Zhongfeng Qiu

A simple optical model to estimate suspended particulate matter in Yellow River Estuary

Distribution of the suspended particulate matter (SPM) concentration is a key issue for analyzing the deposition and erosion variety of the estuary and evaluating the material fluxes from river to sea. Satellite remote sensing is a useful tool to investigate the spatial variation of SPM concentration in estuarial zones. However, algorithm developments and validations of the SPM concentrations in Yellow River Estuary (YRE) have been seldom performed before and therefore our knowledge on the quality of retrieval of SPM concentration is poor. In this study, we developed a new simple optical model to estimate SPM concentration in YRE by specifying the optimal wavelength ratios (600-710 nm)/ (530-590 nm) based on observations of 5 cruises during 2004 and 2011. The simple optical model was attentively calibrated and the optimal band ratios were selected for application to multiple sensors, 678/551 for the Moderate Resolution Imaging Spectroradiometer (MODIS), 705/560 for the Medium Resolution Imaging Spectrometer (MERIS) and 680/555 for the Geostationary Ocean Color Imager (GOCI). With the simple optical model, the relative percentage difference and the mean absolute error were 35.4% and 15.6 gm(-3) respectively for MODIS, 42.2% and 16.3 gm(-3) for MERIS, and 34.2% and 14.7 gm(-3) for GOCI, based on an independent validation data set. Our results showed a good precision of estimation for SPM concentration using the new simple optical model, contrasting with the poor estimations derived from existing empirical models. Providing an available atmospheric correction scheme for satellite imagery, our simple model could be used for quantitative monitoring of SPM concentrations in YRE.

Estimating phycocyanin pigment concentration in productive inland waters using Landsat measurements: A case study in Lake Dianchi

Using remote sensing reflectance (R(rs)(λ), sr(-1)) and phycocyanin (PC, mg m(-3)) pigment data as well as other bio-optical data collected from two cruises in September and December 2009 in Lake Dianchi (a typical plateau lake of China), we developed a practical approach to estimate PC concentrations that could be applied directly to Landsat measurements. The visible and near-IR bands as well as their band ratios of simulated Landsat data were used as inputs to the algorithms, where the algorithm coefficients for each Landsat sensor were determined through multivariate regressions. The coefficients of determination (R(2)) between the R(rs)-modeled and measured PC were all > 0.97 for the spectral bands corresponding to Landsat 8 OLI, Landsat 7 ETM + , Landsat 5 TM, and Landsat 4 TM, with mean absolute percentage errors (MAPE) < 10% for PC ranging between ~80 and 700 mg m(-3) (n = 14). The algorithms were further evaluated using an independent data set (n = 14), yielding larger but still acceptable MAPE (~30%) for PC ranging between ~80 and 500 mg m(-3). Application of the approach to Landsat 8 measurements over Lake Dianchi suggests potential use of the approach for periodical assessment of the lakes bloom conditions, yet its empirical nature together with the lack of specific narrow bands on Landsat sensors to explicitly account for the PC absorption around 625 nm calls for extra caution when applied to other eutrophic lakes.

A New Algorithm to Retrieve Wave Parameters From Marine X-Band Radar Image Sequences

A new algorithm to retrieve wave parameters, such as significant wave height (SWH), peak wave period, wavelength, and wave direction, from marine X-band radar image sequences is proposed. To analyze the heterogeneous nearshore wave field, the empirical orthogonal function is used to extract the principal components (PCs) of the radar image sequence. To retrieve the SWH, a linear relationship between SWH and the standard deviation of PC is established. The maximum entropy power spectral density (PSD) of the first PC is used to derive the peak wave period. The PSD on spatial scale is used to estimate two perpendicular components of the wavelength, and then, the wavelength and wave direction are determined by geometric relationship. Inversion schemes are validated by comparing with data from the in situ buoy; the root-mean-square error between the wave parameters retrieved from radar image sequences and measured by the buoy is 0.21 m for SWH and 0.84 s for the peak wave period, and the biases for these are 0.01 m and 0.33 s.

Retrieval of diffuse attenuation coefficient in the China seas from surface reflectance

Accurate estimation of the diffuse attenuation coefficient is important for our understanding the availability of light to underwater communities, which provide critical information for the China seas ecosystem. However, algorithm developments and validations of the diffuse attenuation coefficient in the China seas have been seldom performed before and therefore our knowledge on the quality of retrieval of the diffuse attenuate coefficient is poor. In this paper optical data at 306 sites collected in coastal waters of the China seas between July 2000 and February 2004 are used to evaluate three typical existing Kd(490) models. The in situ Kd(490) varied greatly among different sites from 0.029 m(-1) to 10.3 m(-1), with a mean of 0.92 ± 1.59 m(-1). Results show that the empirical model and the semi-analytical model significantly underestimate the Kd(490) value, with estimated mean values of 0.24 m(-1) and 0.5 m(-1), respectively. The combined model also shows significant differences when the in situ Kd(490) range from 0.2 m(-1) to 1 m(-1). Thus, the present study proposes that the three algorithms cannot be directly used to appropriately estimate Kd(490) in the turbid coastal waters of the China seas without a fine tuning for regional applications. In this paper, new Kd(490) algorithms are developed based on the semi-analytical retrieval of the absorption coefficient a(m(-1)) and the backscattering coefficient bb(m(-1)) from the reflectance at two wavelengths, 488 and 667 nm for the Moderate Resolution Imaging Spectroradiometer (MODIS) and 490 and 705 nm for the Medium Resolution Imaging Spectrometer (MERIS) applications, respectively. With the new approaches, the mean ratio and the relative percentage difference are 1.05 and 4.6%, respectively, based on an independent in situ data set. Furthermore, the estimates are reliable within a factor of 1.9 (95% confidence interval). Comparisons also show that the Kd(490) derived with the new algorithms are well correlated with the in situ measurements. Our results showed a good improvement in the estimation for Kd(490) using the new approaches, contrasting with existing empirical, semi-analytical and combined models. Therefore, we propose the new approaches for accurate retrieval of Kd(490) in the China seas.

A new method to retrieve significant wave height from X-band marine radar image sequences

A new method is proposed to retrieve significant wave height (SWH) from X-band marine radar image sequences. To reduce the inhomogeneity of the nearshore wave field, the principal component (PC) of the radar image sequence is extracted by empirical orthogonal function (EOF) analysis. To measure the information contained in each PC, the Shannon entropy is introduced after the PC is normalized. Based on the information contained in the wave field, a linear relationship is established to retrieve the SWH from the Shannon entropy of the PC. The method is validated by comparison with measurements from in situ buoys: the root mean square error between the SWH measured by a buoy and the retrieved value is 0.22 m, while the corresponding bias and correlation coefficient are 0.01 m and 0.92, respectively. The physical meanings of different EOF modes decomposed from the wave field are also discussed.

Reconstruction of hyperspectral reflectance for optically complex turbid inland lakes: test of a new scheme and implications for inversion algorithms.

A new scheme has been proposed by Lee et al. (2014) to reconstruct hyperspectral (400 - 700 nm, 5 nm resolution) remote sensing reflectance (R_rs(λ), sr^-1) of representative global waters using measurements at 15 spectral bands. This study tested its applicability to optically complex turbid inland waters in China, where R_rs(λ) are typically much higher than those used in Lee et al. (2014). Strong interdependence of R_rs(λ) between neighboring bands (≤ 10 nm interval) was confirmed, with Pearson correlation coefficient (PCC) mostly above 0.98. The scheme of Lee et al. (2014) for R_rs(λ) re-construction with its original global parameterization worked well with this data set, while new parameterization showed improvement in reducing uncertainties in the reconstructed R_rs(λ). Mean absolute error (MAE_Rrs(λ_i)) in the reconstructed R_rs(λ) was mostly < 0.0002 sr^-1 between 400 and 700nm, and mean relative error (MRE_Rrs(λ_i)) was < 1% when the comparison was made between reconstructed and measured R_rs(λ) spectra. When R_rs(λ) at the MODIS bands were used to reconstruct the hyperspectral R_rs(λ), MAE_Rrs(λ_i) was < 0.001 sr^-1 and MRE_Rrs(λ_i) was < 3%. When R_rs(λ) at the MERIS bands were used, MAE_Rrs(λ_i) in the reconstructed hyperspectral R_rs(λ) was < 0.0004 sr^-1 and MRE_Rrs(λ_i) was < 1%. These results have significant implications for inversion algorithms to retrieve concentrations of phytoplankton pigments (e.g., chlorophyll-a or Chla, and phycocyanin or PC) and total suspended materials (TSM) as well as absorption coefficient of colored dissolved organic matter (CDOM), as some of the algorithms were developed from in situ R_rs(λ) data using spectral bands that may not exist on satellite sensors.

Detection of Total Phosphorus Concentrations of Turbid Inland Waters Using a Remote Sensing Method

Phosphorus (P) is widely known as a limiting nutrient of water eutrophication for inland freshwater ecosystems. Owing to the complexity of P chemistry, remote sensing detection of total phosphorus (TP) concentrations currently remains limited especially for optically complex turbid inland waters. To address this need, a new TP remote sensing algorithm is developed based on prior water optical classification and the use of support vector regression (SVR) machine. The in situ observed datasets, used in this study, were collected at specific times during 2009u2009~u20092011, covering a total of 232 stations from eight cruises in Lakes Taihu, Chaohu, Dianchi, and Three Gorges reservoir of China. Three types of waters were first classified by using a recently developed NTD675 (Normalized Trough Depth of spectral reflectance at 675xa0nm) water classification method. Then, spectral regions sensitive specifically to each water type were explored and expressed via several band ratios and used for retrieval algorithm development. The established type-specific SVR algorithms yield relatively high predictive accuracies. Specifically, the mean absolute percentage errors (MAPE) produced with the independent validation samples were achieved at 32.7, 23.2, and 14.1xa0% for type 1, type 2, and type 3 waters, respectively. Such water type-specific SVR algorithms are more accurate for the classified waters than an aggregated SVR algorithm for the nonclassified water and also superior to commonly used statistical algorithms. Moreover, application of the developed algorithms with HJ1A/HSI image data demonstrates that the algorithms have a large potential for remote sensing estimation of TP concentrations in optically complex turbid inland waters.

Daytime sea fog retrieval based on GOCI data: a case study over the Yellow Sea

In this paper, a new daytime sea fog detection algorithm has been developed by using Geostationary Ocean Color Imager (GOCI) data. Based on spectral analysis, differences in spectral characteristics were found over different underlying surfaces, which include land, sea, middle/high level clouds, stratus clouds and sea fog. Statistical analysis showed that the Rrc (412 nm) (Rayleigh Corrected Reflectance) of sea fog pixels is approximately 0.1-0.6. Similarly, various band combinations could be used to separate different surfaces. Therefore, three indices (SLDI, MCDI and BSI) were set to discern land/sea, middle/high level clouds and fog/stratus clouds, respectively, from which it was generally easy to extract fog pixels. The remote sensing algorithm was verified using coastal sounding data, which demonstrated that the algorithm had the ability to detect sea fog. The algorithm was then used to monitor an 8-hour sea fog event and the results were consistent with observational data from buoys data deployed near the Sheyang coast (121°E, 34°N). The goal of this study was to establish a daytime sea fog detection algorithm based on GOCI data, which shows promise for detecting fog separately from stratus.

Innovative GOCI algorithm to derive turbidity in highly turbid waters: a case study in the Zhejiang coastal area.

An innovative algorithm is developed and validated to estimate the turbidity in Zhejiang coastal area (highly turbid waters) using data from the Geostationary Ocean Color Imager (GOCI). First, satellite-ground synchronous data (n = 850) was collected from 2014 to 2015 using 11 buoys equipped with a Yellow Spring Instrument (YSI) multi-parameter sonde capable of taking hourly turbidity measurements. The GOCI data-derived Rayleigh-corrected reflectance (R(rc)) was used in place of the widely used remote sensing reflectance (R(rs)) to model turbidity. Various band characteristics, including single band, band ratio, band subtraction, and selected band combinations, were analyzed to identify correlations with turbidity. The results indicated that band 6 had the closest relationship to turbidity; however, the combined bands 3 and 6 model simulated turbidity most accurately (R(2) = 0.821, p<0.0001), while the model based on band 6 alone performed almost as well (R(2) = 0.749, p<0.0001). An independent validation data set was used to evaluate the performances of both models, and the mean relative error values of 42.5% and 51.2% were obtained for the combined model and the band 6 model, respectively. The accurate performances of the proposed models indicated that the use of R(rc) to model turbidity in highly turbid coastal waters is feasible. As an example, the developed model was applied to 8 hourly GOCI images on 30 December 2014. Three cross sections were selected to identify the spatiotemporal variation of turbidity in the study area. Turbidity generally decreased from near-shore to offshore and from morning to afternoon. Overall, the findings of this study provide a simple and practical method, based on GOCI data, to estimate turbidity in highly turbid coastal waters at high temporal resolutions.

An approach for estimating absorption and backscattering coefficients from MERIS in the Bohai Sea

Distribution of absorption and backscattering coefficients (a(560) and bb(550)) is important for characterizing the marine optical environment. Satellite remote sensing is a useful tool for investigating the absorption and backscattering coefficients in coastal waters. A simple semi-analytical algorithm (SAABS) was developed for estimating a(560) and bb(550) in the Bohai Sea from Medium Resolution Imaging Spectrometer (MERIS) images. Using field measurements, the SAABS model attained root-mean-square (RMS) values of 13.25% and 12.75% for a(560) and bb(550), respectively. The SAABS model was also used to retrieve a(560) and bb(550) from the MERIS image. The match-up analysis results indicate that the RMS values of a(560) and bb(550) retrievals are 18.75% and 17%, respectively. These findings suggested that if the atmospheric correction scheme is available, the SAABS model may be used for the quantitative monitoring of the absorption and backscattering coefficients in the Bohai Sea from the MERIS images.