An Overall Evaluation of Water Transparency in Lake Malawi from MERIS Data

Vundo, Augusto; Matsushita, Bunkei; Jiang, Dianlu; Gondwe, Mangaliso J.; Hamzah, Rossi; Setiawan, Fajar; Fukushima, Takehiko

doi:10.3390/rs11030279

Cited by 21 publications

(10 citation statements)

References 37 publications

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“…Similar to study results, Sitoki et al [38], Tran et al [45], reported the SD range value of 0.8-1.7 m (Lake Kyoga) and 0.17-2.3 m (Edward), respectively. On the other hand, Stoyneva-Gärtner et al [40], Vundo et al [49] and Ozguven & Demir Yetis [32] reported an SD range significantly higher than the recorded values in the present study.…”

Section: Variability In Physical Water Quality Parameterscontrasting

confidence: 75%

Spatio-temporal variations in physicochemical water quality parameters of Lake Bunyonyi, Southwestern Uganda

et al. 2021

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The current study was carried out to examine the spatial and temporal variations of physicochemical water quality parameters of Lake Bunyonyi. The observations were made on the surface water of Lake Bunyonyi for 1 year to determine the water quality. The basic 12 variables used to determine the quality of water were measured monthly at nine stations. Water temperature, dissolved oxygen (DO), turbidity, electric conductivity (EC), pH and Secchi depth (SD) were measured in the field, while parameters like total nitrogen (TN), total phosphorus (TP), nitrite-nitrogen (NO2-N), nitrate-nitrogen (NO3-N), soluble reactive phosphorus (SRP) were determined following APHA 2017 standard guidelines for physicochemical analysis. Taking into account standard guidelines for drinking water by the Uganda National Bureau of Standards (UNBS) and the World Health Organization (WHO), the water quality index (WQI) was used to determine the water quality. Temperature, DO, pH, turbidity and EC did not differ significantly among the study stations (p > 0.05) but showed significant temporal variations among the study months (p < 0.05). Likewise, TN, TP, NO2-N, NO3-N and SRP did not differ significantly among the study stations (p > 0.05) but showed significant temporal variations among the study months (p < 0.05). The WQI values ranged from 28.36 to 49 across and from 28.2 to 56.2 between study months with an overall mean value of 36.9. The measured water quality variables did not exceed the UNBS and WHO standards for drinking water in all months and at all stations. According to these values, the water quality of Lake Bunyonyi generally belongs to the ‘good’ class in terms of drinking water quality based on the WQI classification. The study findings are fundamentally important for policy makers in setting guidelines for effective lake management.

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Section: Variability In Physical Water Quality Parameterscontrasting

confidence: 75%

Spatio-temporal variations in physicochemical water quality parameters of Lake Bunyonyi, Southwestern Uganda

et al. 2021

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“…The independent validation dataset showed the distribution of the measured and estimated SDD along the 1:1 line, with the mean relative error and normalized root mean square error of 34% and 55%, respectively, indicating that the power function model based on the red band of Landsat reflectance was suitable for estimating the SDD of China's lakes covering SDD from <0.1 m to more than 10 m. Detailed information about the calibration, validation, and assessment of the model is provided in our previous study [36]. Overall, the precision of the SDD estimation model in our study was acceptable compared to similar previous studies [22][23][24][25].…”

Section: Remote Sensing Estimation Of Sdd Before 1995 and After 2005supporting

confidence: 55%

“…In the past few decades, remote sensing is considered an important supplement or substitute to obtain an overview of the temporal-spatial patterns of SDD due to its unparalleled advantages of collecting measurements from historical and large-scale perspectives [21]. Many arduous efforts have attempted to develop a series of empirical, semi-empirical, and semi-analytical algorithms to estimate SDD and explore the longterm changes in different inland waters using Landsat, MERIS, MODIS, and other images [20,[22][23][24][25]. A decrease in water clarity is associated with a reduced water quality, the loss of macrophytes and benthic algae, a decrease in primary production, decreases in habitat and fishery production, biodiversity loss, and a decrease in recreational value [13,18,26,27].…”

Section: Introductionmentioning

confidence: 99%

Radiation dimming and decreasing water clarity fuel underwater darkening in lakes

et al. 2020

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Long-term decreases in the incident total radiation and water clarity might substantially affect the underwater light environment in aquatic ecosystems. However, the underlying mechanism and relative contributions of radiation dimming and decreasing water clarity to the underwater light environment on a national or global scale remains largely unknown. Here, we present a comprehensive dataset of unprecedented scale in China's lakes to address the combined effects of radiation dimming and decreasing water clarity on underwater darkening. Long-term total radiation and sunshine duration showed 5.8% and 7.9% decreases, respectively, after 2000 compared to 1961-1970, resulting in net radiation dimming. An in situ Secchi disk depth (SDD) dataset in 170 lakes showed that the mean SDD significantly decreased from 1.80 ± 2.19 m before 1995 to 1.28 ± 1.82 m after 2005. SDD remote sensing estimations for 641 lakes with areas ! 10 km 2 showed that SDD markedly decreased from 1.26 ± 0.62 m during 1985-1990 to 1.14 ± 0.66 m during 2005-2010. Radiation dimming and decreasing water clarity jointly caused an approximately 10% decrease in the average available photosynthetically active radiation (PAR) in the euphotic layer. Our results revealed a more important role of decreasing water clarity in underwater darkening than radiation dimming. A meta-analysis of long-term SDD observation data from 61 various waters further elucidated a global extensive underwater darkening. Underwater darkening implies a decrease in water quality for potable water supplies, recession in macrophytes and benthic algae, and decreases in benthic primary production, fishery production, and biodiversity.

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“…Since previous studies have confirmed that semianalytical Equations ( 2) and (3) are robust in waters with different optical properties if accurate a(λ) and b b (λ) are provided (e.g., [4,21,22,36,41]), our efforts were focused on how to obtain more accurate a(λ) and b b (λ) values from R rs (λ) spectra. A flowchart of the developed Z SD estimation algorithm is shown in Figure 2.…”

Section: Development Of a New Z Sd Estimation Algorithmmentioning

confidence: 99%

A Semianalytical Algorithm for Estimating Water Transparency in Different Optical Water Types from MERIS Data

2022

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Water transparency (or Secchi disk depth: ZSD) is a key parameter of water quality; thus, it is very important to routinely monitor. In this study, we made four efforts to improve a state-of-the-art ZSD estimation algorithm that was developed in 2019 on the basis of a new underwater visibility theory proposed in 2015. The four efforts were: (1) classifying all water into clear (Type I), moderately turbid (Type II), highly turbid (Type III), or extremely turbid (Type IV) water types; (2) selecting different reference wavelengths and corresponding semianalytical models for each water type; (3) employing an estimation model to represent reasonable shapes for particulate backscattering coefficients based on the water type classification; and (4) constraining likely wavelength range at which the minimum diffuse attenuation coefficient (Kdλ) will occur for each water type. The performance of the proposed ZSD estimation algorithm was compared to that of the original state-of-the-art algorithm using a simulated dataset (N = 91,287, ZSD values 0.01 to 44.68 m) and an in situ measured dataset (N = 305, ZSD values 0.3 to 16.4 m). The results showed a significant improvement with a reduced mean absolute percentage error (MAPE) from 116% to 65% for simulated data and from 32% to 27% for in situ data. Outliers in the previous algorithm were well addressed in the new algorithm. We further evaluated the developed ZSD estimation algorithm using medium resolution imaging spectrometer (MERIS) images acquired from Lake Kasumigaura, Japan. The results obtained from 19 matchups revealed that the estimated ZSD matched well with the in situ measured ZSD, with a MAPE of 15%. The developed ZSD estimation algorithm can probably be applied to different optical water types due to its semianalytical features.

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An Overall Evaluation of Water Transparency in Lake Malawi from MERIS Data

Cited by 21 publications

References 37 publications

Spatio-temporal variations in physicochemical water quality parameters of Lake Bunyonyi, Southwestern Uganda

Spatio-temporal variations in physicochemical water quality parameters of Lake Bunyonyi, Southwestern Uganda

Radiation dimming and decreasing water clarity fuel underwater darkening in lakes

A Semianalytical Algorithm for Estimating Water Transparency in Different Optical Water Types from MERIS Data

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