Optimum tracking of ship routes in 3g-WAM simulated rough weather using IRS-P4 (MSMR) analysed wind fields

Rough weather ship routing is studied using model hindcast wave climate. With the launch of IRS-P4 (OCEANSAT-I), it became possible to carry out routine wave forecasting over the Indian Ocean. The MSMR channel of the satellite gives scalar wind, which is analysed at National center for Medium Range Weather Forecasting (NCMRWF), India for converting to vector winds. The same is used as input to third generation wave model for the rough weather month of July 2000. Simulations are carried out using Cycle-4 of third generation spectral wave model WAM for regional grid system. This simulated wave climate formed the basis for computing effective ship velocity in the irregular seaway. This study gives a quantitative estimation of change in ship velocity in the open Indian Ocean for a Liberty type ship. The optimal route is charted using Dijkstra’s algorithm for minimal time path between Calcutta and Sumatra. The optimum track information has broad scope for obtaining a safer route, least time route by avoiding delay in schedule with minimum fuel consumption.     

Intra-Seasonal Oscillations Associated with Indian Ocean Warm Pool and Summer Monsoon Rainfall and Their Inter-Annual Variability

Sea surface temperature (SST) and wind speed (WS) derived from Multichannel Scanning Microwave Radiometer (MSMR), onboard IRS-P4 (Oceansat-1) satellite were used to generate spatially averaged (80° to 100° E & 0° to 10° S) daily data during June to September from 1999 to 2001 along with collocated outgoing long wave radiation (OLR) data from NCEP/NCAR reanalysis product. Daily rainfall data over the peninsular India during the above period was taken from the weekly weather report published by the Indian Meteorological Department. The data were then subjected to power spectral analysis. Dominant 30 to 60 days oscillations were observed over both ocean and land during all the 3 years investigated. It is quite interesting to note that the intra-seasonal oscillation with 60 days periodicity dominates in both ocean and land during 1999 and then transforms to 30 and 40 days periodicities in 2000 and 2001 respectively with a phase lag of about 25 to 30 days. It was also observed that the time-latitude section of zonally averaged OLR and TMI derived cloud liquid water data clearly depict the propagation of convection and cloud from the equator to the north at the rate of 0.75° to 1° latitude per day which corroborates well with the rate of propagation derived from the phase lag obtained between the processes occurring at the equatorial Indian Ocean and rainfall recorded at the designated land segments. The results are in good agreement with the observations made by earlier investigators.     

Retrieval of chlorophyll from the sea-leaving radiance in the Arabian sea

Error estimates in chlorophyll retrieval in the sea from satellite data have always been higher compared to the errors in retrieval of sea surface temperature or wind speed from satellite data. This is due to the higher percentage of noise in optical signals compared to infrared or microwave signals reaching the satellite. The noise is introduced both at the sea surface itself and also while signal passes passage through atmosphere. In the present work, we have attempted to investigate the noise introduced at the sea surface. During the validation experiment of the optical sensor MOS-B onboard Indian Remote Sensing Satellite IRS-P3, simultaneous measurements of chlorophyll concentration and sea leaving radiance have been made along the satellite passes in the Arabian sea. The algorithms used in the present studies are not found to give satisfactory values of chlorophyll in about 30% of cases. The possible sources of errors in retrieving chlorophyll concentration have been discussed.     

Impact of GPS radio occultation data assimilation on regional weather predictions

The impact of GPS radio occultation (RO) data assimilation on severe weather predictions in East Asia is introduced and reviewed. Both the local observation operator that assimilates the retrieved refractivity as local point measurement, and the nonlocal observation operator that assimilates the integrated retrieved refractivity along a straight raypath have been utilized in WRF 3DVAR to improve the initial analysis of the model. A general evaluation of the impact of these approaches on Asian regional analysis and daily prediction is provided in this paper. In general, the GPS RO data assimilation may improve prediction of severe weather such as typhoons and Mei-yu systems when COSMIC data were available, ranging from several points in 2006 to a maximum of about 60 in 2007 and 2008 in this region. Based on a number of experiments, regional model predictions at 5 km resolution were not significantly influenced by different observation operators, although the nonlocal observation operator sometimes results in slightly better track forecast. These positive impacts are seen not only in typhoon track prediction but also in prediction of local heavy rainfall associated with severe weather over Taiwan. The impact of 56 GPS RO soundings on track prediction of Cyclone Gonu (2007) over the Indian Ocean is also appealing when compared to other tracks assimilated with different observations. From a successive evaluation of skill scores for real-time forecasts on Mei-yu frontal systems operationally conducted over a longer period and predictions of six typhoons in 2008, assimilation of GPS RO data appears to have some positive impact on regional weather predictions, on top of existent assimilation with all other observations.     

Studies on urban heat islands using envisat AATSR data

Urbanization has significant effects on local weather and climate and among these effects one of the most familiar is the urban heat island, for which the temperatures of the central urban locations are several degrees higher than those of nearby rural areas of similar elevation. Satellite data provides important inputs for estimating regional surface albedo and evapotranspiration required in the studies related to surface energy balance. Present study describes the analysis of day and night ENVISAT-AATSR satellite data for Urban heat island and surface thermal inertia. Field campaigns have been conducted in synchronous with the satellite data over pass for validating the surface temperature estimated from AATSR data. Satellite derived surface temperature values are within ±1° C from ground measured values. Heat island formations in urban regions of Hyderabad and environs can be clearly seen in the night time data with core urban regions showing high temperatures. Apparent thermal inertia derived from AATSR day and night data sets have shown typical variations over urban regions.     

Assessment of Fragmentation and Disturbance Patterns in Eastern Ghats: A Case Study in R.V. Nagar Range,Visakhapatnam District,Andhra Pradesh,India

At present the biodiversity in Eastern Ghats is threatened by loss of habitats, exploitation and unscientific management of natural resources, forest fire, biological invasion and other anthropogenic pressures. In this context, we have assessed the forest cover changes, fragmentation and disturbance in the R.V. Nagar Range of Eastern Ghats region, Andhra Pradesh using satellite remote sensing and GIS techniques. Satellite data of IRS-1A LISS II of 1988 and IRS-P6 LISS III of 2006 were assessed for forest cover changes in 1 sq.km grid and generated as Sensitivity Index map. Further the road and settlement buffer of 1000 m was generated to represent Threat Index map. From 1988 to 2006, the forest cover had a total cover loss of 35.2 sq.km and increase in scrub cover by 7.2%. Over all change analysis from 1988 to 2006 with reference to forest cover indicates, negative changes (loss of forest area) accounted for 48.1 sq.km area and positive changes (gain of forest) for an area of 12.1 sq.km of area. The results of the change detection using multi-date satellite imagery suggest degradation in forest cover over two decades, which necessitates the conservation measures in this range with high priority.     

Atmospheric correction to IRS-P6 AWiFS data and its validation with ground measurements: A study over the semi-arid region

In this study, we have implemented a fast atmospheric correction algorithm to IRS-P6 advanced wide field sensor (AWiFS) satellite data for retrieving surface reflectance under different atmospheric and surface conditions. The algorithm is based on MODIS climatology products and simplified use of Second Simulation of Satellite Signal in Solar Spectrum (6S) radiative transfer code. The algorithm requires information on aerosol optical depth (AOD) for correcting the satellite dataset. The atmospheric correction algorithm has been tested for IRS-P6 AWiFS False colour composites covering the International Crops Research Institute for the Semi-Arid Tropics Farm, Patancheru, Hyderabad, India, under varying atmospheric conditions. Ground measurements of surface reflectance representing different land use/land cover, i.e. red soil, chick pea, groundnut and pigeon pea crops were conducted to validate the algorithm. Terra MODIS AOD₅₅₀ validated with Microtops-II sun photometer–derived AOD₅₀₀ over the urban region of Hyderabad exhibited very good correlation of ～0.92, suggesting possible use of satellite-derived AOD for atmospheric correction.     

Vegetation discrimination using irs-p3 wifs temporal dataset - a case study from rampa forests, eastern ghats, a.p.

Multitemporal data sets from coarse resolution sensors of Indian Remote Sensing Satellites provides an opportunity to classify various forest types using their phenological attributes reflected in temporal NDVI profiles. The present study attempts to classify various vegetation classes using time integrated NDVI (T-NDVI) values derived from IRS-P3 WiFS data. The algorithm explores the differential characteristics in T-NDVI values of different features and the results suggest the possible use of the methodology for forest type classification.     

Evaluation and use of Resourcesat-I data for agricultural applications

Resourcesat-1 satellite offers a unique opportunity of simultaneous observations at three different spatial scales through LISS-IV, LISS-III^* (improved LISS-III) and AWiFS sensors from a common platform. The sensors have enhanced capabilities in terms of spectral, spatial and radiometric resolution as compared to earlier Indian Remote sensing Satellite sensors. This paper summarizes the results of various studies such as evaluation of sensor characteristics, inter-sensor comparison studies, derivation and validation of surface reflectance measurements, quantification of improvements due to Resourcesat-1 sensors, and their use for various agricultural applications. The studies presented in this paper demonstrate that suit of sensors onboard Resourcesat-1 satellite provides better prospects for several agricultural applications like crop identification, discrimination and crop inventory for some major Indian crops, than its predecessors on IRS satellites.     

Extracting Regional Pattern of Wheat Sowing Dates Using Multispectral and High Temporal Observations from Indian Geostationary Satellite

Monitoring of Agricultural crops using remote sensing data is an emerging tool in recent years. Spatial determination of sowing date is an important input of any crop model. Geostationary satellite has the capability to provide data at high temporal interval to monitor vegetation throughout the entire growth period. A study was conducted to estimate the sowing date of wheat crop in major wheat growing states viz. Punjab, Haryana, Uttar Pradesh (UP), Madhya Pradesh (MP), Rajasthan and Bihar. Data acquired by Charged Couple Detector (CCD) onboard Indian geostationary satellite INSAT 3A have continental (Asia) coverage at 1 km?×?1 km spatial resolution in optical spectral bands with high temporal frequency. Daily operational Normalized Difference Vegetation Index (NDVI) product from INSAT 3A CCD available through Meteorological and Oceanographic Satellite Data Archival Centre (MOSDAC) was used to estimate sowing date of wheat crop in selected six states. Daily NDVI data acquired from September 1, 2010 to December 31, 2010 were used in this study. A composite of 7 days was prepared for further analysis of temporal profile of NDVI. Spatial wheat crop map derived from AWiFS (56 m) were re-sampled at INSAT 3A CCD parent resolution and applied over each 7 day composite. The characteristic temporal profiles of 7 day NDVI composite was used to determine sowing date. NDVI profile showed decreasing trend during maturity of kharif crop, minimum value after harvest and increasing trend after emergence of wheat crop. A mathematical model was made to capture the persistent positive slope of NDVI profile after an inflection point. The change in behavior of NDVI profile was detected on the basis of change in NDVI threshold of 0.3 and sowing date was estimated for wheat crop in six states. Seven days has been deducted after it reached to threshold value with persistent positive slope to get sowing date. The clear distinction between early sowing and late sowing regions was observed in study area. Variation of sowing date was observed ranging from November 1 to December 20. The estimated sowing date was validated with the reported sowing date for the known wheat crop regions. The RMSD of 3.2 (n?=?45) has been observed for wheat sowing date. This methodology can also be applied over different crops with the availability of crop maps.     

Ground validation of an algorithm for estimating surface suspended sediment concentrations from multi-spectral reflectance data

Pollution of water resources by sediments eroded from degraded watersheds is a critical concern around the world. Current methods for locating these eroding areas and off-site damage to water resources through visual observations and field sampling with subsequent laboratory analysis are time consuming and expensive. There is thus, a justified interest in developing algorithms for quick estimation of suspended sediment concentrations in large water-bodies from remotely sensed data. This paper presents the results of a ground validation study on characterization and quantification of surface suspended sediment concentrations (SSC) in sediment laden water bodies through an n-waveband specific numerical index, total information content. A comparison of SSC-predictive potential of the proposed new index, derived from four broad (100–300 nm) Landsat MSS, five broad (40–300 nm) Landsat TM and eight narrow (20–40 nm) IRS-P4 OCM spectral bands, with that of the conventional (NIR-Red and NIR+Red) indices, computed from the same spectral band data, is also presented. The study reveaied that at SSCs 250 mg/1, the proposed index (derived from either broad / narrow landsat MSS/TM or IRS-P4 OCM spectral data) could lead to SSC predictions (with mean errors within 20%) comparable with those obtained with the conventional indices (derived from the same spectral band data). It could further be observed that, in general, lower sediment concentrations (i.e. SSCs 150 mg/1) were associated with higher prediction inaccuracies. A comparison of the mean errors of predictions associated with the proposed and the conventional (NIR-Red and NIR+Red) indices computed from broad and narrow band data for SSCs 150 mg/I, revealed that an increase in number of wavebands (from 4 MSS to 5 TM or 8 OCM bands) and a decrease in the bandwidth of these wavebands (from broad MSS/ TM bands to narrow OCM bands) led to a significant increase in the prediction accuracy of the proposed new index. These prediction accuracies were observed to be the highest with the proposed index calculated from narrow OCM-P4 spectral data. However this could not be observed with the conventional indices at any of the SSC ranges and with the proposed index at SSCs 250 mg/l. This shows that the lower SSC-predictive potential of proposed index was a significant function of both the number and the bandwidth of spectral bands used for its computation. In fact in one of the cases, lower SSC (150 mg/l) -predictive accuracy of the proposed index was found to be significantly higher than that of the conventional (NIR+R) index. The proposed algorithm could thus compress the information contained in the entire reflectance spectrum of the sediment laden water bodies to their sediment type and concentration specific characteristic values. This characteristic of the proposed index was not shared by any of the conventional indices, based on only two waveband data. In fact the proposed index appears to be the only mean of completely compressing and quantifying the information contained in all the information channels of a narrow band spectrometer (consisting of 200 wavebands) to be shortly launched by ISRO for satellite based inventory of natural resources.     

Mapping of suspended sediments using site specific seasonal algorithms

This article reports a preliminary work in which two site specific seasonal algorithms have been proposed for estimating the suspended sediments concentration (SSC) from the digital numbers recorded on Indian Remote sensing Satellite, IRS-P4 Ocean Colour Monitor (OCM) sensor. For estimation of SSC, the proposed algorithms utilize dark pixel deduction atmospheric correction technique. The computations are performed with respect to north east monsoon phase situations of Palk Strait coastal stretch. The algorithms performance was satisfactory during the north east monsoon period. Although the results obtained cannot be generalized, we suggest that the authority of proposed algorithms can be extended to other seasons with the addition of more temporal experimental validation data sets and with numeric constants adjusted to present existing conditions. (As this area was severely affected by Tsunami, it may have dissimilar conditions at present).     

Evaluation of optical remote sensing-based shallow water bathymetry for recursive mapping

Water depth estimation using optical remote sensing offers a reliable and efficient means of mapping coastal zones. Here, we aim to find a suitable model for fast and practical bathymetry of an estuary using Indian Remote Sensing Satellite (IRS) Linear Imaging Self Scanning Sensor (LISS-3) images. The study examines three different models; (1) least square regression model, (2) spectral band-ratio method and (3) multi-tidal bathymetry model. The findings are supported with in situ observed depth values and statistical estimates. Although the least square regression model has provided best results with root mean square error (RMSE) of 0.4 m, it requires a large number of observed data points for absolute depth estimation. Spectral band-ratio and multi-tidal model provides results with RMSEs 2.1 and 0.9 m, respectively. The present investigation demonstrates that multi-date imagery exploitation at disparate tide levels is the best estimation technique for recursive shallow water bathymetry where in situ observation is not possible.     

Flood forecasting in Niger-Benue basin using satellite and quantitative precipitation forecast data

Availability of reliable, timely and accurate rainfall data is constraining the establishment of flood forecasting and early warning systems in many parts of Africa. We evaluated the potential of satellite and weather forecast data as input to a parsimonious flood forecasting model to provide information for flood early warning in the central part of Nigeria. We calibrated the HEC-HMS rainfall-runoff model using rainfall data from post real time Tropical Rainfall Measuring Mission (TRMM) Multi satellite Precipitation Analysis product (TMPA). Real time TMPA satellite rainfall estimates and European Centre for Medium-Range Weather Forecasts (ECMWF) rainfall products were tested for flood forecasting. The implication of removing the systematic errors of the satellite rainfall estimates (SREs) was explored. Performance of the rainfall-runoff model was assessed using visual inspection of simulated and observed hydrographs and a set of performance indicators. The forecast skill was assessed for 1–6 days lead time using categorical verification statistics such as Probability Of Detection (POD), Frequency Of Hit (FOH) and Frequency Of Miss (FOM). The model performance satisfactorily reproduced the pattern and volume of the observed stream flow hydrograph of Benue River. Overall, our results show that SREs and rainfall forecasts from weather models have great potential to serve as model inputs for real-time flood forecasting in data scarce areas. For these data to receive application in African transboundary basins, we suggest (i) removing their systematic error to further improve flood forecast skill; (ii) improving rainfall forecasts; and (iii) improving data sharing between riparian countries.     

Validations of suspended sediment concentration (ssc) derived using ocean colour monitor (ocm) data off Chennai coast, India

Indian Remote Sensing Satellite (IRS) — P4 Ocean Colour Monitor (OCM) data were used to estimate Suspended Sediment Concentration (SSC) in the coastal waters off Chennai and to study the distribution along the coast. Surface water samples were collected during May and October 2000 synchronized with satellite overpass, and quantitative estimates of Suspended Sediment Concentration (SSC) were done. OCM — Data Analysis System (DAS) software developed by the Space Applications Centre, Ahmedabad was used for OCM data processing and analysis. The field data and OCM derived SSC showed a correlation of r = 0.85 and r = 0.95 for the months of May and October respectively.     

Troposphere delays from space geodetic techniques, water vapor radiometers, and numerical weather models over a series of continuous VLBI campaigns

Continuous, very long baseline interferometry (VLBI) campaigns over 2 weeks have been carried out repeatedly, i.e., CONT02 in October 2002, CONT05 in September 2005, CONT08 in August 2008, and CONT11 in September 2011, to demonstrate the highest accuracy the current VLBI was capable at that time. In this study, we have compared zenith total delays (ZTD) and troposphere gradients as consistently estimated from the observations of VLBI, Global Navigation Satellite Systems (GNSS), and Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS) at VLBI sites participating in the CONT campaigns. We analyzed the CONT campaigns using the state-of-the-art software following common processing strategies as closely as possible. In parallel, ZTD and gradients were derived from numerical weather models, i.e., from the global European Centre for Medium-Range Weather Forecasts (ECMWF) analysis fields, the High Resolution Limited Area Model (European sites), the Japan Meteorological Agency-Operational Meso-Analysis Field (MANAL, over Japan), and the Cloud Resolving Storm Simulator (Tsukuba, Japan). Finally, zenith wet delays were estimated from the observations of water vapor radiometers (WVR) at sites where the WVR observables are available during the CONT sessions. The best ZTD agreement, interpreted as the smallest standard deviation, was found between GNSS and VLBI techniques to be about 5–6 mm at most of the co-located sites and CONT campaigns. We did not detect any significant improvement in the ZTD agreement between various techniques over time, except for DORIS and MANAL. On the other hand, the agreement and thus the accuracy of the troposphere parameters mainly depend on the amount of humidity in the atmosphere.     

Hydrological and hydrodynamic modeling for flood damage mitigation in Brahmani–Baitarani River Basin,India

Stream flow forecast and its inundation simulations prior to the event are an effective and non-structural method of flood damage mitigation. In this paper, a continuous simulation hydrological and hydrodynamic model was developed for stream flow forecast and for spatial inundation simulation in Brahmani–Baitarani river basin, India. The hydrologic modelling approach includes rainfall-run-off modelling, flow routing, calibration and validation of the model with the field discharge data. CARTOSAT Digital Elevation Model of 30 m resolution, land use/land cover derived from the Indian Remote Sensing Satellite (IRS-P6) AWiFS and soil textural data of the study area were used in the modelling to compute topographic and hydraulic parameters. The hydrological model was calibrated with the help of field observed discharge data of 2006 and 2009 and validated with the data of 2008 and 2011. From the results, it is found that computed discharges are very well matching well with the observed discharges. The developed model can provide the stream flow forecast with more than 30 h lead time. Possible flood inundations were simulated using hydrodynamic modelling approach. CARTO Digital Elevation Model of 10 m resolution, landuse and the computed flood hydrographs were used in inundation simulations.     

Implementing a Spatial Model to Derive Potential Fishing Zones in the Northern Bay of Bengal Lying Adjacent to West Bengal Coast,India

Potential fishing zones (PFZ’s) are those regions where the fishes aggregate due to an abundance of food and they are demarcated by tracing those regions in the ocean, where a sharp sea surface temperature (SST) gradient along with optimal chlorophyll (Chl) concentration co-exists at a given time. In this regard, Indian National Centre for Ocean Information Services (INCOIS) disseminates the daily PFZ forecasts in Bay of Bengal and Arabian Sea to aid the fishermen community. The present study is an endeavor to develop a local spatial model derived Potential Fishing Zone (PFZ) in the northern Bay of Bengal (nBoB) lying adjacent to the West Bengal coast. Satellite derived SST and chlorophyll data obtained for two consecutive winter seasons of 2010–11 and 2011–12 were used to generate line density (LD) raster. Shapefiles of INCOIS predicted PFZs were overlaid on these LD raster to extract the corresponding pixel values. Histogram ranges of the extracted pixels were fixed and same values lying in the LD raster of both SST and chlorophyll other than INCOIS PFZs were detected by a spatial model in ERDAS. The PFZs thus derived were validated against the ground fish catch data and it was observed that good fish catch was seen in the model derived additional PFZs also. The catch per unit effort (CPUE) values was found to be very close to that of the CPUE value of PFZ advisories of INCOIS. However, the CPUE in the non PFZ areas were significantly lower than the former two categories.     

北斗高精度长弧历书模型设计

历书参数可同时用于辅助常规导航和自主导航的信号捕获。延长历书参数的有效期不但可以使地面接收机启动时充分利用历书数据,对于基于星间链路观测的自主导航,历书参数的有效期还决定了地面注入历书的频度和占用的星上存储资源。通过对北斗3类导航卫星主要摄动力及其对轨道根数的长期项和长周期项的影响分析,设计了以6个轨道根数和5个摄动参数为播发参数的历书拟合模型。以一个自主运行周期90 d为时间尺度,对北斗在轨卫星进行了长弧段历书拟合试验,并同时分析了卫星位置和速度的拟合精度。结果表明新的历书拟合模型提高了历书拟合的精度,尤其对于地球静止轨道和倾斜地球同步轨道卫星,拟合精度提高显著。对于GEO和IGSO卫星,位置拟合误差大约从200 km降低至十几千米甚至几千米,速度拟合误差大约从15 m/s降低至0.6 m/s,新方法拟合精度提高了约20~30倍;对于中圆地球轨道卫星,无论采用哪种历书模型,位置拟合误差都在5 km左右,速度拟合误差都在0.6 m/s左右,新方法拟合精度提高约15%。针对星间链路卫星10 km位置误差上限的使用需求,对比了新老历书模型的拟合弧长,常规模型最大拟合弧长约为14 d,而新历书模型的最大拟合弧长可延长至45 d,新历书模型延长了历书使用期限,优化了北斗历书模型设计。     

Some issues related with sub-pixel classification using HYSI data from IMS-1 satellite

The mixed pixels are treated as noise or uncertainty in class allocation of a pixel and conventional hard classification algorithms may thus produce inaccurate classification outputs. Thus application of sub-pixel or soft classification methods have been adopted for classification of images acquired in complex and uncertain environment. The main objective of this research work has been to study the effect of feature dimensionality using statistical learning classifier — support vector machine (SVM with sigmoid kernel) while using different single and composite operators in fuzzy-based error matrixes generation. In this work mixed pixels have been used at allocation and testing stages and sub-pixel classification outputs have been evaluated using fuzzy-based error matrixes applying single and composite operators for generating matrix. As subpixel accuracy assessment were not available in commercial software, so in-house SMIC (Sub-pixel Multispectral Image Classifier) package has been used. Data used for this research work was from HySI sensor at 506 m spatial resolution from Indian Mini Satellite-1 (IMS-1) satellite launched on April 28, 2008 by Indian Space Research Organisation using Polar Satellite Launch Vehicle (PSLV) C9, acquired on 18^th May 2008 for classification output and IRS-P6, AWIFS data for testing at sub-pixel reference data. The finding of this research illustrate that the uncertainty estimation at accuracy assessment stage can be carried while using single and composite operators and overall maximum accuracy was achieved while using 40 (13 to 52 bands) band data of HySI (IMS-1).