S.W.M. Peters

Comparison of remote sensing data, model results and in situ data for total suspended matter (TSM) in the southern Frisian lakes.

Suspended matter plays an important role in water quality management since it is related to total primary production and fluxes of heavy metals and micropollutants such as PCBs. Synoptic information on suspended matter at a regular frequency is difficult to obtain from the routine in situ monitoring network since suspended matter is (like chlorophyll) a spatially inhomogeneous parameter. This can be solved by the integrated use of remote sensing data, in situ data and water quality models. A methodology previously developed for integrating information from remote sensing, and models (Vos and Schuttelaar, Neth Remote Sensing Board (1995) report 95-19), was applied for the assessment of suspended matter concentrations in the southern Frisian lakes in the Netherlands. The model is a one-dimensional network model. Remote sensing data (Landsat-TM5 and SPOT-HRV) were atmospherically corrected and converted to total suspended matter maps. The algorithms are based on analytical optical modelling, using the in situ inherent optical properties. This methodology enables the development of multi-temporal algorithms for estimating seston dry weight concentration in lakes from remotely sensed data; thus satellite data can now become an independent measurement tool for water management authorities.

Intercomparison in the field between the new WISP-3 and other radiometers (TriOS Ramses, ASD FieldSpec, and TACCS)

Abstract. Optical close-range instruments can be applied to derive water quality parameters for monitoring purposes and for validation of optical satellite data. In situ radiometers are often difficult to deploy, especially from a small boat or a remote location. The water insight spectrometer (WISP-3) is a new hand-held radiometer for monitoring water quality, which automatically performs measurements with three radiometers ( L sky , L u , E d ) and does not need to be connected with cables and electrical power during measurements. The instrument is described and its performance is assessed by an intercomparison to well-known radiometers, under real fieldwork conditions using a small boat and with sometimes windy and cloudy weather. Root mean squared percentage errors relative to those of the TriOS system were generally between 20% and 30% for remote sensing reflection, which was comparable to those of the other instruments included in this study. From this assessment, it can be stated that for the tested conditions, the WISP-3 can be used to obtain reflection spectra with accuracies in the same range as well-known instruments. When tuned with suitable regional algorithms, it can be used for quick scans for water quality monitoring of Chl, SPM, and aCDOM.

Remote sensing for inland water quality detection and monitoring: State-of-the-art application in Friesland waters

Remote sensing is an emerging technology with respect to water quality detection and monitoring. It must be made clear to end-users that the results of the technique are beneficial to them in their work. For this purpose it is necessary to provide the end-user with adequate water quality information from remote sensing at the right time, in the right format, at a competitive price (as compared to alternative methods). A methodology has been developed in The Netherlands, based on these criteria, applicable anywhere in the world. The methodology embedded in PC-based software is based on sound modeling of the water-atmosphere system. This makes it possible to derive accurate remote sensing algorithms for estimating water quality parameters for the types of water present. The case study in Friesland described here is a representative example of an applied inland water study.

A review on substances and processes relevant for optical remote sensing of extremely turbid marine areas, with a focus on the Wadden Sea

The interpretation of optical remote sensing data of estuaries and tidal flat areas is hampered by optical complexity and often extreme turbidity. Extremely high concentrations of suspended matter, chlorophyll and dissolved organic matter, local differences, seasonal and tidal variations and resuspension are important factors influencing the optical properties in such areas. This review gives an overview of the processes in estuaries and tidal flat areas and the implications of these for remote sensing in such areas, using the Wadden Sea as a case study area. Results show that remote sensing research in extremely turbid estuaries and tidal areas is possible. However, this requires sensors with a large ground resolution, algorithms tuned for high concentrations of various substances and the local specific optical properties of these substances, a simultaneous detection of water colour and land–water boundaries, a very short time lag between acquisition of remote sensing and in situ data used for validation and sufficient geophysical and ecological knowledge of the area.

An Optical Classification Tool for Global Lake Waters

Shallow and deep lakes receive and recycle organic and inorganic substances from within the confines of these lakes, their watershed and beyond. Hence, a large range in absorption and scattering and extreme differences in optical variability can be found between and within global lakes. This poses a challenge for atmospheric correction and bio-optical algorithms applied to optical remote sensing for water quality monitoring applications. To optimize these applications for the wide variety of lake optical conditions, we adapted a spectral classification scheme based on the concept of optical water types. The optical water types were defined through a cluster analysis of in situ hyperspectral remote sensing reflectance spectra collected by partners and advisors of the European Union 7th Framework Programme (FP7) Global Lakes Sentinel Services (GLaSS) project. The method has been integrated in the Envisat-BEAM software and the Sentinel Application Platform (SNAP) and generates maps of water types from image data. Two variations of water type classification are provided: one based on area-normalized spectral reflectance focusing on spectral shape (6CN, six-class normalized) and one that retains magnitude with no modification to the reflectance signal (6C). This resulted in a protocol, or processing scheme, that can also be applied or adapted for Sentinel-3 Ocean and Land Colour Imager (OLCI) datasets. We apply both treatments to MERIS imagery of a variety of European lakes to demonstrate its applicability. The studied target lakes cover a range of biophysical types, from shallow turbid to deep and clear, as well as eutrophic and dark absorbing waters, rich in colored dissolved organic matter (CDOM). In shallow, high-reflecting Dutch and Estonian lakes with high sediment load, 6C performed better, while in deep, low-reflecting clear Italian and Swedish lakes, 6CN performed better. The 6CN classification of in situ data is promising for very dark, high CDOM, absorbing lakes, but we show that our atmospheric correction of the imagery was insufficient to corroborate this. We anticipate that the application of the protocol to other lakes with unknown in-water characterization, but with comparable biophysical properties will suggest similar atmospheric correction (AC) and in-water retrieval algorithms for global lakes.

Chi-square spectral fitting for concentration retrieval, automatic local calibration, quality control, and water type detection

In this study, the inverse bio-optical model HYDROPT was calibrated with regional specific inherent optical properties (SIOPs) and various local SIOPs to examine the effect of these calibrations on the retrievals. The study area, the Wadden Sea, is an estuary and tidal flat area with very high concentrations of chlorophyll a (Chl a), suspended particulate matter (SPM), and coloured dissolved organic matter (CDOM). HYDROPT could derive concentrations of Chl a, SPM, and CDOM with a reasonable degree of accuracy when in situ above-water reflectances were used as input (root mean squared error of 0.19–0.52 mg·m−3 for Chl a, 0.28–0.46 mg·m−3 for SPM, and 0.20–0.34 m−1 for aCDOM). However, quality control showed that 70% of the in situ input reflectance spectra were ambiguous; these spectra could be modelled with various sets of SIOPs. Therefore, automatic local calibration based on the spectral fit (χ2) value of the fitting procedure did not necessarily lead to the best results; this was expected to be an advantage of χ2 fitting. When MERIS data were used as input, the concentration maps showed distributions according to the expectations, although tidal flats and nearby land affected the results at the locations that matched with in situ stations. The water types (water with similar SIOPs) that could be detected based on MERIS data were new in this study.

Validation of a spectral correction procedure for sun and sky reflections in above-water reflectance measurements

A three-component reflectance model (3C) is applied to above-water radiometric measurements to derive remote-sensing reflectance Rrs (λ). 3C provides a spectrally resolved offset Δ(λ) to correct for residual sun and sky radiance (Rayleigh- and aerosol-scattered) reflections on the water surface that were not represented by sky radiance measurements. 3C is validated with a data set of matching above- and below-water radiometric measurements collected in the Baltic Sea, and compared against a scalar offset correction Δ. Correction with Δ(λ) instead of Δ consistently reduced the (mean normalized root-mean-square) deviation between Rrs (λ) and reference reflectances to comparable levels for clear (Δ: 14.3 ± 2.5 %, Δ(λ): 8.2 ± 1.7 %), partly clouded (Δ: 15.4 ± 2.1 %, Δ(λ): 6.5 ± 1.4 %), and completely overcast (Δ: 10.8 ± 1.7 %, Δ(λ): 6.3 ± 1.8 %) sky conditions. The improvement was most pronounced under inhomogeneous sky conditions when measurements of sky radiance tend to be less representative of surface-reflected radiance. Accounting for both sun glint and sky reflections also relaxes constraints on measurement geometry, which was demonstrated based on a semi-continuous daytime data set recorded in a eutrophic freshwater lake in the Netherlands. Rrs (λ) that were derived throughout the day varied spectrally by less than 2 % relative standard deviation. Implications on measurement protocols are discussed. An open source software library for processing reflectance measurements was developed and is made publicly available.

Variability of adjacency effects in sky reflectance measurements

Sky reflectance Rsky(λ) is used to correct in situ reflectance measurements in the remote detection of water color. We analyzed the directional and spectral variability in Rsky(λ) due to adjacency effects against an atmospheric radiance model. The analysis is based on one year of semi-continuous Rsky(λ) observations that were recorded in two azimuth directions. Adjacency effects contributed to Rsky(λ) dependence on season and viewing angle and predominantly in the near-infrared (NIR). For our test area, adjacency effects spectrally resembled a generic vegetation spectrum. The adjacency effect was weakly dependent on the magnitude of Rayleigh- and aerosol-scattered radiance. The reflectance differed between viewing directions 5.4±6.3% for adjacency effects and 21.0±19.8% for Rayleigh- and aerosol-scattered Rsky(λ) in the NIR. Under which conditions in situ water reflectance observations require dedicated correction for adjacency effects is discussed. We provide an open source implementation of our method to aid identification of such conditions.

A Pilot Study for an Enhanced Algal Spatial Pattern Prediction Using RS Images

Accurate and reliable flow forecasting form an important basis for efficient real-time river management, including flood control, flood warning and so on. In order to improve the accuracy of flow forecasting, gain matrix of Kalman filter was applied to real-time correction of hydraulic model for spatial distributing the system deviation (called “expected value of system noise” in Kalman filter). That means Kalman gain matrix is used to distribute model system deviation from measurement cross sections to the entire state of the river system. State functions of Kalman filter were set up based on discretization and linearization Saint-Venant equations by adopting four-point linear implicit form, and the spatial distribution system deviation method (SDM) was used for real-time correction. The calculation of flood forecasting for river section from Cuntan to Fengjie of Yangtze River verifies that SDM is useful in promoting the accuracy of real-time flood forecasting.