Deriving Particulate Organic Carbon in Coastal Waters from Remote Sensing: Inter-Comparison Exercise and Development of a Maximum Band-Ratio Approach

Recently, different algorithms have been developed to assess near-surface particulate organic matter (POC) concentration over coastal waters. In this study, we gathered an extensive in situ dataset representing various contrasted bio-optical coastal environments at low, medium, and high latitudes, with various bulk particulate matter chemical compositions (mineral-dominated, 50% of the data set, mixed, 40%, or organic-dominated, 10%). The dataset includes 606 coincident measurements of POC concentration and remote-sensing reflectance, Rrs, with POC concentrations covering three orders of magnitude. Twelve existing algorithms have then been tested on this data set, and a new one was proposed. The results show that the performance of historical algorithms depends on the type of water, with an overall low performance observed for mineral-dominated waters. Furthermore, none of the tested algorithms provided satisfactory results over the whole POC range. A novel approach was thus developed based on a maximum band ratio of Rrs (red/blue, red/yellow or red/green ratio). Based on the standard statistical metric for the evaluation of inverse models, the new algorithm presents the best performance. The root-mean square deviation for log-transformed data (RMSDlog) is 0.25. The mean absolute percentage difference (MAPD) is 37.48%. The mean bias (MB) and median ratio (MR) values are 0.54 μg L−1 and 1.02, respectively. This algorithm replicates quite well the distribution of in situ data. The new algorithm was also tested on a matchup dataset gathering 154 coincident MERIS (MEdium Resolution Imaging Spectrometer) Rrs and in situ POC concentration sampled along the French coast. The matchup analysis showed that the performance of the new algorithm is satisfactory (RMSDlog = 0.24, MAPD = 34.16%, MR = 0.92). A regional illustration of the model performance for the Louisiana continental shelf shows that monthly mean POC concentrations derived from MERIS with the new algorithm are consistent with those derived from the 2016 algorithm of Le et al. which was specifically developed for this region.

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Mapping Chlorophyll-a Concentrations in the Kaštela Bay and Brač Channel Using Ridge Regression and Sentinel-2 Satellite Images

Electronics ◽

10.3390/electronics10233004 ◽

2021 ◽

Vol 10 (23) ◽

pp. 3004

Author(s):

Antonia Ivanda ◽

Ljiljana Šerić ◽

Marin Bugarić ◽

Maja Braović

Keyword(s):

Remote Sensing ◽

Chlorophyll A ◽

Satellite Data ◽

Coastal Waters ◽

Optical Parameter ◽

Data Set ◽

Chl A ◽

Sentinel 2

In this paper, we describe a method for the prediction of concentration of chlorophyll-a (Chl-a) from satellite data in the coastal waters of Kaštela Bay and the Brač Channel (our case study areas) in the Republic of Croatia. Chl-a is one of the parameters that indicates water quality and that can be measured by in situ measurements or approximated as an optical parameter with remote sensing. Remote sensing products for monitoring Chl-a are mostly based on the ocean and open sea monitoring and are not accurate for coastal waters. In this paper, we propose a method for remote sensing monitoring that is locally tailored to suit the focused area. This method is based on a data set constructed by merging Sentinel 2 Level-2A satellite data with in situ Chl-a measurements. We augmented the data set horizontally by transforming the original feature set, and vertically by adding synthesized zero measurements for locations without Chl-a. By transforming features, we were able to achieve a sophisticated model that predicts Chl-a from combinations of features representing transformed bands. Multiple Linear Regression equation was derived to calculate Chl-a concentration and evaluated quantitatively and qualitatively. Quantitative evaluation resulted in R2 scores 0.685 and 0.659 for train and test part of data set, respectively. A map of Chl-a of the case study area was generated with our model for the dates of the known incidents of algae blooms. The results that we obtained are discussed in this paper.

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Assessment of Polymer Atmospheric Correction Algorithm for Hyperspectral Remote Sensing Imagery over Coastal Waters

Sensors ◽

10.3390/s21124125 ◽

2021 ◽

Vol 21 (12) ◽

pp. 4125

Author(s):

Mariana A. Soppa ◽

Brenner Silva ◽

François Steinmetz ◽

Darryl Keith ◽

Daniel Scheffler ◽

...

Keyword(s):

Remote Sensing ◽

Satellite Data ◽

Coastal Waters ◽

Spectral Region ◽

Atmospheric Correction ◽

Hyperspectral Remote Sensing ◽

Water Colour ◽

Percentage Difference ◽

Green Spectral Region

Spaceborne imaging spectroscopy, also called hyperspectral remote sensing, has shown huge potential to improve current water colour retrievals and, thereby, the monitoring of inland and coastal water ecosystems. However, the quality of water colour retrievals strongly depends on successful removal of the atmospheric/surface contributions to the radiance measured by satellite sensors. Atmospheric correction (AC) algorithms are specially designed to handle these effects, but are challenged by the hundreds of narrow spectral bands obtained by hyperspectral sensors. In this paper, we investigate the performance of Polymer AC for hyperspectral remote sensing over coastal waters. Polymer is, in nature, a hyperspectral algorithm that has been mostly applied to multispectral satellite data to date. Polymer was applied to data from the Hyperspectral Imager for the Coastal Ocean (HICO), validated against in situ multispectral (AERONET-OC) and hyperspectral radiometric measurements, and its performance was compared against that of the hyperspectral version of NASA’s standard AC algorithm, L2gen. The match-up analysis demonstrated very good performance of Polymer in the green spectral region. The mean absolute percentage difference across all the visible bands varied between 16% (green spectral region) and 66% (red spectral region). Compared with L2gen, Polymer remote sensing reflectances presented lower uncertainties, greater data coverage, and higher spectral similarity to in situ measurements. These results demonstrate the potential of Polymer to perform AC on hyperspectral satellite data over coastal waters, thus supporting its application in current and future hyperspectral satellite missions.

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Evaluation of Four Atmospheric Correction Algorithms for GOCI Images over the Yellow Sea

Remote Sensing ◽

10.3390/rs11141631 ◽

2019 ◽

Vol 11 (14) ◽

pp. 1631 ◽

Cited By ~ 3

Author(s):

Xiaocan Huang ◽

Jianhua Zhu ◽

Bing Han ◽

Cédric Jamet ◽

Zhen Tian ◽

...

Keyword(s):

Remote Sensing ◽

Yellow Sea ◽

Near Infrared ◽

Ocean Color ◽

Atmospheric Correction ◽

The Yellow Sea ◽

Optical Parameters ◽

Band Ratio ◽

Percentage Difference

Atmospheric correction (AC) for coastal waters is an important issue in ocean color remote sensing. AC performance is fundamental in retrieving reliable water-leaving radiances and then bio-optical parameters. Unlike polar-orbiting satellites, geostationary ocean color sensors allow high-frequency (15–60 min) monitoring of ocean color over the same area. The first geostationary ocean color sensor, i.e., the Geostationary Ocean Color Imager (GOCI), was launched in 2010. Using GOCI data acquired over the Yellow Sea in summer 2017 at three principal overpass times (02:16, 03:16, 04:16 UTC) with ±1 and ±3 h match-up times, this study compared four GOCI AC algorithms: (1) the standard near infrared (NIR) algorithm of NASA (NASA-STD), (2) the Korea Ocean Satellite Center (KOSC) standard algorithm for GOCI (KOSC-STD), (3) the diffuse attenuation coefficient at 490 nm Kd (490)-based NIR correction algorithm (Kd-based), and (4) the Management Unit of the North Sea Mathematical Models (MUMM). The GOCI-estimated remote sensing reflectance (Rrs), aerosol parameters [aerosol optical thickness (AOT), Angström Exponent (AE)], and chlorophyll-a (Chla) were validated using in situ data. For Rrs, AOT, AE, and Chla, GOCI-retrieved results performed well within the ±1 h temporal window, but the number of match-ups was extended within the ±3 h match-up window. For ±3 h GOCI-derived Rrs, all algorithms had an absolute percentage difference (APD) at 490 and 555 nm of <40%, while other bands showed larger differences (APD > 60%). Compared with in situ values, the APD of the Rrs(490)/Rrs(555) band ratio was <20% for all ACs. For AOT and AE, the APD was >40% and >200%, respectively. Of the four algorithms, the KOSC-STD algorithm demonstrated satisfactory performance in deriving Rrs for the region of interest (Rrs APD: 22.23%–73.95%) in the visible bands. The Kd-based algorithm worked well obtaining Ocean Color 3 GOCI Chla because Rrs(443) is more accurate than the KOSC-STD. The poorest Rrs retrievals were achieved using the NASA-STD and the MUMM algorithms. Statistical analysis indicated that all methods had optimal performance at 04:16 UTC.

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Deep Learning for Monitoring Agricultural Drought in South Asia Using Remote Sensing Data

Remote Sensing ◽

10.3390/rs13091715 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1715

Author(s):

Foyez Ahmed Prodhan ◽

Jiahua Zhang ◽

Fengmei Yao ◽

Lamei Shi ◽

Til Prasad Pangali Sharma ◽

...

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

South Asia ◽

Model Performance ◽

Remote Sensing Data ◽

Agricultural Drought ◽

Gradient Boosting ◽

Complex Nature ◽

Sensing Data

Drought, a climate-related disaster impacting a variety of sectors, poses challenges for millions of people in South Asia. Accurate and complete drought information with a proper monitoring system is very important in revealing the complex nature of drought and its associated factors. In this regard, deep learning is a very promising approach for delineating the non-linear characteristics of drought factors. Therefore, this study aims to monitor drought by employing a deep learning approach with remote sensing data over South Asia from 2001–2016. We considered the precipitation, vegetation, and soil factors for the deep forwarded neural network (DFNN) as model input parameters. The study evaluated agricultural drought using the soil moisture deficit index (SMDI) as a response variable during three crop phenology stages. For a better comparison of deep learning model performance, we adopted two machine learning models, distributed random forest (DRF) and gradient boosting machine (GBM). Results show that the DFNN model outperformed the other two models for SMDI prediction. Furthermore, the results indicated that DFNN captured the drought pattern with high spatial variability across three penology stages. Additionally, the DFNN model showed good stability with its cross-validated data in the training phase, and the estimated SMDI had high correlation coefficient R2 ranges from 0.57~0.90, 0.52~0.94, and 0.49~0.82 during the start of the season (SOS), length of the season (LOS), and end of the season (EOS) respectively. The comparison between inter-annual variability of estimated SMDI and in-situ SPEI (standardized precipitation evapotranspiration index) showed that the estimated SMDI was almost similar to in-situ SPEI. The DFNN model provides comprehensive drought information by producing a consistent spatial distribution of SMDI which establishes the applicability of the DFNN model for drought monitoring.

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Aerosol optical and microphysical retrievals from a hybrid multiwavelength lidar data set – DISCOVER-AQ 2011

Atmospheric Measurement Techniques ◽

10.5194/amt-7-3095-2014 ◽

2014 ◽

Vol 7 (9) ◽

pp. 3095-3112 ◽

Cited By ~ 7

Author(s):

P. Sawamura ◽

D. Müller ◽

R. M. Hoff ◽

C. A. Hostetler ◽

R. A. Ferrare ◽

...

Keyword(s):

Remote Sensing ◽

In Situ Measurements ◽

Index Of Refraction ◽

High Spectral Resolution ◽

Lidar Data ◽

Data Set ◽

Microphysical Properties ◽

Lidar Measurements ◽

Multiwavelength Lidar

Abstract. Retrievals of aerosol microphysical properties (effective radius, volume and surface-area concentrations) and aerosol optical properties (complex index of refraction and single-scattering albedo) were obtained from a hybrid multiwavelength lidar data set for the first time. In July 2011, in the Baltimore–Washington DC region, synergistic profiling of optical and microphysical properties of aerosols with both airborne (in situ and remote sensing) and ground-based remote sensing systems was performed during the first deployment of DISCOVER-AQ. The hybrid multiwavelength lidar data set combines ground-based elastic backscatter lidar measurements at 355 nm with airborne High-Spectral-Resolution Lidar (HSRL) measurements at 532 nm and elastic backscatter lidar measurements at 1064 nm that were obtained less than 5 km apart from each other. This was the first study in which optical and microphysical retrievals from lidar were obtained during the day and directly compared to AERONET and in situ measurements for 11 cases. Good agreement was observed between lidar and AERONET retrievals. Larger discrepancies were observed between lidar retrievals and in situ measurements obtained by the aircraft and aerosol hygroscopic effects are believed to be the main factor in such discrepancies.

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Evaluation of soil moisture in CMIP5 simulations over contiguous United States using in situ and satellite observations

10.5194/hess-2016-477 ◽

2016 ◽

Author(s):

Shanshui Yuan ◽

Steven M. Quiring

Keyword(s):

United States ◽

Soil Moisture ◽

Soil Column ◽

Regional Analysis ◽

Soil Layer ◽

Model Performance ◽

Coupled Model ◽

Cmip5 Models ◽

Near Surface

Abstract. This study provides a comprehensive evaluation of soil moisture simulations in the Coupled Model Intercomparison Project Phase 5 (CMIP5) extended historical experiment (2003 to 2012). Soil moisture from in situ and satellite sources are used to evaluate CMIP5 simulations in the contiguous United States (CONUS). Both near-surface (0–10 cm) and soil column (0–100 cm) simulations from more than 14 CMIP5 models are evaluated during the warm season (April–September). Multi-model ensemble means and the performance of individual models are assessed at a monthly time scale. Our results indicate that CMIP5 models can reproduce the seasonal variability in soil moisture over CONUS. However, the models tend to overestimate the magnitude of both near-surface and soil-column soil moisture in the western U.S. and underestimate it in the eastern U.S. There are large variations in model performance, especially in the near-surface. There are significant regional and inter-model variations in performance. Results of a regional analysis show that in deeper soil layer, the CMIP5 soil moisture simulations tend to be most skillful in the southern U.S. Based on both the satellite-derived and in situ soil moisture, CESM1, CCSM4 and GFDL-ESM2M perform best in the 0–10 cm soil layer and CESM1, CCSM4, GFDL-ESM2M and HadGEM2-ES perform best in the 0–100 cm soil layer.

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Data assimilation of in situ and satellite remote sensing data to 3D hydrodynamic lake models: a case study using Delft3D-FLOW v4.03 and OpenDA v2.4

Geoscientific Model Development ◽

10.5194/gmd-13-1267-2020 ◽

2020 ◽

Vol 13 (3) ◽

pp. 1267-1284 ◽

Cited By ~ 2

Author(s):

Theo Baracchini ◽

Philip Y. Chu ◽

Jonas Šukys ◽

Gian Lieberherr ◽

Stefan Wunderle ◽

...

Keyword(s):

Remote Sensing ◽

Data Assimilation ◽

Satellite Remote Sensing ◽

Model Performance ◽

Remote Sensing Data ◽

Lake Ecosystem ◽

Sensing Data ◽

Localization Scheme ◽

Spatiotemporal Scales

Abstract. The understanding of physical dynamics is crucial to provide scientifically credible information on lake ecosystem management. We show how the combination of in situ observations, remote sensing data, and three-dimensional hydrodynamic (3D) numerical simulations is capable of resolving various spatiotemporal scales involved in lake dynamics. This combination is achieved through data assimilation (DA) and uncertainty quantification. In this study, we develop a flexible framework by incorporating DA into 3D hydrodynamic lake models. Using an ensemble Kalman filter, our approach accounts for model and observational uncertainties. We demonstrate the framework by assimilating in situ and satellite remote sensing temperature data into a 3D hydrodynamic model of Lake Geneva. Results show that DA effectively improves model performance over a broad range of spatiotemporal scales and physical processes. Overall, temperature errors have been reduced by 54 %. With a localization scheme, an ensemble size of 20 members is found to be sufficient to derive covariance matrices leading to satisfactory results. The entire framework has been developed with the goal of near-real-time operational systems (e.g., integration into meteolakes.ch).

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Using Optical Water Types to Monitor Changes in Optically Complex Inland and Coastal Waters

Remote Sensing ◽

10.3390/rs11192297 ◽

2019 ◽

Vol 11 (19) ◽

pp. 2297 ◽

Cited By ~ 6

Author(s):

Kristi Uudeberg ◽

Ilmar Ansko ◽

Getter Põru ◽

Ave Ansper ◽

Anu Reinart

Keyword(s):

Remote Sensing ◽

Coastal Waters ◽

Atmospheric Correction ◽

Suspended Sediments ◽

Reflectance Spectra ◽

Water Type ◽

Colored Dissolved Organic Matter ◽

Water Color ◽

Sentinel 2

The European Space Agency’s Copernicus satellites Sentinel-2 and Sentinel-3 provide observations with high spectral, spatial, and temporal resolution which can be used to monitor inland and coastal waters. Such waters are optically complex, and the water color may vary from completely clear to dark brown. The main factors influencing water color are colored dissolved organic matter, phytoplankton, and suspended sediments. Recently, there has been a growing interest in the use of the optical water type (OWT) classification in the remote sensing of ocean color. Such classification helps to clarify relationships between different properties inside a certain class and quantify variation between classes. In this study, we present a new OWT classification based on the in situ measurements of reflectance spectra for boreal region lakes and coastal areas without extreme optical conditions. This classification divides waters into five OWT (Clear, Moderate, Turbid, Very Turbid, and Brown) and shows that different OWTs have different remote sensing reflectance spectra and that each OWT is associated with a specific bio-optical condition. Developed OWTs are distinguishable by both the MultiSpectral Instrument (MSI) and the Ocean and Land Color Instrument (OLCI) sensors, and the accuracy of the OWT assignment was 95% for both the MSI and OLCI bands. To determine OWT from MSI images, we tested different atmospheric correction (AC) processors, namely ACOLITE, C2RCC, POLYMER, and Sen2Cor and for OLCI images, we tested AC processors ALTNNA, C2RCC, and L2. The C2RCC AC processor was the most accurate and reliable for use with MSI and OLCI images to estimate OWTs.

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A Simple Empirical Band-Ratio Algorithm to Assess Suspended Particulate Matter from Remote Sensing over Coastal and Inland Waters of Vietnam: Application to the VNREDSat-1/NAOMI Sensor

Water ◽

10.3390/w12092636 ◽

2020 ◽

Vol 12 (9) ◽

pp. 2636

Author(s):

Dat Dinh Ngoc ◽

Hubert Loisel ◽

Vincent Vantrepotte ◽

Huy Chu Xuan ◽

Ngoc Nguyen Minh ◽

...

Keyword(s):

Remote Sensing ◽

Particulate Matter ◽

Suspended Particulate Matter ◽

Band Ratio ◽

Data Set ◽

Spectral Bands ◽

Processing Chain ◽

Suspended Particulate ◽

Data Points ◽

Spm Concentration

VNREDSat-1 is the first Vietnamese satellite enabling the survey of environmental parameters, such as vegetation and water coverages or surface water quality at medium spatial resolution (from 2.5 to 10 m depending on the considered channel). The New AstroSat Optical Modular Instrument (NAOMI) sensor on board VNREDSat-1 has the required spectral bands to assess the suspended particulate matter (SPM) concentration. Because recent studies have shown that the remote sensing reflectance, Rrs(λ), at the blue (450–520 nm), green (530–600 nm), and red (620–690 nm) spectral bands can be assessed using NAOMI with good accuracy, the present study is dedicated to the development and validation of an algorithm (hereafter referred to as V1SPM) to assess SPM from Rrs(λ) over inland and coastal waters of Vietnam. For that purpose, an in-situ data set of hyper-spectral Rrs(λ) and SPM (from 0.47 to 240.14 g·m−3) measurements collected at 205 coastal and inland stations has been gathered. Among the different approaches, including four historical algorithms, the polynomial algorithms involving the red-to-green reflectance ratio presents the best performance on the validation data set (mean absolute percent difference (MAPD) of 18.7%). Compared to the use of a single spectral band, the band ratio reduces the scatter around the polynomial fit, as well as the impact of imperfect atmospheric corrections. Due to the lack of matchup data points with VNREDSat-1, the full VNREDSat-1 processing chain (atmospheric correction (RED-NIR) and V1SPM), aiming at estimating SPM from the top-of-atmosphere signal, was applied to the Landsat-8/OLI match-up data points with relatively low to moderate SPM concentration (3.33–15.25 g·m−3), yielding a MAPD of 15.8%. An illustration of the use of this VNREDSat-1 processing chain during a flooding event occurring in Vietnam is provided.

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Factors that influence surface PM<sub>2.5</sub> values inferred from satellite observations: perspective gained for the US Baltimore–Washington metropolitan area during DISCOVER-AQ

Atmospheric Chemistry and Physics ◽

10.5194/acp-14-2139-2014 ◽

2014 ◽

Vol 14 (4) ◽

pp. 2139-2153 ◽

Cited By ~ 35

Author(s):

S. Crumeyrolle ◽

G. Chen ◽

L. Ziemba ◽

A. Beyersdorf ◽

L. Thornhill ◽

...

Keyword(s):

Remote Sensing ◽

Metropolitan Area ◽

Scattering Coefficient ◽

Data Set ◽

Ambient Relative Humidity ◽

Microphysical Properties ◽

The Us ◽

Dry Conditions ◽

The Impact

Abstract. During the NASA DISCOVER-AQ campaign over the US Baltimore, MD–Washington, D.C., metropolitan area in July 2011, the NASA P-3B aircraft performed extensive profiling of aerosol optical, chemical, and microphysical properties. These in situ profiles were coincident with ground-based remote sensing (AERONET) and in situ (PM2.5) measurements. Here, we use this data set to study the correlation between the PM2.5 observations at the surface and the column integrated measurements. Aerosol optical depth (AOD550 nm) calculated with the extinction (550 nm) measured during the in situ profiles was found to be strongly correlated with the volume of aerosols present in the boundary layer (BL). Despite the strong correlation, some variability remains, and we find that the presence of aerosol layers above the BL (in the buffer layer – BuL) introduces significant uncertainties in PM2.5 estimates based on column-integrated measurements (overestimation of PM2.5 by a factor of 5). This suggests that the use of active remote sensing techniques would dramatically improve air quality retrievals. Indeed, the relationship between the AOD550 nm and the PM2.5 is strongly improved by accounting for the aerosol present in and above the BL (i.e., integrating the aerosol loading from the surface to the top of the BuL). Since more than 15% of the AOD values observed during DISCOVER-AQ are dominated by aerosol water uptake, the f(RH)amb (ratio of scattering coefficient at ambient relative humidity (RH) to scattering coefficient at low RH; see Sect. 3.2) is used to study the impact of the aerosol hygroscopicity on the PM2.5 retrievals. The results indicate that PM2.5 can be predicted within a factor up to 2 even when the vertical variability of the f(RH)amb is assumed to be negligible. Moreover, f(RH = 80%) and RH measurements performed at the ground may be used to estimate the f(RH)amb during dry conditions (RHBL < 55%).

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