Lake surface temperature retrieved from Landsat satellite series (1984 to 2021) for the North Slave Region

Attiah, Gifty; Pour, Homa Kheyrollah; Scott, K. Andrea

doi:10.5194/essd-15-1329-2023

Cited by 14 publications

(4 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Single bands are widely used to correlate with in situ data and estimate water quality parameters, such as chlorophyll [30,53], total suspended solids [54], turbidity [55], and temperature [56]. Surface reflectance values have shown good performance in these estimations and are even being used in artificial intelligence [57,58], including in algal bloom detection [24,32,55,59].…”

Section: Preprocessing Of Landsat 8 Satellite Imagesmentioning

confidence: 99%

Estimation of Water Quality Parameters through a Combination of Deep Learning and Remote Sensing Techniques in a Lake in Southern Chile

Rodríguez-López,

Usta,

Duran-Llacer

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

In this study, we combined machine learning and remote sensing techniques to estimate the value of chlorophyll-a concentration in a freshwater ecosystem in the South American continent (lake in Southern Chile). In a previous study, nine artificial intelligence (AI) algorithms were tested to predict water quality data from measurements during monitoring campaigns. In this study, in addition to field data (Case A), meteorological variables (Case B) and satellite data (Case C) were used to predict chlorophyll-a in Lake Llanquihue. The models used were SARIMAX, LSTM, and RNN, all of which showed generally good statistics for the prediction of the chlorophyll-a variable. Model validation metrics showed that all three models effectively predicted chlorophyll as an indicator of the presence of algae in water bodies. Coefficient of determination values ranging from 0.64 to 0.93 were obtained, with the LSTM model showing the best statistics in any of the cases tested. The LSTM model generally performed well across most stations, with lower values for MSE (<0.260 (μg/L)2), RMSE (<0.510 ug/L), MaxError (<0.730 μg/L), and MAE (<0.442 μg/L). This model, which combines machine learning and remote sensing techniques, is applicable to other Chilean and world lakes that have similar characteristics. In addition, it is a starting point for decision-makers in the protection and conservation of water resource quality.

show abstract

Section: Preprocessing Of Landsat 8 Satellite Imagesmentioning

confidence: 99%

Estimation of Water Quality Parameters through a Combination of Deep Learning and Remote Sensing Techniques in a Lake in Southern Chile

Rodríguez-López,

Usta,

Duran-Llacer

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…The quality assurance (QA) band delineating the surface, atmospheric, and sensor conditions included in the Landsat data was used to mask clouds and other obstacles 25 . Cloud pixels with high (67% – 1 00%) and medium (34% – 66%) coverage values were masked, whereas low values (0% – 33%) were saved as cloud-free 23 . Cloud masks were further applied to reduce cloud-distorted pixels.…”

Section: Methodsmentioning

confidence: 99%

“…The process of transportation and sample pre-processing may lead to delayed measurements. In addition, due to multi-year climatic oscillations, even long-term field experiments miss potential phases of these climatic cycles that may affect the temporal patterns of trophic states, causing both spatial and temporal observation constraints 23 .…”

Section: Background and Summarymentioning

confidence: 99%

A dataset of trophic state index for nation-scale lakes in China from 40-year Landsat observations

Hu,

Ma,

Xue

et al. 2024

Sci Data

View full text Add to dashboard Cite

Trophic state index (TSI) serves as a key indicator for quantifying and understanding the lake eutrophication, which has not been fully explored for long-term water quality monitoring, especially for small and medium inland waters. Landsat satellites offer an effective complement to facilitate the temporal and spatial monitoring of multi-scale lakes. Landsat surface reflectance products were utilized to retrieve the annual average TSI for 2693 lakes over 1 km2 in China from 1984 to 2023. Our method first distinguishes lake types by pixels with a decision tree and then derives relationships between trophic state and algal biomass index. Validation with public reports and existing datasets confirmed the good consistency and reliability. The dataset provides reliable annual TSI results and credible trends for lakes under different area scales, which can serve as a reference for further research and provide convenience for lake sustainable management.

show abstract

“…Globally, multiple studies have made efforts to synthesize various sources of LST to produce a comprehensive LST dataset on a global scale [14] and on sub-global scales such as for the lakes located in Europe [15], United States (US) [16], France [17], Tibetan Plateau [18,19], North Slave region [20], Alpine region [21] and sub-Alpine region [22]. The objective for creating these datasets was to offer the scientific community an alternative, extensive, and reliable LST dataset that could be used for multiple purposes such as enhancing the understanding of lake limnology and weather events, evaluating the effects of climate change, and validating models [14,16].…”

Section: Introductionmentioning

confidence: 99%

Reconstructing 42 Years (1979–2020) of Great Lakes Surface Temperature through a Deep Learning Approach

Kayastha,

Liu,

Titze

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

Accurate estimates for the lake surface temperature (LST) of the Great Lakes are critical to understanding the regional climate. Dedicated lake models of various complexity have been used to simulate LST but they suffer from noticeable biases and can be computationally expensive. Additionally, the available historical LST datasets are limited by either short temporal coverage (<30 years) or lower spatial resolution (0.25° × 0.25°). Therefore, in this study, we employed a deep learning model based on Long Short-Term Memory (LSTM) neural networks to produce a daily LST dataset for the Great Lakes that spans an unparalleled 42 years (1979–2020) at a spatial resolution of ~1 km. In our dataset, the Great Lakes are represented by ~33,000 unstructured grid points and the LSTM training incorporated the information from each grid point. The LSTM was trained with seven meteorological variables from reanalysis data as feature variables and the LST from a historical satellite-derived dataset as the target variable. The LSTM was able to capture the spatial heterogeneity of LST in the Great Lakes well and exhibited high correlation (≥0.92) and low bias (limited to ±1.5 °C) for the temporal evolution of LST during the training (1995–2020) and testing (1979–1994) periods.

show abstract

Lake surface temperature retrieved from Landsat satellite series (1984 to 2021) for the North Slave Region

Cited by 14 publications

References 45 publications

Estimation of Water Quality Parameters through a Combination of Deep Learning and Remote Sensing Techniques in a Lake in Southern Chile

Estimation of Water Quality Parameters through a Combination of Deep Learning and Remote Sensing Techniques in a Lake in Southern Chile

A dataset of trophic state index for nation-scale lakes in China from 40-year Landsat observations

Reconstructing 42 Years (1979–2020) of Great Lakes Surface Temperature through a Deep Learning Approach

Contact Info

Product

Resources

About