A system to measure the data quality of spectral remote sensing reflectance of aquatic environments

Abstract

Spectral remote‐sensing reflectance (Rrs, sr−1) is the key for ocean color retrieval of water bio‐optical properties. Since Rrs from in situ and satellite systems are subject to errors or artifacts, assessment of the quality of Rrs data is critical. From a large collection of high quality in situ hyperspectral Rrs data sets, we developed a novel quality assurance (QA) system that can be used to objectively evaluate the quality of an individual Rrs spectrum. This QA scheme consists of a unique Rrs spectral reference and a score metric. The reference system includes Rrs spectra of 23 optical water types ranging from purple blue to yellow waters, with an upper and a lower bound defined for each water type. The scoring system is to compare any target Rrs spectrum with the reference and a score between 0 and 1 will be assigned to the target spectrum, with 1 for perfect Rrs spectrum and 0 for unusable Rrs spectrum. The effectiveness of this QA system is evaluated with both synthetic and in situ Rrs spectra and it is found to be robust. Further testing is performed with the NOMAD data set as well as with satellite Rrs over coastal and oceanic waters, where questionable or likely erroneous Rrs spectra are shown to be well identifiable with this QA system. Our results suggest that applications of this QA system to in situ data sets can improve the development and validation of bio‐optical algorithms and its application to ocean color satellite data can improve the short‐term and long‐term products by objectively excluding questionable Rrs data.

Improving Satellite Global Chlorophyll a Data Products Through Algorithm Refinement and Data Recovery

“…The reason for data scarcity is possibly due to the remote locations, while the reason for erroneous data may possibly be due to difficulty in obtaining reliable in situ R rs (λ) data and due to the requirement of large volume of waters to be filtered to obtain a measurable signal from these extremely low‐Chl waters. Indeed, even after applying additional quality control to the NOMAD R rs (λ) data using a recently developed scoring system (Wei et al, ), it is still impossible to determine which measurements from ocean gyres are more trustable than others, leading to no difference in the statistical regressions. Therefore, more high‐quality Chl and R rs (λ) data need to be collected from ocean gyres, especially from waters with Chl < 0.05 (or even <0.03 mg m −3 ), in order to determine the lower bound of Chl in algorithm development.…”

Semianalytical Derivation of Phytoplankton, CDOM, and Detritus Absorption Coefficients From the Landsat 8/OLI Reflectance in Coastal Waters

“…To estimate R rs (412) from the OLI R rs ( λ 1‐4 ) data, a lookup table (LUT) for the R rs spectral shapes centered at λ 0‐4 was created from two hyperspectral R rs data sets. The first set of data were in situ measurements (400‐800 nm with 3‐nm increment) collected from the global waters; a detailed description can be found in Wei et al (). The second data set was simulated with the Hydrolight radiative transfer simulation software (version 5.1; Mobley & Sundman, ).…”

“…Each individual five‐band R rs spectrum was then normalized by the root of the sum of squares (RSS) of R rs values from λ 0 to λ 4 as in Wei, Lee, and Shang (), where is the normalized R rs spectrum. All such normalized spectra are further illustrated in Figure b.…”

“…Each individual five-band R rs spectrum was then normalized by the root of the sum of squares (RSS) of R rs values from λ 0 to λ 4 as in Wei, Lee, and Shang (2016),…”

Glint Removal Assessment to Estimate the Remote Sensing Reflectance in Inland Waters with Widely Differing Optical Properties

“…The glint component did not provide information from an underwater light field, then, it might be removed from R rs to the retrieve optical information about the optical components within a water column [9,13,14]. There are some strategies to minimize the surface reflected light, such as: (i) to take measurements below the water surface (upwelling radiance, L u ) and then extrapolate it to above water [15,16]; and (ii) to plug a black cone into the sensor's head to protect the readings from the surface reflection (Skylight-blocked approach-SBA in [17]). Both strategies lead to particular constraints in regards to calculus approximations and the self-shadowing of the cone, respectively.…”