Characterizing and Estimating Fungal Disease Severity of Rice Brown Spot with Hyperspectral Reflectance Data

Large-scale farming of agriculture crops requires real-time detection of disease for field pest management. Hyperspectral remote sensing data generally have high spectral resolution, which could be very useful for detecting disease stress in green vegetation at the leaf and canopy levels. In this study, hyperspectral reflectances of rice in the laboratory and field were measured to characterize the spectral regions and wavebands, which were the most sensitive to rice brown spot infected by Bipolaris oryzae （Helminthosporium oryzae Breda. de Hann）. Leaf reflectance increased at the ranges of 450 to 500 nm and 630 to 680 nm with the increasing percentage of infected leaf surface, and decreased at the ranges of 520 to 580 nm, 760 to 790 nm, 1550 to 1750 nm, and 2080 to 2350 nm with the increasing percentage of infected leaf surface respectively. The sensitivity analysis and derivative technique were used to select the sensitive wavebands for the detection of rice brown spot infected by B. oryzae. Ratios of rice leaf reflectance were evaluated as indicators of brown spot. R669/R746 （the reflectance at 669 nm divided by the reflectance at 746 nm, the following ratios may be deduced by analogy）, R702/R718, R692/R530, R692/R732, R535/R746, R521/R718, and R569/R718 increased significantly as the incidence of rice brown spot increased regardless of whether it＇s at the leaf or canopy level. R702/R718, R692/R530, R692/R732 were the best three ratios for estimating the disease severity of rice brown spot at the leaf and canopy levels. This result not only confirms the capability of hyperspectral remote sensing data in characterizing crop disease for precision pest management in the real world, but also testifies that the ratios of crop reflectance is a useful method to estimate crop disease severity.

Quantifying biochemical variables of corn by hyperspectral reflectance at leaf scale

To further develop the methods to remotely sense the biochemical content of plant canopies,we report the results of an experiment to estimate the concentrations of three biochemical variables of corn,i.e.,nitrogen(N),crude fat(EE) and crude fiber(CF) concentrations,by spectral reflectance and the first derivative reflectance at fresh leaf scale. The correlations between spectral reflectance and the first derivative transformation and three biochemical variables were analyzed,and a set of estimation models were established using curve-fitting analyses. Coefficient of determination(R2),root mean square error(RMSE) and relative error of prediction(REP) of estimation models were calculated for the model quality evaluations,and the possible opti-mum estimation models of three biochemical variables were proposed,with R2 being 0.891,0.698 and 0.480 for the estimation models of N,EE and CF concentrations,respectively. The results also indicate that using the first derivative reflectance was better than using raw spectral reflectance for all three biochemical variables estimation,and that the first derivative reflectances at 759 nm,1954 nm and 2370 nm were most suitable to develop the estimation models of N,EE and CF concentrations,respectively. In addition,the high correlation coefficients of the theoretical and the measured biochemical parameters were obtained,especially for nitrogen(r=0.948).

Monitoring Protein and Starch Accumulation in Wheat Grains with Leaf SPAD and Canopy Spectral Reflectance

The research was conducted to determine the relationships of protein and starch accumulation dynamics in grains of wheat to post-heading leaf SPAD values and canopy spectral reflectance. The results showed that leaf nitrogen accumulation was exponentially related to leaf SPAD values and linearly related to canopy spectral reflectance, and that there was negative linear relationship between leaf nitrogen accumulation and grain protein accumulation, but positive linear relationship between post-heading leaf nitrogen transloca-tion and grain protein accumulation at maturity. In addition, leaf SPAD values were parabolically related with and ratio indices R(l 500,610)and R(l 220,560)were exponentially related with protein and starch accumulation in grains. These results indicate that leaf SPAD values and canopy spectral reflectance should be good indicators of quality formation dynamics in wheat grains.

Simple method for extracting the seasonal signals of photochemical reflectance index and normalized difference vegetation index measured using a spectral reflectance sensor

A spectral reflectance sensor(SRS)fixed on the near-surface ground was developed to support the continuous monitoring of vegetation indices such as the normalized difference vegetation index(NDVI)and photochemical reflectance index(PRI).NDVI is useful for indicating crop growth/phenology,whereas PRI was developed for observing physiological conditions.Thus,the seasonal change patterns of NDVI and PRI are two valuable pieces of information in a crop-monitoring system.However,capturing the seasonal patterns is considered challenging because the vegetation index values estimated by the reflection from vegetation are often governed by meteorological conditions,such as solar irradiance and precipitation.Further,unlike growth/phenology,the physiological condition has diurnal changes as well as seasonal characteristics.This study proposed a novel filtering method for extracting the seasonal signals of SRS-based NDVI and PRI in paddy rice,barley,and garlic.First,the measurement accuracy of SRSs was compared with handheld spectrometers,and the R^(2)values between the two devices were 0.96 and 0.81 for NDVI and PRI,respectively.Second,the experimental study of threshold criteria with respect to meteorological variables(i.e.,insolation,cloudiness,sunshine duration,and precipitation)was conducted,and sunshine duration was the most useful one for excluding distorted values of the vegetation indices.After data processing based on sunshine duration,the R^(2)values between the measured vegetation indices and the extracted seasonal signals of vegetation indices increased by approximately 0.002–0.004(NDVI)and 0.065–0.298(PRI)on the three crops,and the seasonal signals of vegetation indices became noticeably improved.This method will contribute to an agricultural monitoring system by identifying the seasonal changes in crop growth and physiological conditions.

Assessing canopy nitrogen and carbon content in maize by canopy spectral reflectance and uninformative variable elimination

Assessing canopy nitrogen content(CNC) and canopy carbon content(CCC) of maize by hyperspectral remote sensing data permits estimating cropland productivity, protecting farmland ecology, and investigating the nitrogen and carbon cycles in the atmosphere. This study aimed to assess maize CNC and CCC using canopy hyperspectral information and uninformative variable elimination(UVE). Vegetation indices(VIs) and wavelet functions were adopted for estimating CNC and CCC under varying water and nitrogen regimes. Linear, nonlinear, and partial least squares(PLS) regression models were fitted to VIs and wavelet functions to estimate CNC and CCC, and were evaluated for their prediction accuracy.UVE was used to eliminate uninformative variables, improve the prediction accuracy of the models, and simplify the PLS regression models(UVE-PLS). For estimating CNC and CCC, the normalized difference vegetation index(NDVI, based on red edge and NIR wavebands) yielded the highest correlation coefficients(r > 0.88). PLS regression models showed the lowest root mean square error(RMSE) among all models. However, PLS regression models required nine VIs and four wavelet functions, increasing their complexity. UVE was used to retain valid spectral parameters and optimize the PLS regression models.UVE-PLS regression models improved validation accuracy and resulted in more accurate CNC and CCC than the PLS regression models. Thus, canopy spectral reflectance integrated with UVE-PLS can accurately reflect maize leaf nitrogen and carbon status.

Predicting Surface Roughness and Moisture of Bare Soils Using Multi- band Spectral Reflectance Under Field Conditions

Soil surface roughness, denoted by the root mean square height(RMSH), and soil moisture(SM) are critical factors that affect the accuracy of quantitative remote sensing research due to their combined influence on spectral reflectance(SR). In regards to this issue, three SM levels and four RMSH levels were artificially designed in this study; a total of 12 plots was used, each plot had a size of 3 m × 3 m. Eight spectral observations were conducted from 14 to 30 October 2017 to investigate the correlation between RMSH, SM, and SR. On this basis, 6 commonly used bands of optical satellite sensors were selected in this study, which are red(675 nm), green(555 nm), blue(485 nm), near infrared(845 nm), shortwave infrared 1(1600 nm), and shortwave infrared 2(2200 nm). A negative correlation was found between SR and RMSH, and between SR and SM. The bands with higher coefficient of determination R^2 values were selected for stepwise multiple nonlinear regression analysis. Four characterized bands(i.e., blue, green, near infrared, and shortwave infrared 2) were chosen as the independent variables to estimate SM with R^2 and root mean square error(RMSE) values equal to 0.62 and 2.6%, respectively. Similarly, the four bands(green, red, near infrared, and shortwave infrared 1) were used to estimate RMSH with R^2 and RMSE values equal to 0.48 and 0.69 cm, respectively. These results indicate that the method used is not only suitable for estimating SM but can also be extended to the prediction of RMSH. Finally, the evaluation approach presented in this paper highly restores the real situation of the natural farmland surface on the one hand, and obtains high precision values of SM and RMSH on the other. The method can be further applied to the prediction of farmland SM and RMSH based on satellite and unmanned aerial vehicle(UAV) optical imagery.

Variations of albedo and spectral reflectance on Qiyi Glacier in Qilian Mountains during the ablation season

Based on the data observed at two sites (site H1, 4,473 m a.s.l., and site H2, 4,696 m a.s.l.) on Qiyi Glacier in Qilian Mountains, China, by automatic weather station and spectral pyranometer during the period of June 9 through September 27, 2006, we investigated the temporal and spatial variations in surface albedo and spectral reflectance on the glacier. At site H1, the daily mean surface albedos fluctuated between 0.233 and 0.866, which were significantly affected by the air temperature on the glacier. It was found that the albedos clearly showed a diurnal cycle with the lowest value at noon at the two observation sites over the study period, and the difference of albedos between the upper site H2 and the lower site H1 also showed diurnal cycle but with the highest value at noon. The reflectance on the glacier was higher in the ultraviolet (0.28–0.4 μm) and visible (0.4–0.76 μm) wavelengths, lower in the near infrared wavelength (0.76–3 μm), which is quite contrary to the spectral reflectance on other ground surfaces. At the two observation sites, the spectral reflectance declined in all wavelengths with the ablation of snow generally. However, it declined drastically in ultraviolet (0.28–0.4 μm) and 0.6–0.7 μm wavelength, and declined less in 0.4–0.5 μm wavelength. On fresh snow surface, the spectral reflectance had the high values of 0.983 and 0.815 in the ultraviolet and visible (0.4–0.76 μm) wavelengths, respectively; but it had a relatively lower value of 0.671 in near infrared (0.76–3 μm) wavelengths. However, on dirty and melting ice surfaces, the reflectance had the very low values of 0.305 and 0.256 in the ultraviolet and visible wavelengths, with the lowest value of 0.082 in near infrared wavelengths. The spectral reflectance also showed a diurnal cycle like that of albedo. The diurnal variations of spectral reflectance on snow surface in ultraviolet and visible wavelength changed to a greater degree than that on ice surface. The diurnal variation curves were asymmetrical before and after the local noontime, but the curves on ice surfaces in every wavelength were relatively flat and symmetrical. Especially, the surface reflectance in near infrared wavelength was flat and symmetry on both snow and ice surfaces. The studies of relations between the snow albedo and snow density and impurity, and the impact of glacier albedo on the glacier runoff are also described in this paper.

Research on the detection of early caries based on hyperspectral imaging

Objective:We applied hyperspectral imaging(HSI)system to distinguish early caries from soundand pigmented areas.It will provide a theoretical basis and technical support,for research anddevelopment of an instrument that could be used for screening and detection of early dentalcaries.Methods:Eighteen extracted human teeth(molars and premolars),with varying degrees ofnatural pathology and no degree of decay involving dentin were obtained.HSI system with awavelength range from 400 to 1000nm was used to obtain images of all 18 teeth containingsound,carious and pigmented areas.We compared the spectra of the wavebands at both 500 nmand 780 nm from the different tooth states,and the reflectance diference bet ween sound versuscarious lesions and sound versus pigmented areas,respectively.Results:There was a slight diference in refectance bet ween carious areas and pigmented areas at500 nm.A substantial difference was additionally noted in refectance bet ween carious areas andpigmented areas at 780 nm.Conclusion:The results have shown that the interference of tooth surface pigment can be elim-inated in the near-infrared(NIR)waveband,and the caries can be effectively identifed from the pigmented areas.Thus,it could be used to detect carious areas of teeth in place of the traditionalvisual inspection method or white light endoscopy.Clinical significance:The NIR difused light signal enables the identification of early caries frompigment and other interference,providing a reasonable detection tool for early detection andearly treatment of teeth diseases.

Low-dimensional multi-spectral space for color reproduction based on nonnegative constrained principal component analysis

In order to overcome the shortcomings that the reconstructed spectral reflectance may be negative when using the classic principal component analysis （PCA）to reduce the dimensions of the multi-spectral data, a nonnegative constrained principal component analysis method is proposed to construct a low-dimensional multi-spectral space and accomplish the conversion between the new constructed space and the multispectral space. First, the reason behind the negative data is analyzed and a nonnegative constraint is imposed on the classic PCA. Then a set of nonnegative linear independence weight vectors of principal components is obtained, by which a lowdimensional space is constructed. Finally, a nonlinear optimization technique is used to determine the projection vectors of the high-dimensional multi-spectral data in the constructed space. Experimental results show that the proposed method can keep the reconstructed spectral data in [ 0, 1 ]. The precision of the space created by the proposed method is equivalent to or even higher than that by the PCA.

Size Effect on the Radiation Stability Silicon Dioxide Sphere Particles

The effect of protons(E = 100 keV,F = 5×10^(15) cm^(-2)) exposure on the diffuse reflectance spectra of the SiO_(2 )with different size particles in wavelength range from 250 to 2500 nm have been investigated.Particles were nanosphere,submicrosphere,microsphere and submacrosphere,as well as solid micro-and nanocrystals.The synthesis of the particles was carried out by the formation of silica shells and dissolution of the polystyrene core particles.The surface morphology,surface area and crystal structure of the particles have been investigated.When evaluating the changes of the solar absorptance,it was found that the radiation stability of the micro-and submacro-hollow particles is higher than that of the other nanostructured particles,except for solid microcrystals.The low radiation stability of the hollow microparticles is due to the large void inside the hollow particles where radiation defects are not formed.

Estimation of As and Cu Contamination in Agricultural Soils Around a Mining Area by Reflectance Spectroscopy:A Case Study

Concentrations of Iron (Fe), As, and Cu in soil samples from the fields near the Baoshan Mine in Hunan Province, China, were analyzed and soil spectral reflectance was measured with an ASD FieldSpec FR spectroradiometer (Analytical Spectral Devices, Inc., USA) under laboratory condition. Partial least square regression (PLSR) models were constructed for predicting soil metal concentrations. The data pre-processing methods, first and second derivatives (FD and SD), baseline correction (BC), standard normal variate (SNV), multiplicative scatter correction (MSC), and continuum removal (CR), were used for the spectral reflectance data pretreatments. Then, the prediction results were evaluated by relative root mean square error (RRMSE) and coefficients of determination (R 2 ). According to the criteria of minimal RRMSE and maximal R 2 , the PLSR models with the FD pretreatment (RRMSE = 0.24, R 2 = 0.61), SNV pretreatment (RRMSE = 0.08, R 2 = 0.78), and BC-pretreatment (RRMSE = 0.20, R 2 = 0.41) were considered as the final models for predicting As, Fe, and Cu, respectively. Wavebands at around 460, 1 400, 1 900, and 2 200 nm were selected as important spectral variables to construct final models. In conclusion, concentrations of heavy metals in contaminated soils could be indirectly assessed by soil spectra according to the correlation between the spectrally featureless components and Fe; therefore, spectral reflectance would be an alternative tool for monitoring soil heavy metals contamination.

Predicting Nitrogen Status of Rice Using Multispectral Data at Canopy Scale

Two field experiments were conducted in Jiashan and Yuhang towns of Zhejiang Province, China, to study the feasibility of predicting N status of rice using canopy spectral reflectance. The canopy spectral reflectance of rice grown with different levels of N inputs was determined at several important growth stages. Statistical analyses showed that as a result of the different levels of N supply, there were significant differences in the N concentrations of canopy leaves at different growth stages. Since spectral reflectance measurements showed that the N status of rice was related to reflectance in the visible and NIR (near-infrared) ranges, observations for rice in 1 nm bandwidths were then converted to bandwidths in the visible and NIR spectral regions with IKONOS (space imaging) bandwidths and vegetation indices being used to predict the N status of rice. The results indicated that canopy reflectance measurements converted to ratio vegetation index (RVI) and normalized difference vegetation index (NDVI) for simulated IKONOS bands provided a better prediction of rice N status than the reflectance measurements in the simulated IKONOS bands themselves. The precision of the developed regression models using RVI and NDVI proved to be very high with R2 ranging from 0.82 to 0.94, and when validated with experimental data from a different site, the results were satisfactory with R2 ranging from 0.55 to 0.70. Thus, the results showed that theoretically it should be possible to monitor N status using remotely sensed data.

Quantitative Analysis of Moisture Effect on Black Soil Reflectance

Several studies have demonstrated that soil reflectance decreases with increasing soil moisture content, or increases when the soil moisture reaches a certain content; however, there are few analyses on the quantitative relationship between soil reflectance and its moisture, especially in the case of black soils in northeast China. A new moisture adjusting method was developed to obtain soil reflectance with a smaller moisture interval to describe the quantitative relationship between soil reflectance and moisture. For the soil samples with moisture contents ranging from air-dry to saturated, the changes in soil reflectance with soil moisture can be depicted using a cubic equation. Both moisture threshold (MT) and moisture inflexion (MI) of soil reflectance can also be determined by the equation. When the moisture range was smaller than MT, soil reflectance can be simulated with a linear model. However, for samples with different soil organic matter (OM), the parameters of the linear model varied regularly with the OM content. Based on their relationship, the soil moisture can be estimated from soil reflectance in the black soil region.

Detection of Internal Leaf Structure Deterioration Using a New Spectral Ratio Index in the Near-Infrared Shoulder Region

Spectral reflectance in the near-infrared （NIR） shoulder （750-900 nm） region is affected by internal leaf structure, but it has rarely been investigated. In this study, a dehydration treatment and three paraquat herbicide applications were conducted to explore how spectral reflectance and shape in the NIR shoulder region responded to various stresses. A new spectral ratio index in the NIR shoulder region （NSRI）, defined by a simple ratio of reflectance at 890 nm to reflectance at 780 nm, was proposed for assessing leaf structure deterioration. Firstly, a wavelength-independent increase in spectral reflectance in the NIR shoulder region was observed from the mature leaves with slight dehydration. An increase in spectral slope in the NIR shoulder would be expected only when water stress developed sufficiently to cause severe leaf dehydration resulting in an alteration in cell structure. Secondly, the alteration of leaf cell structure caused by Paraquat herbicide applications resulted in a wavelength-dependent variation of spectral reflectance in the NIR shoulder region. The NSRI in the NIR shoulder region increased significantly under an herbicide application. Although the dehydration process also occurred with the herbicide injury, NSRI is more sensitive to herbicide injury than the water-related indices （water index and normalized difference water index） and normalized difference vegetation index. Finally, the sensitivity of NSRI to stripe rust in winter wheat was examined, yielding a determination coefficient of 0.61, which is more significant than normalized difference vegetation index （NDVI）, water index （WI） and normalized difference water index （NDWI）, with a determination coefficient of 0.45, 0.36 and 0.13, respectively. In this study, all experimental results demonstrated that NSRI will increase with internal leaf structure deterioration, and it is also a sensitive spectral index for herbicide injury or stripe rust in winter wheat.

Quantitative retrieval of soil salt content based on measured spectral data

Choosing the Minqin Oasis, located downstream of the Shiyang River in Northwest China, as the study area, we used field-measured hyperspectral data and laboratory-measured soil salt content data to analyze the characteristics of saline soil spectral reflectance and its transformation in the area, and elucidated the relations between the soil spectral re-flectance, reflectance transformation, and soil salt content. In addition, we screened sensitive wavebands. Then, a multiple linear regression model was established to predict the soil salt content based on the measured spectral data, and the accuracy of the model was verified using field-measured salinity data. The results showed that the overall shapes of the spectral curves of soils with different degrees of salinity were consistent, and the reflectance in visible and near-infrared bands for salinized soil was higher than that for non-salinized soil. After differential transformation, the correlation coefficient between the spectral reflectance and soil salt content was obviously improved. The first-order differential transformation model based on the logarithm of the reciprocal of saline soil spectral reflectance produced the highest accuracy and stability in the bands at 462 and 636 nm; the determination coefficient was 0.603, and the root mean square error was 5.407. Thus, the proposed model provides a good reference for the quantitative extraction and monitoring of regional soil salinization.

Spectral Characteristics Comparison of Two Summer Corn Cultivars under Different Fertilization Treatments

This study is aimed to explore the spectrum reflection characteristics of summer corn leaves in different fertilization conditions.Using hyperspectral remote sensing technology,the experiments were conducted in fields to collect the hyperspectral images of Denghai 605( DH605) and Ludan 981( LD981) in different growth period under five fertilization treatments,and then the reflectance of corn ear leaves was extracted by ENVI software. The five fertilization treatments included the control( CK) with no fertilization,40 kg and 30 kg of controlled-release fertilizer per 666. 67 m2 as base( K40 and K30),50 kg and 40 kg of compound fertilizer per 666. 67 m2 as base with 15 kg urea as seed fertilizer( F50 + N and F40 + N). The reflectance spectrums of the two corn cultivars under different fertilization treatments showed the approximately same changing trend with a reflection peak at green band( 550 nm) and a higher reflection platform at near infrared band( 760 nm-1050 nm). At the heading to filling stage,the reflectance of DH605 and LD981 was the highest under the CK,followed by the K30 and F40 + N respectively. At the filling to dough stage,the reflectance of DH605 and LD981 was the highest under the treatment K30 and F40 + N respectively,which was obviously higher than that of the other treatments. In the conditions of compound fertilizer,except the late filling stage,LD981 had little higher reflectance than DH605 at the other stages. In the conditions of controlled-release fertilizer and at dough to mature stage,LD981 had obviously higher reflectance compared to the other stages,and also higher than that of DH605; there was not obvious difference in reflectance LD981 and DH605 at the other stages.

Spectral Beam Combining of Fiber Lasers by Using Reflecting Volume Bragg Gratings

By employing three reflecting volume Bragg gratings, a near-infrared 4-channel spectral-beam-combining system is demonstrated to present 720 W combined power with a combining efficiency of 94.7%. The combined laser beam is near-diffraction-limited with a beam factor M^2-1.54. During this 4-channel beam-combining process, no special active cooling measures are used to evaluate the volume Bragg gratings as combining elements are under the higher power laser operation. Thermal expansion and period distortion are verified in a 2 k W 2-channel beam-combining process, and the heat issue in the transmission case is found to be more remarkable than that in the diffraction e-se. Transmitted and diffracted beams experience wave-front aberrations with different degrees, thus leading to distinct beam deterioration.

Spectral decomposition method for predicting magmatic intrusion into a coal bed

Accurate prediction of magmatic intrusion into a coal bed is illustrated using the method of seismic spectral decomposition.The characteristics of coal seismic reflections are first analyzed and the effect of variable time windows and domain frequencies on the spectral decomposition are examined.The higher domain frequency of coal bed reflections using the narrower STFT time window,or the smaller ST scale factor,are acceptable.When magmatic rock intrudes from the bottom of the coal bed the domain frequency of the reflections is decreased slightly,the frequency bandwidth is narrowed correspondingly,and the response from spectral decomposition is significantly reduced.Intrusion by a very thin magmatic rock gives a spectral decomposition response that is just slightly less than what is seen from a normal coal bed.Results from an actual mining area were used to validate the method.Predicting the boundary of magmatic intrusions with the method discussed herein was highly accurate and has been validated by observations from underground mining.

Superelastic Radiative Cooling Metafabric for Comfortable Epidermal Electrophysiological Monitoring

Epidermal electronics with superb passive-cooling capabilities are of great value for both daytime outdoor dressing comfort and low-carbon economy. Herein, a multifunctional and skinattachable electronic is rationally developed on a porous all-elastomer metafabric for efficient passive daytime radiative cooling(PDRC) and human electrophysiological monitoring. The cooling characteristics are realized through the homogeneous impregnation of polytetrafluoroethylene microparticles in the styrene–ethylene–butylene–styrene fibers, and the rational regulation of microporosity in SEBS/PTFE metafabrics, thus synergistically backscatter ultraviolet–visible–near-infrared light(maximum reflectance over 98.0%) to minimize heat absorption while efficiently emit human-body midinfrared radiation to the sky. As a result, the developed PDRC metafabric achieves approximately 17℃ cooling effects in an outdoor daytime environment and completely retains its passive cooling performance even under 50% stretching. Further, high-fidelity electrophysiological monitoring capability is also implemented in the breathable and skin-conformal metafabric through liquid metal printing, enabling the accurate acquisition of human electrocardiograph, surface electromyogram, and electroencephalograph signals for comfortable and lengthy health regulation. Hence, the fabricated superelastic PDRC metafabric opens a new avenue for the development of body-comfortable electronics and low-carbon wearing technologies.

Remote Sensing Retrieval of Surface Suspended Sediment Concentration in the Yellow River Estuary

Accurate assessment of surface suspended sediment concentration （SSSC） in estuary is essential to address several important issues： erosion, water pollution, human health risks, etc. In this study, an empirical cubic retrieval model was developed for the retrieval of SSSC from Yellow River Estuary. Based on sediments and seawater collected from the Yellow River and southeastern Laizhou Bay, SSSC conditions were reproduced in the laboratory at increasing concentrations within a range common to field observations. Continu- ous spectrum measurements of the various SSSCs ranging from 1 to 5700 mg/1 were carried out using an AvaField-3 spectrometer. The results indicated the good correlation between water SSSC and spectral reflectance （Rrs） was obtained in the spectral range of 726-900 nm. At SSSC greater than 2700 mg/L, the 740-900 nm spectral range was less susceptible to the effects of spectral reflectance saturation and more suitable for retrieval of high sediment concentrations. The best correlations were obtained for the reflectance ratio of 820 nm to 490 nm. Informed by the correlation between Rrs and SSSC, a retrieval model was developed （R2 = 0.992）. The novel cubic model, which used the ratio of a near-infrared （NIR） band （740-900 nm） to a visible band （400-600 nm） as factors, provided robust quantifica- tion of high SSSC water samples. Two high SSSC centers, with an order of 103 mg/1, were found in the inversion results around the abandoned Diaokou River mouth, the present Yellow River mouth to the abandoned Qingshuigou River mouth. There was little sedi- ment exchange between the two high SSSC centers due to the directions of the residual currents and vertical mixing.