An improved atmospheric correction algorithm for applying MERIS data to very turbid inland waters

Atmospheric correction (AC) is a necessary process when quantitatively monitoring water quality parameters from satellite data. However, it is still a major challenge to carry out AC for turbid coastal and inland waters. In this study, we propose an improved AC algorithm named N-GWI (new standard Gordon and Wang’s algorithms with an iterative process and a bio-optical model) for applying MERIS data to very turbid inland waters (i.e., waters with a water-leaving reflectance at 864.8 nm between 0.001 and 0.01). The N-GWI algorithm incorporates three improvements to avoid certain invalid assumptions that limit the applicability of the existing algorithms in very turbid inland waters. First, the N-GWI uses a fixed aerosol type (coastal aerosol) but permits aerosol concentration to vary at each pixel; this improvement omits a complicated requirement for aerosol model selection based only on satellite data. Second, it shifts the reference band from 670 nm to 754 nm to validate the assumption that the total absorption coefficient at the reference band can be replaced by that of pure water, and thus can avoid the uncorrected estimation of the total absorption coefficient at the reference band in very turbid waters. Third, the N-GWI generates a semi-analytical relationship instead of an empirical one for estimation of the spectral slope of particle backscattering. Our analysis showed that the N-GWI improved the accuracy of atmospheric correction in two very turbid Asian lakes (Lake Kasumigaura, Japan and Lake Dianchi, China), with a normalized mean absolute error (NMAE) of less than 22% for wavelengths longer than 620 nm. However, the N-GWI exhibited poor performance in moderately turbid waters (the NMAE values were larger than 83.6% in the four American coastal waters). The applicability of the N-GWI, which includes both advantages and limitations, was discussed.     

A soft-classification-based chlorophyll-a estimation method using MERIS data in the highly turbid and eutrophic Taihu Lake

Soft-classification-based methods for estimating chlorophyll-a concentration (C_chla) by satellite remote sensing have shown great potential in turbid coastal and inland waters. However, one of the most important water color sensors, the MEdium Resolution Imaging Spectrometer (MERIS), has not been applied to the study of turbid or eutrophic lakes. In this study, we developed a new soft-classification-based C_chla estimation method using MERIS data for the highly turbid and eutrophic Taihu Lake. We first developed a decision tree to classify Taihu Lake into three optical water types (OWTs) using MERIS reflectance data, which were quasi-synchronous (±3 h) with in situ measured C_chla data from 91 sample stations. Secondly, we used MERIS reflectance and in situ measured C_chla data in each OWT to calibrate the optimal C_chla estimation model for each OWT. We then developed a soft-classification-based C_chla estimation method, which blends the C_chla estimation results in each OWT by a weighted average, where the weight for each MERIS spectra in each OWT is the reciprocal value of the spectral angle distance between the MERIS spectra and the centroid spectra of the OWT. Finally, the soft-classification based C_chla estimation algorithm was validated and compared with no-classification and hard-classification-based methods by the leave-one-out cross-validation (LOOCV) method. The soft-classification-based method exhibited the best performance, with a correlation coefficient (R²), average relative error (ARE), and root-mean-square error (RMSE) of 0.81, 33.8%, and 7.0 μg/L, respectively. Furthermore, the soft-classification-based method displayed smooth values at the edges of OWT boundaries, which resolved the main problem with the hard-classification-based method. The seasonal and annual variations of C_chla were computed in Taihu Lake from 2003 to 2011, and agreed with the results of previous studies, further indicating the stability of the algorithm. We therefore propose that the soft-classification-based method can be effectively used in Taihu Lake, and that it has the potential for use in other optically-similar turbid and eutrophic lakes, and using spectrally-similar satellite sensors.     

Sentinel-3 OLCI数据的内陆湖泊有机悬浮物浓度遥感估算

有机悬浮物OSM (Organic Suspended Matter)是湖泊有机碳库的重要组成成分,对研究湖泊生态环境和初级生产力具有重要意义.本研究以太湖、巢湖、涌湖、小兴凯湖、滇池、洪泽湖、呼伦湖和南漪湖8个内陆湖泊为研究区,发展了适合内陆水体的有机悬浮物浓度遥感估算方法.基于B6、B7波段的斜率和B10、B11波...     

Estimating dissolved organic carbon concentration in turbid coastal waters using optical remote sensing observations

Dissolved Organic Carbon (DOC) is an important component in the global carbon cycle. It also plays an important role in influencing the coastal ocean biogeochemical (BGC) cycles and light environment. Studies focussing on DOC dynamics in coastal waters are data constrained due to the high costs associated with in situ water sampling campaigns. Satellite optical remote sensing has the potential to provide continuous, cost-effective DOC estimates. In this study we used a bio-optics dataset collected in turbid coastal waters of Moreton Bay (MB), Australia, during 2011 to develop a remote sensing algorithm to estimate DOC. This dataset includes data from flood and non-flood conditions. In MB, DOC concentration varied over a wide range (20–520 μM C) and had a good correlation (R² = 0.78) with absorption due to coloured dissolved organic matter (CDOM) and remote sensing reflectance. Using this data set we developed an empirical algorithm to derive DOC concentrations from the ratio of R_rs(412)/R_rs(488) and tested it with independent datasets. In this study, we demonstrate the ability to estimate DOC using remotely sensed optical observations in turbid coastal waters.     

Tracing high time-resolution fluctuations in dissolved organic carbon using satellite and buoy observations: Case study in Lake Taihu,China

Field measurements of dissolved organic carbon (DOC) concentration and remote-sensing reflectance were conducted to develop a regional, empirical red-blue algorithm to retrieve surface DOC from Geostationary Ocean Color Imager (GOCI) data for Lake Taihu, China. The auxiliary data (in-situ observations of the optical properties and water quality, buoy measurements of hydrodynamic data and water chemical parameters) were used to investigate the spatial and temporal variations in DOC. GOCI was shown to be capable of successfully obtaining hourly variations in DOC, with a root mean square error percentage (RMSP) of 17.29% (RMSE = 0.69 mg/L) for the match-up data. The GOCI-derived DOC in Lake Taihu confirms that the highest DOC concentration is in northwest Lake Taihu, followed by Meiliang Bay, Gonghu Bay and northeast Lake Taihu. Hourly DOC variation is significant and presents a different trend for each lake segment due to the variety of influencing factors. Discharge of DOC from surrounding rivers is an important factor to the variation of DOC in northeast Lake Taihu. However, organic products of algae will be the primary contributor to DOC when algal bloom occurred. During the period of algal bloom, high DOC levels in Lake Taihu can lead to hypoxia when coupled with high temperatures and low disturbance.     

内陆湖泊颗粒有机碳反演及日变化初步研究

以太湖及洞庭湖为例,检验海洋一类水体颗粒有机碳(particulate organic carbon, POC)浓度遥感估算方法在内陆湖泊二类水体中的适用性,结果表明,一类水体POC反演模型并不适用于二类水体。针对二类水体建立了以近红外波段(834 nm)为因子的单波段POC反演模型以563及834 nm波段组合为因子的两种双波段反演模型,模型验证结果显示,单波段模型的均方根误差(RMSE)为1.12 mg/L,平均相对误差(MAPE)为35.8%,两个双波段反演模型的RMSE分别为1.09 mg/L及1.11 mg/L,MAPE分别为37.3%及37.8%,三种模型均可用于太湖及洞庭湖水体的POC浓度遥感估算。在此基础上,以太湖为例,建立了基于静止轨道卫星海洋水色(GOCI)卫星数据的太湖POC反演模型,反演模型的MAPE为35%。利用5月13日8景GOCI影像,研究了太湖POC浓度日变化,发现POC浓度日变化存在两个阶段:上午至中午的递减阶段和中午至傍晚的递增阶段。     

Temporal analysis of remotely sensed turbidity in a coastal archipelago

A topographically fragmental archipelago with dynamic waters set the preconditions for assessing coherent remotely sensed information. We generated a turbidity dataset for an archipelago coast in the Baltic Sea from MERIS data (FSG L1b), using CoastColour L1P, L2R and L2W processors. We excluded land and mixed pixels by masking the imagery with accurate (1:10 000) shoreline data. Using temporal linear averaging (TLA), we produced satellite-imagery datasets applicable to temporal composites for the summer seasons of three years. The turbidity assessments and temporally averaged data were compared to in situ observations obtained with coastal monitoring programs. The ability of TLA to estimate missing pixel values was further assessed by cross-validation with the leave-one-out method. The correspondence between L2W turbidity and in situ observations was good (r = 0.89), and even after applying TLA the correspondence remained acceptable (r = 0.78). The datasets revealed spatially divergent temporal water characteristics, which may be relevant to the management, design of monitoring and habitat models. Monitoring observations may be spatially biased if the temporal succession of water properties is not taken into account in coastal areas with anisotropic dispersion of waters and asynchronous annual cycles. Accordingly, areas of varying turbidity may offer a different habitat for aquatic biota than areas of static turbidity, even though they may appear similar if water properties are measured for short annual periods.     

Estimation of higher chlorophylla concentrations using field spectral measurement and HJ-1A hyperspectral satellite data in Dianshan Lake,China

Based on in situ water sampling and field spectral measurements in Dianshan Lake, a semi-analytical three-band algorithm was used to estimate Chlorophylla (Chla) content in case II waters. The three bands selected to estimate Chla for high concentrations included 653, 691 and 748 nm. An equation, based on the difference in reciprocal reflectance between 653 and 691 nm, multiplied by reflectance at 748 nm as [R_rs⁻¹(653) − R_rs⁻¹ (691)] R_rs(748), explained 85.57% of variance in Chla concentration with a root mean square error (RMSE) of <6.56 mg/m³. In order to test the utility of this model with satellite data, HJ-1A Hyperspectral Imager (HSI) data were analyzed using comparable wavelengths selected from the in situ data [B₆₇⁻¹(656) − B₈₀⁻¹(716)] B₈₇(753). This model accounted for 84.3% of Chla variation, estimating Chla concentrations with an RMSE of <4.23 mg/m³. The results illustrate that, based on the determined wavelengths, the spectrum-based model can achieve a high estimation accuracy and can be applied to hyperspectral satellite imagery especially for higher Chla concentration waters.     

Testing estimation of water surface in Italian rice district from MODIS satellite data

Recent changes in rice crop management within Northern Italy rice district led to a reduction of seeding in flooding condition, which may have an impact on reservoir water management and on the animal and plant communities that depend on the flooded paddies. Therefore, monitoring and quantifying the spatial and temporal variability of water presence in paddy fields is becoming important. In this study we present a method to estimate dynamics of presence of standing water (i.e. fraction of flooded area) in rice fields using MODIS data. First, we produced high resolution water presence maps from Landsat by thresholding the Normalised Difference Flood Index (NDFI) made: we made it by comparing five Landsat 8 images with field-obtained information about rice field status and water presence. Using these data we developed an empirical model to estimate the flooding fraction of each MODIS cell. Finally we validated the MODIS-based flooding maps with both Landsat and ground information. Results showed a good predictability of water surface from Landsat (OA = 92%) and a robust usability of MODIS data to predict water fraction (R² = 0.73, EF = 0.57, RMSE = 0.13 at 1 × 1 km resolution). Analysis showed that the predictive ability of the model decreases with the greening up of rice, so we used NDVI to automatically discriminate estimations for inaccurate cells in order to provide the water maps with a reliability flag. Results demonstrate that it is possible to monitor water dynamics in rice paddies using moderate resolution multispectral satellite data. The achievement is a proof of concept for the analysis of MODIS archives to investigate irrigation dynamics in the last 15 years to retrieve information for ecological and hydrological studies.     

Using remote sensing to monitor the influence of river discharge on watershed outlets and adjacent coral Reefs: Magdalena River and Rosario Islands,Colombia

Worldwide, coral reef ecosystems are being increasingly threatened by sediments loads from river discharges, which in turn are influenced by changing rainfall patterns due to climate change and by growing human activity in their watersheds. In this case study, we explored the applicability of using remote sensing (RS) technology to estimate and monitor the relationship between water quality at the coral reefs around the Rosario Islands, in the Caribbean Sea, and the rainfall patterns in the Magdalena River watershed. From the Moderate Resolution Imaging Spectroradiometer (MODIS), this study used the water surface reflectance product (MOD09GQ) to estimate water surface reflectance as a proxy for sediment concentration and the land cover product (MCD12Q1 V51) to characterize land cover of the watershed. Rainfall was estimated by using the 3B43 V7 product from the Tropical Rainforest Measuring Mission (TRMM). For the first trimester of each year, we investigated the inter-annual temporal variation in water surface reflectance at the Rosario Islands and at the three main mouths of the Magdalena River watershed. No increasing or decreasing trends of water surface reflectance were detected for any of the sites for the study period 2001–2014 (p > 0.05) but significant correlations were detected among the trends of each site at the watershed mouths (r = 0.57–0.90, p < 0.05) and between them and the inter-annual variation in rainfall on the watershed (r = 0.63–0.67, p < 0.05). Those trimesters with above-normal water surface reflectance at the mouths and above-normal rainfall at the watershed coincided with La Niña conditions while the opposite was the case during El Niño conditions. Although, a preliminary analysis of inter-annual land cover trends found only cropland cover in the watershed to be significantly correlated with water surface reflectance at two of the watershed mouths (r = 0.58 and 0.63, p < 0.05), the validation analysis draw only a 40.7% of accuracy in this land cover classification. This requires further analysis to confirm the impact of the cropland on the water quality at the watershed outlets. Spatial analysis with MOD09GQ imagery detected the overpass of river plumes from Barbacoas Bay over the Rosario Islands waters.     

Optimising three-band spectral indices to assess aerial N concentration,N uptake and aboveground biomass of winter wheat remotely in China and Germany

Remotely and accurately quantifying the canopy nitrogen status in crops is essential for regional studies of N budgets and N balances. In this study, we optimised three-band spectral algorithms to estimate the N status of winter wheat. This study extends previous work to optimise the band combinations further and identifies the optimised central bands and suitable bandwidths of the three-band nitrogen planar domain index (NPDI) for estimating the aerial N uptake, N concentration and aboveground biomass. Analysis of the influence of bandwidth change on the accuracy of estimating the canopy N status and aboveground biomass indicated that the suitable bandwidths for optimised central bands were 37 nm at 846 nm, 13 nm at 738 nm and 57 nm at 560 nm for assessing the aerial N uptake and were 37 nm at 958 nm, 21 nm at 696 nm and 73 nm at 578 nm for the assessment of the aerial N concentration and were 49 nm at 806 nm, 17 nm at 738 nm and 57 nm at 560 nm for the estimation of aboveground biomass. The optimised three-band NPDI could consistently and stably estimate the aerial N uptake and aboveground biomass of winter wheat in the vegetative stage and the aerial N concentration in the reproductive stage compared to the fixed band combinations. With suitable bandwidths, the broadband NPDI demonstrated excellent performance in estimating the aerial N concentration, N uptake and biomass. We conclude that the band-optimised algorithm represents a promising tool to measure the improved performance of the NPDI in estimating the aerial N uptake and biomass in the vegetative stage and the aerial N concentration in the reproductive stage, which will be useful for designing improved nitrogen diagnosis systems and for enhancing the applications of ground- and satellite-based sensors.     

Remotely detecting canopy nitrogen concentration and uptake of paddy rice in the Northeast China Plain

The influence of morphophysiological variation at different growth stages on the performance of vegetation indices for estimating plant N status has been confirmed. However, the underlying mechanisms explaining how this variation impacts hyperspectral measures and canopy N status are poorly understood. In this study, four field experiments involving different N rates were conducted to optimize the selection of sensitive bands and evaluate their performance for modeling canopy N status of rice at various growth stages in 2007 and 2008. The results indicate that growth stages negatively affect hyperspectral indices in different ways in modeling leaf N concentration (LNC), plant N concentration (PNC) and plant N uptake (PNU). Published hyperspectral indices showed serious limitations in estimating LNC, PNC and PNU. The newly proposed best 2-band indices significantly improved the accuracy for modeling PNU (R² = 0.75–0.85) by using the lambda by lambda band-optimized algorithm. However, the newly proposed 2-band indices still have limitations in modeling LNC and PNC because the use of only 2-band indices is not fully adequate to provide the maximum N-related information. The optimum multiple narrow band reflectance (OMNBR) models significantly increase the accuracy for estimating the LNC (R² = 0.67–0.71) and PNC (R² = 0.57–0.78) with six bands. Results suggest the combinations of center of red-edge (735 nm) with longer red-edge bands (730–760 nm) are very efficient for estimating PNC after heading, whereas the combinations of blue with green bands are more efficient for modeling PNC across all stages. The center of red-edge (730–735 nm) paired with early NIR bands (775–808 nm) are predominant in estimating PNU before heading, whereas the longer red-edge (750 nm) paired with the center of “NIR shoulder” (840–850 nm) are dominant in estimating PNU after heading and across all stages. The OMNBR models have the advantage of modeling canopy N status for the entire growth period. However, the best 2-band indices are much easier to use. Alternatively, it is also possible to use the best 2-band indices to monitor PNU before heading and PNC after heading. This study systematically explains the influences of N dilution effect on hyperspectral band combinations in relating to the different N variables and further recommends the best band combinations which may provide an insight for developing new hyperspectral vegetation indices.     

Understanding long-term (1982–2013) patterns and trends in winter wheat spring green-up date over the North China Plain

Monitoring the spring green-up date (GUD) has grown in importance for crop management and food security. However, most satellite-based GUD models are associated with a high degree of uncertainty when applied to croplands. In this study, we introduced an improved GUD algorithm to extract GUD data for 32 years (1982–2013) for the winter wheat croplands on the North China Plain (NCP), using the third-generation normalized difference vegetation index form Global Inventory Modeling and Mapping Studies (GIMMS3g NDVI). The spatial and temporal variations in GUD with the effects of the pre-season climate and soil moisture conditions on GUD were comprehensively investigated. Our results showed that a higher correlation coefficient (r = 0.44, p < 0.01) and lower root mean square error (22 days) and bias (16 days) were observed in GUD from the improved algorithm relative to GUD from the MCD12Q2 phenology product. In spatial terms, GUD increased from the southwest (less than day of year (DOY) 60) to the northeast (more than DOY 90) of the NCP, which corresponded to spatial reductions in temperature and precipitation. GUD advanced in most (78%) of the winter wheat area on the NCP, with significant advances in 37.8% of the area (p < 0.05). GUD occurred later at high altitudes and in coastal areas than in inland areas. At the interannual scale, the average GUD advanced from DOY 76.9 in the 1980s (average 1982–1989) to DOY 73.2 in the 1990s (average 1991–1999), and to DOY 70.3 after 2000 (average 2000–2013), indicating an average advance of 1.8 days/decade (r = 0.35, p < 0.05). Although GUD is mainly controlled by the pre-season temperature, our findings underline that the effect of the pre-season soil moisture on GUD should also be considered. The improved GUD algorithm and satellite-based long-term GUD data are helpful for improving the representation of GUD in terrestrial ecosystem models and enhancing crop management efficiency.     

Assessment of satellite ocean color products of MERIS,MODIS and SeaWiFS along the East China Coast (in the Yellow Sea and East China Sea)

The validation of satellite ocean-color products is an important task of ocean-color missions. The uncertainties of these products are poorly quantified in the Yellow Sea (YS) and East China Sea (ECS), which are well known for their optical complexity and turbidity in terms of both oceanic and atmospheric optical properties. The objective of this paper is to evaluate the primary ocean-color products from three major ocean-color satellites, namely the Moderate Resolution Imaging Spectroradiometer (MODIS), Medium Resolution Imaging Spectrometer (MERIS), and Sea-viewing Wide Field-of-view Sensor (SeaWiFS). Through match-up analysis with in situ data, it is found that satellite retrievals of the spectral remote sensing reflectance R_rs(λ) at the blue-green and green bands from MERIS, MODIS and SeaWiFS have the lowest uncertainties with a median of the absolute percentage of difference (APD_m) of 15–27% and root-mean-square-error (RMS) of 0.0021–0.0039 sr⁻¹, whereas the R_rs(λ) uncertainty at 412 nm is the highest (APD_m 47–62%, RMS 0.0027–0.0041 sr⁻¹). The uncertainties of the aerosol optical thickness (AOT) τ_a, diffuse attenuation coefficient for downward irradiance at 490 nm K_d(490), concentrations of suspended particulate sediment concentration (SPM) and Chlorophyll a (Chl-a) were also quantified. It is demonstrated that with appropriate in-water algorithms specifically developed for turbid waters rather than the standard ones adopted in the operational satellite data processing chain, the uncertainties of satellite-derived properties of K_d(490), SPM, and Chl-a may decrease significantly to the level of 20–30%, which is true for the majority of the study area. This validation activity advocates for (1) the improvement of the atmosphere correction algorithms with the regional aerosol optical model, (2) switching to regional in-water algorithms over turbid coastal waters, and (3) continuous support of the dedicated in situ data collection effort for the validation task.     

Mapping species of submerged aquatic vegetation with multi-seasonal satellite images and considering life history information

Spatial information of the dominant species of submerged aquatic vegetation (SAV) is essential for restoration projects in eutrophic lakes, especially eutrophic Taihu Lake, China. Mapping the distribution of SAV species is very challenging and difficult using only multispectral satellite remote sensing. In this study, we proposed an approach to map the distribution of seven dominant species of SAV in Taihu Lake. Our approach involved information on the life histories of the seven SAV species and eight distribution maps of SAV from February to October. The life history information of the dominant SAV species was summarized from the literature and field surveys. Eight distribution maps of the SAV were extracted from eight 30 m HJ-CCD images from February to October in 2013 based on the classification tree models, and the overall classification accuracies for the SAV were greater than 80%. Finally, the spatial distribution of the SAV species in Taihu in 2013 was mapped using multilayer erasing approach. Based on validation, the overall classification accuracy for the seven species was 68.4%, and kappa was 0.6306, which suggests that larger differences in life histories between species can produce higher identification accuracies. The classification results show that Potamogeton malaianus was the most widely distributed species in Taihu Lake, followed by Myriophyllum spicatum, Potamogeton maackianus, Potamogeton crispus, Elodea nuttallii, Ceratophyllum demersum and Vallisneria spiralis. The information is useful for planning shallow-water habitat restoration projects.     

Assessing the potential of MODIS/Terra version 5 images to improve near shore lake bathymetric surveys

Estimation of storage volume of inland water bodies is made using bathymetric surveys. Such surveys are often terminated far inward from shores due to operational limitations. The gap in depth data for the shore area is often overcome by extrapolating the established volume-area relationship beyond the survey boundary. This introduces large errors in volume estimation. In this study, the potential of the Moderate Resolution Imaging Spectroradiometer MODIS version-5 images to improve an existing bathymetric model is assessed over Lake Tana, Ethiopia. MODIS images are used to generate a new bathymetric model of the unsurveyed shore. The new model is validated through its ability to reproduce measured lake levels. The result is compared with lake level estimates made from an existing bathymetric survey. Results showed that MODIS improved bathymetric model reproduced lake water level with a RMSE of 0.14 m as compared to 0.82 m using the existing bathymetric model. The Mean Absolute Error (MAE) has also improved from 0.78 m to 0.13 m. These results suggest that despite their unavailability on cloudy days, MODIS images provide significant advantages for improving bathymetric models.     

A three-band semi-analytical model for deriving total suspended sediment concentration from HJ-1A/CCD data in turbid coastal waters

The accurate assessment of total suspended sediment (TSM) concentration in coastal waters by means of remote sensing is quite challenging, due to the optical complexity and significant variability of these waters. In this study, three-band semi-analytical TSM retrieval (TSTM) model with HJ-1A/CCD spectral bands was developed for the retrieval of TSM concentration from turbid coastal waters. This model was calibrated and validated by means of one calibration dataset and three independent validation datasets obtained from three different turbid waters. It was found that the TSTM model may be used to retrieve accurate TSM concentration data from highly turbid waters without the spectral slope of the model requiring further optimization. Finally, the TSM concentration data were quantified from the HJ-1A/CCD images after atmospheric correction using the dark-object subtraction technique. Upon comparing the model-derived and field-measured TSM concentration data, it was observed that the TSTM model produced <29% uncertainty in deriving TSM concentration from the HJ-1A/CCD data. These findings imply that the TSTM model may be used for the quantitative monitoring of TSM concentration in coastal waters, provided that the atmospheric correction scheme for the HJ-1A/CCD imagery is available.     

Retrieval of leaf water content spanning the visible to thermal infrared spectra

The objective of this study was to investigate the entire spectra (from visible to the thermal infrared; 0.390–14.0 μm) to retrieve leaf water content in a consistent manner. Narrow-band spectral indices (calculated from all possible two band combinations) and a partial least square regression (PLSR) were used to assess the strength of each spectral region. The coefficient of determination (R²) and root mean square error (RMSE) were used to report the prediction accuracy of spectral indices and PLSR models. In the visible-near infrared and shortwave infrared (VNIR–SWIR), the most accurate spectral index yielded R² of 0.89 and RMSE of 7.60%, whereas in the mid infrared (MIR) the highest R² was 0.93 and RMSE of 5.97%. Leaf water content was poorly predicted using two-band indices developed from the thermal infrared (R² = 0.33). The most accurate PLSR model resulted from MIR reflectance spectra (R² = 0.96, RMSE = 4.74% and RMSE cross validation RMSE_CV = 6.17%) followed by VNIR–SWIR reflectance spectra (R² = 0.91, RMSE = 6.90% and RMSE_CV = 7.32%). Using thermal infrared (TIR) spectra, the PLSR model yielded a moderate retrieval accuracy (R² = 0.67, RMSE = 13.27% and RMSE_CV = 16.39%). This study demonstrated that the mid infrared (MIR) and shortwave infrared (SWIR) domains were the most sensitive spectral region for the retrieval of leaf water content.     

A Weighted Algorithm for Estimating Chlorophyll-a Concentration from Turbid Waters

Accurately estimating phytoplankton Chlorophyll-a(Chla) concentration from remotely sensed data is particularly challenging in turbid, productive waters. In this study, a weighted Chla concentration algorithm (WCA) are constructed to smooth the performance of three-bands semi-analytical algorithm(TSA) and four-bands semi-analytical algorithm(FSA). The performance of WCA, TSA and FSA algorithms are calibrated and validated by three independently datasets collected from Chesapeake Bay, USA, Yellow River Estuary, China, and Taihu Lake, China. Results of this study indicated that: (1) The accuracy and stability of TSA, FSA and WCA in Chesapeake Bay have a superior performance than it in Taihu Lake and Yellow River Estuary; (2) In Taihu Lake and Yellow River Estuary, the TSA and FSA algorithms are not stable for estimating Chla concentration, especially in Taihu Lake, the accuracy and stability of TSA, FSA and WCA algorithms are quite bad; (3) The WCA can greatly improve the accuracy and stability of TSA and FSA algorithms, but it is greatly depended on the performance of TSA and FSA algorithms; and (4) Although the TSA, FSA and WCA algorithms are semi-analytical algorithm, however, the optimal bands, accuracy and stability of these algorithms are very timely and located dependence.     

Comparison of MODIS-based models for retrieving suspended particulate matter concentrations in Poyang Lake,China

Suspended particulate matter (SPM) is a key parameter describing water quality, and developing the retrieval model of SPM concentration (C_SPM) is fundamental for obtaining the spatiotemporal information of C_SPM and further for understanding, managing and protecting aquatic ecosystems. This study aimed to compare moderate resolution imaging spectroradiometer (MODIS)-based C_SPM retrieval models in order to find the optimal model for improving the C_SPM estimation in Poyang Lake. The C_SPM measurements on 27 September 2007 and their coincident MODIS Terra image were used to calibrate retrieval models with the least-squares technique. The C_SPM measurements on 31 August 2012 and the MODIS Terra image on 30 August 2012 were applied to validate the calibrated models, and the correlation coefficient (r) between the measured and estimated C_SPM values, the root mean square error (RMSE) and relative root mean square error (RRMSE) of estimation as well as the model bias evaluation result were compared to determine the optimal model for estimating the C_SPM values of Poyang Lake from MODIS images. Model calibration showed that, after two samples were removed, the exponential models of blue, green and red band, the linear model of infrared band, the cubic model of red band as well as the exponential model of red minus infrared band explained about 92%, 88%, 90%, 89%, 90% and 76% of the variation of C_SPM, respectively; while model validation indicated that, after removing two samples, the exponential models of blue and green band got biased C_SPM estimations, the agreement between the measured and estimated C_SPM values was not very high (r = <0.8) for the models with single red and infrared band, and the exponential model of red minus infrared band got the best result among all calibrated models (r = 0.87, RMSE = 22.1 mg/l, RRMSE = 52.8%). We concluded that the exponential model of red minus infrared band obtained stable C_SPM estimation and was the optimal model for C_SPM estimation in this study, and more independent datasets should be obtained to further validate our finding for improving the C_SPM estimation in Poyang Lake.