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Pasang surut adalah fluktuasi muka air laut sebagai fungsi waktu karena adanya gaya tarik benda-benda di langit, terutama matahari dan bulan terhadap massa air laut di bumi. Selain dengan adanya pengamatan tradisional menggunakan tide gauge, komponen pasang surut dapat ditentukan menggunakan satelit altimetri. Penggunaan tide gauge, hasil ekstraksi komponen pasang surut hanya dapat dilakukan secara akurat karena memiliki resolusi temporal yang sangat baik meskipun hanya tersedia pada titik-titik stasiun tersebut didirikan saja. Hal ini berkebalikan dengan yang menggunakan satelit altimetri. Data yang didapatkan dari satelit altimetri perlu diolah agar mendapatkan nilai sea surface height yang selanjutnya digunakan untuk penentuan komponen pasang surut dengan analisis harmonik metode kuadrat terkecil pada banyak titik yang berada di permukaan bumi. Akan tetapi, resolusi temporal yang lebih rendah daripada tide gauge juga menjadi batasan dalam pengestraksian komponen pasut. Tujuan penelitian adalah mengekstraksi sea surface height data satelit altimetri Jason-3 dan membandingkan hasil ekstraksi tersebut dengan pengamatan manual. Hasil dari penelitian ini adalah nilai amplitudo dan fase dari tiap komponen pasang surut. Kemudian dilakukan perbandingan dengan hasil komponen dari data pengamatan stasiun pasang surut. Didapatkan bahwa perbedaan rata-rata (RMSE) terbesar sebesar 0,360 m pada komponen pasang surut K1 dan RMSE terkecil yaitu sebesar 0,003 m pada komponen pasang surut M4.

Land cover maps are important documents for local governments to perform urban planning and management. A field survey using measuring instruments can produce an accurate land cover map. However, this method is time-consuming, expensive, and labor-intensive. A number of researchers have proposed using remote sensing, which generates land cover maps using an optical satellite image with various statistical classification procedures. Recently, artificial intelligence (AI) technology, such as deep learning, has been used in multiple fields, including satellite image classification, with satisfactory results. In this study, a WorldView-2 image of Terangun in Aceh Province, which was acquired on Aug 2, 2016, was classified using a commonly used deep-learning-based classification, namely, U-net. There were eight classes used in the experiment: building, road, open land (such as green open space, bare land, grass, or low vegetation), river, farm, field, aquaculture pond, and garden. For comparison, three classification methods: maximum-likelihood, random forest, and support vector machine, were performed compared to U-Net. A land cover map provided by the government was used as a reference to evaluate the accuracy of land cover maps generated using two classification methods. The results with 100 randomly selected pixels revealed that U-Net was able to obtain a 72% and 0.585 for overall and kappa accuracy, respectively; whereas, overall accuracy and kappa accuracy for the maximum likelihood, random forest and support vector machine methods were 49% and 0.148; 59% and 0.392; and 67% and 0. 511; respectively. Therefore, U-Net outperformed those three of classification methods in classifying the image.

Multitemporal cross-sensor imagery is fundamental for the monitoring of the Earth’s surface over time. However, these data often lack visual consistency because of variations in the atmospheric and surface conditions, making it challenging to compare and analyze images. Various image-normalization methods have been proposed to address this issue, such as histogram matching and linear regression using iteratively reweighted multivariate alteration detection (IR-MAD). However, these methods have limitations in their ability to maintain important features and their requirement of reference images, which may not be available or may not adequately represent the target images. To overcome these limitations, a relaxation-based algorithm for satellite-image normalization is proposed. The algorithm iteratively adjusts the radiometric values of images by updating the normalization parameters (slope (α) and intercept (β)) until a desired level of consistency is reached. This method was tested on multitemporal cross-sensor-image datasets and showed significant improvements in radiometric consistency compared to other methods. The proposed relaxation algorithm outperformed IR-MAD and the original images in reducing radiometric inconsistencies, maintaining important features, and improving the accuracy (MAE = 2.3; RMSE = 2.8) and consistency of the surface-reflectance values (R2 = 87.56%; Euclidean distance = 2.11; spectral angle mapper = 12.60).

Particulate matter less than 2.5 microns in diameter (PM2.5) is an air pollutant that has become a major environmental concern for governments around the world. Management and control require air quality monitoring and prediction. However, previous studies did not fully utilize the spectral information in multispectral satellite images and land use data in geographic datasets. To alleviate these problems, this study proposes the extraction of land use information not only from geographic inventory but also from satellite images with a machine learning-based classification. In this manner, near up-to-date land use data and spectral information from satellite images can be utilized, and the integration of geographic and remote sensing datasets boosts the accuracy of PM2.5 concentration modeling. In the experiments, Landsat-8 imagery with a 30-m spatial resolution was used, and cloud-free image generation was performed prior to the land cover classification. The proposed method, which uses predictors from geographic and multispectral satellite datasets in modeling, was compared with an approach which utilizes geographic and remote sensing datasets, respectively. Quantitative assessments showed that the proposed method and the developed model, with a performance of RMSE = 3.06 µg/m3 and R2 = 0.85 comparatively outperform the models with a performance of RMSE = 3.14 µg/m3 and R2 = 0.68 for remote sensing datasets and a performance of RMSE = 3.47 µg/m3 and R2 = 0.79 for geographic datasets.

Chlorophyll-a (Chl-a) concentration, a crucial indicator of phytoplankton biomass, is sensitive to seasonality. The variations in trophic states regarding seasonality and the changes of spectral properties of water bodies pose uncertainties to the accuracy of remote sensing semiempirical models. In particular, lakes in subtropical regions generally experience different trophic states in dry and wet seasons. In this study, a season-insensitive Chl-a retrieval model using multitask convolution neural network with multiple output layers (MCNN) is proposed. A layer-sharing network combined with data augmentation is adopted to alleviate the issue of insufficient quantity of in situ samples. In addition, a hyperparameter optimization is performed to automatically refine the MCNN architecture. To evaluate the accuracy of proposed method, Laguna Lake, one of the largest lakes in Southeast Asia, is selected as the validation target. The lake is characterized by oligotrophic and mesotrophic states in wet season, whereas the states change to mesotrophic and low-level eutrophic states in dry season. A collection of Sentinel-3 Ocean and Land Colour Instrument Level-2 images and 409 in situ samples with the Chl-a concentration range 1.24–22.30 mg$\cdot$m$^{-3}$ were used for model calibration and evaluation. Experimental results showed that MCNN with the performance of average $\boldsymbol{R^{2}}$ = 0.74, RMSE = 2.06 mg$\cdot$m$^{-3}$, Pearson's $\boldsymbol {r}$ = 0.86 outperforms related semiempirical models, including normalized difference chlorophyll index, two-band and three-band models, and WaterNet. The Chl-a prediction accuracy was improved by 7.19–14.6%, in terms of RMSE, compared with WaterNet.

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.

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.