Water Quality Retrieval from PRISMA Hyperspectral Images: First Experience in a Turbid Lake and Comparison with Sentinel-2

Abstract

A new era of spaceborne hyperspectral imaging has just begun with the recent availability of data from PRISMA (PRecursore IperSpettrale della Missione Applicativa) launched by the Italian space agency (ASI). There has been pre-launch optimism that the wealth of spectral information offered by PRISMA can contribute to a variety of aquatic science and management applications. Here, we examine the potential of PRISMA level 2D images in retrieving standard water quality parameters, including total suspended matter (TSM), chlorophyll-a (Chl-a), and colored dissolved organic matter (CDOM) in a turbid lake (Lake Trasimeno, Italy). We perform consistency analyses among the aquatic products (remote sensing reflectance (Rrs) and constituents) derived from PRISMA and those from Sentinel-2. The consistency analyses are expanded to synthesized Sentinel-2 data as well. By spectral downsampling of the PRISMA images, we better isolate the impact of spectral resolution in retrieving the constituents. The retrieval of constituents from both PRISMA and Sentinel-2 images is built upon inverting the radiative transfer model implemented in the Water Color Simulator (WASI) processor. The inversion involves a parameter (gdd) to compensate for atmospheric and sun-glint artifacts. A strong agreement is indicated for the cross-sensor comparison of Rrs products at different wavelengths (average R ≈ 0.87). However, the Rrs of PRISMA at shorter wavelengths (<500 nm) is slightly overestimated with respect to Sentinel-2. This is in line with the estimates of gdd through the inversion that suggests an underestimated atmospheric path radiance of PRISMA level 2D products compared to the atmospherically corrected Sentinel-2 data. The results indicate the high potential of PRISMA level 2D imagery in mapping water quality parameters in Lake Trasimeno. The PRISMA-based retrievals agree well with those of Sentinel-2, particularly for TSM.

Estimation of Bathymetry and Benthic Habitat Composition from Hyperspectral Remote Sensing Data (BIODIVERSITY) Using a Semi-Analytical Approach

“…The results indicated the high potential of PRISMA level 2D imagery in mapping water quality parameters in Lake Trasimeno. The PRISMA-based retrievals agreed closely with those of Sentinel-2, particularly for TSM [35].…”

“…The development of global data products for inland waters and the implementation of existing algorithms to address specific science questions such as water quality parameter estimations have suffered from sensor noise and uncertainties induced by inverse algorithms [33]. PRISMA, launched by the Italian space agency in 2019, has shown potential in aquatic remote sensing in coastal waters and turbid lakes; a comparison with Sentinel-2 was carried out [34,35].…”

Feasibility of the Spatiotemporal Fusion Model in Monitoring Ebinur Lake’s Suspended Particulate Matter under the Missing-Data Scenario

“…The SPM inversion model used in this study is an empirical model that does not involve physical processes. Although physical models based on radiative transfer, e.g., WASI, are also widely used in the field of SPM inversion [20,80,81], they are not restricted to SPM monitoring only. In fact, the models also play a great role in evaluating water quality, bathymetry, substratum type, and the concentrations of the optically active constituents of the water column [82].…”

Water Quality Retrieval and Algal Bloom Detection Using High-Resolution Cubesat Imagery

“…We adapt the physics-based WASI processor (Gege, 2014) for inversion of the PlanetScope imagery to derive the in-water constituents. WASI is a flexible model that can be adapted to different bio-optical conditions either in optically-shallow, i.e., considerable bottom-reflected radiance, or optically-deep waters, i.e., negligible bottom-reflected radiance (Gege, 2014;Niroumand-Jadidi et al, 2020b). WASI simulates a set of waterleaving spectra by varying different parameters (e.g., the concentration of constituents).…”