Enhance field water-color measurements with a Secchi disk and its implication for fusion of active and passive ocean-color remote sensing

Zhongping Lee; Shaoling Shang; Keping Du; Bingyi Liu; Gong Lin; Jianwei Wei; Xiaolong Li

doi:10.1364/AO.57.003463

Applied Optics
Vol. 57,
Issue 13,
pp. 3463-3473
(2018)
•https://doi.org/10.1364/AO.57.003463

Enhance field water-color measurements with a Secchi disk and its implication for fusion of active and passive ocean-color remote sensing

Zhongping Lee, Shaoling Shang, Keping Du, Bingyi Liu, Gong Lin, Jianwei Wei, and Xiaolong Li

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Zhongping Lee, Shaoling Shang, Keping Du, Bingyi Liu, Gong Lin, Jianwei Wei, and Xiaolong Li, "Enhance field water-color measurements with a Secchi disk and its implication for fusion of active and passive ocean-color remote sensing," Appl. Opt. 57, 3463-3473 (2018)

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- Atmospheric and Oceanic Optics
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About this Article
History
- Original Manuscript: February 16, 2018
- Revised Manuscript: March 25, 2018
- Manuscript Accepted: March 25, 2018
- Published: April 26, 2018

Abstract

Inversion of the total absorption ( $a$ ) and backscattering coefficients of bulk water through a fusion of remote sensing reflectance ( $R_{rs}$ ) and Secchi disk depth ( $Z_{SD}$ ) is developed. An application of such a system to a synthesized wide-range dataset shows a reduction of $\sim 3$ folds in the uncertainties of inverted $a (λ)$ (in a range of $\sim 0.01 - 6.8 m^{- 1}$ ) from $R_{rs} (λ)$ for the 350–560 nm range. Such a fusion is further proposed to process concurrent active (ocean LiDAR) and passive (ocean-color) measurements, which can lead to nearly “exact” analytical inversion of an $R_{rs}$ spectrum. With such a fusion, it is found that the uncertainty in the inverted total $a$ in the 350–560 nm range could be reduced to $\sim 2 %$ for the synthesized data, which can thus significantly improve the derivation of $a$ coefficients of other varying components. Although the inclusion of $Z_{SD}$ places an extra constraint in the inversion of $R_{rs}$ , no apparent improvement over the quasi-analytical algorithm (QAA) was found when the fusion of $Z_{SD}$ and $R_{rs}$ was applied to a field dataset, which calls for more accurate determination of the absorption coefficients from water samples.

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	$K_{d} (490)$		$K_{d} (550)$		$K_{d} (640)$
Range of $K_{d}^{tr}$ (or $Z_{SD}$ )	$α$	$β$ ( $m^{- 1}$ )	$α$	$β$ ( $m^{- 1}$ )	$α$	$β$ ( $m^{- 1}$ )
$K_{d}^{tr} < 0.04 m^{- 1}$ ( $Z_{SD} > 25 m$ )	0.598	0.014
$0.04 < K_{d}^{tr} < 0.14 m^{- 1}$ ( $7 < Z_{SD} < 25 m$ )			0.599	0.052
$K_{d}^{tr} > 0.14 m^{- 1}$ ( $Z_{SD} < 7 m$ )					0.813	0.277

	355 nm ( $\sim 0.01 - 6.2$ )	410 nm ( $\sim 0.01 - 2.7$ )	440 nm ( $\sim 0.01 - 1.8$ )	490 nm ( $\sim 0.02 - 0.9$ )	350–560 nm ( $\sim 0.01 - 6.8$ )
“Error-free” data
$Z_{SD} - QAA$	7.4	6.0	6.8	5.1	6.1
QAA_v6	17.3	18.2	19.8	15.9	16.6
After assigning, randomly, $\pm 10 %$ error in $Z_{SD}$
$Z_{SD} - QAA$	7.2	7.0	7.4	6.9	7.5

	355 nm ( $\sim 0.002 - 0.03$ )	440 nm ( $\sim 0.0014 - 0.03$ )	550 nm ( $\sim 0.0009 - 0.03$ )	350–560 nm ( $\sim 0.0009 - 0.03$ )
“Error-free” data
$Z_{SD} - QAA$	12.7	9.7	6.5	8.7
QAA_v6	21.6	19.7	17.4	18.4
After assigning, randomly, $\pm 10 %$ error in $Z_{SD}$
$Z_{SD} - QAA$	14.2	12.1	10.8	11.7

	410 nm ( $\sim 0.01 - 6.3$ )	440 nm ( $\sim 0.01 - 4.5$ )	490 nm ( $\sim 0.02 - 2.8$ )	550 nm ( $\sim 0.06 - 1.3$ )	400–560 nm ( $\sim 0.01 - 6.3$ )
$Z_{SD} - QAA$	26.5	23.4	21.2	13.1	19.2
QAA_v6	24.4	20.7	17.8	11.0	17.7

	355 nm	410 nm	440 nm	490 nm	350–560 nm
“Error-free” data
QAA_v6	17.3	18.2	19.8	15.9	16.6
LiDAR-QAA_355–532	2.7	1.6	3.2	1.7	2.2
LiDAR-QAA_532	9.1	7.6	8.8	3.5	5.7
After assigning, randomly, $\pm 5 %$ error to $K_{LiDAR}$
LIDAR-QAA_355–532	4.9	3.9	4.2	3.8	4.4
LIDAR-QAA_532	9.2	7.9	8.9	5.0	6.7

	355 nm	440 nm	550 nm	350–560 nm
“Error-free” data
QAA_v6	21.6	19.7	17.4	18.4
LiDAR-QAA_355–532	8.3	3.7	2.5	4.1
LiDAR-QAA_532	9.4	6.6	5.4	6.0
After assigning, randomly, $\pm 5 %$ error to $K_{LiDAR}$
LIDAR-QAA_355–532	9.9	6.1	7.1	6.9
LIDAR-QAA_532	10.6	8.3	7.0	7.9

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Applied Optics