Atmospheric Correction Of Ocean Color Remote Sensing Over Coastal Waters

Due to the effects of climate change and human activities on the ecological environment,the monitoring and management of the coastal water environments have become increasingly important[1-4].To achieve these objectives,we need real-time and large-scale observations and analyses of biologic and non-biologic components within coastal waters,which can help us further understand marine biogeochemical processes,the response of the ocean to global climate change,the role of the ocean in the oceanatmosphere carbon cycle,and the response of regional marine ecosystems to disasters.Compared to in situ observations,satellite remote sensing,with wide coverage and high observational frequency,are indispensable for meeting these observation demands[5].Since the Coastal Zone Color Scanner(CZCS)was launched by the National Aeronautics and Space Administratio1(NASA)in 1978[6,7],a series of ocean color satellites have been successfully launched aiming for quantifying marine phytoplankton concentrations.Over the past decades,these conventional ocean color satellites have played a non-negligible role in water quality monitoring and ecosystem protection over oceanic and coastal waters.Compared to conventional ocean color satellites(usually with a spatial resolution of～1 km),high-spatial-resolution(HSR)satellites(usually with a spatial resolution of tens of meters or even a few meters)and Unmanned Aerial Vehicle(UAV)system are more suitable for characterizing the spatial variability of water’s components[8]over coastal areas and inland lakes,which are also an essential tool for monitoring and management of water ecosystems over coastal areas and inland lakes[9].In recent years,the frequent applicatuion of HSR sensors(both satellite-based and airborne)has led to a new phase of scientific research and monitoring of coastal and inland water environments.For remote sensing,to obtain important biogeochemical parameters(e.g.,chlorophyll-a concentration(Chl-a),the concentration of suspended particle matter(SPM),phytoplankton absorption coefficients(aph),absorption coefficients of colored dissolved organic matter(adg),and primary productivity(PP),etc.)based on remote sensing,we first need to obtain the remote-sensing reflectance(Rrs)that contains the bio-optical signals of the water column from the total signals measured by satellite sensors.In general,the total signal measured by the satellite sensors includes the signals from atmosphere,the sea surface,and the water column,while only the last signal contains information on the scattering and absorption processes of various components within the water body.In order to derive Rrs from the total reflectance measured by the satellite,we need to remove the contributions from atmosphere and sea surface reflection,which process is termed atmospheric correction(AC).Since the backscattering signal of the water column accounts for less than 20%of the observed signal at the top of atmosphere,which is much lower than the signals from atmospheric scattering and sea surface reflection,it is particularly important to establish an accurate and reliable atmospheric correction algorithm.At present,many atmospheric correction algorithms have been proposed,and the commonly used Gordon and Wang[10]Gethod based on the "black pixel" assumption in the near-infrared(NIR)band(the marine reflectance can be ignored in the NIR band)has been fully validated in clean waters of open oceans and continental shelves.However,the issues of atmospheric correction over coastal waters are still a challenge,in which two major challenges are faced:one is the atmospheric correction issue in the presence of strongly absorbing aerosols,and the other one is the atmospheric correction issue for extremely turbid coastal waters.Both of these issues lead to low values of satellite-retrieved Rrs in the blue band,especially for 412 nm,even being negative.Meanwhile,the traditional atmospheric correction algorithm for HSR remote sensing images has low applicability due to its low number of bands or low Signal-Noise-Ratio(SNR)in shortwave infrared(SWIR)bands or even no SWIR bands.Therefore,it is necessary to develop suitable atmospheric correction algorithms according to different practical situations and different satellite sensors to address the above-mentioned issues of the current atmospheric correction algorithms over coastal waters.This study focuses on solving the atmospheric correction issues for the presence of strongly absorbing aerosols and turbid coastal waters.For the method of research,the proposed atmospheric correction algorithms are validated using simulated and in situ datasets for different satellite remote sensing images.For the content of research,based on the previous atmospheric correction algorithms,the atmospheric correction algorithms are revised and proposed to solve the issues of strongly absorbing aerosol and turbid coastal waters,respectively.The following describes the main research components and results:(1)Developed an atmospheric correction algorithm based on observations from multi-anglesIn this effort,with a revised POLYMER(POLYnomial based approach applied to MERIS data)atmospheric correction model,we present a novel scheme(two-angle atmospheric correction algorithm,termed as TAACA)to remove atmospheric contributions in polar-orbit satellite ocean color measurements for coastal environments,especially when there are absorbing aerosols.TAACA essentially uses the same water properties within a relative short time interval on the same day as a constraint to determine oceanic and atmospheric properties simultaneously using two same-day consecutive satellite images having different sun-sensor geometries.The performance of TAACA is first evaluated with a synthetic dataset,where the retrieved Rrs by TAACA matches very well(the coefficient of determination(R2)≥0.98)with the simulated Rrs for each wavelength,and the unbiased Root Mean Square Difference(uRMSD)is～12.2%for cases of both non-absorbing and strongly absorbing aerosols.When this dataset is handled by POLYMER,for non-absorbing aerosol cases,the R2 and uRMSD values are～0.99 and～7.5%,respectively;but they are～0.92 and～39.5%for strongly absorbing aerosols.TAACA is further assessed using co-located VIIRS measurements for waters in Boston Harbor and Massachusetts Bay,and the retrieved Rrs from VIIRS agrees with in situ measurements within～27.3%at the visible wavelengths.By contrast,traditional algorithm resulted in uRMSD as 3890.4%and 58.9%at 410 and 443 nm,respectively,for these measurements.The Rrs products derived from POLYMER also show large deviations from in situ measurements.It is envisioned that more reliable Rrs products in coastal waters could be obtained from polar-orbit satellite ocean color measurements with a scheme like TAACA,especially when there are strongly absorbing aerosols.(2)Developed an atmospheric correction algorithm based on observations from multi-pixelsWe propose an innovative multi-pixel atmospheric correction approach(MPACA)to process high-spatial-resolution satellite measurements over coastal waters based on a revised POLYMER model.MPACA assumes the aerosol type to be uniform within a relatively small region,while the aerosol loads and water properties thereof are allowed to vary.Landsat-8 OLI images over six coastal locations with various turbidities were utilized to evaluate the performance of MPACA.The retrieved Rrs by MPACA is validated with in situ matchups obtained from two sources:ship-based field campaigns and the AERONET-OC networks.It is found that,at each of OLI’s four visible bands,MPACA provided accurate Rrs products over such coastal environments,with the Root Mean Square Difference(RMSD)and Mean Absolute Percentage Difference(MAPD)less than 0.0006 sr-1 and 16.2%,respectively.In contrast,the Rrs values retrieved with NASA’s SeaDAS(v7.5),where each pixel was treated independently,showed RMSD and MAPD as～0.0018 sr-1 and～38.8%,respectively.Acolite-DSF,which assumed some spatial dependency,obtained MAPD almost two times that of SeaDAS for each visible band.Further,it appears that Acolite-EXP did not perform well for this evaluation dataset,where RMSD is～0.0062 sr-1 and MAPD～228,2%.These results suggest that MPACA is a promising scheme for atmospheric correction in coastal waters,especially for measurements from multi-band satellites that have a high spatial resolution along with at least two bands in the NIR or SWIR domain.(3)Developed an atmospheric correction algorithm based on Rrs(NIR)estimations from Neural NetworksWe here present an atmospheric correction algorithm(termed as ACANIR-NN),which uses estimated Rrs(NIR)to achieve atmosphere correction over turbid waters for sensors having no spectral bands in SWIR.The estimation of Rrs(NIR)is obtained from available Rrs in the visible bands with a specifically designed Neural Networks.The performance of ACANIR-NN is evaluated over eight coastal locations with ground measurements obtained from the AERONET-OC system.It is found that the MAPD for Rrs(412)and Rrs(443)obtained by ACANIR-NN is 7.5%and 7.7%,respectively,which are 44.0%and 27.5%from the standard SeaDAS algorithm for this dataset.We further demonstrated the applicability of ACANIR-NN to SeaWiFS measurements over turbid waters(i.e.,the mouth of Amazon River).These results indicate that ACANIR-NN can generate reliable Rrs over turbid coastal areas for sensors having no SWIR bands.The above results will significantly improve the quality of Rrs products over coastal areas and lakes,thus laying the foundation for more accurate inversion of biogeochemical parameters in these waters.These results will not only enable traditional ocean color satellites and high-spatial-resolution satellites to provide effective ecological observations for complex water bodies such as coastal areas and inland waters,but also improve the quality of retrieved products for traditional ocean color satellites(e.g.SeaWiFS,MERIS)over complex waters and advance the practical progress of ocean color remote sensing technology.Meanwhile,the atmospheric correction algorithm proposed in this study will also strongly support the development and operation of multi-spectral small satellites and the application of ecological environment over complex waters,which is of great significance.