Radon in groundwater in Tumkur district of Karnataka with special reference to sampling sensitivity

Radon is a naturally occurring colourless and odourless radioactive gas that is soluble in water and is the main source of radioactivity of groundwater. Use of radon contaminated groundwater increased the radon levels in the air, especially in poorly ventilated houses, which is hazardous to health. Ingestion of such water for quite long period may lead to stomach cancer. The drinking water standards proposed by the Bureau of Indian Standards (BIS) exclude the permissible concentration of radon in drinking water. The US Environmental Protection Agency (USEPA) in 1991 proposed a Maximum Concentration Level (MCL) of 11.1 Bq/l for public water supply. The water samples from the bore wells in Tumkur district of Karnataka show radon concentrations in the range of 5 to 250 Bq/l. Ninety percentages of the samples show radon levels above the permissible limit as per USEPA. The spatial variation and geological control over radon concentration in groundwater in the area and sampling sensitivity are discussed here. The study was conducted during March 2012.     

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.     

Talkoot: software tool to create collaboratories for earth science

“Open science,” where researchers share and publish every element of their research process in addition to the final results, can foster novel ways of collaboration among researchers and has the potential to spontaneously create new virtual research collaborations. Based on scientific interest, these new virtual research collaborations can cut across traditional boundaries such as institutions and organizations. Advances in technology allow for software tools that can be used by different research groups and institutions to build and support virtual collaborations and infuse open science. This paper describes Talkoot, a software toolkit designed and developed by the authors to provide Earth Science researchers a ready-to-use knowledge management environment and an online platform for collaboration. Talkoot allows Earth Science researchers a means to systematically gather, tag and share their data, analysis workflows and research notes. These Talkoot features are designed to foster rapid knowledge sharing within a virtual community. Talkoot can be utilized by small to medium sized groups and research centers, as well as large enterprises such a national laboratories and federal agencies.     

Inversion of PROSAIL Model for Retrieval of Plant Biophysical Parameters

The current development of satellite technology particularly in the sensors like POLDER and MISR, has emphasized more on directional reflectance measurements (i.e. spectral reflectance of the target measured from different view zenith and azimuth angles) of the earth surface features mainly the vegetation for retrieval of biophysical parameters at regional scale using radiative transfer models. This approach being physical process based and uses directional reflectance measurement has been found to better and more reliable compared to the conventional statistical approach used till date and takes care of anisotropic nature (i.e. reflectance from the target is different if measured from different view angles) of the target. Keeping this in view a field experiment was conducted in mustard crop to evaluate the radiative transfer model for biophysical parameter retrieval through its inversion with the objectives set as (i) to relate canopy biophysical parameters and geometry to its bidirectional reflectance, (ii) to evaluate a canopy reflectance model to best represent the radiative transfer within the canopy for its inversion and (iii) to retrieve crop biophysical parameters through inversion of the model. Two varieties of the mustard crop (Brassica juncea L) were grown with two nitrogen treatments. The bidirectional reflectance data obtained at 5 nm interval for a range of 400–1100 nm were integrated to IRS LISS–II sensor’s four band values using Newton Cotes Integration technique. Biophysical parameters like leaf area index, leaf chlorophyll content, leaf length, plant height and average leaf inclination angle, biomass etc were estimated synchronizing with the bi-directional reflectance measurements. Radiative transfer model PROSAIL model was validated and its inversion was done to retrieve LAI and ALA. Look Up Table (LUT) of Bidirectional reflectance distribution function (BRDF) was prepared simulating through PROSAIL model varying only LAI (0.2 interval from 1.2 to 5.4 ) and ALA (5° interval from 40° to 55°) parameters and inversion was done using a merit function and numerical optimization technique given by Press et al. (1986). The derived LAI and ALA values from inversion were well matched with observed one with RMSE 0.521 and 5.57, respectively.     

Coccolithophores from the central Arabian Sea: Sediment trap results

Sediment trap samples collected from a depth of 1018 m in the Central Arabian Sea Trap (CAST) at 14°28.2′N, 64°35.8′E were analyzed for temporal variation of coccolithophore fluxes from October 1993 to August 1994. Out of the twenty species of coccolithophores encountered,Gephyrocapsa oceanica, Emiliania huxleyi, Umbilicosphaera sibogae andUmbellosphaera irregularis were the most abundant. The total coccolithophore fluxes ranged from 28.5 × 10⁶m^-2d^-1 to 50.3 × 10⁶m^-2d^-1 showing seasonality with higher fluxes during the northeast (NE) monsoon and lower fluxes during the spring intermonsoon. The higher fluxes were attributed to the enhancement of primary production in the central Arabian Sea due to southward extent of nutrients from the northeast Arabian Sea by the prevailing surface currents. Similarly, the occurrences of relatively lower coccolithophore fluxes during the spring intermonsoon and southwest (SW) monsoon were attributed to the low nutrients in the warm, shallow surface mixed layer and downwelling to the south of Findlater Jet respectively in the central Arabian Sea. Some of the coccolithophore species such asE. huxleyi, G. oceanica, Calcidiscus leptoporus andUmbellosphaera tenuis showed signs of dissolution.     

Estimation of Sediment Properties during Benthic Impact Experiments

Sediment properties, such as water content and density, have been used to estimate the dry and wet weights, as well as the volume of sediment recovered and discharged, during benthic impact experiments conducted in the Pacific and Indian Oceans. The estimates show that the weights of dry (355-1332 t) and wet sediment (1651-4888 t) recovered during the experiments, are not only a function of the total duration and distance covered during the experiment, but also depend on water content and density of the sediment. Estimation of dry sediment and its volumetric ratio in the discharge, are key parameters for calculating the discharged volume. The estimated volume of wet sediment recovered (1427-4049 m 3 ) as well as discharged (2693-6951 m 3 ) during the experiments, provide important inputs to evaluate the sediment resettlement and migration. Using these estimates, the average depth of excavation on the seafloor, can also been calculated. However, the stages of sediment recovery and discharge are expressed differently in some of the experiments. In order to standardize these, different stages have been identified, and definitions of certain terms have been suggested, for use in the future. The methods of calculating different properties, as well as weight and volume of discharged sediment are described in the paper for use in other applications concerning deep-sea discharges.     

Quantitative estimation of seafloor features from photographs and their application to nodule mining

Methods developed for quantitative estimation of seafloor features from seabed photographs and their application for estimation of nodule sizes, coverage, abundance, burial, sediment thickness, extent of rock exposure, density of benthic organisms, and their lebensspuren have been presented. Digitization of the photographs shows variable nodule size (< 1 to 10 cm), coverage (< 1 to 75%) and abundance (< 1 to 20 kg/m²). Nodule population is inversely proportional to the coverage of the sediment (10–100%) and its thickness (0 to > 10 cm), which causes differential burial (0–100%) of nodules. Correlation between nodule parameters (diameter and coverage) in the photographs and grab recovery is used to evolve empirical relationships for estimating nodule abundance in different seabed settings. The rock outcrops (basalts) with a coverage of 6–100% are the sources of nuclei for the nodules, the distribution of which is controlled by the local topography. Higher concentrations of nodules are observed along the slopes, followed by the crests of seamounts, and are lowest in the valleys and plains. A population density of 6–7 benthic organisms per 100 m² belonging to 7 different phyla is observed, with a high frequency of lebensspuren (4–12 traces/m²) in association with nodules. Estimation of these parameters can be used as important inputs in the design of the nodule collector, as it will have to encounter a variety of seafloor conditions, such as patchy nodule distribution, rock outcrops, steep slopes, and frequent microtopographic changes, as well as benthic life. The distribution and relation of various features with one another can also be used to understand the possible impact of nodule mining on the seabed. Estimates show that for a yield of 3 million tonnes of nodules per year, the volume of sediment disturbed will be between 200 × 10⁷ and 500 × 10⁷ m³over an area of 300–600 km², depending upon the average abundance of nodules. Hence, the nodule collector will have to be a self‐propelled system, with photographic and acoustic sensors, to enable selective mining and avoid unfavorable areas.     

Performance analysis of different predictive models for crop classification across an aridic to ustic area of Indian states

The purpose of this study is to present comparative performance analysis of different machine learning algorithms for large area crop classification. Ten Indian districts with significant rabi crops viz. wheat, mustard, gram, red lentils (masoor) have been selected for the study. Most popular classical ensemble models – bagging/ARCing, random forest (RF), gradient boosting and Importance Sampled Learning Ensemble (ISLE) with traditional single model (decision tree) have been selected for comparative analysis. To incorporate dependency of large area crop in different variables viz. parent material and soil, phenology, texture, topography, soil moisture, vegetation, climate etc., 35 digital layers are prepared using different satellite data (ALOS DEM, Landsat-8, MODIS NDVI, RISAT-1, Sentinental-1A) and climatic data (precipitation, temperature). In rabi season, field survey about crop type is carried out to prepare training data. Performance is evaluated on the basis of marginal rates, F-measure and Jaccard’s coefficient of community, Classification Success Index and Agreement Coefficients. Score is calculated to rank the algorithm. RF is best performer followed by gradient boosting for crop classification. Other ensemble methods ARCing, bagging and ISLE are in decreasing order of performance. Traditional non-ensemble method decision tree scored higher than ISLE.