.1 were used to monitor wheat powdery mildew and estimate grain yield under different nitrogen input levels during the 2016–2017, 2017–2018, 2018–2019 and 2019–2020 seasons. The relationships of powdery mildew and grain yield with vegetation indices (VIs) derived from spectral reflectance data across the visible (VIS) and near-infrared (NIR) regions of the spectrum were studied. The relationships of canopy spectral reflectance or first derivative spectral reflectance with powdery mildew did not differ under different nitrogen input levels. However, the dynamics of VIs differed in their sensitivities to nitrogen input levels, disease severity, grain yield, The area of the red edge peak (Σdr680–760 nm) was a better overall predictor for both disease severity and grain yield through linear regression models. The slope parameter estimates did not differ between the two nitrogen input levels at each GSs. Hyperspectral indices can be used to monitor wheat powdery mildew and estimate grain yield under different nitrogen input levels, but such models are dependent on GS and year, further research is needed to consider how to incorporate the growth stage and year-to-year variation into future applications.

Fraction of absorbed photosynthetically active radiation over summer maize canopy estimated by hyperspectral remote sensing under different drought conditions.

Fraction of absorbed photosynthetically active radiation (fAPAR) is one of the important remote sensing model parameters of vegetation productivity. However, the crop canopy fAPAR estimation during growing season under different drought conditions has not been reported yet. In this study, the characteristics of summer maize canopy fAPAR and spectral reflectance during growing season under different drought stresses and the relationships of fAPAR with reflectance, the first derivative spectral reflectance and vegetation indices were examined based on the hyperspectral reflectance and fAPAR data from the summer maize drought manipulation experiment with five irrigation levels in 2015. Under mild water stress and sufficient water supply conditions, fAPAR was higher, with the maximum value of 0.7. Under severe water stress and severe persistent drought, fAPAR was lower, with the minimum value of 0.06. Reflectance of visible and shortwave bands increased and near infrared reflectance decreased with increasing drought. The fAPAR was negatively related with visible bands and shortwave bands, but positively correlated with near infrared. Visible and shortwave band reflectance had significant correlation with fAPAR, especially at 383, 680 and 1980 nm, with all the correlation coefficients being more than -0.87. The strong and stable relationship between the first derivative spectral reflectance and fAPAR appeared at 580, 720 and 1546 nm, with the correlation coefficients being -0.91, 0.89 and 0.88, respectively. There were linear or logarithm relationships between fAPAR with nine vegetation indices. Among the nine indices, the enhanced vegetation index (EVI), renormalized difference vegetation index (RDVI), soil adjusted vegetation index (SAVI), and modified soil adjusted vegetation index (MSAVI) performed well with the correlation coefficient being higher than 0.88, and the average relative error (RMAE) 16.6%, 16.6%, 16.7% and 16.2%, respectively. Based on the logarithmic relationship between first derivative spectral reflectance and fAPAR, the simulation effect was best at the band of (720±5) nm, with a correlation coefficient of 0.86. The correlation coefficient of the relationship between fAPAR and reflectance was less than 0.81. The results could provide fAPAR simulation for remote sensing model of vegetation productivity and drought warning.

The inversion of nitrogen balance index in typical growth period of soybean based on high definition digital image and hyperspectral data on unmanned aerial vehicles

Nitrogen balance index (NBI) is one of the important indicators for crop growth. The high and low status of nitrogen can be quickly monitored by measuring NBI, which can provide accurate information of agricultural production and management. The relationship between NBI and original spectrum and derivative spectrum of infrared and near infrared wavelength from flowering to maturity stage was analyzed based on high definition digital image and hyperspectral data on unmanned aerial vehicles. Then, the sensitive bands were selected and the vegetation indexes were calculated. The inversion models of NBI were constructed by empirical model method. The optimal inversion model was obtained by analysing the determination coefficient (R2) and the root mean square error (RMSE) of validating model. The results showed that the correlation between NBI and derivative spectral reflectance was more stronger than that between it and original spectral reflectance. All the 14 vegetation indices selected in this study, except the derivative spectral photochemical reflectance index, had significant correlation with NBI. The NBI inversion models were constructed based on those 13 vegetation indices and the accuracy was analyzed. The inversion model constructed by derivative spectral difference vegetation index had the highest accuracy, with the R2 and RMSE being 0.771 and 3.077 respectively. The soybean NBI distribution maps of the whole growing stages generated by this model could reflect the soybean growth state. Estimation of NBI using the high definition digital image and hyperspectral data obtained by unmanned aerial vehicle, as shown by our results, could be a real-time, dynamic, non-destructive and effective way to monitor the nitrogen status of soybean. It's a simple and practical method for precise management of nitrogen in soybean.

Comparison of multivariate methods for estimating soil total nitrogen with visible/near-infrared spectroscopy

This study aimed to compare stepwise multiple linear regression (SMLR), partial least squares regression (PLSR) and support vector machine regression (SVMR) for estimating soil total nitrogen (TN) contents with laboratory visible/near-infrared reflectance (Vis/NIR) of selected coarse and heterogeneous soils. Moreover, the effects of the first (1st) vs. second (2nd) derivative of spectral reflectance and the importance wavelengths were explored. The TN contents and the Vis/NIR were measured in the laboratory. Several methods were employed for Vis/NIR data pre-processing. The SMLR, PLSR and SVMR models were calibrated and validated using independent datasets. Results showed that the SVMR and the PLSR models had similar performances, and better performances than the SMLR. The spectral bands near 1450, 1850, 2250, 2330 and 2430 nm in the PLSR model were important wavelengths. In addition, the 1st derivative was more appropriate than the 2nd derivative for spectral data pre-processing. PLSR was the most suitable method for estimating TN contents in this study. SVMR may be a promising technique, and its potential needs to be further explored. Moreover, the future studies using outdoor and airborne/satellite hyperspectral data for estimating TN content are necessary for testing the findings.

Estimation of rice neck blasts severity using spectral reflectance based on BP-neural network

Estimation of rice disease using spectral reflectance is important to non-destructive, rapid, and accurate monitoring of rice health. In this study, the rice reflectance data and disease index (DI) were determined experimentally and analyzed by single wave correlation, regression model and neural network model. The result showed that raw spectral reflectance and first derivative reflectance (FDR) difference of the rice necks under various disease severities is clear and obvious in the different spectral regions. There was also significantly negative or positive correlation between DI and raw spectral reflectance, FDR. The regression model was built with raw and first derivative spectral reflectance, which was correlated highly with the DI. However, due to rather complicated non-linear relations between spectral reflectance data and DI, the results of DI retrieved from the regression model was not so ideal. For this reason, an artificial neural network model (BP model) was constructed and applied in the retrieval of DI. For its superior ability for solving the non-linear problem, the BP model provided better accuracy in retrieval of DI compared with the results from the statistic model. Therefore, it was implied that the rice neck blasts could be predicted by remote sensing technology.

Training a GA-PLS Model for Chl-a Concentration Estimation over Inland Lake in Northeast China

Apparent Spectral properties (AOPs) of phytoplankton pigment concentration were analyzed in Shitoukoumen Reservoir, the Changchun city drinking water resource, in order to investigate the feasibility of the using remote sensing to monitor chlorophyll a (Chl-a) concentration in Northeast China. 225 samples were collected for laboratory Chl-a analysis during 12 field campaigns from 2006 to 2008. Correlation analysis between Chl-a and spectra and derivative spectral reflectance revealed that derivative at spectral band between 420 to 700nm is more sensitive to Chl-a concentration, however correlation coefficient on reflectance is relatively low as Chl-a is not the dominating optical active component in the reservoir. A combination of Genetic Algorithms and Partial Least Square (GA-PLS) model was established for Chl-a estimation in this study. A preprocessing procedure was conducted to selected the comparatively high correlated spectral variable by application of correlation analysis between Chl-a with each spectral band, reflectance derivative ranging from 350 to 1000nm and all possible band ratios (101, 250 combinations all together). 100 sensitive spectral variables were selected for GA-PLS modeling with above mentioned preprocessing procedure. A number of 2/3 samples were selected to train the GA-PLS model, and the rest was utilized to validate the performance of the model. It is found that the relationship between the most sensitive reflectance band, reflectance derivative and band ratio and Chl-a concentration agreed well with linear function with R-Square range from 0.45 to 0.78. However, the GA-PLS model for Chl-a estimation performs much better, with model validation R-Square of 0.81. As our results were derived from large number of ground truth samples, representing a spatio-temporal variation of pigment conditions, so the GA-PLS model has great potential for Chl-a estimation for inland water bodies with similar background.

Moisture, Soil Organic Matter and Nitrate Nitrogen Content Maps Using the Real-Time Soil Spectrophotometer

The soil reflectances, within the wavelength range of 400 to 1700 nm, were collected using the real-time soil spectrophotometer, and investigated for describing the soil parameter variability in the field. Moving average smoothing. Kubelka-Munk transformation and multiple scatter correction were applied to the soil reflectance; and correlation analysis and stepwise multi linear regressions were carried out to the original reflectance, the 1st and the 2nd derivatives. The results indicated that the derivative increased the correlation, and the 2nd derivative gave the best prediction in determination of the soil parameters. The variability distribution maps of soil parameters had been generated by block kriging with the 2nd derivative spectral reflectance.

Derivative reflectance spectroscopy to estimate suspended sediment concentration

Remotely sensed data can be used to estimate successfully the concentration of sediment in water. Such estimation has relied on relationships between suspended sediment concentration (SSC) and radiation in one or two broad wavebands where it is assumed that the effects of environmental variability (irradiance, subpixel cloud, etc.) are either small, or can be considered as spectrally additive constants in all wavebands. Where these assumptions do not hold, an alternative and theoretically more robust relationship is proposed, t that between SSC and derivative radiation (change in radiation per unit wavelength). Measurements of spectral reflectance (R λ), derivative spectral reflectance (DR λ) and SSC were measured in the laboratory, where the effects of environmental variability were small and at sea where the effects of environmental variability were large. There was a strong correlation between SSC and DR λ both in the laboratory (max R 2 = − 0.98) and at sea (max R 2 = − 0.83) and DR λ was used to estimate SSC in the laboratory to an error of less than 8 % of the mean SSC. The correlation between R λ and SSC was weaker at sea (max R 2 = 0.46) than in the laboratory (max R 2 = 0.96). This was due to the presence of large and spectrally variable environmental effects. We recommend the use of derivative spectra for the estimation of SSC when continuous spectra are available.