Arundhati Misra

Oceansat-II Scatterometer: Sensor Performance Evaluation,

The Oceansat-II Scatterometer has completed two years in orbit. The instrument has been declared operational, and the normalized radar cross section (σ0) and wind products are being made routinely available to the global operational Numerical Weather Prediction community. The σ0 data from the sensor have been rigorously analyzed for the past two years. Efforts have been put to systematically correlate the biases observed in the data to the onboard functionality of the instrument and to precisely quantify these biases. These analyses have helped not only in the refinement of the ground-processing algorithm but also in the evaluation of sensor performance. This paper presents some of the analyses that have been carried out related to instrument noise calibration with reference to deep-space observations, estimation of biases in the signal bandwidth, and estimation of fixed remnant attitude biases. This paper also addresses the means for rectifying these instrument-related biases.

\sigma^{0}

This paper departs from the popular usage of the Backus-Gilbert inversion (BGI) method as a tool for inversion of antenna temperature measurements in microwave radiometry. The BGI method is applied in this paper to enhance the information content of an existing set of oversampled brightness-temperature (TB) data. The purpose is to isolate the inversion process from its resolution enhancement counterpart. The advantage gained is that the resolution enhancement can be performed in a simplified way and in a different level of processing that starts with the scan-mode TB data product and simply requires with it the knowledge of the antenna gain pattern and the sensors scan geometry. The technique is demonstrated with the 19.35-GHz Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) channel, which provides oversampled TB data. The radiometric resemblance of this channel with that of the 37 GHz and geocollocation of their TB footprints facilitate validation of the enhancement of features. The significance of oversampling the low-frequency (LF) radiometer channels is underscored in the process, which gives the authors the confidence to propose oversampling of the LF data for the forthcoming sensor Microwave Analysis and Detection of Rain and Atmospheric Structures (MADRAS) onboard the Megha_Tropiques mission, which is a joint ISRO-CNES collaboration (due for launch in 2009).

Analyses, and Estimation of Biases

Chhana based milk products viz. rossogolla, kanchagolla,narampak sandesh and karapak sandesh are very popular in eastern part of India and gaining popularity in other parts of the country. A wide variation in manufacture method, microbial quality and shelf-life of these traditional milk products were observed by previous research. The aim of the present study was to determine the prevalence of contamination of chhana based milk products available in Kolkata city with Enteropathogenic Escherichia coli (EPEC) serogroups. Random samples of different chhana based milk products were collected from different parts of Kolkata city in aseptic condition, cultured in selective media and examined for biochemical tests. Among 240 samples, E. coli was isolated from 67 (27.91%) of them. Potential EPEC was present in 52 samples (21.66%) and 55 of the isolates were EPEC. Eleven serogroups were identified viz. O26, O55, O111, O119, O114, O125, O142, O86, O126, O127, O128. Among all these serogroups, O55 (23.66%) was the most prevalent. Though recent studies on virulence factors indicate that not all strains serologically classified as EPEC are able to attaching/effacing lesion, it is believed that the isolation of EPEC serogroups from chhana based milk products represent a potential risk for public health particularly children, as well as an indicative of the presence of other enteropathogens. Considering the public health importance of sweetmeat consumers, the product should be prepared hygienically reducing the microbial load present in it. The result indicates that strict preventive measures should be adopted to ensure contamination free sweetmeats for the safety of the consumers.

Brightness Temperature Reconstruction Using BGI

This paper presents a new assortment of temperature-sounding channels for a proposed low-Earth-orbit polar Sun-synchronous satelliteborne millimeter-wave atmospheric sounder of the Indian Space Research Organization (ISRO). The newness owes its origin to the exploration of the millimeter-wave O2 absorption spectrum in quest of optimal off-resonance frequencies that impose fewer restrictions on channel bandwidth and temperature sensitivity and yet can sound up to 40 km in the atmosphere with a 4-km vertical resolution. This is ISROs first leap toward millimeter-wave technology. The overall receiver-noise figure for the channels in the 5-mm band (50-60 GHz) has been pessimistically estimated at 5 dB which will severely degrade the system temperature sensitivity. Therefore, channel bandwidth is at premium. The purpose of this design is to limit the number of passbands and simplify the design of frequency-selective filters by choosing center frequencies that have sufficient interleaving and also provide a scope of allotting a reasonable bandwidth. The set of temperature-sounding channels in the current design have 15 channels in 17 passbands in contrast with 15 channels in 29 passbands of the operational Advanced Microwave Sounding Unit (AMSU)-A.

Prevalence of Enteropathogenic Escherichia coli Isolated from Chhana Based Indian Sweets in Relation to Public Health

In this paper we compare, using ISRO’s RISAT-1 FRS-1 mode Compact Polarimetric (CL-Pol) data, two widely used hybrid polarimetric decomposition techniques, m − δ and m − χ decompositions, with regard to classification accuracy for various agricultural crops of north and west India. We show that the classification based on the m − χ decomposition results in better crop separability in general. But the crop stage and existence of orientating structures in the crops affects the efficacy of decomposition; a fact vividly brought out in this paper. Theoretical insights into the effectiveness of these decomposition techniques for different crop geometry are brought forth. We also compare the classification accuracy subsequent to polarimetric speckle filtering vis-a-vis spatial multilooking (downsampling). We show that usage of an appropriate polarimetric filter tends to produce comparable accuracy for most of the agricultural classes, as that of multilook case, without degrading spatial resolution. This work showcases a custom implementation of Stokes parameter based decomposition as well as POLSAR filter based on refined Lee algorithm, written in C and tailored to RISAT-1.

A Selection of Channels for a Proposed Atmospheric Temperature Sounder of ISRO

A hybrid-polarity architecture, consisting of transmitting circular polarisation and receiving two orthogonal linear polarisation and also their relative phase, was used to calculate four Stokes parameters. Different parameters like Degree of Polarisation, Alpha angle, Entropy, Anisotropy, Radar vegetation Index and decompositions like Raney decomposition (m-δ), Freeman-2 and 3 component decompositions were derived from these hybrid data. Crop biophysical parameters viz. plant height, plant age and plant biomass of cotton crops grown under two different environments, i.e., rainfed and irrigated in Guajrat, India were studied with respect to derived polarimetric parameters. Right circular transmitted and horizontally (RH) and vertically (RV ) received backscatter values show good relation with the plant height, age and biomass. RH backscatter −13 dB to −7 dB and RV backscatter from −13 to −10 dB were observed for crop biophysical parameters. Volume component of all decomposition showed strong response to the increase in height, age and biomass of the plant. Radar Vegetation index (RVI) values have also shown significant increase from 0.6 to 0.7 with increasing age of the crop. The rate of growth was slow in the initial phase, but fast post mid-July for both early and late sown cases. The polarimetric parameters were found significantly correlated to the above plant biophysical parameters.

EVALUATION OF HYBRID POLARIMETRIC DECOMPOSITION TECHNIQUES FOR WINTER CROP DISCRIMINATION

The Backus-Gilbert Inversion technique is a proven method of temperature inversion in microwave radiometry with a scope of trade-off between desired resolution and tolerable noise. In this study it is used to super-resolve brightness temperature data independent of the inversion process. The BGI-method is applied on scan-mode TMI (TRMM Microwave Imager) data. The 19.35GHz TMI channel provides brightness temperature data oversampled by a factor of two and geo-collocated with the 37GHz channel data. Consequently, the enhancement of features in the 19.35GHz image after BG-resolving is validated by the identification of such features in the 37GHz image. The potential of the BGImethod as a non-degrading interpolation technique is also tested.

Cotton Crop Biophysical Parameter Study Using Hybrid/Compact Polarimetric RISAT-1 SAR Data

El-Nino refers to a large-scale ocean-atmosphere climate interaction associated with the episodic warming in sea surface temperatures (SST) across the central and east-central Equatorial Pacific. La Nina is an opposite event of El Nino which is termed as the episodic cooling of ocean SST in the central and east-central equatorial pacific. El Nino events are mostly associated with warm and dry conditions in southern and eastern inland areas of Australia, as well as Indonesia, Philippines, Malaysia and central Pacific islands such as Fiji, Tonga and Papua New Guinea. The inter-annual variability of Indian summer monsoon rainfall (ISMR) has been linked to variations of Sea Surface Temperatures (SST) over the equatorial Pacific and Indian Oceans. ENSO events have a profound impact on summer monsoonal rainfall across India and most of the major droughts have occurred during El Nino events. However, its reverse is not always true. Previously El Nino had a strong association with droughts in India but this relationship has been weekend in recent years. El Nino conditions mostly coincide with a period of weak monsoon and rising temperatures in India and thus the probability of drought occurrence surges during El Nino events that could be disturbing for Indian crop production and water supply. Moreover, El Nino resulting in deficit rainfall tends to lower the summer crops production such as rice, sugarcane, cotton and oilseeds and therefore the outcome might be seen in form of high inflation rates and lower GDP due to high contribution of agriculture sector in Indian economy. This paper describes the occurrence of El Nino events, its impact on climate in different parts of world with special reference to Indian monsoon and crop production.

Spatial resolution enhancement and interpolation using Backus-Gilbert inversion: demonstration with TMI brightness temperature data

ABSTRACT A novel fog/low clouds detection technique has been implemented using data from the Atmospheric Infrared Sounder (AIRS) and the Infrared Atmospheric Sounding Interferometer (IASI) along with the Global Forecasting System (GFS) and the European Centre for Medium Weather Forecast (ECMWF) model wind data. The study has been carried out during fog seasons of the years 2015–2016 and 2016–2017 over six locations over the Indo-Gangetic (IG) plains. Fog is indicated if the estimated Fog Stability Index (FSI) is found to be less than the corresponding dynamically derived threshold value. The same algorithm is effective for both day and night-time analysis and has the potential to detect fog even in the presence of higher clouds. Results are validated with visibility data obtained from various meteorological stations of the India Meteorological Department (IMD) located in the IG plains and found to be in agreement in 84.63% of cases. For qualitative comparison with FSI, the Indian National Satellites (INSAT-3D) fog products have also been used. FSI could be the most effective technique for those satellites which could have a sounder sensor along with an infrared and microwave channels like the AIRS and the IASI for an improved and continuous monitoring of fog.

Impact of El-Nino and La-Nina on Indian Climate and Crop Production

Abstract Periodic crop condition monitoring is of prime importance in cotton belt of western India for water stress management. In this article, vegetation water content (VWC) is assessed using Radar Vegetation Index (RVI) derived from the RISAT-1 data during July to September, vegetative to first picking phase, for utilizing its potential for large area cotton condition assessment. The RVI estimation from dual-polarized data has been demonstrated for regional applications. Prediction models of VWC for cotton crop using RVI and in situ ground measurements depicts significant relationship, with R2 varying from 0.5 to 0.6 and RMSE of 0.3–0.7 kg m−2. High correlation exists between RVI with crop age and crop biomass with R2 varying from 0.55 to 0.7, this proves useful for sowing date prediction. The results showed good validation (R2 = 0.8) for operational applications. The estimated VWC was found with 30–35% error above 4 kg m−2 biomasses as compared to 20–25% in lower ranges.