Estimation of Partial Pressure of Carbon Dioxide and Air-Sea Fluxes in Hooghly Estuary Based on In Situ and Satellite Observations

An empirical model is developed and used with remotely sensed predictors: sea surface temperature (SST) and chlorophyll-a concentration (Chl-a), to compute surface water partial pressure of carbon dioxide (pCO_2w) and air-sea fluxes of CO₂ in the Hooghly estuary and its adjacent coastal oceans. In situ observations used here were based on measurements carried out in this region during winter and summer periods in 2008. The estimated pCO_2w compares well with the in situ observations at root mean square error ±18 μatm. In winter, estimated pCO_2w ranges between 320 and 500 μatm with large values (>400 μatm) on the south-western and south-eastern flanks of the coastal domain and lower values (340–375 μatm) on the main-channel. In summer, it remained spatially uniform at 450 μatm. Extrapolation of the results over the study region based on the Moderate Imaging Specroradiometer (MODIS) measured SST and Chl-a suggests that the region is a strong source of atmospheric CO₂ during the summer with net release of 0.095 Tg C year^?1 (equivalent to mean flux of 90 molC m^?2 year^?1) and is a weak source during the winter with net release of 0.006 Tg C yr^?1 (0.5 molC m^?2 year^?1) from the geographical extent of 6000 Km² area.     

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.     

Boreal forest transmissivity in the microwave domain using ground-based measurements

This letter proposes an estimation of microwave transmissivity within the Canadian boreal forest. The aim is to correct the forest effect in snow water equivalent estimation from Special Sensor Microwave Imager and Advanced Microwave Scanning Radiometer microwave measurements. The estimation was carried out using ground-based radiometric measurements, at 19 and 37 GHz, and for both polarizations. The results show that the transmissivity is correlated with the stem volume and is independent of the tree species. For high stem volumes (>100 m/sup 3//ha), the transmissivity is found to be 0.4 and 0.3 for 19 and 37 GHz, respectively.     

Selection of spectral band combination for land cover/land use classification using a brightness value overlapping index (BVOI)

A simply defined, accurate and efficient criterion of selecting a spectral-band combination for improved land use/land cover classification using remote sensing data is discussed. Results indicate that Brightness Value Overlapping Index (BVOI) is very effective in measuring the degree of overlap in brightness values among land cover types and in selecting suitable spectral-band combination for landuse classification. The results of BVOI are also compared with the results of another band-combination selecting index - Optimum Index Factor (OIF).     

The hierarchical tessellation model and its use in spatial analysis

The hierarchid tessellation model belongs to a class of spatial data models based on the recursive decomposition of space. The quadtree is one such tessellation and is characterized by square cells and a 1:4 decomposition ratio. To relax these constraints in the tessellation, a generalized hierarchical tessellation data model, called Adaptive Recursive Tessellations (ART), has been proposed. ART increases flexibility in the tessellation by the use of rectangular cells and variable decomposition ratios. In ART, users can specify cell sizes which are intuitively meaningful to their applications, or which can reflect the scales of data. ART is implemented in a data structure called Adaptive Recursive Run-Encoding (ARRE), which is a variant of two-dimensional run-encoding whose running path can vary with the different tessellation structures incorporated in an ART model. Given the recognition of the benefits of implementing statistical spatial analysis in GIS, the use of hierarchical tessellation models such as ART in spatial analysis is discussed. Three examples are introduced to show how ART can: (1) be applied to solve the quadrat size problem in quadrat analysis of point patterns; (2) act as the data model in the variable resolution block kriging technique for geostatistical data to reduce variation in kriging error; and (3) facilitate the evaluation of spatial autocorrelation for area data at multiple map resolutions via the construction of a connectivity matrix for calculating spatial autocorrelation indices based on ARRE.     

Temporal change detection in river courses and flood plains in an arid environment through satellite remote sensing

Temporal changes in ephemeral river courses and associated flood plains, which could not be detected by Landsat MSS due to its poor spatial resolution of 80m, have been identified and mapped within 10% accuracy by Landsat TM False colour composite because of its higher spectral and spatial resolution of 30 m. Over a period of 28 years (1958–86) the river courses widened upto 1.8 times through bank erosion due to the recurring flash floods. The flash floods have also caused morphological, soil fertility and landuse changes in the associated flood plains, which could also be monitored by the Landsat TM.     

Quantification of suspended solids in Dal lake,Srinagar using remote sensing technology

Indian Remote Sensing satellite (IRS)-1B, Linear Imaging Self Scanner (LISS)-II spectral digital data was analysed to determine the feasibility of quantifying the concentration of suspended solids in the surface water of inland water body, Dal lake, in Srinagar, India. The water samples collected in concurrent with IRS-1B overpass, were analysed to determine the concentration of suspended solids. The results indicate that a positive functional relationship exist between the concentration of suspended solids and the visible wave length bands 1 and 3 and near infrared band 4. It has been observed that as the concentration of suspended solids increase, the spectral response also increases. It is concluded that IRS LISS-H data can be effectively used to quantify suspended sediment concentration in the Dal lake surface water.     

Response of GPS occultation signals to atmospheric gravity waves and retrieval of gravity wave parameters

We show that the amplitude of the Global Positioning System (GPS) signals in the radio occultation (RO) experiments is an indicator of the activity of the gravity waves (GW) in the atmosphere. The amplitude of the GPS RO signals is more sensitive to the atmospheric wave structures than is the phase. Early investigations used only the phase of the GPS occultation signals for statistical investigation of the GW activity in the height interval 10–40 km on a global scale. In this study, we use the polarization equations and Hilbert transform to find the 1-D GW radio image in the atmosphere by analyzing the amplitude of the RO signal. The radio image, also called the GW portrait, consists of the phase and amplitude of the GW as functions of height. We demonstrate the potential of this method using the amplitude data from GPS/Meteorology (GPS/MET) and satellite mission Challenge Mini-satellite Payload (CHAMP) RO events. The GW activity is nonuniformly distributed with the main contribution associated with the tropopause and the secondary maximums related to the GW breaking regions. Using our method we find the vertical profiles of the horizontal wind perturbations and its vertical gradient associated with the GW influence. The estimated values of the horizontal wind perturbations are in fairly good agreement with radiosonde data. The horizontal wind perturbations v(h) are ±1 to ±5 m s with vertical gradients dv/dh ±0.5 to ±15 m s km at height 10–40 km. The height dependence of the GW vertical wavelength was inferred through the differentiation of the GW phase. Analysis of this dependence using the dispersion relationship for the GW gives the estimation of the projection of the horizontal background wind velocity on the direction of the GW propagation. For the event considered, the magnitude of this projection changes between 1.5 and 10 m s at heights of 10–40 km. We conclude that the amplitude of the GPS occultation signals contain important information about the wave processes in the atmosphere on a global scale.