Single frequency processing of Ørsted GPS radio occultation measurements

The Global Positioning System (GPS) radio occultation measurements obtained using the TurboRogue GPS receiver on the Danish satellite Ørsted have been processed using the single frequency method. Atmospheric profiles of refractivity and temperature are derived and validated against numerical weather prediction data from the European Centre for Medium-Range Weather Forecast (ECMWF). Results from the Ørsted GPS measurement campaign in February 2000 indicate that the single frequency method can provide retrievals with accuracy comparable to that of using two frequencies. From comparisons between measured dry temperature profiles and corresponding dry temperature profiles derived from ECMWF analysis fields, we find a mean difference of less than 0.5 K and a standard deviation of 2–4 K between 500 and 30 hPa in height. Above 30 hPa the impact of the ionosphere becomes more dominant and more difficult to eliminate using the single frequency method, and the results show degraded accuracy when compared to previous analysis results of occultation data from other missions using the dual frequency method. At latitudes less than 40° (denoted low latitudes), the standard deviation is generally smaller than at latitudes higher than 40° (denoted high latitudes). A small temperature bias is observed centered at 200 hPa for low latitudes and at 300 hPa for high latitudes. This indicates that the ECMWF analyses do not adequately resolve the tropopause temperature minimum. In the lowest part of the troposphere an observed warm bias is thought to be due to erroneous tracking of the GPS signal in cases of atmospheric multipath propagation.     

GGM02 – An improved Earth gravity field model from GRACE

A new generation of Earth gravity field models called GGM02 are derived using approximately 14 months of data spanning from April 2002 to December 2003 from the Gravity Recovery And Climate Experiment (GRACE). Relative to the preceding generation, GGM01, there have been improvements to the data products, the gravity estimation methods and the background models. Based on the calibrated covariances, GGM02 (both the GRACE-only model GGM02S and the combination model GGM02C) represents an improvement greater than a factor of two over the previous GGM01 models. Error estimates indicate a cumulative error less than 1 cm geoid height to spherical harmonic degree 70, which can be said to have met the GRACE minimum mission goals. Electronic Supplementary Material Supplementary material is available in the online version of this article at     

The negative alpha filter: a new Processing technique for polarimetric SAR interferometry

In this letter we develop a new concept, the negative alpha filter, which we suggest has application for quantitative estimation of surface parameters beneath vegetation using polarimetric synthetic aperture radar (SAR) interferometry (POLInSAR). We first derive the filter and then validate it using simulations of L-band coherent forest scattering. We then show initial results of applying the filter to airborne data from the German Aerospace Center's E-SAR L-band sensor.     

Analysis of geophysical networks derived from multiscale digital elevation models: a morphological approach

We provide a simple and elegant framework based on morphological transformations to generate multiscale digital elevation models (DEMs) and to extract topologically significant multiscale geophysical networks. These terrain features at multiple scales are collectively useful in deriving scaling laws, which exhibit several significant terrain characteristics. We present results derived from a part of Cameron Highlands DEM.     

Hyper Redundancy for Accuracy Enhancement in Automated Close Range Photogrammetry

A generic network design in close range photogrammetry is one where optimal multi-ray intersection geometry is obtained with as few camera stations as practicable. Hyper redundancy is a concept whereby, once the generic network is in place, many additional images are recorded, with the beneficial impact upon object point precision being equivalent to the presence of multiple exposures at each camera position within the generic network. The effective number of images per station within a hyper redundant network might well be in the range of 10 to 20 or more. As is apparent when it is considered that a hyper redundant network may comprise hundreds of images, the concept is only applicable in practice to fully automatic vision metrology systems, where it proves to be a very effective means of enhancing measurement accuracy at the cost of minimal additional work in the image recording phase. This paper briefly reviews the network design and accuracy aspects of hyper redundancy and illustrates the technique by way of the photogrammetric measurement of surface deformation of a radio telescope of 26 m diameter. This project required an object point measurement accuracy of σ  = 0·065 mm, or 1/400 000 of the diameter of the reflector.     

Simulating the effect of Nitrogen application on Wheat Yield by linking remotely sensed measurements with wtgrows simulation model

A field experiment was conducted on wheat at New Delhi with five treatments of Nitrogen (N) fertilizer application (0, 30, 60, 90 and 120 kgha^-1). Relationship has been established between observed leaf area index (LAI) and remotely sensed vegetation indices. These relationships are inverted and used for predicting LAI from vegetation indices on different days after sowing. The “re-initialization” strategy is implemented in model WTGROWS in which initial conditions of model are changed so that the model simulated LAI match remote sensing predicted LAI. The model performance with re-initialization has been evaluated by comparing the simulated grain yield and total above-ground dry matter (TDM) values with the actual observations. The results show that in-season re-initialization is effective in model course correction by improving the simulated results of yield and TDM for different N treatments even though the model was run with no N stress condition. Model re-initialization at different days shows that the closer is the day of re-initialization to crop anthesis the more effective is model course correction. Also, the treatment showing maximum error in yield simulation without re-initialization shows maximum reduction in error by re-initialization. The approach shows that the remote sensing inputs can substitute for some of the inputs or errors in inputs required by crop models for yield prediction.     

Comparison of ERS-SAR and spot data for sugar beet crop cover assessment

The present work was aimed to compare the abilities of radar and optical satellite data to estimate crop canopy cover, which is a key component of productivity estimates. Three ERS-1 SAR images were obtained of East Anglia (UK) in 1995 and one ERS-2 SAR image in 1996. The images covered a study area around the IACR Brooms Barn Sugar Beet Research Institute. Field data comprising radiometric and biophysical measurements of the crop canopy were collected in two fields from June 22 to August 3, 1995 to coincide with ERS-1 SAR overpass dates. In 1996, field data were collected in two fields from June 11 to July 29 on a weekly basis. A previously calibrated version of the water cloud model was inverted to estimate Leaf Area Index (LAI) from ERS-1 and ERS-2 SAR backscatter and soil moisture samples. Canopy cover was estimated from the radar-estimated LAI using a standard exponential relationship that has a well-established coefficient for sugar beet. Radio-metrically and atmospherically corrected data from three SPOT images in 1995 and one SPOT image in 1996 were used to calculate the Optimised Soil Adjusted Vegetation Index (OSAVI), from which crop canopy cover was estimated using a relationship determined previously by canopy modelling. The crop cover values estimated by satellite were in good agreement with those measured on ground with the Parkinson radiometer. Radar data may be able to provide useful estimates of canopy cover for crop production modelling, especially in the case of loss of optical data due to cloud.     

Check dam positioning by prioritization of micro-watersheds using SYI model and morphometric analysis — Remote sensing and GIS perspective

Sediment Yield Index (SYI) model and results of morphometric analysis have been used to prioritize watersheds and to locate sites for checkdam positioning in Tarafeni watershed in Midnapur district. West Bengal. Various thematic maps such as land use/land cover, slope, drainage, soil etc. were prepared from 1RS ID LISS III digital data, SOI toposheets of 1:50,000 scale and other reference maps. Morphometric parameters such as bifurcation ratio (R_b). drainage density (D_d), texture ratio (T), length of overland flow (L_o), stream frequency (F_u), compactness coefficient (C_c), circularity ratio (R_c), elongation ratio (E_r), shape factor (B_s) and form factor (R_f) were computed. Automated demarcation of prioritization of micro-watersheds was done by using GIS overlaying technique by assigning weight factors to all the identified features in each thematic map and ranks were assigned to the morphometric parameters. Five categories of priority viz., very high, high, medium, low and very low, were given to all the watersheds in both the methods. Sixty-two micro-watersheds using SYI method and twenty-three micro-watersheds using morphometric have been prioritized as very high priority. Final priority map was prepared by considering the commonly occurred very high-prioritized micro-watersheds in both SYI model and morphometric analysis. Twenty-four suitable sites were identified for check dam construction in 21 highly prioritized watersheds. It is proved that integrated study of SYI model and morphometric analysis yield good result in prioritization of watersheds.