A chlorophyll-a algorithm for Landsat-8 based on mixture density networks

Material suplementario disponible en:Retrieval of aquatic biogeochemical variables, such as the near-surface concentration of chlorophyll-a (Chla) in inland and coastal waters via remote observations, has long been regarded as a challenging task. This manuscript applies Mixture Density Networks (MDN) that use the visible spectral bands available by the Operational Land Imager (OLI) aboard Landsat-8 to estimate Chla. We utilize a database of co-located in situ radiometric and Chla measurements (N = 4,354), referred to as Type A data, to train and test an MDN model (MDNA). This algorithm’s performance, having been proven for other satellite missions, is further evaluated against other widely used machine learning models (e.g., support vector machines), as well as other domain-specific solutions (OC3), and shown to offer significant advancements in the field. Our performance assessment using a held-out test data set suggests that a 49% (median) accuracy with near-zero bias can be achieved via the MDNA model, offering improvements of 20 to 100% in retrievals with respect to other models. The sensitivity of the MDNA model and benchmarking methods to uncertainties from atmospheric correction (AC) methods, is further quantified through a semi-global matchup dataset (N = 3,337), referred to as Type B data. To tackle the increased uncertainties, alternative MDN models (MDNB) are developed through various features of the Type B data (e.g., Rayleigh-corrected reflectance spectra ρs ). Using held-out data, along with spatial and temporal analyses, we demonstrate that these alternative models show promise in enhancing the retrieval accuracy adversely influenced by the AC process. Results lend support for the adoption of MDNB models for regional and potentially global processing of OLI imagery, until a more robust AC method is developed. Index Terms—Chlorophyll-a, coastal water, inland water, Landsat-8, machine learning, ocean color, aquatic remote sensing

Hyperspectral remote sensing of cyanobacterial pigments as indicators for cell populations and toxins in eutrophic lakes

The growth of mass populations of toxin-producing cyanobacteria is a serious concern for the ecological status of inland waterbodies and for human and animal health. In this study we examined the performance of four semi-analytical algorithms for the retrieval of chlorophyll a (Chl a) and phycocyanin (C-PC) from data acquired by the Compact Airborne Spectrographic Imager-2 (CASI-2) and the Airborne Imaging Spectrometer for Applications (AISA) Eagle sensor. The retrieval accuracies of the semi-analytical models were compared to those returned by optimally calibrated empirical band-ratio algorithms. The best-performing algorithm for the retrieval of Chl a was an empirical band-ratio model based on a quadratic function of the ratio of re!ectance at 710 and 670 nm (R2=0.832; RMSE=29.8%). However, this model only provided a marginally better retrieval than the best semi-analytical algorithm. The best-performing model for the retrieval of C-PC was a semi-analytical nested band-ratio model (R2=0.984; RMSE=3.98 mg m−3). The concentrations of C-PC retrieved using the semi-analytical model were correlated with cyanobacterial cell numbers (R2=0.380) and the particulate and total (particulate plus dissolved) pools of microcystins (R2=0.858 and 0.896 respectively). Importantly, both the empirical and semi-analytical algorithms were able to retrieve the concentration of C-PC at cyanobacterial cell concentrations below current warning thresholds for cyanobacteria in waterbodies. This demonstrates the potential of remote sensing to contribute to early-warning detection and monitoring of cyanobacterial blooms for human health protection at regional and global scales

Robust algorithm for estimating total suspended solids (TSS) in inland and nearshore coastal waters

One of the challenging tasks in modern aquatic remote sensing is the retrieval of near-surface concentrations of Total Suspended Solids (TSS). This study aims to present a Statistical, inherent Optical property (IOP) -based, and muLti-conditional Inversion proceDure (SOLID) for enhanced retrievals of satellite-derived TSS under a wide range of in-water bio-optical conditions in rivers, lakes, estuaries, and coastal waters. In this study, using a large in situ database (N \u3e 3500), the SOLID model is devised using a three-step procedure: (a) water-type classification of the input remote sensing reflectance (Rrs), (b) retrieval of particulate backscattering (bbp) in the red or near-infrared (NIR) regions using semi-analytical, machine-learning, and empirical models, and (c) estimation of TSS from bbp via water-type-specific empirical models. Using an independent subset of our in situ data (N = 2729) with TSS ranging from 0.1 to 2626.8 [g/m3], the SOLID model is thoroughly examined and compared against several state-of-the-art algorithms (Miller and McKee, 2004; Nechad et al., 2010; Novoa et al., 2017; Ondrusek et al., 2012; Petus et al., 2010). We show that SOLID outperforms all the other models to varying degrees, i.e.,from 10 to \u3e100%, depending on the statistical attributes (e.g., global versus water-type-specific metrics). For demonstration purposes, the model is implemented for images acquired by the MultiSpectral Imager aboard Sentinel-2A/B over the Chesapeake Bay, San-Francisco-Bay-Delta Estuary, Lake Okeechobee, and Lake Taihu. To enable generating consistent, multimission TSS products, its performance is further extended to, and evaluated for, other missions, such as the Ocean and Land Color Instrument (OLCI), Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), and Operational Land Imager (OLI). Sensitivity analyses on uncertainties induced by the atmospheric correction indicate that 10% uncertainty in Rrs leads to \u3c20% uncertainty in TSS retrievals from SOLID. While this study suggests that SOLID has a potential for producing TSS products in global coastal and inland waters, our statistical analysis certainly verifies that there is still a need for improving retrievals across a wide spectrum of particle loads

Optical types of inland and coastal waters

Inland and coastal waterbodies are critical components of the global biosphere. Timely monitoring is necessary to enhance our understanding of their functions, the drivers impacting on these functions and to deliver more effective management. The ability to observe waterbodies from space has led to Earth observation (EO) becoming established as an important source of information on water quality and ecosystem condition. However, progress toward a globally valid EO approach is still largely hampered by inconsistences over temporally and spatially variable in-water optical conditions. In this study, a comprehensive dataset from more than 250 aquatic systems, representing a wide range of conditions, was analyzed in order to develop a typology of optical water types (OWTs) for inland and coastal waters. We introduce a novel approach for clustering in situ hyperspectral water reflectance measurements (n = 4045) from multiple sources based on a functional data analysis. The resulting classification algorithm identified 13 spectrally distinct clusters of measurements in inland waters, and a further nine clusters from the marine environment. The distinction and characterization of OWTs was supported by the availability of a wide range of coincident data on biogeochemical and inherent optical properties from inland waters. Phylogenetic trees based on the shapes of cluster means were constructed to identify similarities among the derived clusters with respect to spectral diversity. This typification provides a valuable framework for a globally applicable EO scheme and the design of future EO missions

Optical types of inland and coastal waters

Inland and coastal waterbodies are critical components of the global biosphere. Timely monitoring is necessary to enhance our understanding of their functions, the drivers impacting on these functions and to deliver more effective management. The ability to observe waterbodies from space has led to Earth observation (EO) becoming established as an important source of information on water quality and ecosystem condition. However, progress toward a globally valid EO approach is still largely hampered by inconsistences over temporally and spatially variable in‐water optical conditions. In this study, a comprehensive dataset from more than 250 aquatic systems, representing a wide range of conditions, was analyzed in order to develop a typology of optical water types (OWTs) for inland and coastal waters. We introduce a novel approach for clustering in situ hyperspectral water reflectance measurements (n = 4045) from multiple sources based on a functional data analysis. The resulting classification algorithm identified 13 spectrally distinct clusters of measurements in inland waters, and a further nine clusters from the marine environment. The distinction and characterization of OWTs was supported by the availability of a wide range of coincident data on biogeochemical and inherent optical properties from inland waters. Phylogenetic trees based on the shapes of cluster means were constructed to identify similarities among the derived clusters with respect to spectral diversity. This typification provides a valuable framework for a globally applicable EO scheme and the design of future EO missions

Examination of Abiotic Drivers and Their Influence on Spartina alterniflora Biomass over a Twenty-Eight Year Period Using Landsat 5 TM Satellite Imagery of the Central Georgia Coast

We examined the influence of abiotic drivers on inter-annual and phenological patterns of aboveground biomass for Marsh Cordgrass, Spartina alterniflora, on the Central Georgia Coast. The linkages between drivers and plant response via soil edaphic factors are captured in our graphical conceptual model. We used geospatial techniques to scale up in situ measurements of aboveground S. alterniflora biomass to landscape level estimates using 294 Landsat 5 TM scenes acquired between 1984 and 2011. For each scene we extracted data from the same 63 sampling polygons, containing 1222 pixels covering about 1.1 million m2. Using univariate and multiple regression tests, we compared Landsat derived biomass estimates for three S. alterniflora size classes against a suite of abiotic drivers. River discharge, total precipitation, minimum temperature, and mean sea level had positive relationships with and best explained biomass for all dates. Additional results, using seasonally binned data, indicated biomass was responsive to changing combinations of variables across the seasons. Our 28-year analysis revealed aboveground biomass declines of 33%, 35%, and 39% for S. alterniflora tall, medium, and short size classes, respectively. This decline correlated with drought frequency and severity trends and coincided with marsh die-backs events and increased snail herbivory in the second half of the study period

A comparison between above-water surface and subsurface spectral reflectances collected over inland waters

The objective of the research was to undertake a quantitative comparison of spectral-reflectance measurements made slightly above the surface of water bodies with the measurements made slightly below the surface. The study is focused on three rivers; two in Georgia, USA and one in Japan. As expected, the differences in reflectance are not constant and vary with the wavelength. The contribution of surface-reflection effects to the surface reflectance measured slightly above the water is both pronounced and highly variable, but although they do alter the magnitude of the upwelling signal, they do not change the general shape of the spectral profiles. The correction of surface-reflection effects by assuming a proportionality factor (ρ) is not considered to be efficient for inland fresh water bodies. For in situ spectroscopy, the recommended approach is to measure upwelling radiance slightly below the water’s surface as a means of minimizing extraneous noise. Researchers should be aware of the potential for diminishing the validity of findings because of measurement errors

Usability of Modelling Languages for Model Interpretation: An Empirical Research Report

Models offer visual support for analyzing complex domains such as business processes and information systems. In both cases, models are developed using graphical modelling languages. In our study we focus on usability evaluation of modelling languages for the model interpretation scenario. The study is based on a causal model of hypotheses, which was developed under consideration of psychological cognitive theories and usability theory. Survey data is collected and the causal relations hypotheses are assessed using a structure equation modelling approach. Our study shows important findings for practical and theoretical issues of how differing modelling languages are influencing usability attributes on causal stages in model interpretation

Salt Marsh Elevation and Habitat Mapping Using Hyperspectral and LIDAR Data

Accurate mapping of both elevation and plant distributions in salt marshes is important for management and conservation goals. Although light detection and ranging (LIDAR) is effective at measuring surface elevations, laser penetration is limited in dense salt marsh vegetation. In a previous study, we found that LIDAR-derived digital elevation model (DEM) error varied with vegetation cover. We derived cover-class-specific correction factors to reduce these errors, including separate corrections for three different height classes of Spartina alterniflora, the dominant macrophyte in southeastern U.S. salt marshes. In order to apply these cover class-specific corrections, it is necessary to have information on the distribution of cover classes in a LIDAR-derived DEM. Hyperspectral imagery has been shown to be suitable for the separation of salt marsh vegetation species by spectral signatures, and can be used to determine cover classes; however, there is persistent confusion both among the different height classes of S. alterniflora and between plants and mud (the Spartina problem). This paper presents a method to overcome the respective limitations of LIDAR and hyperspectral imagery through the use of multisensor data. An initial classification of hyperspectral imagery based on the maximum likelihood classification algorithm was used in a decision tree in combination with elevation and normalized difference vegetation index (NDVI) derived from the hyperspectral imagery to map nine salt marsh cover classes. The decision tree appreciably reduced the Spartina problem by reassigning classes using these ancillary data and resulted in a final overall classification accuracy of 90%, with a quantity disagreement of 1% and an allocation disagreement of 9%. The resulting hyperspectral image classification was then used as the basis for applying cover class-specific elevation correction factors to the LIDAR-derived DEM. Applying these correction factors greatly improved the accuracy of the DEM: overall mean error decreased from 0.10 ± 0.12 (SD) to − 0.003 ± 0.10 m, and root mean squared error from 0.15 to 0.10 m. Our results suggest that the use of decision trees to combine elevation and spectral information can aid both hyperspectral image classification and DEM elevation mapping

Validation of real-time ultrasound technology for predicting fat thicknesses and ribeye areas of Brangus bulls from four months to two years of age

Sixty Brangus bulls were evaluated live with two real-time ultrasound instruments to estimate ribeye area (REA) and 12th rib fat thickness (FT) every 4 mo beginning at 4 mo and 12 mo of age, respectively, and continuing until 24 mo of age. At each evaluation period 10 bulls were slaughtered to determine actual REA and FT. Scanned mean FT was accurate (P<.05) at 16 mo and was not different (P=.09) from the actual mean FT. Scanned mean REA was accurate (P<.05) at 12 mo. Absolute differences between scanned and actual mean FT and REA were different (P<.05) from zero for all main effects. Increased level of operator (scanner) skill did not improve accuracy of FT or REA measurements, whereas increased level of interpreter (reader) skill improved accuracy of REA measurements. There was no difference (P>.05) between the two ultrasound units in accuracy of estimating FT or REA. Scanned measurements overestimated bulls with less than .20 in FT and greater than 13.6 in2 REA and underestimated bulls with more than .40 in. FT and less than 12.0 in2 REA. We conclude that REA scanned at 12 mo and FT at 12 or 16 mo were sufficiently accurate to characterize groups of young bulls; however, individual scans were inaccurate. Scanning at other months was not accurate for either individuals or groups of young bulls