Monitoring Coastal Chlorophyll-a Concentrations in Coastal Areas Using Machine Learning Models

Harmful algal blooms have negatively affected the aquaculture industry and aquatic ecosystems globally. Remote sensing using satellite sensor systems has been applied on large spatial scales with high temporal resolutions for effective monitoring of harmful algal blooms in coastal waters. However, oceanic color satellites have limitations, such as low spatial resolution of sensor systems and the optical complexity of coastal waters. In this study, bands 1 to 4, obtained from Landsat-8 Operational Land Imager satellite images, were used to evaluate the performance of empirical ocean chlorophyll algorithms using machine learning techniques. Artificial neural network and support vector machine techniques were used to develop an optimal chlorophyll-a model. Four-band, four-band-ratio, and mixed reflectance datasets were tested to select the appropriate input dataset for estimating chlorophyll-a concentration using the two machine learning models. While the ocean chlorophyll algorithm application on Landsat-8 Operational Land Imager showed relatively low performance, the machine learning methods showed improved performance during both the training and validation steps. The artificial neural network and support vector machine demonstrated a similar level of prediction accuracy. Overall, the support vector machine showed slightly superior performance to that of the artificial neural network during the validation step. This study provides practical information about effective monitoring systems for coastal algal blooms

High-Spatial Resolution Monitoring of Phycocyanin and Chlorophyll-a Using Airborne Hyperspectral Imagery

Hyperspectral imagery (HSI) provides substantial information on optical features of water bodies that is usually applicable to water quality monitoring. However, it generates considerable uncertainties in assessments of spatial and temporal variation in water quality. Thus, this study explored the influence of different optical methods on the spatial distribution and concentration of phycocyanin (PC), chlorophyll-a (Chl-a), and total suspended solids (TSSs) and evaluated the dependence of algal distribution on flow velocity. Four ground-based and airborne monitoring campaigns were conducted to measure water surface reflectance. The actual concentrations of PC, Chl-a, and TSSs were also determined, while four bio-optical algorithms were calibrated to estimate the PC and Chl-a concentrations. Artificial neural network atmospheric correction achieved Nash-Sutcliffe Efficiency (NSE) values of 0.80 and 0.76 for the training and validation steps, respectively. Moderate resolution atmospheric transmission 6 (MODTRAN 6) showed an NSE value >0.8; whereas, atmospheric and topographic correction 4 (ATCOR 4) yielded a negative NSE value. The MODTRAN 6 correction led to the highest R-2 values and lowest root mean square error values for all algorithms in terms of PC and Chl-a. The PC:Chl-a distribution generated using HSI proved to be negatively dependent on flow velocity (p-value = 0.003) and successfully indicated cyanobacteria risk regions in the study area

Optimizing Semi-Analytical Algorithms for Estimating Chlorophyll-a and Phycocyanin Concentrations in Inland Waters in Korea

Several semi-analytical algorithms have been developed to estimate the chlorophyll-a (Chl-a) and phycocyanin (PC) concentrations in inland waters. This study aimed at identifying the influence of algorithm parameters on the output variables and searching optimal parameter values. The optimal parameters of seven semi-analytical algorithms were applied to estimate the Chl-a and PC concentrations. The absorption coefficient measurements were coupled with pigment measurements to calibrate the algorithm parameters. For sensitivity analysis, the elementary effect test was conducted to analyze the influence of the algorithm parameters. The sensitivity analysis results showed that the parameters in the Y function and specific absorption coefficient were the most sensitive parameters. Then, the parameters were optimized via a single-objective optimization that involved one objective function being minimized and a multi-objective optimization that contained more than one objective function. The single-objective optimization led to substantial errors in absorption coefficients. In contrast, the multi-objective optimization improved the algorithm performance with respect to both the absorption coefficient estimates and pigment concentration estimates. The optimized parameters of the absorption coefficient reflected the high-particulate content in waters of the Baekje reservoir using an infrared backscattering wavelength and relatively high value of Y. Moreover, the results indicate the value of measuring the site-specific absorption if site-specific optimization of semi-analyical algorithm parameters was envisioned

Modeling the Fate and Transport of Malathion in the Pagsanjan-Lumban Basin, Philippines

Exposure to highly toxic pesticides could potentially cause cancer and disrupt the development of vital systems. Monitoring activities were performed to assess the level of contamination; however, these were costly, laborious, and short-term leading to insufficient monitoring data. However, the performance of the existing Soil and Water Assessment Tool (SWAT model) can be restricted by its two-phase partitioning approach, which is inadequate when it comes to simulating pesticides with limited dataset. This study developed a modified SWAT pesticide model to address these challenges. The modified model considered the three-phase partitioning model that classifies the pesticide into three forms: dissolved, particle-bound, and dissolved organic carbon (DOC)-associated pesticide. The addition of DOC-associated pesticide particles increases the scope of the pesticide model by also considering the adherence of pesticides to the organic carbon in the soil. The modified SWAT and original SWAT pesticide model was applied to the Pagsanjan-Lumban (PL) basin, a highly agricultural region. Malathion was chosen as the target pesticide since it is commonly used in the basin. The pesticide models simulated the fate and transport of malathion in the PL basin and showed the temporal pattern of selected subbasins. The sensitivity analyses revealed that application efficiency and settling velocity were the most sensitive parameters for the original and modified SWAT model, respectively. Degradation of particulate-phase malathion were also significant to both models. The rate of determination (R2) and Nash-Sutcliffe efficiency (NSE) values showed that the modified model (R2 = 0.52; NSE = 0.36) gave a slightly better performance compared to the original (R2 = 0.39; NSE = 0.18). Results from this study will be able to aid the government and private agriculture sectors to have an in-depth understanding in managing pesticide usage in agricultural watersheds

Optimal Band Selection for Airborne Hyperspectral Imagery to Retrieve a Wide Range of Cyanobacterial Pigment Concentration Using a Data-Driven Approach

Understanding the concentration and distribution of cyanobacteria blooms is an important aspect of managing water quality problems and protecting aquatic ecosystems. Airborne hyperspectral imagery (HSI)-which has high temporal, spatial, and spectral resolutions-is widely used to remotely sense cyanobacteria bloom, and it provides the distribution of the bloom over a wide area. In this study, we determined the input spectral bands that were relevant in effectively estimating the main two pigments (PC, Phycocyanin; Chl-a, Chlorophyll-a) of cyanobacteria by applying data-driven algorithms to HSI and then evaluating the change in the spatio-temporal distribution of cyanobacteria. The input variables for the algorithms consisted of reflectance band ratios associated with the optical properties of PC and Chl-a, which were calculated by the selected hyperspectral bands using a feature selection method. The selected input variable was composed of six reflectance bands (465.7-589.6, 603.6-631.8, 641.2-655.35, 664.8-679.0, 698.0-712.3, and 731.4-784.1 nm). The artificial neural network showed the best results for the estimation of the two pigments with average coefficients of determination 0.80 and 0.74. This study proposes relevant input spectral information and an algorithm that can effectively detect the occurrence of cyanobacteria in the weir pool along the Geum river, South Korea. The algorithm is expected to help establish a preemptive response to the formation of cyanobacterial blooms, and to contribute to the preparation of suitable water quality management plans for freshwater environments

Application of airborne hyperspectral imagery to retrieve spatiotemporal CDOM distribution using machine learning in a reservoir

Colored dissolved organic matter (CDOM) in inland waters is used as a proxy to estimate dissolved organic carbon (DOC) and may be a key indicator of water quality and nutrient enrichment. CDOM is optically active fraction of DOC so that remote sensing techniques can remotely monitor CDOM with wide spatial coverage. However, to effectively retrieve CDOM using optical algorithms, it may be critical to select the absorption co-efficient at an appropriate wavelength as an output variable and to optimize input reflectance wavelengths. In this study, we constructed a CDOM retrieval model using airborne hyperspectral reflectance data and a machine learning model such as random forest. We evaluated the best combination of input wavelength bands and the CDOM absorption coefficient at various wavelengths. Seven sampling events for airborne hyperspectral imagery and CDOM absorption coefficient data from 350 nm to 440 nm over two years (2016-2017) were used, and the collected data helped train and validate the random forest model in a freshwater reservoir. An absorption co-efficient of 355 nm was selected to best represent the CDOM concentration. The random forest exhibited the best performance for CDOM estimation with an R2 of 0.85, Nash-Sutcliffe efficiency of 0.77, and percent bias of 3.88, by using a combination of three reflectance bands: 475, 497, and 660 nm. The results show that our model can be utilized to construct a CDOM retrieving algorithm and evaluate its spatiotemporal variation across a reservoir

Saturated Hydraulic Conductivity of US Soils Grouped According to Textural Class and Bulk Density

The importance of saturated hydraulic conductivity (Ksat) as a soil hydraulic property led to the development of multiple pedotransfer functions for estimating it. One approach to estimating Ksat uses textural classes rather than specific textural fraction contents as a pedotransfer input. The objective of this work was to develop and evaluate a grouping-based pedotransfer procedure to estimate Ksat for sample sizes used in laboratory measurements. A search of publications and reports resulted in the collection of 1245 data sets with coupled data on Ksat, USDA textural class, and bulk density in the United States into a database called USKSAT. A separate database was assembled for the state of Florida that included 24,566 data sets. Data in each textural class were split into high and low bulk density groups using the splitting algorithm that created the most homogeneous groups. Sample diameters and lengths were <10 cm. Peaks of the semi-partial R2 were well defined for loamy soils. The threshold bulk density separating high and low bulk density groups is 1.24 g cm&#8722;3 for clay soils, about 1.33 g cm&#8722;3 for loamy soils, and about 1.65 g cm&#8722;3 for sandy soils. The high bulk density groups included a much broader range of Ksat values than the low bulk density groups for clays and loams but not sandy soils. Inspection of superimposed dependencies of Ksat on bulk density in the USKSAT database and in the Florida database showed their similarity. When geometric means were used as estimates of Ksat within groups, the accuracy was not high and yet was comparable with estimates obtained from far more detailed soil information using sophisticated machine learning methods. Estimating Ksat from textural class and bulk density may have the advantage of utility in data-poor environments and large-scale projects.clos

Developing statistical models for estimating chlorophyll-a and total suspended solid levels at an estuarine reservoir with nutrient inputs from satellite observations

Providing high-resolution monitoring data is essential in promoting decision-making activities for surface water quality protection such as scientific modeling and data analysis. In this study, statistical models using multiple linear regression (MLR) were developed to estimate Chlorophyll-a (Chl-a) and total suspended solid (TSS) concentrations in a mesotrophic reservoir, the Yeongam Reservoir in Korea, from satellite observations. Two types of satellite data that covered different spectral regions, for wavelengths in 412-865nm (for the Geostationary Ocean Color Imager) and those in 405-14,385nm (for the Moderate Resolution Imaging Spectroradiometer), were used as inputs for statistical models, after bias correction. The MLR models for Chl-a and TSS were initially constructed and evaluated with 39 image data-sets obtained during 2011-2014. Subsequently, they compared with their corresponding algorithms that were developed under different environmental settings as well as the CE-QUAL-W2 model, a numerical model of reservoir water quality. Sensitivity analysis showed that specific red and near-infrared wavelengths significantly contributed to improve the accuracy of the Chl-a and TSS estimates, respectively, along with those of blue and green bands typically used. The constructed MLR models showed better performance than the simulation model as well as the classical and recent bio-optical algorithms on average. In particular, poor prediction performance for total nitrogen and total phosphorus caused by a lack of adequate input data and description of transport mechanisms appeared to lower the accuracy of the Chl-a and TSS estimates in the simulation model. Therefore, these results demonstrate that statistical models developed from satellite observations can be used to rapidly screen local water quality and to provide high-quality data for reservoir water quality management.clos

Survival of Manure-borne Escherichia coli and Fecal Coliforms in Soil: Temperature Dependence as Affected by Site-Specific Factors

Understanding pathogenic and indicator bacteria survival in soils is essential for assessing the potential of microbial contamination of water and produce. The objective of this work was to evaluate the effects of soil properties, animal source, experimental conditions, and the application method on temperature dependencies of manure-borne generic Escherichia coli, E. coli O157: H7, and fecal coliforms survival in soils. A literature search yielded 151 survival datasets from 70 publications. Either one-stage or two-stage kinetics was observed in the survival datasets. We used duration and rate of the logarithm of concentration change as parameters of the first stage in the two-stage kinetics data. The second stage of the two-stage kinetics and the one-stage kinetics were simulated with the Q(10) model to find the dependence of the inactivation rate on temperature. Classification and regression trees and linear regressions were applied to parameterize the kinetics. Presence or absence of two-stage kinetics was controlled by temperature, soil texture, soil water content, and for fine-textured soils by setting experiments in the field or in the laboratory. The duration of the first stage was predominantly affected by soil water content and temperature. In the Q(10) model dependencies of inactivation rates on temperature, parameter Q(10) estimates were significantly affected by the laboratory versus field conditions and by the application method, whereas inactivation rates at 20 degrees C were significantly affected by all survival and management factors. Results of this work can provide estimates of coliform survival parameters for models of microbial water quality.clos