The Influence of the Time Equation on Remote Sensing Data Interpretation

The interpretation of optical Earth observation data (remote sensing data from satellites) requires knowledge of the exact geographic position of each pixel as well as the exact local acquisition time. But these parameters are not available in each case. If a satellite has a sun-synchronous orbit, equator crossing time (ECT) can be used to determine the local crossing time (LCT) and its corresponding solar zenith distance. Relation between local equator crossing time (LECT) and LCT is given by orbit geometry. The calculation is based on ECT of satellite. The method of actual ECT determination for different satellites on basis of the two-line-elements (TLE), available for their full lifetime period and with help of orbit prediction package is well known. For land surface temperature (LST) studies mean solar conditions are commonly used in the relation between ECT given in Coordinated Universal Time (UTC) and LECT given in hours, thus neglecting the difference between mean and real Sun time (MST, RST). Its difference is described by the equation of time (ET). Of particular importance is the variation of LECT during the year within about ±15 minutes. This is in each case the variation of LECT of a satellite, including satellites with stable orbit as LANDSAT (L8 around 10:05 a.m.) or ENVISAT (around 10:00 a.m.). In case of NOAA satellites the variation of LECT is overlaid by a long-term orbital drift. Ignatov et al. (2004) developed a method to describe the drift-based variation of LECT that can be viewed as a formal mathematical approximation of a periodic function with one or two Fourier terms. But, nevertheless, ET is not included in actual studies of LST. Our paper aims to demonstrate the possible influence of equation of time on simple examples of data interpretation, e.g. NDVI

Ground Truth Validation of Sentinel-2 Data Using Mobile Wireless Ad Hoc Sensor Networks (MWSN) in Vegetation Stands

Satellite-based remote sensing (RS) data are increasingly used to map and monitor local, regional, and global environmental phenomena and processes. Although the availability of RS data has improved significantly, especially in recent years, operational applications to derive value-added information products are still limited by close-range validation and verification deficits. This is mainly due to the gap between standardized and sufficiently available close-range and RS data in type, quality, and quantity. However, to ensure the best possible linkage of close-range and RS data, it makes sense to simultaneously record close-range data in addition to the availability of environmental models. This critical gap is filled by the presented mobile wireless ad hoc sensor network (MWSN) concept, which records sufficient close-range data automatically and in a standardized way, even at local and regional levels. This paper presents a field study conducted as part of the Durable Environmental Multidisciplinary Monitoring Information Network (DEMMIN), focusing on the information gained with respect to estimating the vegetation state with the help of multispectral data by simultaneous observation of an MWSN during a Sentinel-2A (S2A) overflight. Based on a cross-calibration of the two systems, a comparable spectral characteristic of the data sets could be achieved. Building upon this, an analysis of the data regarding the influence of solar altitude, test side topography and land cover, and sub-pixel heterogeneity was accomplished. In particular, variations due to spatial heterogeneity and dynamics in the diurnal cycle show to what extent such complementary measurement systems can improve the data from RS products concerning the vegetation type and atmospheric conditions

Cloud Classification in JPEG-compressed Remote Sensing Data (LANDSAT 7/ETM+)

Environmental parameters required for geo-information modelling are subject to spatial and temporal dynamics. Remote sensing data can contribute to measure those parameters. For that purpose high-accuracy classifications of remote sensing data are required which can be very time-consuming due to the large data volumes involved. In many applications, however, the rapid provision of classified mass data is of higher priority than classification accuracy. One important focus on research and development efforts in the past years has been to optimise the automated interpretation of remote sensing data. Different investigators have shown that this interpretation can both be effective and efficient in JPEG compressed data with acceptable accuracy. This paper presents an operational processing chain for cloud detection in JPEG-compressed quick-look products of LANDSAT 7/ETM+-scenes (compression ratio is 10:1). Two well-developed conventional algorithms are applied to these datasets for cloud detection. Results show that the processing chain developed is stable and produces quality results with substantially compressed mass data

DEMMIN - Test site for calibration and validation of remote sensing missions, sensors, data and value added products

General conditions of remote sensing: In the next few years, there will be a lot of advanced remote sensing systems availablefor Earth observation. The data collected with help of these systems will be used in the context of GMES. But the analysis of the situation of remote sensing shows that different problems for the data provider and the data users are relevant. The papaer dicusses the problems and possibilities for overcomming some of the identified problems. For this background, the development of DEMMIN as a test site for calibrating and validating remote sensing missions, sensors, data, and value added products (information products) is shown

Analyse von Studien, Berichten und Publikationen zur Kinderarmut in Deutschland in den Jahren von 2006 bis 2008 und deren politische Auswirkungen

Die vorliegende Arbeit befasst sich mit der Analyse von Publikationen zur Kinderarmut in Deutschland in den Jahren 2006 bis 2008 und deren politische Auswirkungen. Hierzu werden eine Auswahl von Studien, die sich mit Kinderarmut in Deutschland befassen, vorgestellt. Im Weiteren wird ebenso eine Auswahl wissenschaftlicher Publikationen zu Kinderarmut in Deutschland analysiert. Im Anschluß daran werden Programme und Maßnahmen zur Kinderarmut der Parteien vorgestellt. Abschließend betrachtet der Autor die Arbeit der Kinderkommission des Bundestages speziell zum Thema Kinderarmut

TERENO – Eine Forschungsplattform für die operative Anwendung

TERENO ist eine langfristig angelegte interdisziplinäre Forschungsinitiative der Helmholtz-Zentren, die den Aufbau eines Netzwerks von Umwelt-Observatorien in Deutschland zur Erforschung der Einflüsse des globalen Wandels auf lokale Ökosysteme innerhalb der nächsten 15 Jahre und darüber hinaus vorantreibt. Untersuchungsgegenstand sind die Interaktionen und Rückkopplungen zwischen den unter-schiedlichen Kompartimenten Atmosphäre, Biosphäre und Überbrückung der räumlichen Diskrepanzen punktueller Messung, Flächenmessung und Modellierung. Dabei stellt die langfristige Dokumentation multitemporaler Umweltdaten auf verschiedenen räumlichen Skalen zur Modellierung verfügbarer Wasserressourcen, Stoffflüsse, ökologischer Systemveränderungen, die wissenschaftliche Basis zur Entwicklung regionaler sozio-ökonomischer Strategien dar

Validation and calibration of remote sensing data products on test site DEMMIN.

One of the main research objectives of the working group Thematic Processors and Validation (TPV) of the Remote Sensing Data Centre (DLR-DFD) is the automation of remote sensing data processing. Whereby, the focal point of research is the derivation of value-added products for agriculture and forestry objectives. Three examples are given to outline past and present research of working group. These are: (1) a standard processor for automated data usability assessment based on multispectral quicklook data; (2) a thematic processor for retrieval of actual evapotranspiration from multispectral image data; and (3) an ongoing research work for the conception of a thematic processor for derivation of soil parameters from hyperspectral image data. Since 1999 DLR Neustrelitz operates the test site DEMMIN (Durable Environmental Multidisciplinary Monitoring Information Network) to assure validation and calibration of data products and algorithms. DEMMIN is an intensively used agricultural ecosystem located in the northeast of Germany and is based on a cooperation between local farmers (IG Demmin) and DLR. It provides a manifold of instrumentation for the measurement of environmental parameters which are stored in the DEMMIN database

DEMMIN - Test Site for Remote Sensing in Agricultural Application

Presentation of DEMMIN (Durable Environmental Multidisciplinary Monitoring Information Network) including environmental measurement network, operative processing chain for in-situ-data, in-situ-data browse products and validation of corresponding products. Objectives for placing a L-band radiometer will be discussed