Estimating and Mapping Chlorophyll-a Concentration of Phewa Lake of Kaski District Using Landsat Imagery

Wagle, Nimisha; Pote, R.; Shahi, Rajesh K.; Lamsal, Sanjay; Thapa, S.; Acharya, Tri Dev

doi:10.5194/isprs-annals-iv-5-w2-127-2019

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…RMSE is used to evaluate the deviation between the estimated and true values, and MAE and MAPE metrics are used to measure the closeness of the estimated values to the true data. The smaller the RMSE, MAE, and MAPE are, the better the predictive performance of the model (Wagle et al, 2019). The following is the calculation for these metrics:…”

Section: Accuracy Evaluationmentioning

confidence: 99%

Mineral content estimation for salt lakes on the Tibetan plateau based on the genetic algorithm-based feature selection method using Sentinel-2 imagery: A case study of the Bieruoze Co and Guopu Co lakes

et al. 2023

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Salt lakes on the Tibetan Plateau (TP) are rich in lithium (Li), boron (B) and other mineral resources, and accurate assessment of the mineral content and spatial distribution of the brine in those salt lakes is important to guide the development and utilization of their mineral resources. There are few studies estimating the mineral content of salt lakes on the TP due to the lack of in situ investigation data. This study introduced an intelligent prediction model combining a feature selection algorithm with a machine learning algorithm using Sentinel-2 satellite data to estimate the Li, B, and TDS contents of Bieruoze Co and Guopu Co lakes on the TP. First, to enrich the spectral information, four mathematical transformations (reciprocal, logarithmic, reciprocal of logarithm, and first-order derivative) were applied to the original bands. Then, feature selection was performed using the genetic algorithm (GA) to select the optimal input variables for the model. Finally, prediction models were constructed by partial least squares regression (PLSR), multiple linear regression (MLR), and random forest (RF). The results showed that: 1) The spectral mathematical transformation provided rich spectral information for the mineral content estimation. 2) The performance of the estimation model constructed by the feature optimization method using GA was better than that of the estimation model constructed based on all spectral bands. Based on GA for feature optimization, the MAPE of GA-RF for estimating Li, B and TDS contents on the testing set was reduced by 77.52%, 28.54% and 36.79%, respectively. 3) Compared with the GA-MLR and GA-PLSR models, GA-RF estimated Li (R2=0.99, RMSE=1.15 mg L-1, MAPE=3.00%), B (R2=0.97, RMSE=10.65 mg L-1, MAPE=2.73%), and TDS (R2=0.93, RMSE=0.60 g L-1, MAPE=1.82%) all obtained the optimal performance. This study showed that the combination of the GA-based feature selection method and the RF model has excellent performance and applicability for monitoring the content of multiple minerals using Sentinel-2 imagery in salt lakes on the TP.

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Section: Accuracy Evaluationmentioning

confidence: 99%

Mineral content estimation for salt lakes on the Tibetan plateau based on the genetic algorithm-based feature selection method using Sentinel-2 imagery: A case study of the Bieruoze Co and Guopu Co lakes

et al. 2023

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“…(5) Over time, there has been a shift from traditional in situ measurement to remote sensing (RS) techniques. (6)(7)(8)(9) RS technology uses spaceborne or airborne sensors to measure the amount of radiation at various wavelengths reflected from the water's surface and extract information from it. (10) The reflections can be used directly or indirectly to determine different water quality parameters.…”

Section: Introductionmentioning

confidence: 99%

“…(13) As the best solution, a combination of ML and satellite RS data is a powerful approach for the routine assessment of spatial and temporal variations in water quality parameters and may offer a suitable method to integrate water quality data collected from traditional in situ measurements. (8) ML algorithms help to estimate water quality parameters in less time and to provide a real-time measurement. (14) ML algorithms in cooperation with RS imagery reduce the human effort of analyzing big data and are highly cost-effective while producing very accurate results.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Review on Application of Machine Learning Algorithms for Water Quality Parameter Estimation Using Remote Sensing Data

Wagle¹,

Acharya²,

Lee³

2020

Sensors and Materials

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Water is an integral aspect of the world necessary for living creatures to thrive. Owing to unplanned urbanization, rapid industrialization, and uncontrollable human intervention, water quality is gradually degrading. This affects not only marine animals but also humans. Thus, the quality of water should be examined regularly. Water quality parameters should be estimated to monitor water quality. In general, water quality parameters are measured by in situ measurements. Although these measurements are accurate, they are costly and do not provide real-time spatial and temporal changes in water quality. To overcome this limitation, water quality parameters can be estimated using machine learning (ML) along with remote sensing (RS) data. A combination of ML and RS data is a powerful approach for the routine assessment of spatial and temporal variations in water quality parameters. In this paper, some articles based on this approach are reviewed. By analyzing the literature, it was found that the integrated use of RS-based geospatial data with ML helps to produce an accurate result. Most of the authors used the regression algorithm in the estimation of the water quality parameters, with a support vector machine (SVM) regression intensively used. The artificial neural network (ANN) algorithm was the most used algorithm of ML in many of the studies. The researchers used multispectral images for their study. By applying ML to RS data, water quality monitoring systems are evolving into real-time artificial intelligence (AI)-enabled models that provide valuable recommendations and insights to support farmers to make decisions and take action in aquaculture.

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Performance of Sentinel-2 and Landsat-8 satellites in estimating Chlorophyll-a concentration in a shallow freshwater lake

Karimi

Hashemi

Aghighi

2022

Preprint

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Remote sensing for monitoring chlorophyll-a (Chl-a) concentration, as a eutrophication indicator, is known as a proper and cost-effective way due to its high temporal and spatial coverage and providing historical data. However, its application in shallow small water bodies is challenging due to its limited spatial and temporal resolutions and effect of depth on accuracy of retrieval models and estimated Chl-a concentration. In this study, the performance of Landsat-8 and Sentinel-2 satellites was investigated for estimating Chl-a concentration in Chitgar Lake in Tehran. The models were constructed using 73 and 122 common data pairs between field observations and Landsat-8 and Sentinel-2 images, respectively. After preprocessing and processing of the satellites’ images, the two bands (2BAND) algorithm (green and red band ratio, R2 = 0.8, RMSE = 1.12 µg.L− 1 and NRMSE = 12.4%) was recognized as the best Landsat-8 based model, and both Normalized Difference Chlorophyll Index (NDCI) algorithm (red and red edge bands, R2 = 0.82, RMSE = 1.29 µg.L− 1, NRMSE = 7.8%) and 2BAND algorithm (red and red edge band ratio, R2 = 0.81, RMSE = 1.28 µg.L− 1 and NRMSE = 7.7%) were the best models for Sentinel-2, all with power = 0.8. Overall, both satellites demonstrated an appropriate performance and can estimate Chl-a concentration with proper accuracy in Chitgar lake as a shallow freshwater body.

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Estimating and Mapping Chlorophyll-a Concentration of Phewa Lake of Kaski District Using Landsat Imagery

Cited by 4 publications

References 14 publications

Mineral content estimation for salt lakes on the Tibetan plateau based on the genetic algorithm-based feature selection method using Sentinel-2 imagery: A case study of the Bieruoze Co and Guopu Co lakes

Mineral content estimation for salt lakes on the Tibetan plateau based on the genetic algorithm-based feature selection method using Sentinel-2 imagery: A case study of the Bieruoze Co and Guopu Co lakes

Comprehensive Review on Application of Machine Learning Algorithms for Water Quality Parameter Estimation Using Remote Sensing Data

Performance of Sentinel-2 and Landsat-8 satellites in estimating Chlorophyll-a concentration in a shallow freshwater lake

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