Total Backscattering Coefficient Research Articles

Aerosol and cloud data processing and optical property retrieval algorithms for the spaceborne ACDL/DQ-1

Abstract. The new-generation atmospheric environment monitoring satellite DQ-1, launched successfully in April 2022, carries the Aerosol and Carbon Detection Lidar (ACDL), which is capable of globally profiling aerosol and cloud optical properties with high accuracy. The ACDL/DQ-1 is a high-spectral-resolution lidar (HSRL) that separates molecular backscatter signals using an iodine filter and has 532 nm polarization detection and dual-wavelength detection at 532 and 1064 nm, which can be utilized to derive aerosol optical properties. The methods have been specifically developed for data processing and optical property retrieval according to the specific characteristics of the ACDL system and are introduced in detail in this paper. Considering the different signal characteristics and different background noise behaviors of each channel during daytime and nighttime, the procedures of data pre-processing, denoising process and quality control are applied to the original measurement signals. The aerosol and cloud optical property products of the ACDL/DQ-1, including the total depolarization ratio, backscatter coefficient, extinction coefficient, lidar ratio and color ratio, can be calculated by the retrieval algorithms presented in this paper. Two measurement cases with use of the ACDL/DQ-1 on 27 June 2022 and the global averaged aerosol optical depth (AOD) from 1 June to 4 August 2022 are provided and analyzed, demonstrating the measurement capability of the ACDL/DQ-1.

Ocean color algorithm for the retrieval of the particle size distribution and carbon-based phytoplankton size classes using a two-component coated-sphere backscattering model

Abstract. The particle size distribution (PSD) of suspended particles in near-surface seawater is a key property linking biogeochemical and ecosystem characteristics with optical properties that affect ocean color remote sensing. Phytoplankton size affects their physiological characteristics and ecosystem and biogeochemical roles, e.g., in the biological carbon pump, which has an important role in the global carbon cycle and thus climate. It is thus important to develop capabilities for measurement and predictive understanding of the structure and function of oceanic ecosystems, including the PSD, phytoplankton size classes (PSCs), and phytoplankton functional types (PFTs). Here, we present an ocean color satellite algorithm for the retrieval of the parameters of an assumed power-law PSD. The forward optical model considers two distinct particle populations: phytoplankton and non-algal particles (NAPs). Phytoplankton are modeled as coated spheres following the Equivalent Algal Populations (EAP) framework, and NAPs are modeled as homogeneous spheres. The forward model uses Mie and Aden–Kerker scattering computations, for homogeneous and coated spheres, respectively, to model the total particulate spectral backscattering coefficient as the sum of phytoplankton and NAP backscattering. The PSD retrieval is achieved via spectral angle mapping (SAM), which uses backscattering end-members created by the forward model. The PSD is used to retrieve size-partitioned absolute and fractional phytoplankton carbon concentrations (i.e., carbon-based PSCs), as well as particulate organic carbon (POC), using allometric coefficients. This model formulation also allows the estimation of chlorophyll a concentration via the retrieved PSD, as well as percent of backscattering due to NAPs vs. phytoplankton. The PSD algorithm is operationally applied to the merged Ocean Colour Climate Change Initiative (OC-CCI) v5.0 ocean color data set. Results of an initial validation effort are also presented using PSD, POC, and picophytoplankton carbon in situ measurements. Validation results indicate the need for an empirical tuning for the absolute phytoplankton carbon concentrations; however these results and comparison with other phytoplankton carbon algorithms are ambiguous as to the need for the tuning. The latter finding illustrates the continued need for high-quality, consistent, large global data sets of PSD, phytoplankton carbon, and related variables to facilitate future algorithm improvements.

Performance Modeling of a Diode-Laser-Based Direct-Detection Doppler Lidar for Vertical Wind Profiling

Abstract Micropulse differential absorption lidars (MPD) for water vapor, temperature, and aerosol profiling have been developed, demonstrated, and are addressing the needs of the atmospheric science community for low-cost ground-based networkable instruments capable of long-term monitoring of the lower troposphere. The MPD instruments use a diode-laser-based (DLB) architecture that can easily be adapted for a wide range of applications. In this study, a DLB direct-detection Doppler lidar based on the current MPD architecture is modeled to better understand the efficacy of the instrument for vertical wind velocity measurements, with the long-term goal of incorporating these measurements into the current network of MPD instruments. The direct-detection Doppler lidar is based on a double-edge receiver that utilizes two Fabry–Pérot interferometers and a vertical velocity retrieval that requires the ancillary measurement of the backscatter ratio, which is the ratio of the total backscatter coefficient to the molecular backscatter coefficient. The modeling in this paper accounts for the major sources of error. It indicates that the vertical velocity can be retrieved with an error of less than 0.56 m s−1 below 4 km with a 150-m range resolution and an averaging time of 5 min. Significance Statement Monitoring the temperature, relative humidity, and winds in the lower atmosphere is important for improving weather forecasting, particularly for severe weather such as thunderstorms. Cost-effective micropulse differential absorption lidar (MPD) instrumentation for continuous temperature and humidity monitoring has been developed and demonstrated, and its effects on weather forecasting are currently being evaluated. The modeling study described in this paper studies the feasibility of using a similar cost-effective MPD instrument architecture for monitoring vertical wind velocity in the lower atmosphere. Modeling indicates that wind velocities can be measured with less than 0.56 m s−1 accuracy and demonstrates the feasibility of adding vertical wind velocity measurements to the MPD instruments.

Statistical validation of Aeolus L2A particle backscatter coefficient retrievals over ACTRIS/EARLINET stations on the Iberian Peninsula

Abstract. The Global Observing System (GOS) has encountered some limitations due to a lack of worldwide real-time wind measurements. In this context, the European Space Agency (ESA) has developed the Aeolus satellite mission, based on the ALADIN (Atmospheric Laser Doppler Instrument) Doppler wind lidar; this mission aims to obtain near-real-time wind retrievals at the global scale. As spin-off products, the instrument retrieves aerosol optical properties such as particle backscatter and extinction coefficients. In this work, a validation of Aeolus reprocessed (baseline 10) co-polar backscatter coefficients (βAeoluspart) is presented through an intercomparison with analogous ground-based measurements taken at the ACTRIS (Aerosols, Clouds and Trace gases Research InfraStructure Network)/EARLINET (European Aerosol Research Lidar Network) stations of Granada (Spain), Évora (Portugal) and Barcelona (Spain) over the period from July 2019 until October 2020. Case studies are first presented, followed by a statistical analysis. The stations are located in a hot spot between Africa and the rest of Europe, which guarantees a variety of aerosol types, from mineral dust layers to continental/anthropogenic aerosol, and allows us to test Aeolus performance under different scenarios. The so called Aeolus-like profiles (βAeoluslike,355part) are obtained from total particle backscatter coefficient and linear particle depolarization ratio (δlinearpart) profiles at 355 and 532 nm measured from the surface, through a thorough bibliographic review of dual-polarization measurements for relevant aerosol types. Finally, the study proposes a relation for the spectral conversion of δlinearpart, which is implemented in the Aeolus-like profile calculation. The statistical results show the ability of the satellite to detect and characterize significant aerosol layers under cloud-free conditions, along with the surface effect on the lowermost measurements, which causes the satellite to largely overestimate co-polar backscatter coefficients. Finally, the Aeolus standard correct algorithm middle bin (SCAmb) shows a better agreement with ground-based measurements than the standard correct algorithm (SCA), which tends to retrieve negative and meaningless coefficients in the clear troposphere. The implementation of Aeolus quality flags entails a vast reduction in the number of measurements available for comparison, which affects the statistical significance of the results.

Measurement report: Characterization of the vertical distribution of airborne <i>Pinus</i> pollen in the atmosphere with lidar-derived profiles – a modeling case study in the region of Barcelona, NE Spain

Abstract. This paper investigates the mechanisms involved in the dispersion, structure, and mixing in the vertical column of atmospheric pollen. The methodology used employs observations of pollen concentration obtained from Hirst samplers (we will refer to this as surface pollen) and vertical distribution (polarization-sensitive lidar), as well as nested numerical simulations with an atmospheric transport model and a simplified pollen module developed especially for this study. The study focuses on the predominant pollen type, Pinus, of the intense pollination event which occurred in the region of Barcelona, Catalonia, NE Spain, during 27–31 March 2015. First, conversion formulas are expressed to convert lidar-derived total backscatter coefficient and model-derived mass concentration into pollen grains concentration, the magnitude measured at the surface by means of aerobiological methods, and, for the first time ever, a relationship between optical and mass properties of atmospheric pollen through the estimation of the so-called specific extinction cross section is quantified in ambient conditions. Second, the model horizontal representativeness is assessed through a comparison between nested pollen simulations at 9, 3, and 1 km horizontal resolution and observed meteorological and aerobiological variables at seven sites around Catalonia. Finally, hourly observations of surface and column concentration in Barcelona are analyzed with the different numerical simulations at increasing horizontal resolution and varying sedimentation/deposition parameters. We find that the 9 or 3 km simulations are less sensitive to the meteorology errors; hence, they should be preferred for specific forecasting applications. The largest discrepancies between measured surface (Hirst) and column (lidar) concentrations occur during nighttime, where only residual pollen is detected in the column, whereas it is also present at the surface. The main reason is related to the lidar characteristics which have the lowest useful range bin at ∼ 225 m, above the usually very thin nocturnal stable boundary layer. At the hour of the day of maximum insolation, the pollen layer does not extend up to the top of the planetary boundary layer, according to the observations (lidar), probably because of gravity effects; however, the model simulates the pollen plume up to the top of the planetary boundary layer, resulting in an overestimation of the pollen load. Besides the large size and weight of Pinus grains, sedimentation/deposition processes have only a limited impact on the model vertical concentration in contrast to the emission processes. For further modeling research, emphasis is put on the accurate knowledge of plant/tree spatial distribution, density, and type, as well as on the establishment of reliable phenology functions.

Vertical assessment of the mineral dust optical and microphysical properties as retrieved from the synergy between polarized micro-pulse lidar and sun/sky photometer observations using GRASP code

The potential of the synergetic use of both polarized Micro-Pulse Lidar (P-MPL) and Sun/sky photometer observations by using the GRASP (Generalized Retrieval of Atmosphere and Surface Properties) code for retrieving dust properties has been analysed in this work. Both the columnar and height-resolved variables have been evaluated in comparison, respectively, with AERONET data and vertical P-MPL-derived profiles of the dust optical and microphysical properties over a mid-latitude site. The linear regression analysis together with the mean fractional bias values and relative differences between datasets have been used as the statistical proxies for assessing the degree of agreement by examining 24 selected dust cases. Regarding columnar variables, the microphysical properties (both the particle volume concentration and effective radius) presented a high confidence level between GRASP and AERONET retrievals by taking into account all the proxies. In addition, the single scattering albedo and the real refractive index presented a good confidence level at all the wavelengths, as well, unlike the imaginary refractive index, which was confident at 440 nm only. By analysing the height-resolved properties of the total particle backscatter coefficient and the total volume concentration, the degree of agreement shows a high confidence between GRASP retrievals and P-MPL-derived variables, which were retrieved by using an alternative, well-validated, polarization-based methodology. Indeed, these results can be essential for application with no-polarized systems (e.g., ceilometers, lidars …). Overall, the synergy between P-MPL and Sun/sky photometer measurements regarding the usual co-location of both MPLNET and AERONET networks in combination with GRASP code represent an improved approach to derive the height-resolved optical and microphysical properties for mineral dust particles with a high degree of confidence.

Improving remote sensing retrieval of water clarity in complex coastal and inland waters with modified absorption estimation and optical water classification using Sentinel-2 MSI

The estimation of water clarity in coastal and inland waters from satellite images is limited by significant uncertainties attributable to the optical complexity of these waters. To address this issue, we propose an improved semi-analytical model for retrieving the Secchi disk depth (ZSD) based on the mechanistic model (Lee model) developed by Lee et al. (2015). In the Lee model, a quasi-analytical algorithm (QAA) was employed to estimate the total absorption coefficient (a(λ)) and total backscattering coefficient (bb(λ)) to retrieve the diffuse attenuation coefficient (Kd(λ)). ZSD was then estimated by remote sensing reflectance (Rrs) and Kd. In this study, two improvements were made to the Lee model based on the band specifications of the Sentinel-2 Multispectral Instrument (MSI). First, the reference wavelength (λ0) was shifted from 560 nm and 665 nm to the longer red edge band 705 nm, and the modified empirical formula for estimating a(λ0) was applied to improve ZSD retrieval. Second, waters were classified into different optical water types (OWTs), and the optimal ZSD algorithm was identified for each OWT to further improve the accuracy of ZSD estimation. The proposed model (denoted as QAA_OWT) was calibrated and evaluated with in situ data (N = 399) collected from waters with various optical properties including a lake (Daihai Lake) dominated by colored dissolved organic matter (CDOM), a macrophytic lake (Ulansuhai Lake), and coastal water (the Bohai Sea) dominated by suspended sediments. The validation showed that QAA_OWT improved the accuracy of ZSD retrieval (0.18–5.0 m), with the averaged percentage difference (MAPD) decreasing from 31.9% to 24.9% and the root mean square deviation (RMSD) decreasing from 0.38 m to 0.32 m. The model was further applied to satellite images from the Sentinel-2 Multispectral Instrument (MSI) and independently assessed based on in situ-satellite match-ups (N = 31). The results verified the reliability of the model (MAPD = 31.8%, RMSD = 0.16 m). Time series MSI-derived ZSD maps clearly characterized the spatio-temporal patterns of the water clarity of Daihai Lake. Such maps are of significance in the accurate evaluation of the effectiveness of pollution control measures and aquatic ecosystem protection for lakes with low water quality. Sensitivity analysis revealed the robustness and insensitivity of the proposed model to input noise. This model could be applicable to many satellite sensors with spectral bands similar to those of Sentinel-2 MSI. Furthermore, it can be used as an effective tool for highly accurate remote sensing retrieval of ZSD in optically complex coastal and inland waters.

Aerosol radiative impact during the summer 2019 heatwave produced partly by an inter-continental Saharan dust outbreak – Part 1: Short-wave dust direct radiative effect

Abstract. The short-wave (SW) direct radiative effect (DRE) during the summer 2019 heatwave produced partly by a moderate, long-lasting Saharan dust outbreak over Europe is analysed in this study. Two European sites (periods) are considered: Barcelona, Spain (23–30 June), and Leipzig, Germany (29 and 30 June), 1350 km apart from each other. Major data are obtained from AERONET and polarised Micro-Pulse Lidar (P-MPL) observations. Modelling is used to describe the different dust pathways, as observed at both sites. The coarse dust (Dc) and fine dust (Df) components (with total dust, DD = Dc + Df) are identified in the profiles of the total particle backscatter coefficient using the POLIPHON (POlarisation LIdar PHOtometer Networking) method in synergy with P-MPL measurements. This information is used to calculate the relative mass loading and the centre-of-mass height, as well as the contribution of each dust mode to the total dust DRE. Several aspects of the ageing of dust are put forward. The mean dust optical depth and its Df/DD ratios are, respectively, 0.153 and 24 % in Barcelona and 0.039 and 38 % in Leipzig; this Df increase in Leipzig is attributed to a longer dust transport path in comparison to Barcelona. The dust produced a cooling effect on the surface with a mean daily DRE of −9.1 and −2.5 W m−2, respectively, in Barcelona and Leipzig, but the Df/DD DRE ratio is larger for Leipzig (52 %) than for Barcelona (37 %). Cooling is also observed at the top of the atmosphere (TOA), although less intense than on the surface. However, the Df/DD DRE ratio at the TOA is even higher (45 % and 60 %, respectively, in Barcelona and Leipzig) than on the surface. Despite the predominance of Dc particles under dusty conditions, the SW radiative impact of Df particles can be comparable to, even higher than, that induced by the Dc ones. In particular, the Df/DD DRE ratio in Barcelona increases by +2.4 % d−1 (surface) and +2.9 % d−1 (TOA) during the dusty period. This study is completed by a second paper about the long-wave and net radiative effects. These results are especially relevant for the next ESA EarthCARE mission (planned in 2022) as it is devoted to aerosol–cloud–radiation interaction research.

Optical Properties Using Adaptive Selection of NIR/SWIR Reflectance Correction and Quasi-Analytic Algorithms for the MODIS-Aqua in Estuarine-Ocean Continuum: Application to the Northern Gulf of Mexico

An adaptive selection of the near/shortwave infrared (NIR/SWIR) reflectance correction and the quasi-analytic algorithms (QAAs) is proposed for the Moderate Resolution Imaging Spectroradiometer (MODIS-Aqua) to utilize the strengths of different correction algorithms and QAAs in a single satellite scene with water types ranging from turbid coastal to clear open ocean waters. A blended satellite product is generated by merging three atmospheric-correction algorithms(AD-ATCOR): 1) iterative NIR correction; 2) management unit of the north sea mathematical models (MUMM); and 3) SWIR, using a spectral threshold-based selection for different water types. The validation analysis of a blended remote sensing reflectance product showed overall good agreement with AERONET-OC observations followed by NASA bio-optical marine algorithm data set (NOMAD) at the blue wavelengths and the estuarine data set at the green and red wavelengths. The results suggest that the adaptive method is a better alternative to address the challenging problem of selecting different correction algorithms for different water types in a single satellite scene. Likewise, an adaptive selection of a QAA (AD-QAA) used the QAA-v5 and the QAA-V to obtain merged inherent optical property (IOP) products in a single MODIS-Aqua scene with varying water types. As a case study, the two adaptive selection procedures were sequentially applied to the MODIS-Aqua imagery representing four environmental conditions in the northern Gulf of Mexico. Improved retrievals of the total absorption and backscattering coefficients along an estuarine to ocean continuum demonstrated the effectiveness of this method in an optically complex and dynamic river-dominated system.

Analysis of the regional spectral properties in northwestern South China Sea based on an empirical orthogonal function

This study presents an analysis of the spectral characteristics of remote sensing reflectance (Rrs) in northwestern South China Sea based on the in situ optical and water quality data for August 2 018. Rrs was initially divided into four classes, classes A to D, using the max-classification algorithm, and the spectral properties of whole Rrs were characterized using the empirical orthogonal function (EOF) analysis. Subsequently, the dominant factors in each EOF mode were determined.The results indicated that more than 95% of the variances of Rrs are partly driven by the back-scattering characteristics of the suspended matter. The initial two EOF modes were well correlated with the total suspended matter and back-scattering coefficient. Furthermore, the first EOF modes of the four classes of Rrs (A-D Rrs-EOF1) significantly contributed to the total variances of each Rrs class. In addition, the correlation coefficients between the amplitude factors of class A-D Rrs-EOF1 and the variances of the relevant water quality and optical parameters were better than those of the unclassified ones. The spectral shape of class A Rrs-EOF1 was governed by the absorption characteristic of chlorophyll a and colored dissolved organic matter (CDOM). The spectral shape of class B Rrs-EOF1 was governed by the absorption characteristic of CDOM since it exhibited a high correlation with the absorption coefficient of CDOM (ag (λ)), whereas the spectral shape of class C Rrs-EOF1 was governed by the back-scattering characteristics but not affected by the suspended matter. The spectral shape of class D Rrs-EOF1 exhibited a relatively good correlation with all the water quality parameters, which played a significant role in deciding its spectral shape.

An OLCI-based algorithm for semi-empirically partitioning absorption coefficient and estimating chlorophyll a concentration in various turbid case-2 waters

Accurate remote assessment of phytoplankton chlorophyll-a (Chla) concentration in turbid case-2 waters is a challenge, owing largely to terrestrial substances (such as minerals and humus) that are optically significant but do not co-vary with phytoplankton. Here, we propose an improved Quasi-Analytical Algorithm (QAA) (denoted as TC2) for retrieving Chla concentrations from remote sensing reflectance (Rrs(λ)) which can be applied to Sentinel-3 Ocean and Land Colour Instrument (OLCI) images in turbid case-2 waters. TC2 has two main extensions when compared with QAA. First, TC2 makes an additional assumption to separate the total non-water absorption at 665 nm (anw(665)) into phytoplankton absorption (aph(665)) and yellow matter (aym(665)), which is the sum of colored dissolved matter (CDOM) and detritus. Second, for selecting the position of the near-infrared (NIR) band which is used to estimate the signal of total backscattering coefficient (bb(λ0)) at QAA reference band (λ0), we take into account the assumption that the absorption of pure water should be dominant at this band, as well as the impact of the signal-to-noise ratio (SNR) in the NIR band on the Chla concentration estimating model. When applied to in situ Rrs(λ) and OLCI match-up Rrs(λ) data in this study, TC2 provided more accurate Chla estimation than previous Cha concentration retrieval algorithms for turbid case-2 waters. TC2 has the potential for use as a simple and effective algorithm for monitoring Chla concentrations in the turbid case-2 waters at a global scale from space.

An integrated approach to estimate surface soil moisture in agricultural lands

Sentinel-1 and Landsat-8 data were used to retrieve soil moisture from top soil surface (0–5 cm depth) at agricultural land (area under wheat crop). After pre-processing of satellite data and removal of vegetation influence (σ°veg) using Water Cloud Model (WCM), total backscattering coefficient (σ°total) and Normalized Difference Vegetation Index (NDVI) were used to simulate backscattering from soil (σ°soil). Modified Dubois Model (MDM) and Topp's Model were used to retrieve soil moisture using ε. Further, modelled soil moisture was evaluated using in situ soil moisture measurements and a Time Domain Reflectometer during Sentinel-1 overpass (24 January, 25 February and 13 March 2018). Statistical tests showed that an integrated approach has potential to improve soil moisture estimates over the vegetated/cropped area for agricultural and hydrological studies.

An improved algorithm for estimating the Secchi disk depth from remote sensing data based on the new underwater visibility theory

The Secchi disk depth (ZSD) is a widely used parameter for evaluating water clarity. Here we propose an improved algorithm, which is based on a new underwater visibility theory, for retrieving more accurate ZSD from remote sensing reflectance (Rrs) in various waters. Two improvements were carried out in the new algorithm. First, we used a hybrid quasi-analytical algorithm (QAA_hybrid) instead of the sixth version of QAA (QAA_v6) for retrieving more accurate total absorption coefficient (aλ) and total backscattering coefficient (bbλ) even in turbid inland waters. Second, we used a dynamic KT/Kd ratio (i.e., ratio of diffuse attenuation coefficient of upwelling radiance and diffuse attenuation coefficient of downwelling irradiance) instead of using the fixed ratio (i.e., 1.5). The results obtained from in situ Rrs show that the improved ZSD estimation algorithm gave more accurate ZSD estimations, with the root mean square error (RMSE) reduced from 0.2 to 0.1 in log10 unit, mean absolute percentage error (MAPE) reduced from 39% to 20% (N = 178 with in situ ZSD values between 0.3 and 20.8 m). We then applied the improved ZSD estimation algorithm to the 2003–2012 MERIS images for Lake Kasumigaura to further confirm the performance of the improved ZSD estimation algorithm. The results obtained from 19 matchups demonstrate that the estimated ZSD matched well with the in situ ZSD, with the RMSE of 0.11 m and the MAPE of 15%. The improved ZSD estimation algorithm shows a potential to estimate more accurate ZSD values from remote sensing data in various waters.

An estuarine-tuned quasi-analytical algorithm (QAA-V): assessment and application to satellite estimates of SPM in Galveston Bay following Hurricane Harvey

Abstract. The standard quasi-analytical algorithm (Lee et al., 2002) was tuned as QAA-V using a suite of synthetic data and in situ measurements to improve its performance in optically complex and shallow estuarine waters. Two modifications were applied to the standard QAA: (1) the semi-analytical relationship for obtaining remote sensing reflectance just below the water surface as a function of absorption and backscattering coefficients was updated using Hydrolight® simulations, and (2) an empirical model of the total non-water absorption coefficient was proposed using a ratio of green to red bands of an ocean color sensor, which is known to work well in various inland and estuarine environments. The QAA-V-derived total absorption and backscattering coefficients, which were evaluated in a variety of waters ranging from highly absorbing and turbid to relatively clear shelf waters, showed satisfactory performance on a Hydrolight-simulated synthetic dataset (R2 > 0.87, MRE < 17 %), an in situ estuarine and nearshore dataset (R2 > 0.70, MRE < 35 %), and the NOMAD (R2 > 0.90, MRE < 30 %). When compared to the standard QAA (QAA-v6), the QAA-V showed an obvious improvement with ∼ 30–40 % reduction in absolute mean relative error for the Hydrolight-simulated synthetic and in situ estuarine and nearshore datasets, respectively. The methodology of tuning QAA was applied to the VIIRS ocean color sensor and validation results suggest that the proposed methodology can also be applied to other ocean color and land-observing sensors. The QAA-V was also assessed on VIIRS imagery using a regional relationship between suspended particulate matter (SPM) and particulate backscattering coefficient at 532 nm (bbtnw532; R2 = 0.89, N = 33). As a case study, the QAA-V processing chain and VIIRS imagery were used to generate a sequence of SPM maps of Galveston Bay, Texas following the unprecedented flooding of Houston and the surrounding regions due to Hurricane Harvey in August 2017. The record discharge of floodwaters through two major rivers into the bay resulted in very high SPM concentrations over several days throughout the bay, with wind forcing additionally influencing its distribution into the coastal waters of the northern Gulf of Mexico. The promising results of this study suggest that the application of QAA-V to various ocean color and land-observing satellite imagery could be used to assess the bio-optical state and water quality dynamics in a variety of coastal systems around the world.

Enhance field water-color measurements with a Secchi disk and its implication for fusion of active and passive ocean-color remote sensing.

Inversion of the total absorption (a) and backscattering coefficients of bulk water through a fusion of remote sensing reflectance (Rrs) and Secchi disk depth (ZSD) is developed. An application of such a system to a synthesized wide-range dataset shows a reduction of ∼3 folds in the uncertainties of inverted a(λ) (in a range of ∼0.01-6.8 m-1) from Rrs(λ) for the 350-560nm range. Such a fusion is further proposed to process concurrent active (ocean LiDAR) and passive (ocean-color) measurements, which can lead to nearly "exact" analytical inversion of an Rrs spectrum. With such a fusion, it is found that the uncertainty in the inverted total a in the 350-560nm range could be reduced to ∼2% for the synthesized data, which can thus significantly improve the derivation of a coefficients of other varying components. Although the inclusion of ZSD places an extra constraint in the inversion of Rrs, no apparent improvement over the quasi-analytical algorithm (QAA) was found when the fusion of ZSD and Rrs was applied to a field dataset, which calls for more accurate determination of the absorption coefficients from water samples.

Spatial–temporal characteristics of haze and vertical distribution of aerosols over the Yangtze River Delta of China

Variation of haze events occurred in the Yangtze River Delta (YRD) of China, the characteristics of meteorological elements and the vertical distribution of aerosols during haze episodes were analyzed by utilizing data of ground observation, radiosonde and CALIPSO. The results illustrate that the frequency of haze events between 1981 and 2010 peaked in winter but bottomed out in summer and decreased from north to south in the YRD region, reaching at the lowest point in “low frequency center” — Shanghai. When haze happened, the most seriously affected area was 2–4km above the ground and the concentrated range of total backscattering coefficient (TBC) that decreased with altitude was 0.8×10−3–2.5×10−3km−1·sr−1. Particulate depolarization ratio (PDR) was less than 40% in a large part and 93% aerosols over the YRD area were regular particles, while the irregular ones concentrated on 2km above the surface and the irregularity rose up but the diversity diminished when altitude increased. Color ratio (CR) was lower than 1.2 mostly at all altitudes and distributed asymmetrically above the ground. Nearly 80% aerosols under 10km were fine particles (CR<1.0) and 22.54% coarse particles (CR>1.0) clustered at 2–4km. Large particles (CR>1.2) aggregated in lower troposphere massively yet relatively smaller ones gathered in middle and upper troposphere. In the YRD region, aerosols with more powerful capabilities were wider and less regular than the ones of Northwestern China.

VIIRS captures phytoplankton vertical migration in the NE Gulf of Mexico

In summer 2014, a toxic Karenia brevis bloom (red tide) occurred in the NE Gulf of Mexico, during which vertical migration of K. brevis has been observed from glider measurements. The current study shows that satellite observations from the Visible Infrared Imaging Radiometer Suite (VIIRS) can capture changes in surface reflectance and chlorophyll concentration occurring within 2h, which may be attributed this K. brevis vertical migration. The argument is supported by earlier glider measurements in the same bloom, by the dramatic changes in the VIIRS-derived surface chlorophyll, and by the consistency between the short-term reflectance changes and those reported earlier from field-measured K. brevis vertical migration. Estimates using the quasi-analytical algorithm also indicate significant increases in both total absorption coefficient and backscattering coefficient in two hours. The two observations in a day from a single polar-orbiting satellite sensor are thus shown to be able to infer phytoplankton vertical movement within a short timeframe, a phenomenon difficult to capture with other sensors as each sensor can provide at most one observation per day, and cross-sensor inconsistency may make interpretation of merged-sensor data difficult. These findings strongly support geostationary satellite missions to study short-term bloom dynamics.

A Model for Deriving the Spectral Backscattering Properties of Particles in Inland and Marine Waters From &lt;italic&gt;In Situ&lt;/italic&gt; and Remote Sensing Data

A model is developed for deriving the backscattering properties of phytoplankton [ $b_{\mathrm{ bph}}(\boldsymbol {\lambda })$ ] and nonalgal particles [ $b_{\mathrm{ bNAP}}(\boldsymbol {\lambda })$ ] from the total particulate backscattering coefficient [ $b_{\mathrm{ bp}}(\boldsymbol {\lambda })$ ] in turbid and productive coastal and inland water environments. In situ spectral particulate backscattering data acquired from several field campaigns on the turbid and productive coastal regions of southern India are used to develop a partitioning method and establish robust relationships between the partitioned $b_{\mathrm{ bph}}(\boldsymbol {\lambda })$ and $b_{\mathrm{ bNAP}}(\boldsymbol {\lambda })$ versus chlorophyll and turbidity. The performance of this method is assessed using independent data sets from marine and inland productive waters. The $b_{\mathrm{ bph}}(\boldsymbol {\lambda })$ and $b_{\mathrm{ bNAP}}(\boldsymbol {\lambda })$ products derived from this method are found in good agreement with in situ data, with the overall percentile error of a few percent which is well within the benchmark for a validated uncertainty of ±35% endorsed for the chlorophyll-a retrieval in oceanic waters. Further comparison with other inversion methods demonstrated the relative performance of the new model, especially in productive and algal bloom-dominated waters. To illustrate the use of the new model for remote sensing applications, it was applied to both multispectral Moderate Resolution Imaging Spectroradiometer-Aqua and hyperspectral (Hyperspectral Imager for the Coastal Ocean) images from the algal bloom-dominated waters of the Arabian Sea and turbid productive lagoon waters on the coastal region of the Bay of Bengal. The results were interesting, i.e., the existence of strong spectral features and inflections in the phytoplankton backscattering spectra in bloom-dominated waters and the nearly featureless NAP backscattering spectra varying approximately according to a power law in sediment-laden waters. Corroborating the previous experimental and theoretical studies, our results for algal bloom waters show a steep slope in the blue region and enhanced backscattering in the green and near-infrared regions due to the influence of phytoplankton absorption and backscattering. These results further suggest that living algal matter in productive waters may be a source of significant backscattering. Under typical nonbloom conditions in turbid coastal waters, our analyses confirm that the minerogenic and detrital particles are likely the most important because of their high abundance relative to living organic particles and their spectral dependences have profound implications for our understanding of the relationship between the backscattering to scattering ratio and particle size distribution and related properties. The new model clearly represents as an important tool permitting the analysis of the spectral backscattering coefficients associated with phytoplankton and nonliving components in sediment-laden and algal-bloom-dominated productive waters within coastal environments.

A modified vegetation backscattering model for leaf area index retrieval from SAR time series

ABSTRACTIn this study, a semi-empirical modified vegetation backscattering model was developed to retrieve leaf area index (LAI) based on multi-temporal Radarsat-2 data and ground observations collected in China. This model combined the contribution of the vegetation and bare soil at the pixel level by adding vegetation coverage and the influence of bare soil on the total backscatter coefficients. Then, a lookup table algorithm was applied to calculate the value of vegetation water content and retrieve the LAI based on the linear relationship between the vegetation water content and LAI. The results indicated that the modified model was effective in evaluating and reproducing the total backscatter coefficients. Meanwhile, the LAI retrieval was well conducted with coefficient of determination (R2) and root mean square error (RMSE) of 89% and 0.19 m2 m−2, respectively. Additionally, this method offers insight into the required application accuracy of LAI retrieval in the agricultural regions.

Ku-, X- and C-band measured and modeled microwave backscatter from a highly saline snow cover on first-year sea ice

Abstract In this study, we inter-compare observed and modeled Ku-, X- and C-band microwave backscatter for two snow temperature conditions for a highly saline snow cover on smooth first-year sea ice. A new surface-based multi-frequency (Ku-, X- and C-bands) microwave scatterometer system is used quasi-coincident with in situ geophysical snow measurements. A multilayer snow and ice backscatter model is used to calculate the total co-polarized backscatter coefficient for two snow temperature conditions. The model provides the surface and volume scattering contributions for each snow layer, as well as the frequency-dependent penetration depth. These results aid interpretation of observed backscatter. Joint use of Ku-, X- and C-band microwaves provide an enhanced understanding of diverse variations in geophysical, thermodynamic and electrical state of snow/sea ice system. Our results indicate that the effect of dielectric loss associated with highly saline snow covers is the dominant factor affecting microwave penetration and backscatter from all three frequencies. The observed and modeled C-band backscatter shows good agreement, followed by X- and Ku-bands, at both snow temperature conditions. Microwave backscatter shows greater sensitivity to variations in plot-scale surface roughness, for all three frequencies. Additionally, Ku-band wavelength exhibits greater sensitivity to snow grain radius, over X-, and C-bands. Our results demonstrate the future potential of a multi-frequency approach towards the development of snow thickness and snow water equivalent algorithms on first-year sea ice.