Lake Balaton is the largest lake (~587 km2) in middle Europe but very shallow with an average depth of 3 m, whose abundant fauna and flora have irreplaceable ecological values. Its natural beaches and artificial facilities attract millions of tourists all over the world. To facilitate long-term monitoring and management of water quality, Landsat satellites, the longest and most consistent spectral archive of earth observation, are used for Chl-a concentration retrieval. Based on the cloud-based geospatial data platform Google Earth Engine (GEE), this study integrates satellite spectral bands with temporal information to improve Chl-a concentration retrieval with Landsat. Results show that the proposed method has an RMSE 6.41 mg/m3. The yearly mean Chl-a shows a good consistency with in-situ measurements and can capture key variability between years and discover the years with high Chl-a concentration. Therefore, this work can benefit exploring long-term spatio-temporal patterns of water quality in Lake Balaton. An online interface has also been developed with GEE for the easy access and visualization of Chl-a variations.

Marine macroalgae form underwater "blue forests" with several important functions. Hyperspectral imaging from unmanned aerial vehicles provides a rich set of spectral and spatial data that can be used to map the distribution of such macroalgae. Results from a study using 81 annotated hyper-spectral images from the Norwegian coast are presented. A U-net convolutional network was used for classification, and accuracies for all macroalgae classes were above 90%, indicating the potential of the method as an accurate tool for blue forest monitoring.

The synergistic use of Landsat-8 operational land imager (OLI) and Sentinel-2 multispectral instrument (MSI) data products provides an excellent opportunity to monitor the dynamics of aquatic ecosystems. However, the merging of data products from multisensors is often adversely affected by the difference in their spectral characteristics. In addition, the errors in the atmospheric correction (AC) methods further increase the inconsistencies in downstream products. This work proposes an improved spectral harmonization method for OLI and MSI-derived remote sensing reflectance (${R_{rs}}$) products, which significantly reduces uncertainties compared to those in the literature. We compared${R_{rs}}$retrieved via state-of-the-art AC processors, i.e., Acolite, C2RCC, and Polymer, against ship-based in situ${R_{rs}}$observations obtained from the Barents Sea waters, including a wide range of optical properties. Results suggest that the Acolite-derived${R_{rs}}$has a minimum bias for our study area with median absolute percentage difference (MAPD) varying from 9% to 25% in the blue–green bands. To spectrally merge OLI and MSI, we develop and apply a new machine learning-based bandpass adjustment (BA) model to near-simultaneous OLI and MSI images acquired in the years from 2018 to 2020. Compared to a conventional linear adjustment, we demonstrate that the spectral difference is significantly reduced from$\sim 6$% to 12% to$\sim 2$% to${< } {10\%}$in the common OLI-MSI bands using the proposed BA model. The findings of this study are useful for the combined use of OLI and MSI${R_{rs}}$products for water quality monitoring applications. The proposed method has the potential to be applied to other waters.

This work introduces a novel method that makes use of machine learning (ML) techniques to classify hyper- and multi spectral observations into optical water types (OWTs). Classification was done using$k$-means clustering, which was followed by a feature relevance step based on the sensitivity analysis (SA) of the predictive mean and variance function of a Gaussian process (GP) regression model. The method was used both in training and predictive mode. The latter allows applying the approach for new unlabeled observations, so that the OWTs and the associated relevant features can automatically be assessed. The methods were studied on hyperspectral synthesized and in situ Arctic data, and were further evaluated on a test image acquired over Arctic seas. Good empirical results encourage wide adoption of the methodology to be applied in operational processing and assessment of water types.

This work introduces a novel method that combines machine learning (ML) techniques with dual-polarimetric (dual-pol) synthetic aperture radar (SAR) observations for estimating quad-polarimetric (quad-pol) parameters, which are presumed to contain geophysical sea ice information. In the training phase, the output parameters are generated from quad-pol observations obtained by Radarsat-2 (RS2), and the corresponding input data consist of features obtained from overlapping dual-pol Sentinel-1 (S1) data. Then, two, well-recognized ML methods are studied to learn the functional relationship between the output and input data. These ML approaches are the Gaussian process regression (GPR) and neural network (NN) for regression models. The goal is to use the aforementioned ML techniques to generate Arctic sea ice information from freely available dual-pol observations acquired by S1, which can, in general, only be generated from quad-pol data. Eight overlapping RS2 and S1 scenes were used to train and test the GPR and NN models. Statistical regression performance measures were computed to evaluate the strength of the ML regression methods. Then, two scenes were selected for further evaluation, where overlapping optical images were available as well. This allowed the visual interpretation of the maps estimated by the ML models. Finally, one of the methods was tested on an entire S1 scene to perform prediction on areas outside of the RS2 and S1 overlap. Our results indicate that the studied ML techniques can be utilized to increase the information retrieval capacity of the wide swath dual-pol S1 imagery while embedding physical properties in the methodology.

This work presents an approach how machine learning techniques can be utilized for upcoming hyper-spectral missions to improve water quality monitoring globally by using a combination of classification and regression methods, and at the same time understanding the spectral relevance for various Water Quality Parameters (WQPs) in different Optical Water Types (OWTs). Machine learning methods are studied to classify OWTs and assign relevance to spectral bands for the WQPs from hyper-spectral observations. The model takes hyper-spectral Level-2 Rrs to classify the data into OWTs by using a non-linear kernel Support Vector Machine (SVM). Then, for selected WQPs feature ranking is performed to assign relevance to the spectral bands in the OWTs. In this study, two WQPs were selected, Chlorophyll-a (Chl-a) content and absorption from Colored Dissolved Organic Matter (aCDOM). The results are presented in spectral relevance maps, showing how spectral relevance varies with increasing optical complexity. These spectral relevance maps are used to select the relevant hyper-spectral bands for a given WQP, which then can be used for regression. Two regression models are evaluated, the kernel Support Vector Regression and Neural Nets.

This work evaluates three machine learning methods with respect to their ability of learning the functional relationship between dual-polarimetric (dual-pol) input data and quad-polarimetric (quad-pol) output parameters. We chose to study and compare the learning strength of a Neural Network (NN) approach, two kernel-methods, the Support Vector Machine (SVM) and the Gaussian Process Regression (GPR). Overlapping quad-pol Radarsat-2 (RS2) and dual-pol ScanSAR Sentinel-l (S1) sea ice Synthetic Aperture Radar (SAR) scenes, with 20 minutes time difference, were used for establishing the relationship between the dual-pol input data and corresponding quad-pol output parameters. We then used the learned relationship to predict quad-pol parameters for the overlapping S1 dual-pol scene, and show the results of the three machine learning methods, visually, by showing images of the predicted polarimetric features, and quantitively, by computing statistical performance measures. The results indicate that all three methods have strong learning capacity, however, the computed statistical measures and the visual comparisons suggest the best performance for the GPR model.

In this paper, we evaluate the possibility of using a machine learning Gaussian Process Regression (GPR) approach to monitor Chlorophyll-a content in Arctic waters by using the Sentinel 3 Ocean and Land Color Instrument. We develop the GPR model on a synthetic dataset, which represents both open ocean and coastal Arctic waters. This allows the model to be exposed to and trained on data from both kinds of aquatic environments. The chosen GPR model has previously been trained and tested in a different aquatic environment, representing a variety of complexity conditions, where it was demonstrated to have strong generalization capabilities. Our results suggest that this model can also be used for diverse Arctic water conditions as well.

This paper addresses the problem of up-scaling full polarimetric (quad-pol) parameters from small quad-pol synthetic aperture radar (SAR) scenes to large dual-pol scenes, using a sophisticated Machine Learning (ML) method, namely the Gaussian Process Regression (GPR). The approach is to let the GPR model learn the relationships between the dual-pol input data and the quad-pol parameters on a quad-pol scene, and then extrapolate the relationships to the whole dual-pol scene. We demonstrate the procedure on two pairs of quadpol Radarsat-2 (RS2) and dual-pol ScanSAR Sentinel-1 (S1) scenes, acquired less than 20 minutes apart. The results are visualised as pixel-wise parametric maps, supported by three quantitative regression performance measures. In addition, we show certainty level maps for the estimated parameters. Our results indicate the potential of using the ML GPR model to upscale quad-pol scenes to large dual-pol images.

Gaussian Process Regression (GPR) for machine learning has lately been successfully introduced for chlorophyll content mapping from remotely sensed data. The method provides a fast, stable and accurate prediction of biophysical parameters. However, since GPR is a non-linear kernel regression method, the relevance of the features are not accessible. In this paper, we introduce a probabilistic approach for feature sensitivity analysis (SA) of the GPR in order to reveal the relative importance of the features (bands) being used in the regression process. We evaluated the SA on GPR ocean chlorophyll content prediction. The method revealed the importance of the spectral bands, thus allowing the discrimination between Case-1 water and Case-2 water conditions.