GPS sidereal filtering: coordinate- and carrier-phase-level strategies

Multipath error is considered one of the major errors affecting GPS observations. One can benefit from the repetition of satellite geometry approximately every sidereal day, and apply filtering to help minimize this error. For GPS data at 1 s interval processed using a double-difference strategy, using the day-to-day coordinate or carrier-phase residual autocorrelation determined with a 10-h window leads to the steadiest estimates of the error-repeat lag, although a window as short as 2 h can produce an acceptable value with > 97% of the optimal lag’s correlation. We conclude that although the lag may vary with time, such variation is marginal and there is little advantage in using a satellite-specific or other time-varying lag in double-difference processing. We filter the GPS data either by stacking a number of days of processed coordinate residuals using the optimum “sidereal” lag (23 h 55 m 54 s), and removing these stacked residuals from the day in question (coordinate space), or by a similar method using double-difference carrier-phase residuals (observational space). Either method results in more consistent and homogeneous set of coordinates throughout the dataset compared with unfiltered processing. Coordinate stacking reduces geometry-related repeating errors (mainly multipath) better than carrier-phase residual stacking, although the latter takes less processing time to achieve final filtered coordinates. Thus, the optimal stacking method will depend on whether coordinate precision or computational time is the over-riding criterion.     

Analysis of Seasonal Variability of Vegetation and Methane Concentration over India using SPOT-VEGETATION and ENVISAT-SCIAMACHY Data

This paper highlights the spatial and temporal variability of atmospheric columnar methane (CH₄) concentration over India and its correlation with the terrestrial vegetation dynamics. SCanning IMaging Absorption spectrometer for Atmospheric CHartographY (SCIAMACHY) on board ENVIronmental SATellite (ENVISAT) data product (0.5° × 0.5°) was used to analyze the atmospheric CH₄ concentration. Satellite Pour l'Observation de la Terre (SPOT)-VEGETATION sensor’s Normalized Difference Vegetation Index (NDVI) product, aggregated at 0.5° × 0.5° grid level for the same period (2004 and 2005), was used to correlate the with CH₄ concentration. Analysis showed mean monthly CH₄ concentration during the Kharif season varied from 1,704 parts per billion volume (ppbv) to 1,780 ppbv with the lowest value in May and the highest value in September. Correspondingly, mean NDVI varied from 0.28 (May) to 0.53 (September). Analysis of correlation between CH₄ concentration and NDVI values over India showed positive correlation (r = 0.76; n = 6) in Kharif season. Further analysis using land cover information showed characteristic low correlation in natural vegetation region and high correlation in agricultural area. Grids, particularly falling in the Indo-Gangetic Plains showed positive correlation. This could be attributed to the rice crop which is grown as a predominant crop during this period. The CH₄ concentration pattern matched well with growth pattern of rice with the highest concentration coinciding with the peak growth period of crop in the September. Characteristically low correlation was observed (r = 0.1; n = 6) in deserts of Rajasthan and forested Himalayan ecosystem. Thus, the paper emphasizes the synergistic use of different satellite based data in understanding the variability of atmospheric CH₄ concentration in relation to vegetation.     

Comparison of Two Data Smoothing Techniques for Vegetation Spectra Derived From EO-1 Hyperion

Hyperspectral data are generally noisier compared to broadband multispectral data because their narrow bandwidth can only capture very little energy that may be overcome by the self-generated noise inside the sensors. It is desirable to smoothen the reflectance spectra. This study was carried out to see the effect of smoothing algorithms - Fast-Fourier Transform (FFT) and Savitzky–Golay (SG) methods on the statistical properties of the vegetation spectra at varying filter sizes. The data used in the study is the reflectance spectra data obtained from Hyperion sensor over an agriculturally dominated area in Modipuram (Uttar Pradesh). The reflectance spectra were extracted for wheat crop at different growth stages. Filter sizes were varied between 3 and 15 with the increment of 2. Paired t-test was carried out between the original and the smoothed data for all the filter sizes in order to see the extent of distortion with changing filter sizes. The study showed that in FFT, beyond filter size 11, the number of locations within the spectra where the smooth spectra were statistically different from its original counterpart increased to 14 and reaches 21 at the filter size 15. However, in SG method, number of statistically different locations were more than those found in the FFT, but the number of locations did not changing drastically. The number of statistically disturbed locations in SG method varied between 16 and 19. The optimum filter size for smoothing the vegetation spectra was found to be 11 in FFT and 9 in SG method.     

GPS tomography in the polar cap: comparison with ionosondes and in situ spacecraft data

Tomographic 4D reconstructions of ionospheric anomalies appearing in the high-latitude polar cap region are compared with plasma density measurements by digital ionosonde located near the north magnetic pole at Eureka station and with in situ plasma measurements on-board DMSP spacecraft. The moderate magnetic storm of 14–17 October 2002 is taken as an example of a geomagnetic disturbance which generates large-scale ionospheric plasma anomalies at mid-latitudes and in the polar cap region. Comparison of the GPS tomographic reconstructions over Eureka station with the ionosonde measurements of the F layer peak densities indicates that the GPS tomography correctly predicts the time of arrival and passage of the ionospheric tongue of ionization over the magnetic pole area, although the tomographic technique appears to under-estimate the value of F peak plasma density. Comparison with the in situ plasma measurements by the DMSP SSIES instruments shows that the GPS tomography correctly reproduces the large-scale spatial structure of ionospheric anomalies over a wide range of latitudes from mid-latitudes to the high-latitude polar cap region, though the tomographic reconstructions tend to over-estimate the density of the topside ionosphere at 840 km DMSP orbit. This study is essential for understanding the quality and limitations of the tomographic reconstruction techniques, particularly in high-latitude regions where GPS TEC measurements and other ionospheric data sources are limited.     

A Spatial Database of Cropping System and its Characteristics to Aid Climate Change Impact Assessment Studies

Cropping system study is not only useful to understand the overall sustainability of agricultural system, but also it helps in generating many important parameters which are useful in climate change impact assessment. Considering its importance, Space Applications Centre, took up a project for mapping and characterizing major cropping systems of Indo-Gangetic Plains of India. The study area included the five states of Indo-Gangetic Plains (IGP) of India, i.e. Punjab, Haryana, Uttar Pradesh, Bihar and West Bengal. There were two aspects of the study. The first aspect included state and district level cropping system mapping using multi-date remote sensing (IRS-AWiFS and Radarsat ScanSAR) data. The second part was to characterize the cropping system using moderate spatial resolution multi-date remote sensing data (SPOT VGT NDVI) and ground survey. The remote sensing data was used to compute three cropping system performance indices (Multiple Cropping Index, Area Diversity Index and Cultivated Land Utilization Index). Ground survey was conducted using questionnaires filled up by 1,000 farmers selected from 103 villages based on the cropping systems map. Apart from ground survey, soil and water sampling and quality analysis were carried out to understand the effect of different cropping systems and their management practices. The results showed that, rice-wheat was the major cropping system of the IGP, followed by Rice-Fallow-Fallow and Maize-Wheat. Other major cropping systems of IGP included Sugarcane based, Pearl millet-Wheat, Rice-Fallow-Rice, Cotton-Wheat. The ground survey could identify 77 cropping systems, out of which 38 are rice-based systems. Out of these 77 cropping systems, there were 5 single crop systems, occupying 6.5% coverage (of all cropping system area), 56 double crop systems with 72.7% coverage, and 16 triple crop systems with 20.8% coverage. The cropping system performance analysis showed that the crop diversity was found to be highest in Haryana, while the cropping intensity was highest in Punjab state.     

Evaluating a thermal image sharpening model over a mixed agricultural landscape in India

Fine spatial resolution (e.g., <300 m) thermal data are needed regularly to characterise the temporal pattern of surface moisture status, water stress, and to forecast agriculture drought and famine. However, current optical sensors do not provide frequent thermal data at a fine spatial resolution. The TsHARP model provides a possibility to generate fine spatial resolution thermal data from coarse spatial resolution (≥1 km) data on the basis of an anticipated inverse linear relationship between the normalised difference vegetation index (NDVI) at fine spatial resolution and land surface temperature at coarse spatial resolution. The current study utilised the TsHARP model over a mixed agricultural landscape in the northern part of India. Five variants of the model were analysed, including the original model, for their efficiency. Those five variants were the global model (original); the resolution-adjusted global model; the piecewise regression model; the stratified model; and the local model. The models were first evaluated using Advanced Space-borne Thermal Emission Reflection Radiometer (ASTER) thermal data (90 m) aggregated to the following spatial resolutions: 180 m, 270 m, 450 m, 630 m, 810 m and 990 m. Although sharpening was undertaken for spatial resolutions from 990 m to 90 m, root mean square error (RMSE) of <2 K could, on average, be achieved only for 990–270 m in the ASTER data. The RMSE of the sharpened images at 270 m, using ASTER data, from the global, resolution-adjusted global, piecewise regression, stratification and local models were 1.91, 1.89, 1.96, 1.91, 1.70 K, respectively. The global model, resolution-adjusted global model and local model yielded higher accuracy, and were applied to sharpen MODIS thermal data (1 km) to the target spatial resolutions. Aggregated ASTER thermal data were considered as a reference at the respective target spatial resolutions to assess the prediction results from MODIS data. The RMSE of the predicted sharpened image from MODIS using the global, resolution-adjusted global and local models at 250 m were 3.08, 2.92 and 1.98 K, respectively. The local model consistently led to more accurate sharpened predictions by comparison to other variants.     

A Dutch multi-date land use database: Identification of real and methodological changes

Land cover and land use are important information sources for environmental issues. One of the most important changes at the Earth's surface concerns land cover and land use. Knowledge about the location and type of these changes is essential for environmental modeling and management. Remote sensing data in combination with additional spatial data are recognized as an important source of information to detect these land cover and land use changes.     

Interferometry with ENVISAT wide swath ScanSAR data

The possibility to get efficient topographic mapping and monitoring of large-scale motions with ScanSAR interferometry has been demonstrated with the Shuttle Radar Topography Mission and RADARSAT mission. The Environmental Satellite Advanced Synthetic Aperture Radar (ASAR) sensor has been designed to provide enhanced capabilities for interferometric applications. Different types of interferometric products can be obtained by combining the various ASAR modes as stripmap synthetic aperture radar [image mode (IM)] and ScanSAR [wide swath (WS) mode]. This letter deals with the possibility to use WS data to get either mixed-mode (IM/WS) or ScanSAR mode (WS/WS) differential interferograms. The impact of digital elevation model localization errors on IM/WS interferograms and of scan pattern synchronization on WS/WS interferograms is investigated. Experimental results are encouraging and show that ASAR ScanSAR data can be routinely used for interferometric applications in both cases.     

The Global Impact of Clouds on the Production of MODIS Bidirectional Reflectance Model-Based Composites for Terrestrial Monitoring

A global data set of cloud occurrence probability derived from Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua gridded daily data is analyzed to investigate the probability of obtaining at least a minimum number of cloud-free observations within various compositing periods. The probabilities derived from Terra and Aqua, with morning and afternoon overpass times, respectively, are similar and increase with compositing period. Compositing both Terra and Aqua observations results in considerably higher probabilities of obtaining a sufficient number of observations for bidirectional reflectance model-based compositing. Given that the only alternative to obtaining sufficient samples is to extend the observation period, which can cause significant problems when the surface state changes, it is concluded that using data from the two MODIS sensors provides the most effective way of generating composited products. Findings with respect to the availability of cloud-free composites when n-day composites are generated on a temporally overlapping daily rolling basis, i.e., every day, rather than every n-days, are also discussed for regional and global applications