A current review of empirical procedures of remote sensing in inland and near-coastal transitional waters

Abstract

The empirical approach of remote sensing has a proven capability to provide timely and accurate information on inland and near-coastal transitional waters. This article gives a thorough review of empirical algorithms for quantitatively estimating a variety of parameters from space-borne, airborne and in situ remote sensors in inland and transitional waters, including chlorophyll-a, total suspended solids, Secchi disk depth (z SD ), turbidity, absorption by coloured dissolved organic matter (a CDOM ) and other parameters, for example, phycocyanin. Current remote-sensing instruments are also reviewed. The theoretical basis of the empirical algorithms is given using fundamental bio-optical theory of the inherent optical properties (IOPs). Bands, band ratios and band arithmetic algorithms that could be used to produce common biogeophysical products for inland/transitional waters are identified. The article discusses the potential role that empirical algorithms could play alongside more advanced model-based algorithms in the future of water remote sensing, especially for near real-time operational monitoring systems. The article aims to describe the current status of empirical remote sensing in inland and near-coastal transitional waters and provide a useful reference to workers. It does not cover 'inversion' algorithms.

Evaluation of Unified Algorithms for Remote Sensing of Chlorophyll-a and Turbidity in Lake Shinji and Lake Nakaumi of Japan and the Vaal Dam Reservoir of South Africa under Eutrophic and Ultra-Turbid Conditions

“…Various remote sensing algorithms have been suggested to measure Chla and turbidity (or TSS) for inland waters with complex optical properties [17]. For example, the effectiveness of the following 2-band model (2-BM; hereinafter referred to as a C1 model) as a Chla algorithm has been shown using the red and near-infrared bands [18,19].…”

“…Moreover, the current situation cannot be dealt with using conventional point source monitoring; thus, it is vital to establish technology such as remote sensing that can simultaneously monitor wide areas. Conventional water quality remote sensing relies mainly on algorithms dealing with blue and green light, but in optically-complex inland waters, the red and near-infrared parts of the spectrum are often used [17]. For example, methods such as two-band algorithms [18,19], three-band algorithms [20,21] and four-band algorithms [22,23] have been proposed to measure chlorophyll-a (Chla).…”

Developments in Earth observation for the assessment and monitoring of inland, transitional, coastal and shelf-sea waters

“…Earth observation provides a synoptic view of systems that cannot be replicated through field-based sampling and one that can be obtained at relatively high temporal frequencies to build long-term observational records of environmental change. However, many previous studies have shown that the performance of in-waters models for the retrieval of physical and biogeochemical parameters from Earth observation data can vary tremendously between water bodies with very different optical properties and overlying atmospheres (Guanter et al, 2010;Matthews et al 2011). The Danube basin and its diversity of water bodies from the large, deep clear water lakes to the south of the Black Forest to the highly turbid environments of the coastal lagoons and waters of the Black Sea will undoubtedly pose a challenge for the operational monitoring of water quality from satellites.…”

“…Most algorithms for the estimation of Secchi depth take advantage of the bands in red because as water clarity decreases, the water-leaving radiative signal in the red spectrum usually increases (Matthews, 2011). The retrieval of Secchi depth can also be achieved successfully using relatively broad spectral bands and thus many studies have capitalized on Landsat-MSS/-TM/-ETM+/-OLI data as they offer high spatial resolution ( There are also a relatively large number of studies dedicated to the retrieval of TSM in inland waters, using sensors ranging from Landsat-TM/ETM+/OLI to ocean colour sensors such as MERIS and MODIS.…”

“…This reflects the fact that TSM is arguably the most straightforward inwater constituent to retrieve from remote sensing data. In similar fashion to algorithms for turbidity, reflectance in red bands is often used in TSM retrieval algorithms (Matthews, 2011) although many different approaches have been used including ratios of reflectance in the green and infrared. Gitelson et al (1993) developed an algorithm based on reflectance in the red that was effective in retrieving TSM in lakes for concentrations up to 66 mg/L.…”