Machine Learning for Arctic Sea Ice Physical Properties Estimation Using Dual-Polarimetric SAR Data

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.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

Remote Sensing of Water Quality Parameters over Lake Balaton by Using Sentinel-3 OLCI

Source at https://doi.org/10.3390/w10101428.The Ocean and Land Color Instrument (OLCI) onboard Sentinel 3A satellite was launched in February 2016. Level 2 (L2) products have been available for the public since July 2017. OLCI provides the possibility to monitor aquatic environments on 300 m spatial resolution on 9 spectral bands, which allows to retrieve detailed information about the water quality of various type of waters. It has only been a short time since L2 data became accessible, therefore validation of these products from different aquatic environments are required. In this work we study the possibility to use S3 OLCI L2 products to monitor an optically highly complex shallow lake. We test S3 OLCI-derived Chlorophyll-a (Chl-a), Colored Dissolved Organic Matter (CDOM) and Total Suspended Matter (TSM) for complex waters against in situ measurements over Lake Balaton in 2017. In addition, we tested the machine learning Gaussian process regression model, trained locally as a potential candidate to retrieve water quality parameters. We applied the automatic model selection algorithm to select the combination and number of spectral bands for the given water quality parameter to train the Gaussian Process Regression model. Lake Balaton represents different types of aquatic environments (eutrophic, mesotrophic and oligotrophic), hence being able to establish a model to monitor water quality by using S3 OLCI products might allow the generalization of the methodology

Mapping Marine Macroalgae along the Norwegian Coast Using Hyperspectral UAV Imaging and Convolutional Nets for Semantic Segmentation

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

A new spectral harmonization algorithm for Landsat-8 and Sentinel-2 remote sensing reflectance products using machine learning: a case study for the Barents Sea (European Arctic)

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 ( Rrs ) products, which significantly reduces uncertainties compared to those in the literature. We compared Rrs retrieved via state-of-the-art AC processors, i.e., Acolite, C2RCC, and Polymer, against ship-based in situ Rrs observations obtained from the Barents Sea waters, including a wide range of optical properties. Results suggest that the Acolite-derived Rrs 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 ∼6 % to 12% to ∼2 % to Rrs products for water quality monitoring applications. The proposed method has the potential to be applied to other waters

Supervised Classifications of Optical Water Types in Spanish Inland Waters

Remote sensing of lake water quality assumes there is no universal method or algorithm that can be applied in a general way on all inland waters, which usually have different in-water components affecting their optical properties. Depending on the place and time of year, the lake dynamics, and the particular components of the water, non-tailor-designed algorithms can lead to large errors or lags in the quantification of the water quality parameters, such as the suspended mineral sediments, dissolved organic matter, and chlorophyll-a concentration. Selecting the most suitable algorithm for each type of water is not a simple matter. One way to make selecting the most suitable water quality algorithm easier on each occasion is by knowing ahead of time the type of water being handled. This approach is used, for instance, in the Lake Water Quality production chain of the Copernicus Global Land Service. The objective of this work is to determine which supervised classification approach might give the most accurate results. We use a dataset of manually labeled pixels on lakes and reservoirs in Eastern Spain. High-resolution images from the Multispectral Instrument sensor on board the ESA Sentinel-2 satellite, atmospherically corrected with the Case 2 Regional Coast Colour algorithm, are used as the basis for extracting the pixels for the dataset. Three families of different supervised classifiers have been implemented and compared: the K-nearest neighbor, decision trees, and support vector machine. Based on the results, the most appropriate for our study area is the random forest classifier, which was selected and applied on a series of images to derive the temporal series of the optical water types per lake. An evaluation of the results is presented, and an analysis is made using expert knowledge

Earlier sea-ice melt extends the oligotrophic summer period in the Barents Sea with low algal biomass and associated low vertical flux

The decrease in Arctic sea-ice extent and thickness as a result of global warming will impact the timing, duration, magnitude and composition of phytoplankton production with cascading effects on Arctic marine food-webs and biogeochemical cycles. Here, we elucidate the environmental drivers shaping the composition, abundance, biomass, trophic state and vertical flux of protists (unicellular eukaryotes), including phytoplankton, in the Barents Sea in late August 2018 and 2019. The two years were characterized by contrasting sea-ice conditions. In August 2018, the sea-ice edge had retreated well beyond the shelf break into the Nansen Basin (>82°N), while in 2019, extensive areas of the northwestern Barents Sea shelf (>79°N) were still ice-covered. These contrasting sea-ice conditions resulted in marked interannual differences in the pelagic protist community structure in this area. In August 2018, the protist community was in a post-bloom stage of seasonal succession characterized by oligotrophic surface waters and dominance of small-sized phytoplankton and heterotrophic protists (predominantly flagellates and ciliates) at most stations. In 2019, a higher contribution of autotrophs and large-celled phytoplankton, particularly diatoms, to total protist biomass compared to 2018 was reflected in higher chlorophyll a concentrations and suggested that the protist community was still in a late bloom stage at some stations. It is noteworthy that particularly diatoms contributed a considerably higher proportion to the protist biomass at the ice-covered stations in both years compared to the open-water stations. This pattern was also evident in the higher vertical protist biomass flux in 2019, dominated by dinoflagellates and diatoms, compared to 2018. Our results suggest that the predicted transition toward an ice-free Barents Sea will lengthen the oligotrophic summer period with low algal biomass and associated low vertical flux.publishedVersio

Sensitivity analysis of Gaussian process machine learning for chlorophyll prediction from optical remote sensing

The machine learning method, Gaussian Process Regression (GPR), has lately been introduced for chlorophyll content mapping from remotely sensed data. It has been shown that GPR has outperformed other machine learning and empirical methods in accuracy, speed and stability. Moreover, GPR not only estimates the chlorophyll content, it also provides the certainty level of the prediction, allowing the assessment of additional certainty maps. However, since GPR is a non-linear kernel based regression method, the relevance of the features are not accessible directly from the weights. The main contribution of this thesis is to develop a procedure for feature sensitivity analysis in order to assign relative importance to the features. The sensitivity analysis was introduced for the predictive mean function and for the predictive variance function of the Gaussian process. Then the empirical estimates for the derived sensitivity functions were applied to a land chlorophyll dataset and to two ocean chlorophyll datasets. The sensitivity analysis revealed the most important spectral bands for land chlorophyll and for ocean chlorophyll prediction. Applying the proposed methodology to the land chlorophyll dataset discovered that bands outside the chlorophyll absorption spectrum also contribute to the prediction of chlorophyll. The results of the sensitivity analysis of the ocean chlorophyll datasets open the possibility of discriminating between Case-1 water and Case-2 water condition. The method also provides additional information through the sensitivity of the predictive variance. Thus, not only the most relevant spectral bands can be revealed, but also the stability of the variance for the feature in interest can be accessed

Machine Learning Water Quality Monitoring

This work utilizes Machine Learning (ML) regression and feature ranking techniques for water quality monitoring from remotely sensed data. The investigated regression methods include the Gaussian Process Regression (GPR), Suport Vector Regression (SVR) and Partial Least Squares Regression (PLSR). Feature relevance in the GPR model is as- sessed by the probabilistic Sensitivity Analysis (SA) approach.This thesis introduces the SA of the predictive mean and variance function of the GPR, which reveals the relev- ance of the input features and the spectral spacing of the input space, respectively. The approach was applied to both controlled and Chlorophyll-a (Chl-a)/ Remote sensing reflectance (Rrs) matchup datasets with promising results. The SA of the predictive mean function of the GPR was compared and evaluated with the Automatic Relevance Determination (ARD) and Variable Importance in Pro- jection (VIP) feature ranking methods. The ARD is associated with GPR model, and the VIP is used to assign relevance to the input features in the PLSR model. The comparison results showed that feature ranking methods can not only be used to reduce dimension, while still obtaining satisfactory regression, but also to reveal the underlying biophys- ical properties of aquatic environments. Feature ranking methods and ML regression models were combined to design an Automatic Model Selection Approach (AMSA). AMSA automatically compares and val- idates regression models by evaluating the number and combination of ranked input features. The output of AMSA is a regression model and the number and position of features used for obtaining the strongest model based on user defined statistical meas- ures. AMSA was tested on several Chl-a/ Rrs matchups representing various water conditions. Finally, AMSA was applied to an aquatic environment showing a large variety of water conditions. The chosen test site was Lake Balaton, due to its unique optical prop- erties. Lake Balaton represents eutrophic, oligotrophic, turbid and clear, open ocean like conditions. Thus, being able to retrieve water quality by using a unified model es- tablished by AMSA, for all these different water conditions of the lake might allow the generalization of the model

Machine Learning Automatic Model Selection Algorithm for Oceanic Chlorophyll-a Content Retrieval

Ocean Color remote sensing has a great importance in monitoring of aquatic environments. The number of optical imaging sensors onboard satellites has been increasing in the past decades, allowing to retrieve information about various water quality parameters of the world’s oceans and inland waters. This is done by using various regression algorithms to retrieve water quality parameters from remotely sensed multi-spectral data for the given sensor and environment. There is a great number of such algorithms for estimating water quality parameters with different performances. Hence, choosing the most suitable model for a given purpose can be challenging. This is especially the fact for optically complex aquatic environments. In this paper, we present a concept to an Automatic Model Selection Algorithm (AMSA) aiming at determining the best model for a given matchup dataset. AMSA automatically chooses between regression models to estimate the parameter in interest. AMSA also determines the number and combination of features to use in order to obtain the best model. We show how AMSA can be built for a certain application. The example AMSA we present here is designed to estimate oceanic Chlorophyll-a for global and optically complex waters by using four Machine Learning (ML) feature ranking methods and three ML regression models. We use a synthetic and two real matchup datasets to find the best models. Finally, we use two images from optically complex waters to illustrate the predictive power of the best models. Our results indicate that AMSA has a great potential to be used for operational purposes. It can be a useful objective tool for finding the most suitable model for a given sensor, water quality parameter and environment

Evaluation of feature ranking and regression methods for oceanic chlorophyll-a estimation

This paper evaluates two alternative regression techniques for oceanic chlorophyll-a (Chl-a) content estimation. One of the investigated methodologies is the recently introduced Gaussian process regression (GPR) model. We explore two feature ranking methods derived for the GPR model, namely sensitivity analysis (SA) and automatic relevance determination (ARD). We also investigate a second regression method, the partial least squares regression (PLSR) for oceanic Chl-a content estimation. Feature relevance in the PLSR model can be accessed through the variable importance in projection (VIP) feature ranking algorithm. This paper thus analyzes three feature ranking models, SA, ARD, and VIP, which are all derived from different fundamental principles, and uses the ranked features as inputs to the GPR and PLSR to assess regression strengths. We compare the regression performances using some common performance measures, and show how the feature ranking methods can be used to find the lowest number of features to estimate oceanic Chl-a content by using the GPR and PLSR models, while still producing comparable performance to the state-of-the-art algorithms. We evaluate the models on a global MEdium Resolution Imaging Spectrometer matchup dataset. Our results show that the GPR model has the best regression performance for most of the input feature sets we used, and our conclusion is this model can favorably be used for Chl-a content retrieval, already with two features, ranked by either the SA or ARD methods