Search

Ocean color sensors, typically installed on polar-orbiting satellites, have been used to monitor oceanic processes for last three decades. However, their temporal resolution is not considered to be adequate for monitoring highly dynamic oceanic processes, especially when considering data gaps due to cloud contamination. The Advanced Himawari Imager (AHI) onboard the Himawari-8, a geostationary satellite operated by the Japan Meteorological Agency (JMA), acquires imagery every 10 min at 500 m to 2000 m spatial resolution. The AHI sensor with three visible, one near-infrared (NIR), and two shortwave-infrared (SWIR) bands displays good potential in monitoring oceanic processes at high temporal resolution. This study investigated and identified an appropriate atmospheric correction method for AHI data; developed a model for Total Suspended Solids (TSS) concentrations estimation using hyperspectral data and in-situ measurements of TSS; validated the model; and assessed its potential to capture diurnal changes using AHI imagery. Two image-based atmospheric correction methods, the NIR-SWIR method and the SWIR method were tested for correcting the AHI data. Then, the new model was applied to the atmospherically corrected AHI data to map TSS and its diurnal changes in the Pearl River Estuary (PRE) and neighboring coastal areas. The results indicated that the SWIR method outperformed the NIR-SWIR method, when compared to in-situ water-leaving reflectance data. The results showed a good agreement between the AHI-derived TSS and in-situ measured data with a coefficient of determination (R²) of 0.85, mean absolute error (MAE) of 3.1 mg/L, a root mean square error (RMSE) of 3.9 mg/L, and average percentage difference (APD) of 30% (TSS range 1–40 mg/L). Moreover, the diurnal variation in the turbidity front, using the Normalized Suspended Material Index (NSMI), showed the capability of AHI data to track diurnal variation in turbidity fronts, due to high TSS concentrations at high temporal frequency. The present study indicates that AHI data with high image capturing frequency can be used to map surface TSS concentrations. These TSS measurements at high frequency are not only important for monitoring the sensitive coastal areas but also for scientific understanding of the spatial and temporal variation of TSS.

The accurate estimate of surface chlorophyll a concentrations (Chla) by remote sensing presents a number of challenges where inherent and apparent optical properties have significant spatial or temporal variability. Indeed, Chla algorithms for Case 2 waters are often lake or region specific, and they are usually highly sensitive to changes in the dominant chromophoric constituents. This study develops and validates an absorption-specific approach to estimating Chla across an optically heterogeneous dataset. The approach is based on the classification of the optically dominant constituent. We tested this approach with in situ data from Taihu Lake, Poyang Lake, Chaohu Lake, Shitoukoumen Reservoir, Pearl River Estuary and Daya Bay as well as using HydroLight simulated data. The results show an improved performance when compared to most single Chla algorithms. We validated the approach with data from the Visible Infrared Imager Radiometer Suite (VIIRS). Results showed that this absorption-specific approach provided good Chla estimates over clear to very turbid waters with a wide range of optical conditions (R2 = 0.76, rRMSE = 35%, n = 230, p

An absorption-based approach was used to determine surface particulate organic carbon (POC) concentrations in both inland and coastal waters. The improved performance of this approach was based on the specification of local POC absorption characteristics based on dominant POC sources; phytoplankton or detritus based. This specification was made using a new POC-Index (PI), developed and tested across a range of POC (300–10,000 mg/m3) conditions in temporally and spatially heterogeneous inland and coastal waterbodies. The POC model was based on remote sensing reflectance (Rrs, sr−1) in four wavebands: Rrs(751), Rrs(488) and Rrs(R/G), where R is the red band [Rrs(672)] for detritus dominated waters and G is the green band [Rrs(555)] in the phytoplankton dominated waters. The model provided a high R2 (0.74) and relatively low rRMSE (42.0%, N = 136, p

This study applied a multivariate model based on three simulated sensors to estimating water quality variables in Shitoukoumen Reservoir, Changchun City, Jilin Province, China, including concentration of total suspended matter, concentration of chlorophyll-a and non-pigment matter absorption. Two field campaigns for spectra measurements with a total of 40 samples were carried out on June 13 and September 23, 2008. The in-situ spectra were recalculated to the spectral bands and sensitivities of the instruments applied in this paper, i.e. Landsat TM, Alos and P6, by using the average method. And the recalculated spectra were used for estimating water quality variables by the single model and multivariate model. The results show that the multivariate model is superior to the single model as the multivariate model takes the combined effects of water components into consideration and can estimate water quality variables simultaneously. According to R2 and RMSE, Alos is superior to other sensors for water quality variables estimation although the precision of non-pigment matter absorption inversion performed the second.

The Songnen Plain in Northeast China, one of the key national bases of agricultural production, went through remarkable land use/cover changes in recent years. This study aimed to explore the long-term land use/cover changes and the effects of these changes on the environment. The Landsat-based analysis showed that, during 1986–2000, cropland, built-up land and barren land had increased, among which cropland had the largest increase of 9,198km2 with an increase rate of 7.5%. Woodland, grassland, water body and swampland had decreased correspondingly, among which grassland had the most dramatic decrease of 6,127km2 with a decrease rate of 25.6%. The transition matrix results revealed that grassland, woodland and swampland were the three main land use types converted to cropland. Climate warming created the potential environment for the conversion of grassland and swampland into cropland. Land resources policy made by central and provincial governments of China affected the pattern and intensity of land use. Land use/cover changes accompanied by climatic variation brought out a series of environmental consequences, such as sand desertification of land, land salinization and alkalinization, grassland degradation, and more frequent floods. Under this circumstance, optimized land use structure and restoration measures are needed.

A two step approach was developed to estimate POC concentrations in a shallow eutrophic lake, where dissolved carbon and inorganic particulate matter strongly influence optical properties. The approach was based on the identification of a co-variability of POC concentrations with one or more inherent optical properties (IOPs). In the present case, a relationship between POC and absorption at 620 nm was identified and was combined with a reflectance based algorithm for case II waters. Compared with other published POC algorithms, this approach provided better estimates over a wide range of POC concentrations in two hypereutrophic lake waters with the predominance of cyanobacteria (R(2) > 0.80). While it is unlikely that a single POC algorithm can be created that is independent of the variability of POC sources, this approach allows for a common method to identify appropriate algorithms with which POC dynamics can be explored.

Spectral absorption properties of total suspended matter (TSM) and colored dissolved organic matter (CDOM) are important for the use of the bio-optical model to estimate water quality parameters. This study aims to investigate the variation in the absorption coefficients of TSM and CDOM of inland waters. A total of 92 water samples were collected from Shitoukoumen Reservoir and Songhua Lake in Northeast China, analyzed for TSM and Chl-a, and measured for the absorption coefficient of TSM, CDOM and total pigments using a laboratory spectrophotometer. The absorption coefficient of TSM has been decomposed for phytoplankton and inorganic sediments. The results show that for Shitoukoumen Reservoir, CDOM has strong absorptions with shallow absorption slopes (i.e., the coefficient S in a(λ)=a(λ0)exp[-S(λ- λ0)]) and large absorption at 355 nm; and for Songhua Lake, CDOM follows similar spectral absorption curves but less variation in the S value. The results also show TSM has the average absorption coefficient 5.7 m-1 at 440 nm and 0.93 m-1 at 675 nm, and their concentration is well correlated to TSM with R2 larger than 0.85 at 440 nm over both Songhu Lake and Shitoukoumen Reservoir. In summer, CDOM was mainly terrigenous and had a high proportion of humic acid derived from the decomposition of phytoplankton and there were no obvious difference of S value. The results indicate that inorganic sediments contributed much more absorption than phytoplankton pigments in Shitoukoumen Reservoir than that in Songhua Lake, and there is strong association of TSM concentration to absorption coefficient at 440 nm.

The availability of underwater light is a critical factor in the growth and abundance of primary producers in shallow embayments. The aim of this study was to examine the spatial distribution and the possible source of CDOM for inland water in summer in the northeast china. Absorption spectra of inland water samples were measured from 200nm to 800nm. Highest mean-value of a(375) occurred in Chagan Lake. A significant spatial difference was found among four different inland waters, and evident spatial variation was in Chagan Lake. A consistent negative non-linear relationship was recorded between S value and CDOM absorption coefficient. Furthermore, S value was used as a proxy for CDOM composition and source. Fulvic acids is primary contribution for CDOM absorption in Songhua Lake and Shitoukoumen Reservoir, but humic acids in Nanhu Lake and Chagan Lake. The relationships between CDOM absorption and total suspended matter concentration and chlorophyll-a concentration were analyzed. It demonstrated the biological processes source for Nanhu Lake, Shitoukoumen Reservoir and Chagan Lake. But for Songhua Lake, the dominating source is from river inputs, but biological process was also an important portion for CDOM concentration.

The spectral distribution of upwelling and downwelling irradiance were used to estimate the effective upwelling irradiance depth as well as examine the angular distribution of the downwelling radiance. The effective upwelling depth was seen to undergo spectral “shifts” in wavelength maxima in relation to elevated particulate concentrations. Wavelengths of the UVA minimum and mid visible maximum depths were found to be shifted to higher wavelengths (red shifted) at high particulate concentrations, while expected minimums at chlorophyll and phycocyanin absorption peaks and in the NIR were shifted to lower wavelengths (blue shifted). By comparing upwelling and downwelling irradiance profiles, the wavelength limits of the asymptotic angular radiance distribution were found to correspond to the visible spectral domain (390 – 740 nm).

Lake Chagan represents a complex situation of major optical constituents and emergent spectral signals for remote sensing analysis of water quality in the Songnen Plain. As such it provides a good test of the combined radiometric correction methods developed for optical remote sensing data to monitor water quality. Landsat thematic mapper (TM) data and in situ water samples collected concurrently with satellite overpass were used for the analysis, in which four important water quality parameters are considered: chlorophyll-a, turbidity, total dissolved organic matter, and total phosphorus in surface water. Both empirical regressions and neural networks were established to analyze the relationship between the concentrations of these four water parameters and the satellite radiance signals. It is found that the neural network model performed at better accuracy than empirical regressions with TM visible and near-infrared bands as spectral variables. The relative root mean square error (RMSE) for the neural network was < 10%, while the RMSE for the regressions was less than 25% in general. Future work is needed on establishing the dynamic characteristic of Chagan Lake water quality with TM or other optical remote sensing data. The algorithms developed in this study need to be further tested and refined with multidate imagery data.