Salt marshes play a crucial role in coastal biogeochemical cycles and provide unique ecosystem se... more Salt marshes play a crucial role in coastal biogeochemical cycles and provide unique ecosystem services. Salt marsh biomass, which can strongly influence such services, varies over time in response to hydrologic conditions and other environmental drivers. We used gap-filled monthly observations of Spartina alterniflora aboveground biomass derived from Landsat 5 and Landsat 8 satellite imagery from 1984-2018 to analyze temporal patterns in biomass in comparison to air temperature, precipitation, river discharge, nutrient input, sea level, and drought index for a southeastern US salt marsh. Wavelet analysis and ensemble empirical mode decomposition identified month to multi-year periodicities in both plant biomass and environmental drivers. Wavelet coherence detected cross-correlations between annual biomass cycles and precipitation, temperature, river discharge, nutrient concentrations (NOx and PO43–) and sea level. At longer periods we detected coherence between biomass and all vari...
Understanding the complex and unpredictable ways ecosystems are changing and predicting the state... more Understanding the complex and unpredictable ways ecosystems are changing and predicting the state of ecosystems and the services they will provide in the future requires coordinated, long-term research. This paper is a product of a U.S. National Science Foundation funded Long Term Ecological Research (LTER) network synthesis effort that addressed anticipated changes in future populations and communities. Each LTER site described what their site would look like in 50 or 100 yr based on long-term patterns and responses to global change drivers in each ecosystem. Common themes emerged and predictions were grouped into state change, connectivity, resilience, time lags, and cascading effects. Here, we report on the "state change" theme, which includes examples from the Georgia Coastal (coastal marsh), Konza Prairie (mesic grassland), Luquillo (tropical forest), Sevilleta (arid grassland), and Virginia Coastal (coastal grassland) sites. Ecological thresholds (the point at which small changes in an environmental driver can produce an abrupt and persistent state change in an ecosystem quality, property, or phenomenon) were most commonly predicted. For example, in coastal ecosystems, sea-level rise and climate change could convert salt marsh to mangroves and coastal barrier dunes to shrub thicket. Reduced fire frequency has converted grassland to shrubland in mesic prairie, whereas overgrazing combined with drought drive shrub encroachment in arid grasslands. Lastly, tropical cloud forests are susceptible to climate-induced changes in cloud base altitude leading to shifts in species distributions. Overall, these examples reveal that state change is a likely outcome of global environmental change across a diverse range of ecosystems and highlight the need for long-term studies to sort out the causes and consequences of state change. The diversity of sites within the LTER network facilitates the emergence of overarching concepts about state changes as an important driver of ecosystem structure, function, services, and futures.
This dataset is from a sampling campaign completed over the course of 48 hours to investigate the... more This dataset is from a sampling campaign completed over the course of 48 hours to investigate the effects of tidally induced water level variation on aqueous chemistry, dissolved organic matter chemistry, and metabolic potential of microbial communities at the Altamaha River, GA, USA. Filtered surface water (SW) and pore water (PP) samples were collected every three hours in October 2018. This dataset is one of seven companion datasets that were part of the WHONDRS 48 Hour Diel Cycling Study of freshwater rivers. All seven datasets are available on ESS-DIVE; they can be found by searching for WHONDRS 48 Hour.This package contains a variety of chemical and hydrologic variables. All data types are provided in a standard format. Basic geochemistry analyses (e.g., non-purgeable organic carbon concentration, cations, anions, SpC, pH) were conducted, standardized photos were taken, surface water hydrographs were collated from existing instrumentation, and extensive metadata were captured....
Water quality measures for inland and coastal waters are available as discrete samples from profe... more Water quality measures for inland and coastal waters are available as discrete samples from professional and volunteer water quality monitoring programs and higher-frequency, near-continuous data from automated in situ sensors. Water quality parameters also are estimated from model outputs and remote sensing. The integration of these data, via data assimilation, can result in a more holistic characterization of these highly dynamic ecosystems, and consequently improve water resource management. It is becoming common to see combinations of these data applied to answer relevant scientific questions. Yet, methods for scaling water quality data across regions and beyond, to provide actionable knowledge for stakeholders, have emerged only recently, particularly with the availability of satellite data now providing global coverage at high spatial resolution. In this paper, data sources and existing data integration frameworks are reviewed to give an overview of the present status and iden...
Retrieval of aquatic biogeochemical variables, such as the near-surface concentration of chloroph... more 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 impr...
Abstract Total suspended solids (TSS) concentration is an important biogeochemical parameter for ... more Abstract Total suspended solids (TSS) concentration is an important biogeochemical parameter for water quality management and sediment-transport studies. In this study, we propose a novel semi-analytical method for estimating TSS in clear to extremely turbid waters from remote-sensing reflectance (Rrs). The proposed method includes three sub-algorithms used sequentially. First, the remotely sensed waters are classified into clear (Type I), moderately turbid (Type II), highly turbid (Type III), and extremely turbid (Type IV) water types by comparing the values of Rrs at 490, 560, 620, and 754 nm. Second, semi-analytical models specific to each water type are used to determine the particulate backscattering coefficients (bbp) at a corresponding single wavelength (i.e., 560 nm for Type I, 665 nm for Type II, 754 nm for Type III, and 865 nm for Type IV). Third, a specific relationship between TSS and bbp at the corresponding wavelength is used in each water type. Unlike other existing approaches, this method is strictly semi-analytical and its sub-algorithms were developed using synthetic datasets only. The performance of the proposed method was compared to that of three other state-of-the-art methods using simulated (N = 1000, TSS ranging from 0.01 to 1100 g/m3) and in situ measured (N = 3421, TSS ranging from 0.09 to 2627 g/m3) pairs of Rrs and TSS. Results showed a significant improvement with a Median Absolute Percentage Error (MAPE) of 16.0% versus 30.2–90.3% for simulated data and 39.7% versus 45.9–58.1% for in situ data, respectively. The new method was subsequently applied to 175 MEdium Resolution Imaging Spectrometer (MERIS) and 498 Ocean and Land Colour Instrument (OLCI) images acquired in the 2003–2020 timeframe to produce long-term TSS time-series for Lake Suwa and Lake Kasumigaura, Japan. Performance assessments using MERIS and OLCI matchups showed good agreements with in situ TSS measurements.
The eastern oyster (Crassostrea virginica) is an important proxy for examining historical traject... more The eastern oyster (Crassostrea virginica) is an important proxy for examining historical trajectories of coastal ecosystems. Measurement of ~40,000 oyster shells from archaeological sites along the Atlantic Coast of the United States provides a long-term record of oyster abundance and size. The data demonstrate increases in oyster size across time and a nonrandom pattern in their distributions across sites. We attribute this variation to processes related to Native American fishing rights and environmental variability. Mean oyster length is correlated with total oyster bed length within foraging radii (5 and 10 km) as mapped in 1889 and 1890. These data demonstrate the stability of oyster reefs despite different population densities and environmental shifts and have implications for oyster reef restoration in an age of global climate change.
One of the challenging tasks in modern aquatic remote sensing is the retrieval of near-surface co... more 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 > 3500), the SOLID model is devised using a three-step procedure: (a) water-type classification of the input remote sensing reflectance (R rs ), (b) retrieval of particulate backscattering (b bp ) in the red or near-infrared (NIR) regions using semi-analytical, machine-learning, and empirical models, and (c) estimation of TSS from b bp 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/m 3 ], the SOLID model is thoroughly examined and compared against several state-of-the-art algorithms . We show that SOLID outperforms all the other models to varying degrees, i.e.,from 10 to > 100%, 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 R rs leads to < 20% uncertainty in TSS retrievals from SOLID. While this study suggests that SOLID
Consistent, cross-mission retrievals of near-surface concentration of chlorophyll-a (Chla) in var... more Consistent, cross-mission retrievals of near-surface concentration of chlorophyll-a (Chla) in various aquatic ecosystems with broad ranges of trophic levels have long been a complex undertaking. Here, we introduce a machine-learning model, the Mixture Density Network (MDN), that largely outperforms existing algorithms when applied across different bio-optical regimes in inland and coastal waters. The model is trained and validated using a sizeable database of co-located Chla measurements (n = 2943) and in situ hyperspectral radiometric data resampled to simulate the Multispectral Instrument (MSI) and the Ocean and Land Color Imager (OLCI) onboard Sentinel-2A/B and Sentinel-3A/B, respectively. Our performance evaluations of the model, via two-thirds of the in situ dataset with Chla ranging from 0.2 to 1209 mg/m 3 and a mean Chla of 21.7 mg/m 3 , suggest significant improvements in Chla retrievals. For both MSI and OLCI, the mean absolute logarithmic error (MAE) and logarithmic bias (Bias) across the entire range reduced by 40-60%, whereas the root mean squared logarithmic error (RMSLE) and the median absolute percentage error (MAPE) improved two-to-three times over those from the state-of-the-art algorithms. Using independent Chla matchups (n < 800) for Sentinel-2A/B and -3A, we show that the MDN model provides most accurate products from recorded images processed via three different atmospheric correction processors, namely the SeaWiFS Data Analysis System (SeaDAS), POLYMER, and ACOLITE, though the model is found to be sensitive to uncertainties in remote-sensing reflectance products. This manuscript serves as a preliminary study on a machine-learning algorithm with potential utility in seamless construction of Chla data records in inland and coastal waters, i.e., harmonized, comparable products via a single algorithm for MSI and OLCI data processing. The model performance is anticipated to enhance by improving the global representativeness of the training data as well as simultaneous retrievals of multiple optically active components of the water column.
Inland and coastal waterbodies are critical components of the global biosphere. Timely monitoring... more 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 5 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. The global importance of aquatic systems is incontestable since they play a fundamental role in biogeochemical cycling, the maintenance of biodiversity, and human wellbeing and prosperity (
Abstract Knowledge of light partitioning into different optically active constituents, particular... more Abstract Knowledge of light partitioning into different optically active constituents, particularly chromophoric dissolved organic matter (CDOM) in the ultraviolet (UV) is indispensable for understanding UV dependent biogeochemical issues including photochemical processes in optically complex waters. Herein a new approach is presented to investigate photochemistry by blending two ocean color algorithms, namely the composite SeaUV (Cao et al., 2014) and the SeaCDOM (Cao and Miller, 2015) algorithms, and applying them to visible remote sensing reflectance (Rrs) measured using the Hyperspectral Imager for the Coastal Ocean (HICO). As illustrated using photochemical carbon monoxide (CO) production from CDOM, this model approach allows high resolution examination of UV optical details with estimates of both depth-specific and depth-integrated photoproduction rates in a dynamic estuarine/coastal environment. Decoupled retrievals of inherent and apparent optical properties (i.e. diffuse attenuation coefficient (Kd) and CDOM absorption coefficient (ag)) using two distinct ocean color algorithms over the entire UV spectrum allow a synoptically dynamic view of CDOM's contribution to light attenuation (ag/Kd). This provides new potential to probe UV processes in complex coastal waters on regional as well as global scales using remote sensing of ocean color.
We examined the influence of abiotic drivers on inter-annual and phenological patterns of abovegr... more 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 m 2. 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.
Photogrammetric Engineering and Remote Sensing, 1995
Our spectrometer is a high spectral resolution instrument which has two sensing heads called came... more Our spectrometer is a high spectral resolution instrument which has two sensing heads called cameras. The two cameras were observed to be misregistered relative to one another by up to 6 nm. The cameras were brought to a common wavelength base to correctly compare the data when the two cameras are used in a near-simultaneous mode. The wavelengths were assigned to the respective corresponding camera spectral wavelength bands and spectral bandwidths and sampling intervals calculated for each camera. Spectral bandwidths were larger than expected, but the sampling intervals were in close agreement with manufacturer specifications. Both cameras were calibrated for radiance and irradiance. The calibration coefficients developed during the radiometric calibrations showed an apparent light intensity dependence for wavelengths greater than 1000 nm. A closure experiment was conducted to test the accuracy of radiometric calibrations. Our results showed that estimates of irradiance from both cameras were within 5 percent of each other.
Salt marshes play a crucial role in coastal biogeochemical cycles and provide unique ecosystem se... more Salt marshes play a crucial role in coastal biogeochemical cycles and provide unique ecosystem services. Salt marsh biomass, which can strongly influence such services, varies over time in response to hydrologic conditions and other environmental drivers. We used gap-filled monthly observations of Spartina alterniflora aboveground biomass derived from Landsat 5 and Landsat 8 satellite imagery from 1984-2018 to analyze temporal patterns in biomass in comparison to air temperature, precipitation, river discharge, nutrient input, sea level, and drought index for a southeastern US salt marsh. Wavelet analysis and ensemble empirical mode decomposition identified month to multi-year periodicities in both plant biomass and environmental drivers. Wavelet coherence detected cross-correlations between annual biomass cycles and precipitation, temperature, river discharge, nutrient concentrations (NOx and PO43–) and sea level. At longer periods we detected coherence between biomass and all vari...
Understanding the complex and unpredictable ways ecosystems are changing and predicting the state... more Understanding the complex and unpredictable ways ecosystems are changing and predicting the state of ecosystems and the services they will provide in the future requires coordinated, long-term research. This paper is a product of a U.S. National Science Foundation funded Long Term Ecological Research (LTER) network synthesis effort that addressed anticipated changes in future populations and communities. Each LTER site described what their site would look like in 50 or 100 yr based on long-term patterns and responses to global change drivers in each ecosystem. Common themes emerged and predictions were grouped into state change, connectivity, resilience, time lags, and cascading effects. Here, we report on the "state change" theme, which includes examples from the Georgia Coastal (coastal marsh), Konza Prairie (mesic grassland), Luquillo (tropical forest), Sevilleta (arid grassland), and Virginia Coastal (coastal grassland) sites. Ecological thresholds (the point at which small changes in an environmental driver can produce an abrupt and persistent state change in an ecosystem quality, property, or phenomenon) were most commonly predicted. For example, in coastal ecosystems, sea-level rise and climate change could convert salt marsh to mangroves and coastal barrier dunes to shrub thicket. Reduced fire frequency has converted grassland to shrubland in mesic prairie, whereas overgrazing combined with drought drive shrub encroachment in arid grasslands. Lastly, tropical cloud forests are susceptible to climate-induced changes in cloud base altitude leading to shifts in species distributions. Overall, these examples reveal that state change is a likely outcome of global environmental change across a diverse range of ecosystems and highlight the need for long-term studies to sort out the causes and consequences of state change. The diversity of sites within the LTER network facilitates the emergence of overarching concepts about state changes as an important driver of ecosystem structure, function, services, and futures.
This dataset is from a sampling campaign completed over the course of 48 hours to investigate the... more This dataset is from a sampling campaign completed over the course of 48 hours to investigate the effects of tidally induced water level variation on aqueous chemistry, dissolved organic matter chemistry, and metabolic potential of microbial communities at the Altamaha River, GA, USA. Filtered surface water (SW) and pore water (PP) samples were collected every three hours in October 2018. This dataset is one of seven companion datasets that were part of the WHONDRS 48 Hour Diel Cycling Study of freshwater rivers. All seven datasets are available on ESS-DIVE; they can be found by searching for WHONDRS 48 Hour.This package contains a variety of chemical and hydrologic variables. All data types are provided in a standard format. Basic geochemistry analyses (e.g., non-purgeable organic carbon concentration, cations, anions, SpC, pH) were conducted, standardized photos were taken, surface water hydrographs were collated from existing instrumentation, and extensive metadata were captured....
Water quality measures for inland and coastal waters are available as discrete samples from profe... more Water quality measures for inland and coastal waters are available as discrete samples from professional and volunteer water quality monitoring programs and higher-frequency, near-continuous data from automated in situ sensors. Water quality parameters also are estimated from model outputs and remote sensing. The integration of these data, via data assimilation, can result in a more holistic characterization of these highly dynamic ecosystems, and consequently improve water resource management. It is becoming common to see combinations of these data applied to answer relevant scientific questions. Yet, methods for scaling water quality data across regions and beyond, to provide actionable knowledge for stakeholders, have emerged only recently, particularly with the availability of satellite data now providing global coverage at high spatial resolution. In this paper, data sources and existing data integration frameworks are reviewed to give an overview of the present status and iden...
Retrieval of aquatic biogeochemical variables, such as the near-surface concentration of chloroph... more 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 impr...
Abstract Total suspended solids (TSS) concentration is an important biogeochemical parameter for ... more Abstract Total suspended solids (TSS) concentration is an important biogeochemical parameter for water quality management and sediment-transport studies. In this study, we propose a novel semi-analytical method for estimating TSS in clear to extremely turbid waters from remote-sensing reflectance (Rrs). The proposed method includes three sub-algorithms used sequentially. First, the remotely sensed waters are classified into clear (Type I), moderately turbid (Type II), highly turbid (Type III), and extremely turbid (Type IV) water types by comparing the values of Rrs at 490, 560, 620, and 754 nm. Second, semi-analytical models specific to each water type are used to determine the particulate backscattering coefficients (bbp) at a corresponding single wavelength (i.e., 560 nm for Type I, 665 nm for Type II, 754 nm for Type III, and 865 nm for Type IV). Third, a specific relationship between TSS and bbp at the corresponding wavelength is used in each water type. Unlike other existing approaches, this method is strictly semi-analytical and its sub-algorithms were developed using synthetic datasets only. The performance of the proposed method was compared to that of three other state-of-the-art methods using simulated (N = 1000, TSS ranging from 0.01 to 1100 g/m3) and in situ measured (N = 3421, TSS ranging from 0.09 to 2627 g/m3) pairs of Rrs and TSS. Results showed a significant improvement with a Median Absolute Percentage Error (MAPE) of 16.0% versus 30.2–90.3% for simulated data and 39.7% versus 45.9–58.1% for in situ data, respectively. The new method was subsequently applied to 175 MEdium Resolution Imaging Spectrometer (MERIS) and 498 Ocean and Land Colour Instrument (OLCI) images acquired in the 2003–2020 timeframe to produce long-term TSS time-series for Lake Suwa and Lake Kasumigaura, Japan. Performance assessments using MERIS and OLCI matchups showed good agreements with in situ TSS measurements.
The eastern oyster (Crassostrea virginica) is an important proxy for examining historical traject... more The eastern oyster (Crassostrea virginica) is an important proxy for examining historical trajectories of coastal ecosystems. Measurement of ~40,000 oyster shells from archaeological sites along the Atlantic Coast of the United States provides a long-term record of oyster abundance and size. The data demonstrate increases in oyster size across time and a nonrandom pattern in their distributions across sites. We attribute this variation to processes related to Native American fishing rights and environmental variability. Mean oyster length is correlated with total oyster bed length within foraging radii (5 and 10 km) as mapped in 1889 and 1890. These data demonstrate the stability of oyster reefs despite different population densities and environmental shifts and have implications for oyster reef restoration in an age of global climate change.
One of the challenging tasks in modern aquatic remote sensing is the retrieval of near-surface co... more 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 > 3500), the SOLID model is devised using a three-step procedure: (a) water-type classification of the input remote sensing reflectance (R rs ), (b) retrieval of particulate backscattering (b bp ) in the red or near-infrared (NIR) regions using semi-analytical, machine-learning, and empirical models, and (c) estimation of TSS from b bp 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/m 3 ], the SOLID model is thoroughly examined and compared against several state-of-the-art algorithms . We show that SOLID outperforms all the other models to varying degrees, i.e.,from 10 to > 100%, 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 R rs leads to < 20% uncertainty in TSS retrievals from SOLID. While this study suggests that SOLID
Consistent, cross-mission retrievals of near-surface concentration of chlorophyll-a (Chla) in var... more Consistent, cross-mission retrievals of near-surface concentration of chlorophyll-a (Chla) in various aquatic ecosystems with broad ranges of trophic levels have long been a complex undertaking. Here, we introduce a machine-learning model, the Mixture Density Network (MDN), that largely outperforms existing algorithms when applied across different bio-optical regimes in inland and coastal waters. The model is trained and validated using a sizeable database of co-located Chla measurements (n = 2943) and in situ hyperspectral radiometric data resampled to simulate the Multispectral Instrument (MSI) and the Ocean and Land Color Imager (OLCI) onboard Sentinel-2A/B and Sentinel-3A/B, respectively. Our performance evaluations of the model, via two-thirds of the in situ dataset with Chla ranging from 0.2 to 1209 mg/m 3 and a mean Chla of 21.7 mg/m 3 , suggest significant improvements in Chla retrievals. For both MSI and OLCI, the mean absolute logarithmic error (MAE) and logarithmic bias (Bias) across the entire range reduced by 40-60%, whereas the root mean squared logarithmic error (RMSLE) and the median absolute percentage error (MAPE) improved two-to-three times over those from the state-of-the-art algorithms. Using independent Chla matchups (n < 800) for Sentinel-2A/B and -3A, we show that the MDN model provides most accurate products from recorded images processed via three different atmospheric correction processors, namely the SeaWiFS Data Analysis System (SeaDAS), POLYMER, and ACOLITE, though the model is found to be sensitive to uncertainties in remote-sensing reflectance products. This manuscript serves as a preliminary study on a machine-learning algorithm with potential utility in seamless construction of Chla data records in inland and coastal waters, i.e., harmonized, comparable products via a single algorithm for MSI and OLCI data processing. The model performance is anticipated to enhance by improving the global representativeness of the training data as well as simultaneous retrievals of multiple optically active components of the water column.
Inland and coastal waterbodies are critical components of the global biosphere. Timely monitoring... more 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 5 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. The global importance of aquatic systems is incontestable since they play a fundamental role in biogeochemical cycling, the maintenance of biodiversity, and human wellbeing and prosperity (
Abstract Knowledge of light partitioning into different optically active constituents, particular... more Abstract Knowledge of light partitioning into different optically active constituents, particularly chromophoric dissolved organic matter (CDOM) in the ultraviolet (UV) is indispensable for understanding UV dependent biogeochemical issues including photochemical processes in optically complex waters. Herein a new approach is presented to investigate photochemistry by blending two ocean color algorithms, namely the composite SeaUV (Cao et al., 2014) and the SeaCDOM (Cao and Miller, 2015) algorithms, and applying them to visible remote sensing reflectance (Rrs) measured using the Hyperspectral Imager for the Coastal Ocean (HICO). As illustrated using photochemical carbon monoxide (CO) production from CDOM, this model approach allows high resolution examination of UV optical details with estimates of both depth-specific and depth-integrated photoproduction rates in a dynamic estuarine/coastal environment. Decoupled retrievals of inherent and apparent optical properties (i.e. diffuse attenuation coefficient (Kd) and CDOM absorption coefficient (ag)) using two distinct ocean color algorithms over the entire UV spectrum allow a synoptically dynamic view of CDOM's contribution to light attenuation (ag/Kd). This provides new potential to probe UV processes in complex coastal waters on regional as well as global scales using remote sensing of ocean color.
We examined the influence of abiotic drivers on inter-annual and phenological patterns of abovegr... more 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 m 2. 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.
Photogrammetric Engineering and Remote Sensing, 1995
Our spectrometer is a high spectral resolution instrument which has two sensing heads called came... more Our spectrometer is a high spectral resolution instrument which has two sensing heads called cameras. The two cameras were observed to be misregistered relative to one another by up to 6 nm. The cameras were brought to a common wavelength base to correctly compare the data when the two cameras are used in a near-simultaneous mode. The wavelengths were assigned to the respective corresponding camera spectral wavelength bands and spectral bandwidths and sampling intervals calculated for each camera. Spectral bandwidths were larger than expected, but the sampling intervals were in close agreement with manufacturer specifications. Both cameras were calibrated for radiance and irradiance. The calibration coefficients developed during the radiometric calibrations showed an apparent light intensity dependence for wavelengths greater than 1000 nm. A closure experiment was conducted to test the accuracy of radiometric calibrations. Our results showed that estimates of irradiance from both cameras were within 5 percent of each other.
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Papers by John Schalles