Improved modelling of surface forces in the orbit determination of ERS and ENVISAT

Doornbos, E.; Scharroo, Remko; Klinkrad, H.; Zandbergen, R.; Fritsche, B.

doi:10.5589/m02-055

Cited by 29 publications

(13 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It mainly depends on the location error of the position of the sensor platform during image acquisition and the error of the used DEM [44]. The first error is known to be low for TerraSAR-X [45], Radarsat-2 [46], and Envisat [47]. Concerning the second error, Rodriguez et al [48] state the absolute error of the SRTM to be below 10 m. The resulting low positional error of the images is required for the combined pixel-based processing of the scenes.…”

Section: Preprocessing Of the Remote Sensing Datamentioning

confidence: 99%

Best Accuracy Land Use/Land Cover (LULC) Classification to Derive Crop Types Using Multitemporal, Multisensor, and Multi-Polarization SAR Satellite Images

et al. 2016

View full text Add to dashboard Cite

When using microwave remote sensing for land use/land cover (LULC) classifications, there are a wide variety of imaging parameters to choose from, such as wavelength, imaging mode, incidence angle, spatial resolution, and coverage. There is still a need for further study of the combination, comparison, and quantification of the potential of multiple diverse radar images for LULC classifications. Our study site, the Qixing farm in Heilongjiang province, China, is especially suitable to demonstrate this. As in most rice growing regions, there is a high cloud cover during the growing season, making LULC from optical images unreliable. From the study year 2009, we obtained nine TerraSAR-X, two Radarsat-2, one Envisat-ASAR, and an optical FORMOSAT-2 image, which is mainly used for comparison, but also for a combination. To evaluate the potential of the input images and derive LULC with the highest possible precision, two classifiers were used: the well-established Maximum Likelihood classifier, which was optimized to find those input bands, yielding the highest precision, and the random forest classifier. The resulting highly accurate LULC-maps for the whole farm with a spatial resolution as high as 8 m demonstrate the beneficial use of a combination of x-and c-band microwave data, the potential of multitemporal very high resolution multi-polarization TerraSAR-X data, and the profitable integration and comparison of microwave and optical remote sensing images for LULC classifications.

show abstract

Section: Preprocessing Of the Remote Sensing Datamentioning

confidence: 99%

Best Accuracy Land Use/Land Cover (LULC) Classification to Derive Crop Types Using Multitemporal, Multisensor, and Multi-Polarization SAR Satellite Images

et al. 2016

View full text Add to dashboard Cite

show abstract

“…given by EIGEN1S, see Reigber et al, 2002), air-drag (e.g. from the model MSISE-90, see Hedin, 1991;Doornbos et al, 2002), solar radiation pressure, contributions of direct tidal effects from all third bodies as the Sun, the Moon and the planets, as well as indirect tidal effects (solid Earth tides, ocean tides) and general relativistic effects. Earth albedo could also be included, although its modeling is relatively complicated due to the fact that information on cloud coverage and ocean surfaces has to be taken into account.…”

Section: Leo Dynamic Precise Orbit Determinationmentioning

confidence: 99%

Kinematic and reduced-dynamic precise orbit determination of low earth orbiters

Švehla¹,

Rothacher²

2003

Adv. Geosci.

128

View full text Add to dashboard Cite

Abstract.Various methods for kinematic and reduced-dynamic precise orbit determination (POD) of Low Earth Orbiters (LEO) were developed based on zero-and double-differencing of GPS carrier-phase measurements with and without ambiguity resolution. In this paper we present the following approaches in LEO precise orbit determination:-double-difference kinematic POD with and without ambiguity resolution, -double-difference dynamic POD with and without ambiguity resolution,-combined GPS/SLR reduced-dynamic POD.All developed POD approaches except the combination of GPS/SLR were tested using real CHAMP data (May 20-30, 2001) and independently validated with Satellite Laser Ranging (SLR) data over the same 11 days. With SLR measurements, additional combinations are possible and in that case one can speak of combined kinematic or combined reduced-dynamic POD. First results of such a combined GPS/SLR POD will be presented, too.This paper shows what LEO orbit accuracy may be achieved with GPS using different strategies including zerodifference and double-difference approaches. Kinematic versus dynamic orbit determination is presently an interesting issue that will also be discussed in this article.

show abstract

“…DELFT and PRC orbits for ERS-1/2, DELFT and DORIS orbits for ENVISAT ASAR. The DELFT orbit data is freely provided by the Department of Earth Observation and Space Systems (DEOS) at the Delft University of Technology (Scharroo et al, 1998;Doornbos et al, 2002). The PRC and DORIS orbit data are released by the European Space Agency (ESA) (Zandbergen et al, 1995(Zandbergen et al, , 2003.…”

Section: Evaluation Of the Impact Of Orbit Precision On Rfm Solutionmentioning

confidence: 99%

Rational function modeling for spaceborne SAR datasets

Zhang

Balz

et al. 2011

ISPRS Journal of Photogrammetry and Remote Sensing

View full text Add to dashboard Cite

Improved modelling of surface forces in the orbit determination of ERS and ENVISAT

Cited by 29 publications

References 6 publications

Best Accuracy Land Use/Land Cover (LULC) Classification to Derive Crop Types Using Multitemporal, Multisensor, and Multi-Polarization SAR Satellite Images

Best Accuracy Land Use/Land Cover (LULC) Classification to Derive Crop Types Using Multitemporal, Multisensor, and Multi-Polarization SAR Satellite Images

Kinematic and reduced-dynamic precise orbit determination of low earth orbiters

Rational function modeling for spaceborne SAR datasets

Contact Info

Product

Resources

About