ABSTRACT:The Landsat-8 satellite imagery is now highly developed compares to the former of Landsat projects. Both land and water area are possibly mapped using this satellite sensor. Considerable approaches have been made to obtain a more accurate method for extracting the information of water area from the images. It is difficult to generate an accurate water quality information from Landsat images by using some existing algorithm provided by researchers. Even though, those algorithms have been validated in some water area, but the dynamic changes and the specific characteristics of each area make it necessary to get them evaluated and validated over another water area. This paper aims to make a new algorithm by correlating the measured and estimated TSS and Chla concentration. We collected in-situ remote sensing reflectance, TSS and Chl-a concentration in 9 stations surrounding the Poteran islands as well as Landsat 8 data on the same acquisition time of April 22, 2015. The regression model for estimating TSS produced high accuracy with determination coefficient (R 2 ), NMAE and RMSE of 0.709; 9.67 % and 1.705 g/m 3 respectively. Whereas, Chla retrieval algorithm produced R 2 of 0.579; NMAE of 10.40% and RMSE of 51.946 mg/m 3 . By implementing these algorithms to Landsat 8 image, the estimated water quality parameters over Poteran island water ranged from 9.480 to 15.801 g/m 3 and 238.546 to 346.627 mg/m 3 for TSS and Chl-a respectively.

ABSTRACT:The Sea Surface Temperature (SST) retrieval from satellites data Thus, it could provide SST data for a long time. Since, the algorithms of SST estimation by using Landsat 8 Thermal Band are sitedependence, we need to develop an applicable algorithm in Indonesian water. The aim of this research was to develop SST algorithms in the North Java Island Water. The data used are in-situ data measured on April 22, 2015 and also estimated brightness temperature data from Landsat 8 Thermal Band Image (band 10 and band 11). The algorithm was established using 45 data by assessing the relation of measured in-situ data and estimated brightness temperature. Then, the algorithm was validated by using another 40 points. The results showed that the good performance of the sea surface temperature algorithm with coefficient of determination (R 2 ) and Root Mean Square Error (RMSE) of 0.912 and 0.028, respectively.

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.

Regional water quality mapping is the key practical issue in environmental monitoring. Global regression models transform measured spectral image data to water quality information without the consideration of spatially varying functions. However, it is extremely difficult to find a unified mapping algorithm in multiple reservoirs and lakes. The local model of water quality mapping can estimate water quality parameters effectively in multiple reservoirs using spatial regression. Experiments indicate that both models provide fine water quality mapping in low chlorophyll-a (Chla) concentration water (study area 1; root mean square error, RMSE: 0.435 and 0.413 mg m−3 in the best global and local models), whereas the local model provides better goodness-of-fit between the observed and derived Chla concentrations, especially in high-variance Chla concentration water (study area 2; RMSE: 20.75 and 6.49 mg m−3 in the best global and local models). In-situ water quality samples are collected and correlated with water surface reflectance derived from Sentinel-2 images. The blue-green band ratio and Maximum Chlorophyll Index (MCI)/Fluorescence Line Height (FLH) are feasible for estimating the Chla concentration in these waterbodies. Considering spatially-varying functions, the local model offers a robust approach for estimating the spatial patterns of Chla concentration in multiple reservoirs. The local model of water quality mapping can greatly improve the estimation accuracy in high-variance Chla concentration waters in multiple reservoirs.

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