Level-2 Products Research Articles

Evaluation of GPM Dual-Frequency Precipitation Radar (DPR) Rainfall Products Using the Rain Gauge Network over China

AbstractThe Dual-Frequency Precipitation Radar (DPR) on board the Global Precipitation Measurement (GPM) mission core satellite provides the new-generation global observation of rain since 2014. The main objective of this paper is to evaluate the suitability and limitation of GPM-DPR level-2 products over China. The DPR rain rate products are compared with rain gauge data during the summers of 5 years (2014–18). The ground observation network is composed of more than 50 000 rain gauges. The DPR precipitation products for all scans (DPR_NS, DPR_MS, and DPR_HS) generally underestimate rain rates. However, DPR_MS agrees better with gauge estimates than DPR_NS and DPR_HS, yielding the lowest mean error, systematic deviation, and highest Pearson correlation coefficient. In addition, all three swath types show obvious overestimation over gauge estimates between 0.5 and 1 mm h−1 and underestimation when gauge estimates are larger than 1 mm h−1. The DPR_HS and DPR_MS agree better with gauge estimates below and above 2.5 mm h−1, respectively. A deeper investigation was carried out to analyze the variation of DPR_MS’s performance with respect to terrains over China. An obvious underestimation, relative to gauge estimates, occurs in Tibetan Plateau while a slight overestimation occurs in the North China Plain. Furthermore, our comprehensive analysis suggests that in Sichuan Basin, the DPR_MS exhibit the best agreement with gauge estimates.

Objective Analysis of SMOS and SMAP Sea Surface Salinity to Reduce Large-Scale and Time-Dependent Biases from Low to High Latitudes

AbstractTen years of L-band radiometric measurements have proven the capability of satellite sea surface salinity (SSS) to resolve large-scale-to-mesoscale SSS features in tropical to subtropical ocean. In mid-to-high latitudes, L-band measurements still suffer from large-scale and time-varying errors. Here, a simple method is proposed to mitigate the large-scale and time-varying errors. First, an optimal interpolation using a large correlation scale (~500 km) is used to map independently Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) level-3 (L3) data. The mapping is compared with the equivalent mapping of in situ observations to estimate the large-scale and seasonal biases. A second mapping is performed on adjusted SSS at the scale of SMOS/SMAP spatial resolution (~45 km). This procedure merges both products and increases the signal-to-noise ratio of the absolute SSS estimates, reducing the root-mean-square difference of in situ satellite products by about 26%–32% from mid- to high latitudes, respectively, in comparison with the existing SMOS and SMAP L3 products. However, in the Arctic Ocean, some issues on satellite retrieved SSS related to, for example, radio frequency interferences, land–sea contamination, and ice–sea contamination remain challenging to reduce given the low sensitivity of L-band radiometric measurements to SSS in cold water. Using the International Thermodynamic Equation Of Seawater—2010 (TEOS-10), the resulting level-4 SSS satellite product is combined with satellite-microwave SST products to estimate sea surface density, spiciness, and haline contraction and thermal expansion coefficients. For the first time, we illustrate how useful these satellite-derived parameters are to fully characterize the surface ocean water masses at large mesoscale.

Evaluation of IMERG Level-3 Products in Depicting the July to October Rainfall over Taiwan: Typhoon Versus Non-Typhoon

This study assesses the performance of satellite precipitation products (SPPs) from the latest version, V06B, Integrated Multi-satellitE Retrievals for Global Precipitation Mission (IMERG) Level-3 (including early, late, and final runs), in depicting the characteristics of typhoon season (July to October) rainfall over Taiwan within the period of 2000–2018. The early and late runs are near-real-time SPPs, while final run is post-real-time SPP adjusted by monthly rain gauge data. The latency of early, late, and final runs is approximately 4 h, 14 h, and 3.5 months, respectively, after the observation. Analyses focus on the seasonal mean, daily variation, and interannual variation of typhoon-related (TC) and non-typhoon-related (non-TC) rainfall. Using local rain-gauge observations as a reference for evaluation, our results show that all IMERG products capture the spatio-temporal variations of TC rainfall better than those of non-TC rainfall. Among SPPs, the final run performs better than the late run, which is slightly better than the early run for most of the features assessed for both TC and non-TC rainfall. Despite these differences, all IMERG products outperform the frequently used Tropical Rainfall Measuring Mission 3B42 v7 (TRMM7) for the illustration of the spatio-temporal characteristics of TC rainfall in Taiwan. In contrast, for the non-TC rainfall, the final run performs notably better relative to TRMM7, while the early and late runs showed only slight improvement. These findings highlight the advantages and disadvantages of using IMERG products for studying or monitoring typhoon season rainfall in Taiwan.

Assessment of the TROPOMI tropospheric NO<sub>2</sub> product based on airborne APEX observations

Abstract. Sentinel-5 Precursor (S-5P), launched in October 2017, carrying the TROPOspheric Monitoring Instrument (TROPOMI) nadir-viewing spectrometer, is the first mission of the Copernicus Programme dedicated to the monitoring of air quality, climate, and ozone. In the presented study, the TROPOMI tropospheric nitrogen dioxide (NO2) level-2 (L2) product (OFFL v1.03.01; 3.5 km × 7 km at nadir observations) has been validated over strongly polluted urban regions by comparison with coincident high-resolution Airborne Prism EXperiment (APEX) remote sensing observations (∼ 75 m × 120 m). Satellite products can be optimally assessed based on (APEX) airborne remote sensing observations, as a large amount of satellite pixels can be fully mapped at high accuracy and in a relatively short time interval, reducing the impact of spatiotemporal mismatches. In the framework of the S-5P validation campaign over Belgium (S5PVAL-BE), the APEX imaging spectrometer has been deployed during four mapping flights (26–29 June 2019) over the two largest urban regions in Belgium, i.e. Brussels and Antwerp, in order to map the horizontal distribution of tropospheric NO2. For each flight, 10 to 20 TROPOMI pixels were fully covered by approximately 2700 to 4000 APEX measurements within each TROPOMI pixel. The TROPOMI and APEX NO2 vertical column density (VCD) retrieval schemes are similar in concept. Overall, for the ensemble of the four flights, the standard TROPOMI NO2 VCD product is well correlated (R = 0.92) but biased negatively by −1.2 ± 1.2 × 1015 molec cm−2 or −14 ± 12 %, on average, with respect to coincident APEX NO2 retrievals. When replacing the coarse 1∘ × 1∘ the massively parallel (MP) version of the Tracer Model version 5 (TM5) a priori NO2 profiles by NO2 profile shapes from the Copernicus Atmospheric Monitoring Service (CAMS) regional chemistry transport model (CTM) ensemble at 0.1∘ × 0.1∘, R is 0.94 and the slope increases from 0.82 to 0.93. The bias is reduced to −0.1 ± 1.0 × 1015 molec cm−2 or −1.0 ± 12 %. The absolute difference is on average 1.3 × 1015 molec cm−2 (16 %) and 0.7 × 1015 molec cm−2 (9 %), when comparing APEX NO2 VCDs with TM5-MP-based and CAMS-based NO2 VCDs, respectively. Both sets of retrievals are well within the mission accuracy requirement of a maximum bias of 25 %–50 % for the TROPOMI tropospheric NO2 product for all individual compared pixels. Additionally, the APEX data set allows the study of TROPOMI subpixel variability and impact of signal smoothing due to its finite satellite pixel size, typically coarser than fine-scale gradients in the urban NO2 field. For a case study in the Antwerp region, the current TROPOMI data underestimate localized enhancements and overestimate background values by approximately 1–2 × 1015 molec cm−2 (10 %–20 %).

Variational Retrievals of High Winds Using Uncalibrated CyGNSS Observables

This study presents a new retrieval approach for obtaining wind speeds from CyGNSS level-1 observables. Unlike other existing approaches, (1) this one is a variational technique that is based on a physical forward model, (2) it uses uncalibrated bin raw counts observables, (3) the geophysical information content comes from only one pixel of the broader delay-Doppler map, finest achievable resolution in level-1 products over the sea, and (4) calibrates them against track-wise polynomial adjustments to a background numerical weather prediction model. Through comparisons with the background model, other spaceborne sensors (SMAP, SMOS, ASCAT-A/B), and CyGNSS wind retrievals by other organizations, the study shows that this approach has skills to infer wind speeds, including hurricane force winds. For example, the Pearson’s correlation coefficient between these CyGNSS retrievals and ERA5 is 0.884, 0.832 with NOAA CyGNSS results, and 0.831 with respect to SMAP co-located measurements. Furthermore, the variational retrieval algorithm is a simplified version of the more general equations that are used in data assimilation, and the calibration scheme could also be integrated in the assimilation process. Therefore, this approach is also a good tool for analyzing the potential performance of ingesting uncalibrated level-1 single-pixel observables into NWP.

Study on the inter-annual hydrology-induced deformations in Europe using GRACE and hydrological models

Abstract Earth’s crust deforms in various time and spatial resolutions. To estimate them, geodetic observations are widely employed and compared to geophysical models. In this research, we focus on the Earth’s crust deformations resulting from hydrology mass changes, as observed by GRACE (Gravity Recovery and Climate Experiment) gravity mission and modeled using WGHM (WaterGAP Global Hydrological Model) and GLDAS (Global Land Data Assimilation System), hydrological models. We use the newest release of GRACE Level-2 products, i. e. RL06, provided by the CSR (Center for Space Research, Austin) analysis center in the form of a mascon solution. The analysis is performed for the European area, divided into 29 river basins. For each basin, the average signal is estimated. Then, annual amplitudes and trends are calculated. We found that the eastern part of Europe is characterized by the largest annual amplitudes of hydrology-induced Earth’s crust deformations, which decrease with decreasing distance to the Atlantic coast. GLDAS largely overestimates annual amplitudes in comparison to GRACE and WGHM. Hydrology models underestimate trends, which are observed by GRACE. For the basin-related average signals, we also estimate the non-linear variations over time using the Singular Spectrum Analysis (SSA). For the river basins situated on the southern borderline of Europe and Asia, large inter-annual deformations between 2004 and 2009 reaching a few millimeters are found; they are related to high precipitation and unexpectedly large drying. They were observed by GRACE but mismodelled in the GLDAS and WGHM models. Few smaller inter-annual deformations were also observed by GRACE between 2002-2017 for central and eastern European river basins, but these have been also well-covered by the WGHM and GLDAS hydrological models.

Soil Moisture Retrievals by Combining Passive Microwave and Optical Data

This paper aims to retrieve the temporal dynamics of soil moisture from 2015 to 2019 over an agricultural site in Southeast Australia using the Soil Moisture Active Passive (SMAP) brightness temperature. To meet this objective, two machine learning approaches, Random Forest (RF), Support Vector Machine (SVM), as well as a statistical Ordinary Least Squares (OLS) model were established, with the auxiliary data including the 16-day composite MODIS NDVI (MOD13Q1) and Surface Temperature (ST). The entire data were divided into two parts corresponding to ascending (6:00 p.m. local time) and descending (6:00 a.m. local time) orbits of SMAP overpasses. Thus, the three models were trained using the descending data acquired during the five years (2015 to 2019), and validated using the ascending product of the same period. Consequently, three different temporal variations of the soil moisture were obtained based on the three models. To evaluate their accuracies, the retrieved soil moisture was compared against the SMAP level-2 soil moisture product, as well as to in-situ ground station data. The comparative results show that the soil moisture obtained using the OLS, RF and SVM algorithms are highly correlated to the SMAP level-2 product, with high coefficients of determination (R2OLS = 0.981, R2SVM = 0.943, R2RF = 0.983) and low RMSE (RMSEOLS = 0.016 cm3/cm3, RMSESVM = 0.047 cm3/cm3, RMSERF = 0.016 cm3/cm3). Meanwhile, the estimated soil moistures agree with in-situ station data across different years (R2OLS = 0.376~0.85, R2SVM = 0.376~0.814, R2RF = 0.39~0.854; RMSEOLS = 0.049~0.105 cm3/cm3, RMSESVM = 0.073~0.1 cm3/cm3, RMSERF = 0.047~0.102 cm3/cm3), but an overestimation issue is observed for high vegetation conditions. The RF algorithm outperformed the SVM and OLS, in terms of the agreement with the ground measurements. This study suggests an alternative soil moisture retrieval scheme, in complementary to the SMAP baseline algorithm, for a fast soil moisture retrieval.

OCO-3 early mission operations and initial (vEarly) XCO2 and SIF retrievals

NASA's Orbiting Carbon Observatory-3 (OCO-3) was installed on the International Space Station (ISS) on 10 May 2019. OCO-3 combines the flight spare spectrometer from the Orbiting Carbon Observatory-2 (OCO-2) mission, which has been in operation since 2014, with a new Pointing Mirror Assembly (PMA) that facilitates observations of non-nadir targets from the nadir-oriented ISS platform. The PMA is a new feature of OCO-3, which is being used to collect data in all science modes, including nadir (ND), sun-glint (GL), target (TG), and the new snapshot area mapping (SAM) mode.This work provides an initial assessment of the OCO-3 instrument and algorithm performance, highlighting results from the first 8 months of operations spanning August 2019 through March 2020. During the In-Orbit Checkout (IOC) phase, critical systems such as power and cooling were verified, after which the OCO-3 spectrometer and PMA were subjected to a series of rigorous tests. First light of the OCO-3 spectrometer was on 26 June 2019, with full science operations beginning on 6 August 2019. The OCO-3 spectrometer on-orbit performance is consistent with that seen during preflight testing. Signal to noise ratios are in the expected range needed for high quality retrievals of the column-averaged carbon dioxide (CO2) dry-air mole fraction (XCO2) and solar-induced chlorophyll fluorescence (SIF), which will be used to help quantify and constrain the global carbon cycle.The first public release of OCO-3 Level 2 (L2) data products, called “vEarly”, is being distributed by NASA's Goddard Earth Sciences Data and Information Services Center (GES DISC). The intent of the vEarly product is to evaluate early mission performance, facilitate comparisons with OCO-2 products, and identify key areas to improve for the next data release. The vEarly XCO2 exhibits a root-mean-squared-error (RMSE) of ≃ 1, 1, 2 ppm versus a truth proxy for nadir-land, TG&SAM, and glint-water observations, respectively. The vEarly SIF shows a correlation with OCO-2 measurements of >0.9 for highly coincident soundings. Overall, the Level 2 SIF and XCO2 products look very promising, with performance comparable to OCO-2. A follow-on version of the OCO-3 L2 product containing a number of refinements, e.g., instrument calibration, pointing accuracy, and retrieval algorithm tuning, is anticipated by early in 2021.

Evaluation Analysis of Landsat Level-1 and Level-2 Data Products Using In Situ Measurements

Landsat Level-1 products are delivered as quantized and calibrated scaled Digital Numbers (DN). The Level-1 DN data can be rescaled to Top-of-Atmosphere (TOA) reflectance applying radiometric rescaling coefficients. Currently, the Level-1 product is the standard data product of the Landsat sensors. The more recent Level-2 data products contain surface reflectance values, i.e., reflectance as it would be measured at ground level in the absence of atmospheric effects; in the near future, these products are anticipated to become the standard products of the Landsat sensors. The purpose of this paper is to present a radiometric performance evaluation of Level-1 and Level-2 data products for the Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) sensors. TOA reflectance and derived surface reflectance values from both data products were evaluated and compared to in situ measurements from eight test sites located in Turkey, Brazil, Chile, the United States, France, and Namibia. The results indicate an agreement between the ETM+ and OLI Level-1 TOA reflectance data and the in situ measurements of 3.9% to 6.5% and 3.9% to 6.0%, respectively, across all spectral bands. Agreement between the in situ measurements and both Level-2 surface reflectance data products were consistently decreased in the shorter wavelength bands, and slightly better in the longer wavelength bands. The mean percent absolute error for Level-2 surface reflectance data ranged from 3.3% to 10% for both Landsat sensors. The significant decay in agreement with the data collected in situ in the short wavelength spectral bands with Level-2 data suggests issues with retrieval of aerosol concentration at some sites. In contrast, the results indicate a reasonably accurate estimate of water vapor in the mid-infrared spectrum. Lastly, despite the less reliable performance of Level-2 data product in the visible spectral region (compared with Level-1 data) in both sensors, the Landsat-8 OLI Level-2 showed an improvement of surface reflectance product over all spectral bands in common with the Landsat-7 ETM+ Level-2 data.

Research on distribution of suspended sediment concentration in long time series based on mid-resolution imaging spectrometer data and quantile trend analysis

In order to explore the long-term variation trend of suspended sediment concentration (SSC) in the Pearl River Estuary, a multi-voyage in-situ measurements and Moderate-Resolution Imagine Spectrometry (MODIS) Level-1 products were used to establish a quantitative inversion model of the SSC in the Pearl River Estuary. In situ data were gathered with multiple campaigns, while the MODIS sensor was passing over the study area. Then, the Mann–Kendall​ algorithm and the Sen’s slope estimation method were used to analyze the trend of various quantiles SSC. The accuracy of the SSC inversion model was verified by using multiple sets of measured data in which the correlation coefficient was 0.90 and the root mean square error was 4.76. The results showed that: (1) The SSC in the Pearl River Estuary shown a zonal distribution in the northeast–southwest direction, which was gradually reduced from the nearshore to the sea, with an average concentration of more than 90 mg L−1. (2) The SSC in the PRE displayed a decreasing trend, with an averaged SSC reduction rate of about 1.02 mg L−1 yr−1 during 2004–2016. Decreasing rate in high SSC was generally larger than those was in low SSC, with a marked spatial difference. (3) The seasonal variation of dam-induced reduction of the sediment load into the PRE contributed to most of the low-percentile SSC decreases. The water-depth change in the PRE induced by human activities dominated the high-percentile SSC trends. The decrease of the wind speed over the PRE also affected the high-percentile SSC trend.

Impact of land–water sensitivity contrast on MOPITT retrievals and trends over a coastal city

Abstract. We compare MOPITT Version 7 (V7) Level 2 (L2) and Level 3 (L3) carbon monoxide (CO) products for the 1∘×1∘ L3 grid box containing the coastal city of Halifax, Canada (longitude −63.58∘, latitude 44.65∘), with a focus on the seasons DJF and JJA, and highlight a limitation in the L3 products that has significant consequences for the temporal trends in near-surface CO identified using those data. Because this grid box straddles the coastline, the MOPITT L3 products are created from the finer spatial resolution L2 products that are retrieved over both land and water, with a greater contribution from retrievals over water because more of the grid box lies over water than land. We create alternative L3 products for this grid box by separately averaging the bounded L2 retrievals over land (L3L) and water (L3W) and demonstrate that profile and total column CO (TCO) concentrations, retrieved at the same time, differ depending on whether the retrieval took place over land or water. These differences (ΔRET) are greatest in the lower troposphere (LT), where mean retrieved volume mixing ratios (VMRs) are greater in L3W than L3L, with maximum mean differences of 11.6 % (14.3 ppbv, p=0.001) at the surface level in JJA. Retrieved CO concentrations are more similar, on average, in the middle and upper troposphere (MT and UT), although large differences (in excess of 40 %) do infrequently occur. TCO is also greater in L3W than L3L in both seasons. By analysing L3L and L3W retrieval averaging kernels and simulations of these retrievals, we demonstrate that, in JJA, ΔRET is strongly influenced by differences in retrieval sensitivity over land and water, especially close to the surface where L3L has significantly greater information content than L3W. In DJF, land–water differences in retrieval sensitivity are much less pronounced and appear to have less of an impact on ΔRET, which analysis of wind directions suggests is more likely to reflect differences in true profile concentrations (i.e. real differences). The original L3 time series for the 1∘×1∘ grid box containing Halifax (L3O) corresponds much more closely to L3W than L3L, owing to the greater contribution from L2 retrievals over water than land. Thus, in JJA, variability in retrieved CO concentrations close to the surface in L3O is suppressed compared to L3L, and a declining trend detected using weighted least squares (WLS) regression analysis is significantly slower in L3O (strongest surface level trend identifiable is −1.35 (±0.35) ppbv yr−1) than L3L (−2.85 (±0.60) ppbv yr−1). This is because contributing L2 retrievals over water are closely tied to a priori CO concentrations used in the retrieval, owing to their lack of near-surface sensitivity in JJA, and these are based on monthly climatological CO profiles from a chemical transport model and therefore have no yearly change (surface level trend in L3W is −0.60 (±0.33) ppbv yr−1). Although our analysis focuses on DJF and JJA, we demonstrate that the findings also apply to MAM and SON. The results that we report here suggest that similar analyses be performed for other coastal cities before using MOPITT surface CO.

Evaluation of V05 Precipitation Estimates from GPM Constellation Radiometers Using KuPR as the Reference

AbstractThis study assesses the level-2 precipitation estimates from 10 radiometers relative to Global Precipitation Measurement (GPM) Ku-band precipitation radar (KuPR) in two parts. First, nine sensors—four imagers [Advanced Microwave Scanning Radiometer 2 (AMSR2) and three Special Sensor Microwave Imager/Sounders (SSMISs)] and five sounders [Advanced Technology Microwave Sounder (ATMS) and four Microwave Humidity Sounders (MHSs)]—are evaluated over the 65°S–65°N region. Over ocean, imagers outperform sounders, primarily due to the usage of low-frequency channels. Furthermore, AMSR2 is clearly superior to SSMISs, likely due to the finer footprint size. Over land all sensors perform similarly except the noticeably worse performance from ATMS and SSMIS-F17. Second, we include the Sondeur Atmospherique du Profil d’Humidite Intertropicale par Radiometrie (SAPHIR) into the evaluation process, contrasting it against other sensors in the SAPHIR latitudes (30°S–30°N). SAPHIR has a slightly worse detection capability than other sounders over ocean but comparable detection performance to MHSs over land. The intensity estimates from SAPHIR show a larger normalized root-mean-square-error over both land and ocean, likely because only 183.3-GHz channels are available. Currently, imagers are preferred to sounders when level-2 estimates are incorporated into level-3 products. Our results suggest a sensor-specific priority order. Over ocean, this study indicates a priority order of AMSR2, SSMISs, MHSs and ATMS, and SAPHIR. Over land, SSMIS-F17, ATMS and SAPHIR should be given a lower priority than the other sensors.

Pre-Processing of Landsat-8 Data for Population Estimation: A Study With Respect to Dimoria Development Block of Assam

This paper reviews various works on population estimation and concentrates on revealing features of the study area’s satellite data. The work emphasizes data pre-processing which is a crucial stage in population estimation. The study region is a rural area of Kamrup Metro district of Assam. Our work is deprived of gridded data, so the satellite data is cropped into grids of images. We have collected landsat8 level-1 products and performed the atmospheric effect correction. Satellite brightness temperature for the bands 10 and 11 are observed. The vegetation coverage is also visualized for the bands 3, 4 and 5. In the end we have proposed a modular based approach for analyzing population of the selected site.

Ensemble-based satellite-derived carbon dioxide and methane column-averaged dry-air mole fraction data sets (2003–2018) for carbon and climate applications

Abstract. Satellite retrievals of column-averaged dry-air mole fractions of carbon dioxide (CO2) and methane (CH4), denoted XCO2 and XCH4, respectively, have been used in recent years to obtain information on natural and anthropogenic sources and sinks and for other applications such as comparisons with climate models. Here we present new data sets based on merging several individual satellite data products in order to generate consistent long-term climate data records (CDRs) of these two Essential Climate Variables (ECVs). These ECV CDRs, which cover the time period 2003–2018, have been generated using an ensemble of data products from the satellite sensors SCIAMACHY/ENVISAT and TANSO-FTS/GOSAT and (for XCO2) for the first time also including data from the Orbiting Carbon Observatory 2 (OCO-2) satellite. Two types of products have been generated: (i) Level 2 (L2) products generated with the latest version of the ensemble median algorithm (EMMA) and (ii) Level 3 (L3) products obtained by gridding the corresponding L2 EMMA products to obtain a monthly 5∘×5∘ data product in Obs4MIPs (Observations for Model Intercomparisons Project) format. The L2 products consist of daily NetCDF (Network Common Data Form) files, which contain in addition to the main parameters, i.e., XCO2 or XCH4, corresponding uncertainty estimates for random and potential systematic uncertainties and the averaging kernel for each single (quality-filtered) satellite observation. We describe the algorithms used to generate these data products and present quality assessment results based on comparisons with Total Carbon Column Observing Network (TCCON) ground-based retrievals. We found that the XCO2 Level 2 data set at the TCCON validation sites can be characterized by the following figures of merit (the corresponding values for the Level 3 product are listed in brackets) – single-observation random error (1σ): 1.29 ppm (monthly: 1.18 ppm); global bias: 0.20 ppm (0.18 ppm); and spatiotemporal bias or relative accuracy (1σ): 0.66 ppm (0.70 ppm). The corresponding values for the XCH4 products are single-observation random error (1σ): 17.4 ppb (monthly: 8.7 ppb); global bias: −2.0 ppb (−2.9 ppb); and spatiotemporal bias (1σ): 5.0 ppb (4.9 ppb). It has also been found that the data products exhibit very good long-term stability as no significant long-term bias trend has been identified. The new data sets have also been used to derive annual XCO2 and XCH4 growth rates, which are in reasonable to good agreement with growth rates from the National Oceanic and Atmospheric Administration (NOAA) based on marine surface observations. The presented ECV data sets are available (from early 2020 onwards) via the Climate Data Store (CDS, https://cds.climate.copernicus.eu/, last access: 10 January 2020) of the Copernicus Climate Change Service (C3S, https://climate.copernicus.eu/, last access: 10 January 2020).

Introducing the 4.4 km spatial resolution Multi-Angle Imaging SpectroRadiometer (MISR) aerosol product

Abstract. The Multi-angle Imaging SpectroRadiometer (MISR) instrument has been operational on the National Aeronautics and Space Administration (NASA) Earth Observing System (EOS) Terra satellite since early 2000, creating an extensive data set of global Earth observations. Here we introduce the latest version of the MISR aerosol products. The level 2 (swath) product, which is reported on a 4.4 km spatial grid, is designated as version 23 (V23) and contains retrieved aerosol optical depth (AOD) and aerosol particle property information derived from MISR's multi-angle observations over both land and water. The changes from the previous version of the algorithm (V22) have significant impacts on the data product and its interpretation. The V23 data set is created from two separate retrieval algorithms that are applied over dark water and land surfaces, respectively. Besides increasing the horizontal resolution to 4.4 km compared with the coarser 17.6 m resolution in V22 and streamlining the format and content, the V23 product has added geolocation information, pixel-level uncertainty estimates, and improved cloud screening. MISR data can be obtained from the NASA Langley Research Center Atmospheric Science Data Center at https://eosweb.larc.nasa.gov/project/misr/misr_table (last access: 11 October 2019). The version number for the V23 level 2 aerosol product is F13_0023. The level 3 (gridded) aerosol product is still reported at 0.5∘×0.5∘ spatial resolution with results aggregated from the higher-resolution level 2 data. The format and content at level 3 have also been updated to reflect the changes made at level 2. The level 3 product associated with the V23 level 2 product version is designated as F15_0032. Both the level 2 and level 3 products are now provided in NetCDF format.

Aerosol optical depth (AOD) retrieval for atmospheric correction in Landsat-8 imagery using second simulation of a satellite signal in the solar spectrum-vector (6SV)

Atmospheric correction has been challenging task in digital image processing. It requires several atmospheric parameters in order to obtain accurate surface reflectance of objects within the image scene. One of the most crucial parameters required for accurate atmospheric correction is aerosol optical depth (AOD). AOD can be obtained by in-situ measurement or estimated from remote sensing observation. In this experiment, atmospheric correction was performed using second simulation of a satellite signal in the solar spectrum-vector (6SV) algorithm on Landsat-8 imagery in which AOD parameter was retrieved from surface reflectance inversion involving daily-global surface reflectance product of moderate resolution imaging spectroradiometer (MODIS). Furthermore, AOD retrieved from surface reflectance inversion was also validated using ground-based sun photometer observation data from aerosol robotic network (AERONET) station in Bandung, Indonesia. Our experiment shows the consistency between AOD from surface reflectance inversion and AOD from ground-based observation. Finally, 6SV was performed on Landsat-8 imagery to obtain the surface reflectance. We further compared surface reflectance of 6SV atmospheric correction and surface reflectance of Landsat-8 Level 2 product. The atmospherically corrected image also shared agreeable result with Landsat 8 Level-2 product.

Monitoring Tangier (Morocco) coastal waters for As, Fe and P concentrations using ESA Sentinels-2 and 3 data: An exploratory study

An exploratory study is presented to investigate human activities pollution in the coastal waters of a popular tourist destination in the west coast of Tangier, Morocco. A series of bathing water samples were examined for arsenic (As), iron (Fe) and phosphorus (P) concentrations in the western coastal waters of Tangier. Additionally, quasi-simultaneous satellite images from Sentinels-2 and 3 were processed to retrieve water leaving reflectances in the same locations. The bathing water samples showed high concentrations of As, P and Fe in the Boukhalef River mouth and in two nearby beaches; Jbila and Sidikacem, which appeared closely correlated with Sentinel-3 reflectances at bands 6 (560 nm) and 7 (620 nm). Since algorithms for Chlorophyll-a and Total Suspended Matter Concentrations rely on these wavelengths, they too were found to be closely related to high As, Fe and P concentrations. Therefore, caution must be taken when analysing these level-2 products in close proximity to the Boukhalef River mouth, since results may be biased due to the presence of significant As, Fe and P concentrations. In this case, Sentinel data proved to be a potentially useful tool with prospects to be used for monitoring environmental hazardous substances such as metals (e.g. As and Fe) spatial distributions in the west coast of Tangier, Morocco.

Synergistic RADARSAT-2 and Sentinel-1 SAR Images for Ocean Feature Analysis

Using a case study approach, the utility of synergistic RADARSAT-2 (R2) and Sentinel-1 (S1) synthetic aperture radar (SAR) imagery is demonstrated for ocean feature signature analysis in the vicinity of the Gulf Stream. The R2 and S1 images considered are either spatially adjacent or spatially overlapping, and were quasi-simultaneously collected (i.e., within minutes of each other). Spatially adjacent R2 and S1 imagery allows ocean feature signatures to be delineated over large spatial areas, while spatially overlapping R2 and S1 imagery collected within short time intervals provides independent ‘looks’ at the same ocean features. This permits determination of the surface displacement of features, potentially leading to improved classification of the origin of ocean feature signatures (quasi-stationary features are likely related to sea surface temperature fronts, while mobile features are likely related to atmospheric conditions). Further, we demonstrate how the use of S1 Level-2 products (i.e. radial velocity datasets) can be leveraged as contextual data to improve the interpretation and classification of ocean feature signatures extracted from R2 imagery. Despite the straight-forward approach taken here, this work demonstrates that there are practical, real-world applications that would benefit from exploiting these on-going imaging opportunities in operational environments.

Sentinel-2 Level-1 Radiometry Assessment Using Vicarious Methods From DIMITRI Toolbox and Field Measurements From RadCalNet Database

The Sentinel-2A and Sentinel-2B constellation is an Earth observation optical mission developed and operated by the European Space Agency in the frame of the Copernicus program of the European Commission. The novel observation capacity offered by the multispectral instruments (MSI) on-board Sentinel-2 mission provides a massive resource for the Earth observation community. The accurate radiometric calibration is a critical element of the success of the mission; therefore, in-orbit calibration and validation activities are conducted within the Sentinel-2 Mission Performance Centre. The database of the imaging multispectral instrument and tool for radiometric intercomparison and the ground measurements of the radiometric calibration network (RadCalNet) are used to perform the vicarious validation of the radiometry of the Level-1 products delivered to the users. The aims of this validation are to assess the quality of the data product, to monitor the evolution of the radiometry of both instruments, and to ensure that the products meet the mission requirement accuracy. This article outlines the vicarious methods—Rayleigh scattering, desert pseudoinvariant calibration sites, sensor-to-sensor intercalibration, and the ground reflectance-based approach—that are used for the Sentinel-2 radiometry validation. The validation results indicate good performance and stability of both sensors MSI-A and MSI-B and consistency up to ∼1% (∼2% for red-edge bands) between them. The results of the intercomparison with the in situ measurements from RadCalNet illustrate the relevance of such dataset for the vicarious validation activity.

Evaluation of Sentinel-3A OLCI Products Derived Using the Case-2 Regional CoastColour Processor over the Baltic Sea.

In this study, the Level-2 products of the Ocean and Land Colour Instrument (OLCI) data on Sentinel-3A are derived using the Case-2 Regional CoastColour (C2RCC) processor for the SentiNel Application Platform (SNAP) whilst adjusting the specific scatter of Total Suspended Matter (TSM) for the Baltic Sea in order to improve TSM retrieval. The remote sensing product “kd_z90max” (i.e., the depth of the water column from which 90% of the water-leaving irradiance are derived) from C2RCC-SNAP showed a good correlation with in situ Secchi depth (SD). Additionally, a regional in-water algorithm was applied to derive SD from the attenuation coefficient Kd(489) using a local algorithm. Furthermore, a regional in-water relationship between particle scatter and bench turbidity was applied to generate turbidity from the remote sensing product “iop_bpart” (i.e., the scattering coefficient of marine particles at 443 nm). The spectral shape of the remote sensing reflectance (Rrs) data extracted from match-up stations was evaluated against reflectance data measured in situ by a tethered Attenuation Coefficient Sensor (TACCS) radiometer. The L2 products were evaluated against in situ data from several dedicated validation campaigns (2016–2018) in the NW Baltic proper. All derived L2 in-water products were statistically compared to in situ data and the results were also compared to results for MERIS validation from the literature and the current S3 Level-2 Water (L2W) standard processor from EUMETSAT. The Chl-a product showed a substantial improvement (MNB 21%, RMSE 88%, APD 96%, n = 27) compared to concentrations derived from the Medium Resolution Imaging Spectrometer (MERIS), with a strong underestimation of higher values. TSM performed within an error comparable to MERIS data with a mean normalized bias (MNB) 25%, root-mean square error (RMSE) 73%, average absolute percentage difference (APD) 63% n = 23). Coloured Dissolved Organic Matter (CDOM) absorption retrieval has also improved substantially when using the product “iop_adg” (i.e., the sum of organic detritus and Gelbstoff absorption at 443 nm) as a proxy (MNB 8%, RMSE 56%, APD 54%, n = 18). The local SD (MNB 6%, RMSE 62%, APD 60%, n = 35) and turbidity (MNB 3%, RMSE 35%, APD 34%, n = 29) algorithms showed very good agreement with in situ data. We recommend the use of the SNAP C2RCC with regionally adjusted TSM-specific scatter for water product retrieval as well as the regional turbidity algorithm for Baltic Sea monitoring. Besides documenting the evaluation of the C2RCC processor, this paper may also act as a handbook on the validation of Ocean Colour data.