Optical remote sensing of water quality parameters retrieval in the Barents Sea

This thesis addresses various aspects of monitoring water quality indicators (WQIs) using optical remote sensing technologies. The dynamic nature of aquatic systems necessitate frequent monitoring at high spatial resolution. Machine learning (ML)-based algorithms are becoming increasingly common for these applications. ML algorithms are required to be trained by a significant amount of training data, and their accuracy depends on the performance of the atmospheric correction (AC) algorithm being used for correcting atmospheric effects. AC over open oceanic waters generally performs reasonably well; however, limitations still exist over inland and coastal waters. AC becomes more challenging in the high north waters, such as the Barents Sea, due to the unique in-water optical properties at high latitudes, long ray pathways, as well as the scattering of light from neighboring sea ice into the sensors’ field of view adjacent to ice-infested waters. To address these challenges, we evaluated the performances of state-of-the-art AC algorithms applied to the high-resolution satellite sensors Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI), both for high-north (Paper II) and for global inland and coastal waters (Paper III). Using atmospherically corrected remote sensing reflectance (Rrs ) products, estimated after applying the top performing AC algorithm, we present a new bandpass adjustment (BA) method for spectral harmonization of Rrs products from OLI and MSI. This harmonization will enable an increased number of ocean color (OC) observations and, hence, a larger amount of training data. The BA model is based on neural networks (NNs), which perform a pixel-by-pixel transformation of MSI-derived Rrs to that of OLI equivalent for their common bands. In addition, to accurately retrieve concentrations of Chlorophyll-a (Chl-a) and Color Dissolved Organic Matter (CDOM) from remotely sensed data, we propose in the thesis (Paper 1) an NN-based WQI retrieval model dubbed Ocean Color Net (OCN). Our results indicate that Rrs retrieved via the Acolite Dark Spectrum Fitting (DSF) method is in best agreement with in-situ Rrs observations in the Barents Sea compared to the other methods. The median absolute percentage difference (MAPD) in the blue-green bands ranges from 9% to 25%. In the case of inland and coastal waters (globally), we found that OC-SMART is the top performer, with MAPD Rrs products for varying optical regimes than previously presented methods. Additionally, to improve the analysis of remote sensing spectral data, we introduce a new spatial window-based match-up data set creation method which increases the training data set and allows for better tuning of regression models. Based on comparisons with in-water measured Chl-a profiles in the Barents Sea, our analysis indicates that the MSI-derived Rrs products are more sensitive to the depth-integrated Chl-a contents than near-surface Chl-a values (Paper I). In the case of inland and coastal waters, our study shows that using combined OLI and BA MSI-derived Rrs match-ups results in considerable improvement in the retrieval of WQIs (Paper III). The obtained results for the datasets used in this thesis illustrates that the proposed OCN algorithm shows better performance in retrieving WQIs than other semi-empirical algorithms such as the band ratio-based algorithm, the ML-based Gaussian Process Regression (GPR), as well as the globally trained Case-2 Regional/Coast Colour (C2RCC) processing chain model C2RCC-networks, and OC-SMART. The work in this thesis contributes to ongoing research in developing new methods for merging data products from multiple OC missions for increased coverage and the number of optical observations. The developed algorithms are validated in various environmental and aquatic conditions and have the potential to contribute to accurate and consistent retrievals of in-water constituents from high-resolution satellite sensors

Remote sensing in shallow lake ecology

Shallow lakes are an important ecological and socio-economic resource. However, the impact of human pressures, both at the lake and catchment scale, has precipitated a decline in the ecological status of many shallow lakes, both in the UK, and throughout Europe. There is now, as direct consequence, unprecedented interest in the assessment and monitoring of ecological status and trajectory in shallow lakes, not least in response to the European Union Water Framework Directive (2000/60/EC). In this context, the spatially-resolving and panoramic data provided by remote sensing platforms may be of immense value in the construction of effective and efficient strategies for the assessment and monitoring of ecological status in shallow lakes and, moreover, in providing new, spatially-explicit, insights into the function of these ecosystems and how they respond to change. This thesis examined the use of remote sensing data for the assessment of (i) phytoplankton abundance and species composition and (ii) aquatic vegetation distribution and ecophysiological status in shallow lakes with a view to establishing the credence of such an approach and its value in limnological research and monitoring activities. High resolution in-situ and airborne remote sensing data was collected during a 2-year sampling campaign in the shallow lakes of the Norfolk Broads. It was demonstrated that semi-empirical algorithms could be formulated and used to provide accurate and robust estimations of the concentration of chlorophyll-a, even in these optically-complex waters. It was further shown that it was possible to differentiate and quantify the abundance of cyanobacteria using the biomarker pigment C-phycocyanin. The subsequent calibration of the imagery obtained from the airborne reconnaissance missions permitted the construction of diurnal and seasonal regional-scale time-series of phytoplankton dynamics in the Norfolk Broads. This approach was able to deliver unique spatial insights into the migratory behaviour of a potentially-toxic cyanobacterial bloom. It was further shown that remote sensing can be used to map the distribution of aquatic plants in shallow lakes, importantly including the extent of submerged vegetation, which is central to the assessment of ecological status. This research theme was subsequently extended in an exploration of the use of remote sensing for assessing the ecophysiological response of wetland plants to nutrient enrichment. It was shown that remote sensing metrics could be constructed for the quantification of plant vigour. The extrapolation of these techniques enabled spatial heterogeneity in the ecophysiological response of Phragmites australis to lake nutrient enrichment to be characterised and assisted the formulation of a mechanistic explanation for the variation in reedswamp performance in these shallow lakes. It is therefore argued that the spatially synoptic data provided by remote sensing has much to offer the assessment, monitoring and policing of ecological status in shallow lakes and, in particular, for facilitating the development of pan-European scale lake surveillance capabilities for the Water Framework Directive (2000/60/EC). It is also suggested that remote sensing can make a valuable contribution to furthering ecological understanding and, most significantly, in enabling ecosystem processes and functions to be examined at the lake-scale

Feasibility Study for an Aquatic Ecosystem Earth Observing System Version 1.2.

International audienceMany Earth observing sensors have been designed, built and launched with primary objectives of either terrestrial or ocean remote sensing applications. Often the data from these sensors are also used for freshwater, estuarine and coastal water quality observations, bathymetry and benthic mapping. However, such land and ocean specific sensors are not designed for these complex aquatic environments and consequently are not likely to perform as well as a dedicated sensor would. As a CEOS action, CSIRO and DLR have taken the lead on a feasibility assessment to determine the benefits and technological difficulties of designing an Earth observing satellite mission focused on the biogeochemistry of inland, estuarine, deltaic and near coastal waters as well as mapping macrophytes, macro-algae, sea grasses and coral reefs. These environments need higher spatial resolution than current and planned ocean colour sensors offer and need higher spectral resolution than current and planned land Earth observing sensors offer (with the exception of several R&D type imaging spectrometry satellite missions). The results indicate that a dedicated sensor of (non-oceanic) aquatic ecosystems could be a multispectral sensor with ~26 bands in the 380-780 nm wavelength range for retrieving the aquatic ecosystem variables as well as another 15 spectral bands between 360-380 nm and 780-1400 nm for removing atmospheric and air-water interface effects. These requirements are very close to defining an imaging spectrometer with spectral bands between 360 and 1000 nm (suitable for Si based detectors), possibly augmented by a SWIR imaging spectrometer. In that case the spectral bands would ideally have 5 nm spacing and Full Width Half Maximum (FWHM), although it may be necessary to go to 8 nm wide spectral bands (between 380 to 780nm where the fine spectral features occur -mainly due to photosynthetic or accessory pigments) to obtain enough signal to noise. The spatial resolution of such a global mapping mission would be between ~17 and ~33 m enabling imaging of the vast majority of water bodies (lakes, reservoirs, lagoons, estuaries etc.) larger than 0.2 ha and ~25% of river reaches globally (at ~17 m resolution) whilst maintaining sufficient radiometric resolution

Remote Sensing of Plant Biodiversity

At last, here it is. For some time now, the world has needed a text providing both a new theoretical foundation and practical guidance on how to approach the challenge of biodiversity decline in the Anthropocene. This is a global challenge demanding global approaches to understand its scope and implications. Until recently, we have simply lacked the tools to do so. We are now entering an era in which we can realistically begin to understand and monitor the multidimensional phenomenon of biodiversity at a planetary scale. This era builds upon three centuries of scientific research on biodiversity at site to landscape levels, augmented over the past two decades by airborne research platforms carrying spectrometers, lidars, and radars for larger-scale observations. Emerging international networks of fine-grain in-situ biodiversity observations complemented by space-based sensors offering coarser-grain imagery—but global coverage—of ecosystem composition, function, and structure together provide the information necessary to monitor and track change in biodiversity globally. This book is a road map on how to observe and interpret terrestrial biodiversity across scales through plants—primary producers and the foundation of the trophic pyramid. It honors the fact that biodiversity exists across different dimensions, including both phylogenetic and functional. Then, it relates these aspects of biodiversity to another dimension, the spectral diversity captured by remote sensing instruments operating at scales from leaf to canopy to biome. The biodiversity community has needed a Rosetta Stone to translate between the language of satellite remote sensing and its resulting spectral diversity and the languages of those exploring the phylogenetic diversity and functional trait diversity of life on Earth. By assembling the vital translation, this volume has globalized our ability to track biodiversity state and change. Thus, a global problem meets a key component of the global solution. The editors have cleverly built the book in three parts. Part 1 addresses the theory behind the remote sensing of terrestrial plant biodiversity: why spectral diversity relates to plant functional traits and phylogenetic diversity. Starting with first principles, it connects plant biochemistry, physiology, and macroecology to remotely sensed spectra and explores the processes behind the patterns we observe. Examples from the field demonstrate the rising synthesis of multiple disciplines to create a new cross-spatial and spectral science of biodiversity. Part 2 discusses how to implement this evolving science. It focuses on the plethora of novel in-situ, airborne, and spaceborne Earth observation tools currently and soon to be available while also incorporating the ways of actually making biodiversity measurements with these tools. It includes instructions for organizing and conducting a field campaign. Throughout, there is a focus on the burgeoning field of imaging spectroscopy, which is revolutionizing our ability to characterize life remotely. Part 3 takes on an overarching issue for any effort to globalize biodiversity observations, the issue of scale. It addresses scale from two perspectives. The first is that of combining observations across varying spatial, temporal, and spectral resolutions for better understanding—that is, what scales and how. This is an area of ongoing research driven by a confluence of innovations in observation systems and rising computational capacity. The second is the organizational side of the scaling challenge. It explores existing frameworks for integrating multi-scale observations within global networks. The focus here is on what practical steps can be taken to organize multi-scale data and what is already happening in this regard. These frameworks include essential biodiversity variables and the Group on Earth Observations Biodiversity Observation Network (GEO BON). This book constitutes an end-to-end guide uniting the latest in research and techniques to cover the theory and practice of the remote sensing of plant biodiversity. In putting it together, the editors and their coauthors, all preeminent in their fields, have done a great service for those seeking to understand and conserve life on Earth—just when we need it most. For if the world is ever to construct a coordinated response to the planetwide crisis of biodiversity loss, it must first assemble adequate—and global—measures of what we are losing

Remote Sensing in Mangroves

The book highlights recent advancements in the mapping and monitoring of mangrove forests using earth observation satellite data. New and historical satellite data and aerial photographs have been used to map the extent, change and bio-physical parameters, such as phenology and biomass. Research was conducted in different parts of the world. Knowledge and understanding gained from this book can be used for the sustainable management of mangrove forests of the worl

Remote Sensing of the Aquatic Environments

The book highlights recent research efforts in the monitoring of aquatic districts with remote sensing observations and proximal sensing technology integrated with laboratory measurements. Optical satellite imagery gathered at spatial resolutions down to few meters has been used for quantitative estimations of harmful algal bloom extent and Chl-a mapping, as well as winds and currents from SAR acquisitions. The knowledge and understanding gained from this book can be used for the sustainable management of bodies of water across our planet