Parametrization and calibration of a quasi-analytical algorithm for tropical eutrophic waters

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• 作者：Fernanda Watanabe^a ; Deepak R. Mishra^b ; Ike Astuti^b ; Thanan Rodrigues^a ; Enner Alcâ ; ntara^a ; ^{ennerha@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Nilton N. Imai^a ; Clá ; udio Barbosa^c
• 关键词：Quasi-analytical algorithm ; Inland waters ; Algal bloom ; Bio-optical model ; Remote sensing reflectance ; Inherent optical properties
• 刊名：ISPRS Journal of Photogrammetry and Remote Sensing
• 出版年：2016
• 出版时间：November 2016
• 年：2016
• 卷：121
• 期：Complete
• 页码：28-47
• 全文大小：4400 K

文摘

Quasi-analytical algorithm (QAA) was designed to derive the inherent optical properties (IOPs) of water bodies from above-surface remote sensing reflectance (R_rs). Several variants of QAA have been developed for environments with different bio-optical characteristics. However, most variants of QAA suffer from moderate to high negative IOP prediction when applied to tropical eutrophic waters. This research is aimed at parametrizing a QAA for tropical eutrophic water dominated by cyanobacteria. The alterations proposed in the algorithm yielded accurate absorption coefficients and chlorophyll-a (Chl-a) concentration. The main changes accomplished were the selection of wavelengths representative of the optically relevant constituents (ORCs) and calibration of values directly associated with the pigments and detritus plus colored dissolved organic material (CDM) absorption coefficients. The re-parametrized QAA eliminated the retrieval of negative values, commonly identified in other variants of QAA. The calibrated model generated a normalized root mean square error (NRMSE) of 21.88% and a mean absolute percentage error (MAPE) of 28.27% for a_t(λ), where the largest errors were found at 412 nm and 620 nm. Estimated NRMSE for a_CDM(λ) was 18.86% with a MAPE of 31.17%. A NRMSE of 22.94% and a MAPE of 60.08% were obtained for a_φ(λ). Estimated a_φ(665) and a_φ(709) was used to predict Chl-a concentration. a_φ(665) derived from QAA for Barra Bonita Hydroelectric Reservoir (QAA_BBHR) was able to predict Chl-a accurately, with a NRMSE of 11.3% and MAPE of 38.5%. The performance of the Chl-a model was comparable to some of the most widely used empirical algorithms such as 2-band, 3-band, and the normalized difference chlorophyll index (NDCI). The new QAA was parametrized based on the band configuration of MEdium Resolution Imaging Spectrometer (MERIS), Sentinel-2A and 3A and can be readily scaled-up for spatio-temporal monitoring of IOPs in tropical waters.