Chlorophyll-a Concentration Retrieval Using Convolutional Neural Networks in Laguna Lake, Philippines

Syariz, Muhammad Aldila; Lin, Ching Hsuan; Blanco, A. C.

doi:10.5194/isprs-archives-xlii-4-w19-401-2019

Cited by 3 publications

(3 citation statements)

References 16 publications

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“…The study also revealed that the optimal ANN model outclassed the other machine learning methods, including random forest, cubist regression, and support vector regression, in terms of Chla concentration estimation. Furthermore, several researchers utilized convolutional neural networks (CNNs), which consider neighborhood spectral information in Chla concentration modelling using convolutional layers with 3D kernels [26][27][28][29].…”

Section: More Than 56mentioning

confidence: 99%

A Transfer Learning Technique for Inland Chlorophyll-a Concentration Estimation Using Sentinel-3 Imagery

et al. 2021

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Chlorophyll-a (Chla) concentration, which serves as a phytoplankton substitute in inland waters, is one of the leading indicators for water quality. Generally, water samples are analyzed in professional laboratories, and Chla concentrations are measured regularly for the purpose of water quality monitoring. However, limited spatial water sampling and the labor-intensive nature of data collection make global and long-term monitoring difficult. The developments of remote-sensing optical sensors and technologies make the long-term monitoring of Chla concentrations for an entire water body more achievable. Many studies based on machine learning techniques, such as regression and artificial neural network (ANN) methods, have recently been proposed for Chla concentration estimation using optical satellite images. The methods based on machine learning can achieve accurate estimation. However, overfitting problems may arise because the in situ Chla dataset is generally insufficient to train a complicated machine learning model, which makes trained models inapplicable. In this study, an ANN model containing three convolutional and two fully connected layers with 4953 unknown parameters is designed. A transfer learning method, consisting of model pretraining, main-training, and fine-tuning stages, is proposed to ease the problem of insufficient in situ samples. In the model pretraining stage, the ANN model is pretrained and initialized using samples derived from an existing Chla concentration model. The pretrained ANN model is then fine-tuned using the proposed transfer learning technique with in situ samples collected in five different campaigns carried out during early 2019 from Laguna Lake, the Philippines. Before the transfer learning, data augmentation and rebalancing methods are conducted to enrich the variability and to near-uniformly distribute the in situ samples in Chla concentration space, respectively. To estimate the alleviation of model overfitting, the trained ANN model, using an in situ dataset from Laguna Lake, was tested using an in situ dataset from Lake Victoria, Uganda, obtained in 2019, which has a similar trophic state as Laguna Lake. The experimental results from Sentinel-3 imagery indicated that the overfitting problem was significantly alleviated and the trained ANN model outperformed related models in terms of the root-mean-squared error of the estimated Chla concentrations.

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Section: More Than 56mentioning

confidence: 99%

A Transfer Learning Technique for Inland Chlorophyll-a Concentration Estimation Using Sentinel-3 Imagery

et al. 2021

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“…Several studies have also investigated the case of Chl-a concentration in Laguna Lake. Specifically, Blanco et al (2020) used regression analysis of Sentinel-3 OLCI images to produce algal classification maps, Syariz et al (2019) used neural networks trained on the same Sentinel-3 data to produce models that out-perform existing 3-band and 2-band models in terms of accuracy, and Jalbuena et al ( 2019) inputted Landsat-8 data in the Bio-Optical Model Based tool for Estimating water quality and bottom properties from Remote sensing images (BOMBER) tool and processed this data through the Water Color Simulator (WASI) tool to produce Chl-a maps.…”

Section: Introductionmentioning

confidence: 99%

Modelling the Chlorophyll-a Concentration of Laguna Lake Using Himawari-8 Satellite Imagery and Machine Learning Algorithms for Near Real Time Monitoring

Martínez¹,

Argamosa

Torres

et al. 2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Recent studies have investigated the use of satellite imaging combined with machine learning for modelling the Chlorophyll-a (Chl-a) concentration of bodies of water. However, most of these studies use satellite data that lack the temporal resolution needed to monitor dynamic changes in Chl-a in productive lakes like Laguna Lake. Thus, the aim of this paper is to present the methodology for modelling the Chl-a concentration of Laguna Lake in the Philippines using satellite imaging and machine learning algorithms. The methodology uses images from the Himawari-8 satellite, which have a spatial resolution of 0.5–2 km and are taken every 10 minutes. These are converted into a GeoTIFF format, where differences in spatial resolution are resolved. Additionally, radiometric correction, resampling, and filtering of the Himawari-8 bands to exclude cloud-contaminated pixels are performed. Subsequently, various regression and gradient boosting machine learning algorithms are applied onto the train dataset and evaluated, namely: Simple Linear Regression, Ridge Regression, Lasso Regression, and Light Gradient Boosting Model (LightGBM). The results of this study show that it is indeed possible to integrate algorithms in Machine Learning in modelling the near real-time variations in Chl-a content in a body of water, specifically in the case of Laguna Lake, to an acceptable margin of error. Specifically, the regression models performed similarly with a train RMSE of 1.44 and test RMSE of 2.51 for Simple Linear Regression and 2.48 for Ridge and Lasso Regression. The linear regression models exhibited a larger degree of overfitting than the LightGBM model, which had a 2.18 train RMSE.

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“…Artificial neural networks (ANNs) have been utilized in geospatial fields with various applications [8,[38][39][40]. ANN is called glorified regression because of the network's nonlinear modeling feature.…”

Section: Introductionmentioning

confidence: 99%

WaterNet: A Convolutional Neural Network for Chlorophyll-a Concentration Retrieval

et al. 2020

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The retrieval of chlorophyll-a (Chl-a) concentrations relies on empirical or analytical analyses, which generally experience difficulties from the diversity of inland waters in statistical analyses and the complexity of radiative transfer equations in analytical analyses, respectively. Previous studies proposed the utilization of artificial neural networks (ANNs) to alleviate these problems. However, ANNs do not consider the problem of insufficient in situ samples during model training, and they do not fully utilize the spatial and spectral information of remote sensing images in neural networks. In this study, a two-stage training is introduced to address the problem regarding sample insufficiency. The neural network is pretrained using the samples derived from an existing Chl-a concentration model in the first stage, and the pretrained model is refined with in situ samples in the second stage. A novel convolutional neural network for Chl-a concentration retrieval called WaterNet is proposed which utilizes both spectral and spatial information of remote sensing images. In addition, an end-to-end structure that integrates feature extraction, band expansion, and Chl-a estimation into the neural network leads to an efficient and effective Chl-a concentration retrieval. In experiments, Sentinel-3 images with the same acquisition days of in situ measurements over Laguna Lake in the Philippines were used to train and evaluate WaterNet. The quantitative analyses show that the two-stage training is more likely than the one-stage training to reach the global optimum in the optimization, and WaterNet with two-stage training outperforms, in terms of estimation accuracy, related ANN-based and band-combination-based Chl-a concentration models.

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Chlorophyll-a Concentration Retrieval Using Convolutional Neural Networks in Laguna Lake, Philippines

Cited by 3 publications

References 16 publications

A Transfer Learning Technique for Inland Chlorophyll-a Concentration Estimation Using Sentinel-3 Imagery

A Transfer Learning Technique for Inland Chlorophyll-a Concentration Estimation Using Sentinel-3 Imagery

Modelling the Chlorophyll-a Concentration of Laguna Lake Using Himawari-8 Satellite Imagery and Machine Learning Algorithms for Near Real Time Monitoring

WaterNet: A Convolutional Neural Network for Chlorophyll-a Concentration Retrieval

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