Chromophoric Dissolved Organic Matter and Dissolved Organic Carbon from Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS) and MERIS Sensors: Case Study for the Northern Gulf of Mexico

Empirical band ratio algorithms for the estimation of colored dissolved organic matter (CDOM) and dissolved organic carbon (DOC) for Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS) and MERIS ocean color sensors were assessed and developed for the northern Gulf of Mexico. Match-ups between in situ measurements of CDOM absorption coefficients at 412 nm (aCDOM(412)) with that derived from SeaWiFS were examined using two previously reported reflectance band ratio algorithms. Results indicate better performance using the Rrs(510)/Rrs(555) (Bias = −0.045; RMSE = 0.23; SI = 0.49, and R2 = 0.66) than the Rrs(490)/Rrs(555) reflectance band ratio algorithm. Further, a comparison of aCDOM(412) retrievals using the Rrs(488)/Rrs(555) for MODIS and Rrs(510)/Rrs(560) for MERIS reflectance band ratios revealed better CDOM retrievals with MERIS data. Since DOC cannot be measured directly by remote sensors, CDOM as the colored component of DOC is utilized as a proxy to estimate DOC remotely. A seasonal relationship between CDOM and DOC was established for the summer and spring-winter with high correlation for both periods (R2~0.9). Seasonal band ratio empirical algorithms to estimate DOC were thus developed using the relationships between CDOM-Rrs and seasonal CDOM-DOC for SeaWiFS, MODIS and MERIS. Results of match-up comparisons revealed DOC estimates by both MODIS and MERIS to be relatively more accurate during summer time, while both of them underestimated DOC during spring-winter time. A better DOC estimate from MERIS in comparison to MODIS in spring-winter could be attributed to its similarity with the SeaWiFS band ratio CDOM algorithm

Semi-Supervised Adversarial Domain Adaptation for Seagrass Detection Using Multispectral Images in Coastal Areas

Seagrass form the basis for critically important marine ecosystems. Previously, we implemented a deep convolutional neural network (CNN) model to detect seagrass in multispectral satellite images of three coastal habitats in northern Florida. However, a deep CNN model trained at one location usually does not generalize to other locations due to data distribution shifts. In this paper, we developed a semi-supervised domain adaptation method to generalize a trained deep CNN model to other locations for seagrass detection. First, we utilized a generative adversarial network loss to align marginal data distribution between source domain and target domain using unlabeled data from both data domains. Second, we used a few labelled samples from the target domain to align class specific data distributions between the two domains, based on the contrastive semantic alignment loss. We achieved the best results in 28 out of 36 scenarios as compared to other state-of-the-art domain adaptation methods

Adding Remote Sensing Data Products to the Nutrient Management Decision Support Toolbox

Some of the primary issues that manifest from nutrient enrichment and eutrophication (Figure 1) may be observed from satellites. For example, remotely sensed estimates of chlorophyll a (chla), total suspended solids (TSS), and light attenuation (Kd) or water clarity, which are often associated with elevated nutrient inputs, are data products collected daily and globally for coastal systems from satellites such as NASA s MODIS (Figure 2). The objective of this project is to inform water quality decision making activities using remotely sensed water quality data. In particular, we seek to inform the development of numeric nutrient criteria. In this poster we demonstrate an approach for developing nutrient criteria based on remotely sensed chla

Quantifying Seagrass Distribution in Coastal Water With Deep Learning Models

Coastal ecosystems are critically affected by seagrass, both economically and ecologically. However, reliable seagrass distribution information is lacking in nearly all parts of the world because of the excessive costs associated with its assessment. In this paper, we develop two deep learning models for automatic seagrass distribution quantification based on 8-band satellite imagery. Specifically, we implemented a deep capsule network (DCN) and a deep convolutional neural network (CNN) to assess seagrass distribution through regression. The DCN model first determines whether seagrass is presented in the image through classification. Second, if seagrass is presented in the image, it quantifies the seagrass through regression. During training, the regression and classification modules are jointly optimized to achieve end-to-end learning. The CNN model is strictly trained for regression in seagrass and non-seagrass patches. In addition, we propose a transfer learning approach to transfer knowledge in the trained deep models at one location to perform seagrass quantification at a different location. We evaluate the proposed methods in three WorldView-2 satellite images taken from the coastal area in Florida. Experimental results show that the proposed deep DCN and CNN models performed similarly and achieved much better results than a linear regression model and a support vector machine. We also demonstrate that using transfer learning techniques for the quantification of seagrass significantly improved the results as compared to directly applying the deep models to new locations

Satellite Remote Sensing of Cyanobacteria: Success Stories of Management Taking Action and the CyAN Data Sharing App

Support the environmental management and public use of U.S. lakes by detecting and quantifying algal blooms and related water quality indicators using satellite data records

MODIS-derived spatiotemporal water clarity patterns in optically shallow Florida Keys waters: A new approach to remove bottom contamination

Retrievals of water quality parameters from satellite measurements over optically shallow waters have been problematic due to bottom contamination of the signals. As a result, large errors are associated with derived water column properties. These deficiencies greatly reduce the ability to use satellites to assess the shallow water environments around coral reefs and seagrass beds. Here, a modified version of an existing algorithm is used to derive multispectral diffuse attenuation coefficient (Kd) from MODIS/Aqua measurements over optically shallow waters in the Florida Keys. Results were validated against concurrent in situ data (Kd(488) from 0.02 to 0.20 m−1, N = 22, R2 = 0.68, Mean Ratio = 0.93, unbiased RMS = 31%), and showed significant improvement over current products when compared to the same in situ data (N = 13, R2 = 0.37, Mean Ratio = 1.61, unbiased RMS = 50%). The modified algorithm was then applied to time series of MODIS/Aqua data over the Florida Keys (in particular, the Florida Keys Reef Tract), whereby spatial and temporal patterns of water clarity between 2002 and 2011 were elucidated. Climatologies, time series, anomaly images, and empirical orthogonal function analysis showed primarily nearshore–offshore gradients in water clarity and its variability, with peaks in both at the major channels draining Florida Bay. ANOVA revealed significant differences in Kd(488) according to distance from shore and geographic region. Excluding the Dry Tortugas, which had the lowest climatological Kd(488), water was clearest at the northern extent of the Reef Tract, and Kd(488) significantly decreased sequentially for every region along the tract. Tests over other shallow-water tropical waters such as the Belize Barrier Reef also suggested general applicability of the algorithm. As water clarity and light availability on the ocean bottom are key environmental parameters in determining the health of shallow-water plants and animals, the validated new products provide unprecedented information for assessing and monitoring of coral reef and seagrass health, and could further assist ongoing regional zoning efforts

Paths to research-driven decision making in the realms of environment and water

Now more than ever it is critical for researchers and decision makers to work together to improve how we manage and preserve the planet\u27s natural resources. Water managers in the western U.S., as in many regions of the world, are facing unprecedented challenges including increasing water demands and diminishing or unpredictable supplies. The transfer of knowledge (KT) and technology (TT) between researchers and entities that manage natural resources can help address these issues. However, numerous barriers impede the advancement of such transfer, particularly between organizations that do not operate in a profit-oriented context and for which best practices for university-industry collaborative engagement may not be sufficient. Frameworks designed around environmental KT – such as the recently-developed Research-Integration-Utilization (RIU) model – can be leveraged to address these barriers. Here, we examine two examples in which NASA Earth science satellite data and remote-sensing technology are used to improve the management of water availability and quality. Despite differences in scope and outcomes, both of these case studies adopt KT and TT best practices and can be further understood through the lens of the RIU model. We show how these insights could be adopted by NASA through a conceptual framework that charts individual- and organizational-level integration milestones alongside technical milestones. Environmental organizations can learn from this approach and adapt it to fit their own institutional needs, integrating KT/TT models and best practices while recognizing and leveraging existing institutional logics that suit their organization\u27s unique history, technical capability and priorities

Performance Metrics for the Assessment of Satellite Data Products: An Ocean Color Case Study

Performance assessment of ocean color satellite data has generally relied on statistical metrics chosen for their common usage and the rationale for selecting certain metrics is infrequently explained. Commonly reported statistics based on mean squared errors, such as the coefficient of determination (r2), root mean square error, and regression slopes, are most appropriate for Gaussian distributions without outliers and, therefore, are often not ideal for ocean color algorithm performance assessment, which is often limited by sample availability. In contrast, metrics based on simple deviations, such as bias and mean absolute error, as well as pair-wise comparisons, often provide more robust and straightforward quantities for evaluating ocean color algorithms with non-Gaussian distributions and outliers. This study uses a SeaWiFS chlorophyll-a validation data set to demonstrate a framework for satellite data product assessment and recommends a multi-metric and user-dependent approach that can be applied within science, modeling, and resource management communities

Impact of Atmospheric Correction on Classification and Quantification of Seagrass Density from WorldView-2 Imagery

Mapping the seagrass distribution and density in the underwater landscape can improve global Blue Carbon estimates. However, atmospheric absorption and scattering introduce errors in space-based sensors’ retrieval of sea surface reflectance, affecting seagrass presence, density, and above-ground carbon (AGCseagrass) estimates. This study assessed atmospheric correction’s impact on mapping seagrass using WorldView-2 satellite imagery from Saint Joseph Bay, Saint George Sound, and Keaton Beach in Florida, USA. Coincident in situ measurements of water-leaving radiance (Lw), optical properties, and seagrass leaf area index (LAI) were collected. Seagrass classification and the retrieval of LAI were compared after empirical line height (ELH) and dark-object subtraction (DOS) methods were used for atmospheric correction. DOS left residual brightness in the blue and green bands but had minimal impact on the seagrass classification accuracy. However, the brighter reflectance values reduced LAI retrievals by up to 50% compared to ELH-corrected images and ground-based observations. This study offers a potential correction for LAI underestimation due to incomplete atmospheric correction, enhancing the retrieval of seagrass density and above-ground Blue Carbon from WorldView-2 imagery without in situ observations for accurate atmospheric interference correction

Performance Across Worldview-2 and RapidEye for Reproducible Seagrass Mapping

Satellite remote sensing offers an effective remedy to challenges in ground-based and aerial mapping that have previously impeded quantitative assessments of global seagrass extent. Commercial satellite platforms offer fine spatial resolution, an important consideration in patchy seagrass ecosystems. Currently, no consistent protocol exists for image processing of commercial data, limiting reproducibility and comparison across space and time. Additionally, the radiometric performance of commercial satellite sensors has not been assessed against the dark and variable targets characteristic of coastal waters. This study compared data products derived from two commercial satellites: DigitalGlobe\u27s WorldView-2 and Planet\u27s RapidEye. A single scene from each platform was obtained at St. Joseph Bay in Florida, USA, corresponding to a November 2010 field campaign. A reproducible processing regime was developed to transform imagery from basic products, as delivered from each company, into analysis-ready data usable for various scientific applications. Satellite-derived surface reflectances were compared against field measurements. WorldView-2 imagery exhibited high disagreement in the coastal blue and blue spectral bands, chronically overpredicting. RapidEye exhibited better agreement than WorldView-2, but overpredicted slightly across all spectral bands. A deep convolutional neural network was used to classify imagery into deep water, land, submerged sand, seagrass, and intertidal classes. Classification results were compared to seagrass maps derived from photointerpreted aerial imagery. This study offers the first radiometric assessment of WorldView-2 and RapidEye over a coastal system, revealing inherent calibration issues in shorter wavelengths of WorldView-2. Both platforms demonstrated as much as 97% agreement with aerial estimates, despite differing resolutions. Thus, calibration issues in WorldView-2 did not appear to interfere with classification accuracy, but could be problematic if estimating biomass. The image processing routine developed here offers a reproducible workflow for WorldView-2 and RapidEye imagery, which was tested in two additional coastal systems. This approach may become platform independent as more sensors become available