assessment of scattering error correction techniques for ac-s meter in a tropical eutrophic reservoir

assessment of scattering error correction techniques for ac-s meter in a tropical eutrophic reservoir

;Fernanda Watanabe;Thanan Rodrigues;Alisson do Carmo;Enner Alcântara;Milton Shimabukuro;Nilton Imai;Nariane Bernardo;Luiz Henrique Rotta
Journal of pharmacological sciences 2018 Vol. 10 pp. 740-
104
watanabe2018remoteassessment

Abstract

Measurements of absorption coefficients (a(λ), in m−1) collected by spectrophotometers in situ are overestimated due to the scattering of the reflecting tube absorption meter. Accurate correction of these data is essential in order to characterize water bodies bio-optically, as well as retrieve the remote sensing reflectance (Rrs, in sr−1), when applying a forward model. There are various methods of scattering error correction; however, they were all developed for clear water. In this research, different techniques were attempted in order to define the most appropriate method for correcting a(λ) values acquired by an absorption and attenuation spectral (ac-s) meter (WET Labs Inc., Philomath, OR, USA) in a tropical eutrophic reservoir. Three methods recommended by the manufacturer of the ac-s meter were tested: “flat” or “baseline”, “constant fraction”, and “proportional”. These methods were applied to two datasets that were measured in May and October 2014. The flat technique exhibited the lowest errors, with an average normalized root mean square error (NRMSE) of 7.95%, and a mean absolute percentage error (MAPE) of 29.26% for May. Meanwhile, proportional was the most suitable technique for most of the samples in October, with a mean NRMSE of 11.19% and a MAPE of 31.03% for October. In addition, the proportional method maintained the shape of the a(λ) values better than the other methods. Despite that, both the flat and proportional methods gave a similar performance statistically. Moreover, the flat method produced the best estimations of chla content for both datasets. Therefore, this method is recommended to correct ac-s data in retrieving such phytoplankton pigments.

Citation

ID: 251467
Ref Key: watanabe2018remoteassessment
Use this key to autocite in SciMatic or Thesis Manager

References

Blockchain Verification

Account:
NFT Contract Address:
0x95644003c57E6F55A65596E3D9Eac6813e3566dA
Article ID:
251467
Unique Identifier:
10.3390/rs10050740
Network:
Scimatic Chain (ID: 481)
Loading...
Blockchain Readiness Checklist
Authors
Abstract
Journal Name
Year
Title
5/5
Creates 1,000,000 NFT tokens for this article
Token Features:
  • ERC-1155 Standard NFT
  • 1 Million Supply per Article
  • Transferable via MetaMask
  • Permanent Blockchain Record
Blockchain QR Code
Scan with Saymatik Web3.0 Wallet

Saymatik Web3.0 Wallet