Determination of the downwelling diffuse attenuation coefficient of lakewater with the sentinel-3A OLCI

The Ocean and Land Color Imager (OLCI) on the Sentinel-3A satellite, which was launched by the European Space Agency in 2016, is a new-generation water color sensor with a spatial resolution of 300 m and 21 bands in the range of 400-1020 nm. The OLCI is important to the expansion of remote sensing monitoring of inland waters using water color satellite data. In this study, we developed a dual band ratio algorithm for the downwelling diffuse attenuation coefficient at 490 nm (Kd(490)) for the waters of Lake Taihu, a large shallow lake in China, based on data measured during seven surveys conducted between 2008 and 2017 in combination with Sentinel-3A-OLCI data. The results show that: (1) Compared to the available Kd(490) estimation algorithms, the dual band ratio (681 nm/560 nm and 754 nm/560 nm) algorithm developed in this study had a higher estimation accuracy (N = 26, coefficient of determination (R2) = 0.81, root-mean-square error (RMSE) = 0.99m-1and mean absolute percentage error (MAPE) = 19.55%) and validation accuracy (N = 14, R2= 0.83, RMSE = 1.06 m-1and MAPE = 27.30%), making it more suitable for turbid inland waters; (2) A comparison of the OLCI Kd(490) product and a similar Moderate Resolution Imaging Spectroradiometer (MODIS) product reveals a high consistency between the OLCI and MODIS products in terms of the spatial distribution of Kd(490). However, the OLCI product has a smoother spatial distribution and finer textural characteristics than the MODIS product and contains notably higher-quality data; (3) The Kd(490) values for Lake Taihu exhibit notable spatial and temporal variations. Kd(490) is higher in seasons with relatively high wind speeds and in open waters that are prone to wind- and wave-induced sediment resuspension. Finally, the Sentinel-3A-OLCI has a higher spatial resolution and is equipped with a relatively wide dynamic range of spectral bands suitable for inland waters. The Sentinel-3B satellite will be launched soon and, together with the Sentinel-3A satellite, will form a two-satellite network with the ability to make observations twice every three days. This satellite network will have a wider range of application and play an important role in the monitoring of inland waters with complex optical properties

Inversion of inherent optical properties in optically complex waters using sentinel-3A/OLCI images: A case study using China\u27s three largest freshwater lakes

Inherent optical properties (IOPs) play an important role in underwater light field, and are difficult to estimate accurately using satellite data in optically complex waters. To study water quality in appropriate temporal and spatial scales, it is necessary to develop methods to obtain IOPs form space-based observation with quantified uncertainties. Field-measured IOP data (N = 405) were collected from 17 surveys between 2011 and 2017 in the three major largest freshwater lakes of China (Lake Chaohu, Lake Taihu, and Lake Hongze) in the lower reaches of the Yangtze River and Huai River (LYHR). Here we provide a case-study on how to use in-situ observation of IOPs to devise an improved algorithm for retrieval of IOPs. We then apply this algorithm to observation with Sentinel-3A OLCI (Ocean and Land Colour Instrument, corrected with our improved AC scheme), and use in-situ data to show that the algorithm performs better than the standard OLCI IOP product. We use the satellite derived products to study the spatial and seasonal distributions of IOPs and concentrations of optically active constituents in these three lakes, including chlorophyll-a (Chla) and suspended particulate matter (SPM), using all cloud-free OLCI images (115 scenes) over the lakes in the LYHR basin in 2017. Our study provides a strategy for using local and remote observations to obtain important water quality parameters necessary to manage resources such as reservoirs, lakes and coastal waters

Algorithm to derive inherent optical properties from remote sensing reflectance in turbid and eutrophic lakes

Inherent optical properties play an important role in understanding the biogeochemical processes of lakes by providing proxies for a variety of biogeochemical quantities, including phytoplankton pigments. However, to date, it has been difficult to accurately derive the absorption coefficient of phytoplankton [aph(λ)] in turbid and eutrophic waters from remote sensing. A large dataset of remote sensing of reflectance [ Rrs (λ)] and absorption coefficients was measured for samples collected from lakes in the middle and lower reaches of the Yangtze River and Huai River basin (MLYHR), China. In the process of scattering correction of spectrophotometric measurements, the particulate absorption coefficients [ap(λ)] were first assumed to have no absorption in the near-infrared (NIR) wavelength. This assumption was corrected by estimating the particulate absorption coefficients at 750 nm [ap(750)] from the concentrations of chlorophyll-a (Chla) and suspended particulate matter, which was added to the ap(λ) as a baseline. The resulting mean spectral mass-specific absorption coefficient of the nonalgal particles (NAPs) was consistent with previous work. A novel iterative IOP inversion model was then designed to retrieve the total nonwater absorption coefficients [anw(λ)] and backscattering coefficients of particulates [bbp(λ)], aph(λ), and adg (λ) [absorption coefficients of NAP and colored dissolved organic matter (CDOM)] from Rrs (λ) in turbid inland lakes. The proposed algorithm performed better than previously published models in deriving anw(λ) and bbp(λ) in this region. The proposed algorithm performed well in estimating the aph(λ) for wavelengths \u3e 500 nm for the calibration dataset [N = 285, unbiased absolute percentage difference (UAPD) = 55.22%, root mean square error (RMSE) = 0.44 m−1] and for the validation dataset (N = 57, UAPD = 56.17%, RMSE = 0.71 m−1). This algorithm was then applied to Sentinel-3A Ocean and Land Color Instrument (OLCI) satellite data, and was validated with field data. This study provides an example of how to use local data to devise an algorithm to obtain IOPs, and in particular, a ph (λ), using satellite Rr s (λ) data in turbid inland waters

Shifting Patterns of Summer Lake Color Phenology in Over 26,000 US Lakes

Lakes are often defined by seasonal cycles. The seasonal timing, or phenology, of many lake processes are changing in response to human activities. However, long-term records exist for few lakes, and extrapolating patterns observed in these lakes to entire landscapes is exceedingly difficult using the limited number of available in situ observations. Limited landscape-level observations mean we do not know how common shifts in lake phenology are at macroscales. Here, we use a new remote sensing data set, LimnoSat-US, to analyze U.S. summer lake color phenology between 1984 and 2020 across more than 26,000 lakes. Our results show that summer lake color seasonality can be generalized into five distinct phenology groups that follow well-known patterns of phytoplankton succession. The frequency with which lakes transition from one phenology group to another is tied to lake and landscape level characteristics. Lakes with high inflows and low variation in their seasonal surface area are generally more stable, while lakes in areas with high interannual variations in climate and catchment population density show less stability. Our results reveal previously unexamined spatiotemporal patterns in lake seasonality and demonstrate the utility of LimnoSat-US, which, with over 22 million remote sensing observations of lakes, creates novel opportunities to examine changing lake ecosystems at a national scale

Multidecadal Remote Sensing of Inland Water Dynamics

Remote sensing approaches to measuring inland water dynamics date back more than 50 years. These approaches rely on the unique spectral properties of different waterbodies to delineate surface extents and estimate optically active water quality parameters. Until recently, inland water remote sensing focused largely on localized study domains due to limitations in modelling methods, computing power, and data access. Recent advances in these areas have created novel opportunities for data-driven-multidecadal remote sensing of inland waters at the landscape scale. Here, I highlight the history of inland water remote sensing along with the dominant methodologies, water quality constituents, and limitations involved. I then use this background to contextualize three macroscale inland water remote sensing studies of increasing complexity. The first combines field measurements with remotely sensed surface water extents to identify the impacts of small-scale gold mining in Peru. Our results suggest that mining is leading to synergistic increases in lake area and mercury loading that are significantly heightening exposure risk for people and wildlife. I move from measuring lake extents in Peru to measuring lake color in over 26,000 lakes across the United States. This analysis shows that lake color seasonality can be generalized into five distinct phenology groups that follow well-known patterns of algae growth and succession. The stability of a given lake (i.e. the likelihood it will move from one phenology group to another) is tied to lake and landscape level characteristics including climate and population density. Finally, I move from simple parameters such as quantity and color to estimating multidecadal changes in water clarity in U.S. lakes. I show that lake water clarity in the U.S. has increased by an average of 0.52 cm yr-1 since 1984, largely as a result of extensive U.S. freshwater pollution abatement measures. In combination, these three studies highlight that data intensive remote sensing approaches are expanding the capabilities of inland water remote sensing from local to global scales, and that macroscale remote sensing of inland waters reveals trends and processes that are unobservable using field data alone.Doctor of Philosoph

Role of Coastal Environmental Conditions During Austral Winter On Plankton Community Dynamics And The Occurrence Of Pseudo-nitzschia Spp. And Domoic Acid In Inhambane Province, Mozambique

Harmful algal blooms (HABs) are increasing globally in frequency, persistence, and geographic extent. HABs pose a threat to economic stability, and ecosystem and human health. To date no incidences of marine toxins produced by phytoplankton have been recorded in Mozambique, which may be due to the absence of a monitoring program and general awareness of the potential threat. This study is the first documentation of the occurrence of a neurotoxin, domoic acid (DA), produced by the diatom genus Pseudo-nitzschia spp. along the east coast of Africa. The coast of Inhambane Province is a biodiversity hotspot where year-round Rhincodon typus (whale shark) sightings are among the highest in the world, supporting an emerging ecotourism industry. Links between primary productivity and biodiversity in this area have not previously been considered or reported. My research focused on identifying environmental factors, specifically nutrients, influencing coastal productivity and DA concentrations and highlights variations within the system across four regions during May-August 2018. During this time, the coastal phytoplankton community was diatom-dominated, with high abundances of Pseudo-nitzschia spp. which were influenced by nutrient pulses resulting from wind-driven upwelling. In late July 2018, primary production was enhanced and corresponded with a peak in DA located within a biodiversity hotspot for Rhincodon typus (whale shark). Increases in DA concentration were correlated to phosphorus limitation. Domoic acid was also found to be present in mesozooplankton samples, providing evidence for trophic transfer of the toxin and the potential for bioaccumulation within a system, serving as a vector for higher trophic level organisms. Continued and comprehensive monitoring along southern Mozambique would provide critical information to develop predictive models to assess ecosystem and human health threats and impacts to the local economy from marine toxins under challenges posed by global change

Remote Sensing of Plant Biodiversity

This Open Access volume aims to methodologically improve our understanding of biodiversity by linking disciplines that incorporate remote sensing, and uniting data and perspectives in the fields of biology, landscape ecology, and geography. The book provides a framework for how biodiversity can be detected and evaluated—focusing particularly on plants—using proximal and remotely sensed hyperspectral data and other tools such as LiDAR. The volume, whose chapters bring together a large cross-section of the biodiversity community engaged in these methods, attempts to establish a common language across disciplines for understanding and implementing remote sensing of biodiversity across scales. The first part of the book offers a potential basis for remote detection of biodiversity. An overview of the nature of biodiversity is described, along with ways for determining traits of plant biodiversity through spectral analyses across spatial scales and linking spectral data to the tree of life. The second part details what can be detected spectrally and remotely. Specific instrumentation and technologies are described, as well as the technical challenges of detection and data synthesis, collection and processing. The third part discusses spatial resolution and integration across scales and ends with a vision for developing a global biodiversity monitoring system. Topics include spectral and functional variation across habitats and biomes, biodiversity variables for global scale assessment, and the prospects and pitfalls in remote sensing of biodiversity at the global scale

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