Inherent Optical Properties in Lake Taihu Derived from VIIRS Satellite Observations

Abstract

Using in situ remote sensing reflectance and inherent optical property (IOP) measurements, a near-infrared (NIR)-based IOP algorithm is developed and tuned for Lake Taihu, in order to derive the particle backscattering coefficient bbp(λ), total absorption coefficient at(λ), dissolved and detrital absorption coefficient adg(λ), and phytoplankton absorption coefficient aph(λ), with satellite observations from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP). The IOP algorithm for Lake Taihu has a reasonably good accuracy. In fact, the determination coefficients between the retrieved and in situ IOPs are 0.772, 0.638, and 0.487 for at(λ), adg(λ), and aph(λ), respectively. The IOP products in Lake Taihu that have been derived from VIIRS-SNPP observations show significant spatial and temporal variations. Southern Lake Taihu features enhanced bbp(λ) and adg(λ), while northern Lake Taihu shows higher aph(λ). The seasonal and interannual variability of adg(λ) and bbp(λ) in Lake Taihu is quantified and characterized with the highest bbp(λ) and adg(λ) in the winter, and the lowest in the summer. In the winter, bbp(443) and adg(443) can reach over ~1.5 and ~5.0 m−1, respectively, while they are ~0.5–1.0 and ~2.0 m−1 in the summer. This study shows that in Lake Taihu adg(λ) is the most significant IOP, while aph(λ) is the least in terms of the IOP values and contributions to remote sensing reflectance. The highest bbp(λ) and adg(λ) occurred in the winter between 2017–2018, and the lowest bbp(λ) and adg(λ) occurred in the summer of 2014. In comparison, the seasonal and interannual variability of mean aph(λ) for Lake Taihu is less significant, even though enhanced seasonal and interannual variability can be found in some parts of Lake Taihu, such as in the northern Lake Taihu region.

Semi-Empirical Models for the Bidirectional Water-Leaving Radiance: An Analysis of a Turbid Inland Lake

“…In order to compare the performance levels of models in different water types, we calculated the statistical numbers of sampled points with various IOPs. Based on the analysis of the IOPs and the previous research on long-term observations of Taihu Lake (Shi et al, 2019), we divided the data from Taihu Lake into five types. The range of IOPs and the statistical numbers of the sampled points in five water types are presented in Table 2.…”

An Extended Quasi−Analytical Algorithm for Retrieving Absorption Coefficient Using 510–620 nm Bands from OLCI and MERIS Satellite Data

“…Several of the currently used algorithms have been further applied to the ocean color satellite data, i.e., sea−viewing wide field−of−view sensor (SeaWiFS) [32,33], moderate resolution imaging spectroradiometer (MODIS) [20,34], visible infrared imaging radiometer (VIIRS) [35,36], medium resolution imaging spectrometer (MERIS) [37,38], geostationary ocean color imager (GOCI) [39], Landsat series [18,40], etc. However, few studies have taken advantage of the ocean and land color instrument (OLCI) on Sentinel−3A, which is a relatively new ocean color sensor launched in 2016.…”