Light emerging from natural water bodies and measured by remote sensing radiometers contains information about the local type and concentrations of phytoplankton, non-algal particles and colored dissolved organic matter in the underlying waters. An increase in spectral resolution in forthcoming satellite and airborne remote sensing missions is expected to lead to new or improved capabilities to characterize aquatic ecosystems. Such upcoming missions include NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) Mission; the NASA Surface Biology and Geology observable mission; and NASA Airborne Visible / Infrared Imaging Spectrometer (AVIRIS) - Next Generation airborne missions. In anticipation of these missions, we present an organized dataset of geographically diverse, quality-controlled, high spectral resolution inherent and apparent optical property (IOP/AOP) aquatic data. The data are intended to be of use to increase our understanding of aquatic optical properties, to develop aquatic remote sensing data product algorithms, and to perform calibration and validation activities for forthcoming aquatic-focused imaging spectrometry missions. The dataset is comprised of contributions from several investigators and investigating teams collected over a range of geographic areas and water types, including inland waters, estuaries and oceans. Specific in situ measurements include coefficients describing particulate absorption, particulate attenuation, non-algal particulate absorption, colored dissolved organic matter absorption, phytoplankton absorption, total absorption, total attenuation, particulate backscattering, and total backscattering, as well as remote sensing reflectance, and irradiance reflectance.
This dataset contains 6-year averages of global filtered tropospheric NO2 slant column densities (tSCDs) retrieved from the Sentinel-5 Precursor (S5P) satellite sensor TROPOMI (Tropospheric Monitoring Instrument) for the period from 1 May 2018 to 30 April 2024. All data are available on a 0.03° x 0.03° grid. The NO2 tSCDs are derived from the total slant columns by subtracting the across-track NO2 slant column stripe offset and spatially averaged stratospheric vertical column densities (VCDs) multiplied with the stratospheric air mass factor (AMF), provided in the TROPOMI NO2 product. The filtered NO2 tSCDs are developed to detect global shipping signals in the NO2 TROPOMI data. Therefore, only pixels over water are available in this dataset. The filtering methods include a high-pass filter with different box sizes (1°, 0.5°, 0.25°) and a Fourier filter. In addition, different flagging criteria are applied to the data with the standard box size of 1° for the high-pass filtering: no flagging, quality (qa) flagging, cloud fraction (CF) flagging, cloud height (CH) flagging, wind speed (wind) flagging, and sun glint (sg) flagging.
This raster dataset shows the main type of crop grown on each field in Germany each year. Crop types and crop rotation are of great economic importance and have a strong influence on the functions of arable land and ecology. Information on the crops grown is therefore important for many environmental and agricultural policy issues. With the help of satellite remote sensing, the crops grown can be recorded uniformly for whole Germany. Based on Sentinel-1 and Sentinel-2 time series as well as LPIS data from some Federal States of Germany, 18 different crops or crop groups were mapped per pixel with 10 m resolution for Germany on an annual basis since 2018. These data sets enable a comparison of arable land use between years and the derivation of crop rotations on individual fields. More details and the underlying (in the meantime slightly updated) methodology can be found in Asam et al. 2022.
Confronting Climate Change is one of the paramount societal challenges of our time. The main cause for global warming is the increase of anthropogenic greenhouse gases in the Earth's atmosphere. Together, carbon dioxide and methane, being the two most important greenhouse gases, globally contribute to about 81% of the anthropogenic radiative forcing. However, there are still significant deficits in the knowledge about the budgets of these two major greenhouse gases such that the ability to accurately predict our future climate remains substantially compromised. Different feedback mechanisms which are insufficiently understood have significant impact on the quality of climate projections. In order to accurately predict future climate of our planet and support observing emission targets in the framework of international agreements, the investigation of sources and sinks of the greenhouse gases and their feedback mechanisms is indispensable. In the past years, inverse modelling has emerged as a key method for obtaining quantitative information on the sources and sinks of the greenhouse gases. However, this technique requires the availability of sufficient amounts of precise and independent data on various spatial scales. Therefore, observing the atmospheric concentrations of the greenhouse gases is of significant importance for this purpose. In contrast to point measurements, airborne instruments are able to provide regional-scale data of greenhouse gases which are urgently required, though currently lacking. Providing such data from remote sensing instruments supported by the best currently available in-situ sensors, and additionally comparing the results of the greenhouse gas columns retrieved from aircraft to the network of ground-based stations is the mission goal of the HALO CoMet campaign. The overarching objective of HALO CoMet is to improve our understanding and to better quantify the carbon dioxide and methane cycles. Through analysing the CoMet data, scientists will accumulate new knowledge on the global distribution and temporal variation of the greenhouse gases. These findings will help to better understand the global carbon cycle and its influence on climate. These new findings will be utilized for predicting future climate change and assessing its impact. Within the frame of CoMet and due to the operational possibilities we will concentrate on small to sub-continental scales. This does not only allow to identify local emission sources of greenhouse gases, but also opens up the opportunity to use important remote sensing and in-situ data information for the inverse modelling approach for regional budgeting. The project also aims at developing new methodologies for greenhouse gas measurements, and promotes technological developments necessary for future Earth-observing satellites.
Im Rahmen der GMES Initial Operation Phase 2011-2013 (GIO) werden basierend auf multispektralen und teilweise multitemporalen Satellitenbilddaten von ganz Europa ('IMAGE2012') fünf sog. High Resolution Layer (HRL) im Rasterformat zu den fünf Themen Bodenversiegelung, Waldflächen, Ackerland/Grünland, Feuchtgebiete, Gewässerflächen erzeugt. Dies geschieht im Rahmen einer seitens der Europäischen Umweltagentur (EEA) getätigten Ausschreibung. Die HRL sollen von den davon betroffenen EU-Mitgliedstaaten über ihrem jeweiligen Staatsgebiet einem Validierungsverfahren unterzogen werden und systematisch auf qualitative Mängel hin bewertet werden. Als Referenz sollen dabei national verfügbare Georeferenzdaten (z.B. ATKIS® Basis-DLM) und andere bundesweit verfügbare Landbedeckungsinformationen (z.B. Digitales Landbedeckungsmodell DLM-DE) herangezogen werden. Die Ergebnisse dieser Validierung sollen wiederum als Grundlage herangezogen werden, um die Rasterdaten der HRL im Rahmen des darauffolgenden separaten 'Enhancements' inhaltlich quantitativ und qualitativ zu verbessern.
Mapping and monitoring the break-up events on Wilkins Ice Shelf and identification of mechanisms and processes leading to break-up. Within this activity we integrate various high and moderate-resolution satellite images with special emphasis on SAR data. The analysis covers currently a time period back to 1986 (Landsat TM) with increasing dense time series to present. In close collaboration with the European Space Agency (ESA) and the German Aerospace Center (DLR) acquisition plans for the ENVISAT ASAR and TerraSAR-X instruments are implemented and the respective data analysed. Since September 2009, this activity is supported by a DFG research grant. Main aim is to derive surface velocity fields of the ice shelf and its tributary glaciers by satellite remote sensing as input for icedynamic modelling and fracture mechanical analyses.
Das Thermosphären/Ionosphären (T/I) System wird sowohl von oben (solar, geomagnetisch), als auch von unten stark beeinflusst. Einer der wichtigsten Einflüsse von unten sind Wellen (z.B. planetare Wellen, Gezeiten, oder Schwerewellen), die größtenteils in der Troposphäre bzw. an der Tropopause angeregt werden. Die vertikale Ausbreitung der Wellen bewirkt hierbei eine vertikale Kopplung der T/I mit der unteren und mittleren Atmosphäre. Vor allem der Einfluss von Schwerewellen (GW) ist hierbei weitestgehend unverstanden. Einer der Gründe hierfür ist, dass GW sehr kleinskalig sind (einige zehn bis zu wenigen tausend km) - eine Herausforderung, sowohl für Beobachtungen, als auch für Modelle. Wir werden GW Verteilungen in der T/I aus verschiedenen in situ Satelliten-Datensätzen ableiten (z.B., sowohl in Neutral-, als auch in Elektronendichten). Hierfür werden Datensätze der Satelliten(-konstellationen) SWARM, CHAMP, GOCE und GRACE verwendet werden. Es sollen charakteristische globale Verteilungen bestimmt, und die wichtigsten zeitlichen Variationen (z.B. Jahresgang, Halbjahresgang und solarer Zyklus) untersucht werden. Diese GW Verteilungen werden dann mit von den Satelliteninstrumenten HIRDLS und SABER gemessenen Datensätzen (GW Varianzen, GW Impulsflüssen und Windbeschleunigungen durch GW) in der Stratosphäre und Mesosphäre verglichen. Einige Datensätze (CHAMP, GRACE, SABER) sind mehr als 10 Jahre lang. Räumliche und zeitliche Korrelationen zwischen den GW Verteilungen in der T/I (250-500km Höhe) und den GW Verteilungen in der mittleren Atmosphäre (Stratosphäre und Mesosphäre) für den gesamten Höhenbereich 20-100km werden untersucht werden. Diese Korrelationen sollen Aufschluss darüber geben, welche Höhenbereiche und Regionen in der mittleren Atmosphäre den stärksten Einfluss auf die GW Verteilung in der T/I haben. Insbesondere Windbeschleunigungen durch GW, beobachtet von HIRDLS und SABER, können zusätzliche Hinweise darauf geben, ob Sekundär-GW, die mutmaßlich in Gebieten starker GW Dissipation angeregt werden, in entscheidendem Maße zur globalen GW Verteilung in der T/I beitragen. Zusätzlich wird der Versuch unternommen, sowohl GW Impulsfluss, als auch Windbeschleunigungen durch GW aus den Messungen in der T/I abzuleiten. Solche Datensätze sind von besonderem Interesse für einen direkten Vergleich mit von globalen Zirkulationsmodellen simulierten GW Verteilungen in der T/I. Diese werden für eine konsistente Simulation der T/I in Zirkulationsmodellen (GCM) benötigt, stellen dort aber auch eine Hauptunsicherheit dar, da eine Validierung der modellierten GW durch Messungen fehlt.
This pre-study pilot project will be carried out in Kenya and Tanzania and is part of a more extensive remote sensing project (initiated by the European Space Agency, ESA) aiming to develop a monitoring system for the assessment of land cover change of farmlands, rangelands and forest standings (logging, fires, uncontrolled deforestation, new settlements, etc.) at a national regional level. An integrated approach of remote sensing techniques (both through the use of satellite and ground data), physical vegetation models and ground measurements will be adopted. Operatively, the execution will consist of a 6-month period (pre-study) consisting in a ground campaign along a north-south transect, which is almost unknown to the current vegetation cartography. Based on the field results of the pre-study and within an on-going 30 month period (extended study, see Annexed 3), new classification methods and algorithms will be developed for assessment of land use and cover change using ENVISAT-data. An outcoming of this research will be a system capable to monitor and plan the available agricultural food resources for those developing regions.
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