Retrieval of water quality from airborne imaging spectrometry of various lake types in different seasons

Kallio, Kari; Kutser, Tiit; Hannonen, T.; Koponen, Sampsa; Pulliainen, Jouni; Vepsäläinen, J.; Pyhälahti, Timo

doi:10.1016/s0048-9697(00)00685-9

Cited by 187 publications

(104 citation statements)

References 24 publications

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“…In contrast, the turbidity algorithm uses an empirical single-band model (hereinafter referred to as the T1 model) [24,25] and a 2-BM (hereinafter referred to as the T2 model) [26,27] using red to near-infrared bands as follows.…”

Section: Chlorophyll-a and Turbidity Algorithmsmentioning

confidence: 99%

“…For example, methods such as two-band algorithms [18,19], three-band algorithms [20,21] and four-band algorithms [22,23] have been proposed to measure chlorophyll-a (Chla). For measuring turbidity (Turb) or total suspended solids (TSS), which is an indicator of turbidity, methods such as one-band algorithms [24,25] and two-band algorithms [26,27] have been proposed. Furthermore, Chla and turbidity algorithms, such as SCI (Synthetic Chlorophyll Index), which applied fluorescence line height [28] using the fluorescence band for more turbid conditions and a turbidity algorithm [29] using an 810-nm reflection peak under rich colored dissolved organic matter (CDOM) conditions, have been proposed.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Unified Algorithms for Remote Sensing of Chlorophyll-a and Turbidity in Lake Shinji and Lake Nakaumi of Japan and the Vaal Dam Reservoir of South Africa under Eutrophic and Ultra-Turbid Conditions

Sakuno

Yajima

Yoshioka

et al. 2018

Water

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Abstract:We evaluated unified algorithms for remote sensing of chlorophyll-a (Chla) and turbidity in eutrophic and ultra-turbid waters such as Japan's Lake Shinji and Lake Nakaumi (SJNU) and the Vaal Dam Reservoir (VDR) in South Africa. To realize this objective, we used 38 remote sensing reflectance (R rs ), Chla and turbidity datasets collected in these waters between July 2016 and March 2017. As a result, we clarified the following items. As a unified Chla model, we obtained strong correlation (R 2 = 0.7, RMSE = 2 mg m −3 ) using a two-band model (2-BM) and three-band model (3-BM), with R rs (687)/R rs (672) and [R rs −1 (687) − R rs −1 (672)] × R rs (832). As a unified turbidity model, we obtained strong correlation (R 2 = 0.7, RMSE = 260 NTU) using 2-BM and 3-BM, with R rs (763)/R rs (821) and R rs (810) − [R rs (730) + R rs (770)]/2. When targeting the Sentinel-2 Multispectral Imager (MSI) frequency band, we focused on MSI Bands 4 and 5 (R rs (740) and R rs (775)) for the Chla algorithm. When optically separating SJNU and VDR data, it is effective to use the slopes of MSI Bands 3 and 4 (R rs (560) and R rs (665)) and the slopes of MSI Bands 7 and 9 (R rs (775) and R rs (865)).

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Section: Chlorophyll-a and Turbidity Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of Unified Algorithms for Remote Sensing of Chlorophyll-a and Turbidity in Lake Shinji and Lake Nakaumi of Japan and the Vaal Dam Reservoir of South Africa under Eutrophic and Ultra-Turbid Conditions

Sakuno

Yajima

Yoshioka

et al. 2018

Water

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“…However, even if the satellite spatial resolution is improving and it is suitable for the open ocean, it is less accurate in coastal and inland waters (Blondeau-Patissier et al 2004;Gohin et al 2005). Relatively complex radiometers, airborne or at ground level, are used to improve the resolution and are used to validate satellite images (Kallio et al 2001;Deschamps et al 2004;Nechad et al 2010). However, these actions are expensive and laborious.…”

mentioning

confidence: 99%

WACODI: A generic algorithm to derive the intrinsic color of natural waters from digital images

Novoa

Wernand

Woerd

2015

Limnology & Ocean Methods

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This document presents the WAter COlor from Digital Images (WACODI) algorithm, which extracts the color of natural waters from images collected by low-cost digital cameras, in the context of participatory science and water quality monitoring. SRGB images are converted to the CIE XYZ color space, undergoing a gamma expansion and illumination correction that includes the specular reflection at the air-water interface.The XYZ values obtained for each pixel of the image are converted to chromaticity coordinates and Hue color angle (a w ), which is a measure of color. Based on the distributions of a w in sub-sections of the image, an approximation of the intrinsic color of the water is obtained. This algorithm was applied to images acquired in 2013 during two field campaigns in Northern Europe. The Hue color angles were derived from hyperspectral measurements above and below the surface, carried out simultaneously with image acquisition. When for each station a specific illumination correction was applied, based on the corresponding hyperspectral data, a good fit (r 2 5 0.93) was obtained between the image and the spectra Hue color angles (slope 5 0.98, intercept 5 20.03). When a more generic illumination correction was applied to the same images, based on the sky conditions at the time of the image acquisition (either overcast or sunny), a slightly inferior, but still satisfactory fit resulted. Results on the application of the WACODI algorithm to the first images collected by the public via the smartphone application or "APP," developed within the European FP7 Citclops, are presented at the end of this study.

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“…Thus, it is possible to designate spectral channels centered at specific wavelengths (e.g., Kallio et al, 2001) and at desired spectral resolutions (e.g., as fine as 2.9 nm for AISA-Eagle). The control on the timing of data acquisition enables collecting data at optimal time periods and helps in mitigating adverse weather conditions such as cloud cover and haze, which often hinder reliable data collection from spaceborne sensors.…”

Section: Introductionmentioning

confidence: 99%

“…The optimal spectral ranges for the location of these wavelengths were found to be, l 1 ¼ 660e670 nm, l 2 ¼ 700e730 nm, and l 3 ¼ 740e760 nm Gitelson et al, 2008). Koponen et al (2002) and Kallio et al (2001) applied several empirical NIR-red algorithms to estimate chl-a concentration in several lakes in Finland using AISA data and discovered that the performance of the algorithms they tested varied seasonally. Such seasonal variations preclude algorithms from being used operationally in a routine manner for data from different seasons and different geographic locations.…”

Section: Introductionmentioning

confidence: 99%

Estimation of chlorophyll-a concentration in turbid productive waters using airborne hyperspectral data

et al. 2012

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Retrieval of water quality from airborne imaging spectrometry of various lake types in different seasons

Cited by 187 publications

References 24 publications

Evaluation of Unified Algorithms for Remote Sensing of Chlorophyll-a and Turbidity in Lake Shinji and Lake Nakaumi of Japan and the Vaal Dam Reservoir of South Africa under Eutrophic and Ultra-Turbid Conditions

Evaluation of Unified Algorithms for Remote Sensing of Chlorophyll-a and Turbidity in Lake Shinji and Lake Nakaumi of Japan and the Vaal Dam Reservoir of South Africa under Eutrophic and Ultra-Turbid Conditions

WACODI: A generic algorithm to derive the intrinsic color of natural waters from digital images

Estimation of chlorophyll-a concentration in turbid productive waters using airborne hyperspectral data

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