Brockmann Consult
  • Geesthacht, Germany
Recent publications
In this paper, we made use of PRISMA imaging spectroscopy data for retrieving surface snow properties in the Nansen Ice Shelf (East Antarctica). PRISMA satellite mission has been launched in 2019 and it features 239 spectral bands covering the 400-2500 nm interval. These data are promising for cryospheric applications, since several snow and ice parameters can be derived from reflectance in the Visible Near InfraRed - Short Wave InfraRed (VNIR-SWIR) wavelength interval. Here we analyze, for the first time, PRISMA data collected in Antarctica. Our scene was acquired on December 2020 over the Nansen Ice Shelf (NIS). Using PRISMA data we estimated different snow parameters (effective grain diameter, snow specific surface area, snow spectral and broadband albedo, bottom of atmosphere snow reflectance, type of impurities in snow and their concentration), and we compared them with data presented in the scientific literature.
Coarse resolution sensors are not very sensitive at detecting small fire patches, making current estimations of global burned areas (BA) very conservative. Using medium or high-resolution sensors to generate BA products becomes then a priority, particularly in areas where fires tend to be small and frequent. Building on previous work that developed a small fire dataset (SFD) for Sub-Saharan Africa for 2016, this paper presents a new version of the dataset for 2019 using the two Sentinel-2 satellites (A and B) and VIIRS active fires. Total estimated BA was 4.8 Mkm². This value was much higher than estimations from two global, coarser-spatial resolution BA products based on MODIS data for the same area and period: 80 % greater than estimates from FireCCI51 (based on MODIS 250 m bands) and 120 % larger than MCD64A1 (based on MODIS 500 m bands). The main differences were observed in those months with higher fire occurrence (November to January for the Northern Hemisphere regions and June to September for the Southern hemisphere ones). Accuracy assessment of the SFD product was based on a novel sampling strategy designed to obtain independent fire reference perimeters. Validation results showed remarkable high accuracy values comparing to existing global BA products. Overall omission errors (OE) were estimated as 8.5 %, commission errors (CE) as 15.0 %, with a Dice Coefficient of 87.7 %. All of these estimations implied significant improvements over the global, coarser spatial resolution BA products (OE > 50 % and CE > 20 % for the same area and period), as well as over the previous SFD product for 2016 of the same area, generated from a single Sentinel-2 satellite and MODIS active fires (OE = 26.5 % and CE = 19.3 %). Temporal accuracies greatly increased as well with the new product, with 92.5 % of fires detected within the first 10 days of occurrence.
Cloud cover is a major limiting factor in exploiting time-series data acquired by optical spaceborne remote sensing sensors. Multiple methods have been developed to address the problem of cloud detection in satellite imagery and a number of cloud masking algorithms have been developed for optical sensors but very few studies have carried out quantitative intercomparison of state-of-the-art methods in this domain. This paper summarizes results of the first Cloud Masking Intercomparison eXercise (CMIX) conducted within the Committee Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV). CEOS is the forum for space agency coordination and cooperation on Earth observations, with activities organized under working groups. CMIX, as one such activity, is an international collaborative effort aimed at intercomparing cloud detection algorithms for moderate-spatial resolution (10–30 m) spaceborne optical sensors. The focus of CMIX is on open and free imagery acquired by the Landsat 8 (NASA/USGS) and Sentinel-2 (ESA) missions. Ten algorithms developed by nine teams from fourteen different organizations representing universities, research centers and industry, as well as space agencies (CNES, ESA, DLR, and NASA), are evaluated within the CMIX. Those algorithms vary in their approach and concepts utilized which were based on various spectral properties, spatial and temporal features, as well as machine learning methods. Algorithm outputs are evaluated against existing reference cloud mask datasets. Those datasets vary in sampling methods, geographical distribution, sample unit (points, polygons, full image labels), and generation approaches (experts, machine learning, sky images). Overall, the performance of algorithms varied depending on the reference dataset, which can be attributed to differences in how the reference datasets were produced. The algorithms were in good agreement for thick cloud detection, which were opaque and had lower uncertainties in their identification, in contrast to thin/semi-transparent clouds detection. Not only did CMIX allow identification of strengths and weaknesses of existing algorithms and potential areas of improvements, but also the problems associated with the existing reference datasets. The paper concludes with recommendations on generating new reference datasets, metrics, and an analysis framework to be further exploited and additional input datasets to be considered by future CMIX activities.
The severity of wildfires is increasing globally. In this study, we used data from the Global Change Observation Mission-Climate/Second-generation Global Imager (GCOM-C/SGLI) to characterize the biomass burning aerosols that are generated by large-scale wildfires. We used data from the September 2020 wildfires in western North America. The target area had a complex topography, comprising a basin among high mountains along a coastal region. The SGLI was essential for dealing with the complex topographical changes in terrain that we encountered, as it contains 19 polarization channels ranging from near ultraviolet (380 nm and 412 nm) to thermal infrared (red at 674 nm and near-infrared at 869 nm) and has a fine spatial resolution (1 km). The SGLI also proved to be efficient in the radiative transfer simulations of severe wildfires through the mutual use of polarization and radiance. We used a regional numerical model SCALE (Scalable Computing for Advanced Library and Environment) to account for variations in meteorological conditions and/or topography. Ground-based aerosol measurements in the target area were sourced from the National Aeronautics and Space Administration-Aerosol Robotic Network; currently, official satellite products typically do not provide the aerosol properties for very optically thick cases of wildfires. This paper used satellite observations, ground-based observations, and a meteorological model to define an algorithm for retrieving the aerosol properties caused by severe wildfire events.
The observed gradual change in the Earth's climate most noticeably affects the snow cover and ice sheets in the polar regions, especially during the long polar summer, when solar radiation leads to considerable increase in temperature and partial melting at some distance from the snow or ice surface. This effect, which in the polar regions is more pronounced in the snow cover, deserves serious attention as an important geophysical problem. In this article, for the first time, a theoretical analysis is made of the conditions under which the absorption of directional radiation penetrating a weakly absorbing scattering medium has a maximum at some distance from the illuminated surface. It is shown that the maximum absorption of radiation inside an optically thick medium exists only at illumination angles less than 60°from the normal. An analytical solution was obtained that gives both the magnitude of this maximum absorption and its depth below the illuminated surface. Calculations of solar radiation transfer and heat propagation in the snow layer are also performed. Various experimental data on the ice absorption index in the visible range are taken into account when determining the optical properties of snow. To calculate the transient temperature profile in the snow layer, the heat conduction equation with volumetric absorption of radiation is solved. The boundary conditions take into account the variation of solar irradiation, convective heat transfer, and radiative cooling of snow in the infrared transparency window of the cloudless atmosphere. The calculations show that the radiative cooling should be taken into account even during the polar summer.
This work is aimed at the development of the snow albedo product for the SGLI/GCOM-C JAXA space mission. The retrieval technique is based on the analytical solution of the radiative transfer equation valid at small values of probability of light absorption in snowpack, which is valid assumption in the visible and near-infrared regions of the electromagnetic spectrum.
Water quality monitoring is relevant for protecting the designated, or beneficial uses, of water such as drinking, aquatic life, recreation, irrigation, and food supply that support the economy, human well-being, and aquatic ecosystem health. Managing finite water resources to support these designated uses requires information on water quality so that managers can make sustainable decisions. Chlorophyll- a (chl- a , µg L ⁻¹ ) concentration can serve as a proxy for phytoplankton biomass and may be used as an indicator of increased anthropogenic nutrient stress. Satellite remote sensing may present a complement to in situ measures for assessments of water quality through the retrieval of chl- a with in-water algorithms. Validation of chl- a algorithms across US lakes improves algorithm maturity relevant for monitoring applications. This study compares performance of the Case 2 Regional Coast Colour (C2RCC) chl- a retrieval algorithm, a revised version of the Maximum-Peak Height (MPH (P) ) algorithm, and three scenarios merging these two approaches. Satellite data were retrieved from the MEdium Resolution Imaging Spectrometer (MERIS) and the Ocean and Land Colour Instrument (OLCI), while field observations were obtained from 181 lakes matched with U.S. Water Quality Portal chl- a data. The best performance based on mean absolute multiplicative error (MAE mult ) was demonstrated by the merged algorithm referred to as C 15 −M 10 (MAE mult = 1.8, bias mult = 0.97, n = 836). In the C 15 −M 10 algorithm, the MPH (P) chl- a value was retained if it was > 10 µg L ⁻¹ ; if the MPH (P) value was ≤ 10 µg L ⁻¹ , the C2RCC value was selected, as long as that value was < 15 µg L ⁻¹ . Time-series and lake-wide gradients compared against independent assessments from Lake Champlain and long-term ecological research stations in Wisconsin were used as complementary examples supporting water quality reporting requirements. Trophic state assessments for Wisconsin lakes provided examples in support of inland water quality monitoring applications. This study presents and assesses merged adaptations of chl- a algorithms previously reported independently. Additionally, it contributes to the transition of chl- a algorithm maturity by quantifying error statistics for a number of locations and times.
This article presents the burned area (BA) product of the Copernicus Climate Change Service (C3S) of the European Commission. This product, named C3SBA10, is based on the adaptation to Sentinel-3 OLCI images of a BA algorithm developed within the Fire Climate Change Initiative (FireCCI) project, which used MODIS data. We first reviewed the adaptation process and then analysed the results of both products for common years (2017–2019). Comparisons were performed using four different grid sizes (0.05, 0.10, 0.25, and 0.50 degrees). Annual correlations between the two products ranged from 0.94 to 0.99. Global BA estimates were found to be more similar when the two Sentinel-3 satellites were active (2019), as the temporal resolution was closer to that of the MODIS sensor. Global validation was performed using reference data derived from Landsat-8 images, following a stratified random sampling design. The C3SBA10 showed commission errors between 16 and 21% and omission errors from 48 to 50%, similar to those found in the FireCCI product. The temporal reporting accuracy was also validated using 19 million active fires. In total, 87% of the detections were made within 10 days after the fire by both products. The high consistency between both products ensures global BA data provision from 2001 to the present. The datasets are freely available through the Copernicus Climate Data Store (CDS) repository
Earth Observation satellite data allows for the monitoring of the surface of our planet at predefined intervals covering large areas. However, there is only one medium resolution sensor family in orbit that enables an observation time span of 40 and more years at a daily repeat interval. This is the AVHRR sensor family. If we want to investigate the long-term impacts of climate change on our environment, we can only do so based on data that remains available for several decades. If we then want to investigate processes with respect to climate change, we need very high temporal resolution enabling the generation of long-term time series and the derivation of related statistical parameters such as mean, variability, anomalies, and trends. The challenges to generating a well calibrated and harmonized 40-year-long time series based on AVHRR sensor data flown on 14 different platforms are enormous. However, only extremely thorough pre-processing and harmonization ensures that trends found in the data are real trends and not sensor-related (or other) artefacts. The generation of European-wide time series as a basis for the derivation of a multitude of parameters is therefore an extremely challenging task, the details of which are presented in this paper.
Common aquatic remote sensing algorithms estimate the trophic state (TS) of inland and nearshore waters through the inversion of remote sensing reflectance (Rrs (λ)) into chlorophyll-a (chla) concentration. In this study we present a novel method that directly inverts Rrs (λ) into TS without prior chla retrieval. To successfully cope with the optical diversity of inland and nearshore waters the proposed method stacks supervised classification algorithms and combines them through meta-learning. We demonstrate the developed methodology using the waveband configuration of the Sentinel-3 Ocean and Land Colour Instrument on 49 globally distributed inland and nearshore waters (567 observations). To assess the performance of the developed approach, we compare the results with TS derived through optical water type (OWT) switching of chla retrieval algorithms. Meta-classification of TS was on average 6.75% more accurate than TS derived via OWT switching of chla algorithms. The presented method achieved > 90% classification accuracies for eutrophic and hypereutrophic waters and was > 12% more accurate for oligotrophic waters than derived through OWT chla retrieval. However, mesotrophic waters were estimated with lower accuracy from both our developed method and through OWT chla retrieval (52.17% and 46.34%, respectively), highlighting the need for improved base algorithms for low - moderate biomass waters. Misclassified observations were characterised by highly absorbing and/or scattering optical properties for which we propose adaptations to our classification strategy.
Climate Data Records (CDRs) of Essential Climate Variables (ECVs) as defined by the Global Climate Observing System (GCOS) derived from satellite instruments help to characterize the main components of the Earth system, to identify the state and evolution of its processes, and to constrain the budgets of key cycles of water, carbon and energy. The Climate Change Initiative (CCI) of the European Space Agency (ESA) coordinates the derivation of CDRs for 21 GCOS ECVs. The combined use of multiple ECVs for Earth system science applications requires consistency between and across their respective CDRs. As a comprehensive definition for multi-ECV consistency is missing so far, this study proposes defining consistency on three levels: (1) consistency in format and metadata to facilitate their synergetic use (technical level); (2) consistency in assumptions and auxiliary datasets to minimize incompatibilities among datasets (retrieval level); and (3) consistency between combined or multiple CDRs within their estimated uncertainties or physical constraints (scientific level). Analysing consistency between CDRs of multiple quantities is a challenging task and requires coordination between different observational communities, which is facilitated by the CCI program. The inter-dependencies of the satellite-based CDRs derived within the CCI program are analysed to identify where consistency considerations are most important. The study also summarizes measures taken in CCI to ensure consistency on the technical level, and develops a concept for assessing consistency on the retrieval and scientific levels in the light of underlying physical knowledge. Finally, this study presents the current status of consistency between the CCI CDRs and future efforts needed to further improve it.
The editorial team are delighted to present this Special Issue of Sensors focused on Remote Sensing of Ocean Color: Theory and Applications. We believe that this is a timely opportunity to showcase current developments across a broad range of topics in ocean color remote sensing (OCRS). Although the field is well-established, in this Special Issue we are able to highlight advances in the applications of the technology, our understanding of the underpinning science, and its relevance in the context of monitoring climate change and engaging public participation.
Sea surface temperature (SST) is observed by a constellation of sensors, and SST retrievals are commonly combined into gridded SST analyses and climate data records (CDRs). Differential biases between SSTs from different sensors cause errors in such products, including feature artefacts. We introduce a new method for reducing differential biases across the SST constellation, by reconciling the brightness temperature (BT) calibration and SST retrieval parameters between sensors. We use the Advanced Along-Track Scanning Radiometer (AATSR) and the Sea and Land Surface Temperature Radiometer (SLSTR) as reference sensors, and the Advanced Very High Resolution Radiometer (AVHRR) of the MetOp-A mission to bridge the gap between these references. Observations across a range of AVHRR zenith angles are matched with dual-view three-channel skin SST retrievals from the AATSR and SLSTR. These skin SSTs act as the harmonization reference for AVHRR retrievals by optimal estimation (OE). Parameters for the harmonized AVHRR OE are iteratively determined, including BT bias corrections and observation error covariance matrices as functions of water-vapor path. The OE SSTs obtained from AVHRR are shown to be closely consistent with the reference sensor SSTs. Independent validation against drifting buoy SSTs shows that the AVHRR OE retrieval is stable across the reference-sensor gap. We discuss that this method is suitable to improve consistency across the whole constellation of SST sensors. The approach will help stabilize and reduce errors in future SST CDRs, as well as having application to other domains of remote sensing.
The change of mapping methods for seagrass beds, here species of eelgrasses, from aerial surveys to automated classification of optical satellite data is described. Both methods are compared with respect to availability and suitability of their data. Differences in the detection capability of the methods are shown as well as results of the validation of the satellite image classification. In North Friesia, where the largest area of eelgrass occurs in the Wadden Sea, eelgrass beds have been mapped regularly using aerial surveys since 1994. After a significant decline in the 1930s and 1960s, monitoring results show a steady increase in the size of the area covered by eelgrass beds up to 2017. Since 2006, the aerial surveys have been complemented by ground surveys, which, however, only cover one sixth of the area of the Schleswig-Holstein Wadden Sea each year. Results show that size estimates of individual beds can vary significantly between aerial and ground surveys. In recent years, satellite-borne remote sensing technology and subsequent analysis methods have reached a level of quality, which makes them an alternative and cost-efficient method for mapping eelgrass. The technology has advantages such as the coverage of large areas at single points in time, repeatable and transferable image analysis methods, and high spatial resolution of the satellite images, as well as frequent repetition of acquisition of data. This provides standardised results, which allow direct comparisons over time and between areas.
PROBA-V (Project for On-Board Autonomy-Vegetation) is a global vegetation monitoring satellite. The spectral quality of the data and the coverage of PROBA-V over coastal waters provide opportunities to expand its use to other applications. This study tests PROBA-V data for the retrieval of turbidity in the North Sea region. In the first step, clouds were masked and an atmospheric correction, using an adapted version of iCOR, was performed. The resulted water leaving radiance reflectance was validated against AERONET-OC stations, yielding a coefficient of determination of 0.884 in the RED band. Next, turbidity values were retrieved using the RED band. The PROBA-V retrieved turbidity data was compared with turbidity data from CEFAS Smartbuoys and ad-hoc measurement campaigns. This resulted in a coefficient of determination of 0.69. Finally, a time series of 1.5 year of PROBA-V derived turbidity data was plotted over MODIS data to check consistencies in both datasets. Seasonal dynamics were noted with high turbidity in autumn and winter and low values in spring and summer. For low values, PROBA-V and MODIS yielded similar results, but while MODIS seems to saturate around 50 FNU, PROBA-V can reach values up till almost 80 FNU.
This article presents and analyses the modular architecture and capabilities of CODE-DE (Copernicus Data and Exploitation Platform – Deutschland, www.code-de.org), the integrated German operational environment for accessing and processing Copernicus data and products, as well as the methodology to establish and operate the system. Since March 2017, CODE-DE has been online with access to Sentinel-1 and Sentinel-2 data, to Sentinel-3 data shortly after this time, and since March 2019 with access to Sentinel-5P data. These products are available and accessed by 1,682 registered users as of March 2019. During this period 654,895 products were downloaded and a global catalogue was continuously updated, featuring a data volume of 814 TByte based on a rolling archive concept supported by a reload mechanism from a long-term archive. Since November 2017, the element for big data processing has been operational, where registered users can process and analyse data themselves specifically assisted by methods for value-added product generation. Utilizing 195,467 core and 696,406 memory hours, 982,948 products of different applications were fully automatically generated in the cloud environment and made available as of March 2019. Special features include an improved visualization of available Sentinel-2 products, which are presented within the catalogue client at full 10 m resolution.
A climate data record of global sea surface temperature (SST) spanning 1981-2016 has been developed from 4 × 10¹² satellite measurements of thermal infra-red radiance. The spatial area represented by pixel SST estimates is between 1 km² and 45 km². The mean density of good-quality observations is 13 km⁻² yr⁻¹. SST uncertainty is evaluated per datum, the median uncertainty for pixel SSTs being 0.18 K. Multi-annual observational stability relative to drifting buoy measurements is within 0.003 K yr⁻¹ of zero with high confidence, despite maximal independence from in situ SSTs over the latter two decades of the record. Data are provided at native resolution, gridded at 0.05° latitude-longitude resolution (individual sensors), and aggregated and gap-filled on a daily 0.05° grid. Skin SSTs, depth-adjusted SSTs de-aliased with respect to the diurnal cycle, and SST anomalies are provided. Target applications of the dataset include: climate and ocean model evaluation; quantification of marine change and variability (including marine heatwaves); climate and ocean-atmosphere processes; and specific applications in ocean ecology, oceanography and geophysics.
The Sentinel Application Platform (SNAP) architecture facilitates Earth Observation data processing. In this work, we present results from a new Snow Processor for SNAP. We also describe physical principles behind the developed snow property retrieval technique based on the analysis of Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3A/B measurements over clean and polluted snow fields. Using OLCI spectral reflectance measurements in the range 400–1020 nm, we derived important snow properties such as spectral and broadband albedo, snow specific surface area, snow extent and grain size on a spatial grid of 300 m. The algorithm also incorporated cloud screening and atmospheric correction procedures over snow surfaces. We present validation results using ground measurements from Antarctica, the Greenland ice sheet and the French Alps. We find the spectral albedo retrieved with accuracy of better than 3% on average, making our retrievals sufficient for a variety of applications. Broadband albedo is retrieved with the average accuracy of about 5% over snow. Therefore, the uncertainties of satellite retrievals are close to experimental errors of ground measurements. The retrieved surface grain size shows good agreement with ground observations. Snow specific surface area observations are also consistent with our OLCI retrievals. We present snow albedo and grain size mapping over the inland ice sheet of Greenland for areas including dry snow, melted/melting snow and impurity rich bare ice. The algorithm can be applied to OLCI Sentinel-3 measurements providing an opportunity for creation of long-term snow property records essential for climate monitoring and data assimilation studies—especially in the Arctic region, where we face rapid environmental changes including reduction of snow/ice extent and, therefore, planetary albedo.
Drought in Australia has widespread impacts on agriculture and ecosystems. Satellite-based Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) has great potential to monitor and assess drought impacts on vegetation greenness and health. Various FAPAR products based on satellite observations have been generated and made available to the public. However, differences remain among these datasets due to different retrieval methodologies and assumptions. The Quality Assurance for Essential Climate Variables (QA4ECV) project recently developed a quality assurance framework to provide understandable and traceable quality information for Essential Climate Variables (ECVs). The QA4ECV FAPAR is one of these ECVs. The aim of this study is to investigate the capability of QA4ECV FAPAR for drought monitoring in Australia. Through spatial and temporal comparison and correlation analysis with widely used Moderate Resolution Imaging Spectroradiometer (MODIS), Satellite Pour l’Observation de la Terre (SPOT)/PROBA-V FAPAR generated by Copernicus Global Land Service (CGLS), and the Standardized Precipitation Evapotranspiration Index (SPEI) drought index, as well as the European Space Agency’s Climate Change Initiative (ESA CCI) soil moisture, the study shows that the QA4ECV FAPAR can support agricultural drought monitoring and assessment in Australia. The traceable and reliable uncertainties associated with the QA4ECV FAPAR provide valuable information for applications that use the QA4ECV FAPAR dataset in the future.
This study focuses on mean sea-level variability at the West African coast in the observational period (1993–2013) and its offshore waters, investigating its decadal variability, long-term trends and the large-scale climate patterns that are connected to its variability. To achieve this objective, statistically analyses is performed on several available data sets: sea-level data from tide gauges (Takoradi, Tema and Forcados), satellite altimetry (combined TOPEX/Poseidon, Jason-1 and Jason-2/OSTM), gridded sea-level reconstruction (Church et al., J Clim 17(13):2609–2625, 2004), meteorological reanalysis (NCEP), a high-resolution ocean model simulation driven by this meteorological reanalysis, and, observational data sets (The Hadley Centre Global Sea Ice and Sea Surface Temperature (HadISST1), and the Atlantic Multi-decadal Oscillation (AMO) index). Ghana is the only country along the West African coast with two relatively long sea-level records available (Takoradi and Tema), but with data quality concerns (Woodworth et. al., Afr J Mar Sci 29(3):321–330, 2007). Attempts are made to combine these two records, which cover different but overlapping periods, to construct a regional sea-level curve for Ghana (1929–1981) that may be regionally representative. A physical connection is identified between the AMO, sea-surface temperature and sea level in the Gulf of Guinea and mean sea-level trends and variability of the West African coast. It has been found that a stronger AMO is connected with higher mean sea-level in the Tropical Atlantic and in particular also at the Gulf of Guinea sea-level. This connection may explain the multidecadal variability of sea-level there, and in particular the negative trends between 1955 and 1975 and the positive trends thereafter. In addition, warmer sea surface temperatures in the Gulf of Guinea are also connected with higher sea-level, although a simple estimation based on reasonable assumptions of the thermal expansion of the water column is not sufficient to explain the connection between sea-surface-temperature and sea-level. More detailed modelling studies will be needed to explain this link. Although this study provides useful information for adaption strategies in Ghana, the research is unable to provide sea-level information between the years 1981 and 1993 because of lack of data.
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