Wolfgang Koppe

Rice monitoring with multi-temporal and dual-polarimetric TerraSAR-X data

Abstract This study assesses the use of TerraSAR-X data for monitoring rice cultivation in the Sanjiang Plain in Heilongjiang Province, Northeast China. The main objective is the understanding of the coherent co-polarized X-band backscattering signature of rice at different phenological stages in order to retrieve growth status. For this, multi-temporal dual polarimetric TerraSAR-X High Resolution SpotLight data (HH/VV) as well as single polarized StripMap (VV) data were acquired over the test site. In conjunction with the satellite data acquisition, a ground truth field campaign was carried out. The backscattering coefficients at HH and VV of the observed fields were extracted on the different dates and analysed as a function of rice phenology to provide a physical interpretation for the co-polarized backscatter response in a temporal and spatial manner. Then, a correlation analysis was carried out between TerraSAR-X backscattering signal and rice biomass of stem, leaf and head to evaluate the relationship with different vertical layers within the rice vegetation. HH and VV signatures show two phases of backscatter increase, one at the beginning up to 46 days after transplanting and a second one from 80 days after transplanting onwards. The first increase is related to increasing double bounce reflection from the surface–stem interaction. Then, a decreasing trend of both polarizations can be observed due to signal attenuation by increasing leaf density. A second slight increase is observed during senescence. Correlation analysis showed a significant relationship with different vertical layers at different phenological stages which prove the physical interpretation of X-band backscatter of rice. The seasonal backscatter coefficient showed that X-band is highly sensitive to changes in size, orientation and density of the dominant elements in the upper canopy.

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

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.

Evaluation of Vertical Accuracy of the WorldDEM™ Using the Runway Method

Accuracy assessment of a global digital elevation model (DEM) is an important and challenging task primarily because of the difficulties and costs associated with securing a reliable and representative reference dataset. In this article, we report on the vertical accuracy assessment of the WorldDEM™, the latest global DEM using the synthetic aperture radar interferometry (InSAR) method, based on the German TanDEM-X mission data. For reference data we use vertical profiles along the centerline of 47 paved runways located in different areas around the world. Our accuracy statement is based on the analysis of discrepancies between the reference data and the corresponding vertical profiles extracted from the WorldDEM™ dataset. Since the runways are nearly flat and have homogenous surfaces, the observed discrepancies are mainly due to instrument-induced error. Therefore, the derived accuracy statement has a universal character, e.g., it is not biased by other error sources including target- or environment-induced errors. Our main conclusions are that the WorldDEM™ is the most accurate global DEM to date in terms of its vertical accuracy; it appears that the accuracy is spatially independent.

Quality assessment of TerraSAR-X derived ground control points

The orbit accuracy of TerraSAR-X and TanDEM-X radar satellites is in the range of centimeters. This information is useful to carry out high precision surveying from space. The objective of this study is the quality assessment of ground control points (GCPs) retrieved from TerraSAR-X and TanDEM-X imagery. The focus is put on the optimized stereo constellation for the GCP retrieval procedure. GCPs are important inputs for precise orthorectification of other image sources. Depending on the input image parameters it is possible to retrieve GCPs with a 3D accuracy up to 1 m.

Nitrogen Status Estimation of Winter Wheat by Using an IKONOS Satellite Image in the North China Plain

The objective of this study was to determine relationship between high resolution satellite image and wheat N status, and develop a methodology to predict wheat N status in the farmers’ fields. Field experiment with 5 different N rates was conducted in Huimin County in the North China Plain, and farmers’ fields in 3 separated sites were selected as validation plots. The IKONOS image covering all research sites was obtained at shooting stage in 2006. The results showed that single band reflectance of NIR, Red and Green and vegetation indices of NDVI, GNDVI, RVI and OSAVI all well correlated with wheat N status parameters. Field validation results indicated that the prediction models using OSAVI performed well in predicting N uptake in the farmers’ fields (R2 = 0.735). We conclude that high resolution satellite images like IKONOS are useful tools in N fertilization management in the North China Plain.

Development of Operational Applications for TerraSAR-X

In the course of the TerraSAR-X mission, various new applications based on X-Band Synthetic Aperture Radar (SAR) data have been developed and made available as operational products or services. In this article, we elaborate on proven characteristics of TerraSAR-X that are responsible for development of operational applications. This article is written from the perspective of a commercial data and service provider and the focus is on the following applications with high commercial relevance, and varying operational maturity levels: Surface Movement Monitoring (SMM), Ground Control Point (GCP) extraction and Automatic Target Recognition (ATR). Based on these applications, the article highlights the successful transition of innovative research into sustainable and operational use within various market segments. TerraSAR-X’s high orbit accuracy, its precise radar beam tracing, the high-resolution modes, and high-quality radiometric performance have proven to be the instrument’s advanced characteristics, through, which reliable ground control points and surface movement measurements are obtained. Moreover, TerraSAR-X high-resolution data has been widely exploited for the clarity of its target signatures in the fields of target intelligence and identification. TerraSAR-X’s multi temporal interferometry applications are non-invasive and are now fully standardised autonomous tools to measure surface deformation. In particular, multi-baseline interferometric techniques, such as Persistent Scatter Interferometry (PSI) and Small Baseline Subsets (SBAS) benefit from TerraSAR-X’s highly precise orbit information and phase stability. Similarly, the instrument’s precise orbit information is responsible for sub-metre accuracy of Ground Control Points (GCPs), which are essential inputs for orthorectification of remote sensing imagery, to locate targets, and to precisely georeference a variety of datasets. While geolocation accuracy is an essential ingredient in the intelligence field, high-resolution TerraSAR-X data, particularly in Staring SpotLight mode has been widely used in surveillance, security and reconnaissance applications in real-time and also by automatic or assisted target recognition software.

Hyperspactral data analysis of nitrogen fertilization effects on winter wheat using spectrometer in North China Plain

This article presents results from hyperspectral analysis for winter wheat (Tricitum Aestivum L.) in the North China Plain during a research study in 2006. In the first part the focus was set on canopy spectral reflectance during the vegetation period under different N supplies. Four different experiments with variable N-inputs and winter wheat cultivars were set up in the study area of Huimin County, Shandong Province. Spectral reflectance data and agronomic data like biomass, plant height, N-uptake and LAI were collected at different phenological stages. In the second part of the study a spectral and agronomic library was set up. For this purpose, spectral reflectance was related to agronomic parameters. The results indicated significant difference in spectra characteristics, cultivars and N-inputs. Vegetation indices like NDVI, HNDVI, RVI, HVI, OSAVI and MCARI2 had the best performance in estimating agronomic parameters among the vegetation indices evaluated.

Deriving winter wheat characteristics from combined radar and hyperspectral data analysis

The main objective of this study is to derive plant nitrogen (N) status and aboveground biomass via satellite remote sensing. To understand canopy spectral reflectance, the focus of the first part was set on the analysis of spectral signatures of winter wheat during its vegetation period under different N treatments. Spectral reflectance at different phenological stages, measured by a spectroradiometer (ASD HandHeld), is related to agronomy parameters like plant N, aboveground biomass and leaf area index (LAI). For this purpose, an extensive field survey was carried out in Huimin County in the North China Plain. For detection of plant N status of winter wheat and biomass on regional scale, hyperspectral (EO-1 Hyperion) and radar (Envisat ASAR) remote sensing data were obtained. First results of preprocessing of remote sensing data are presented in this contribution.