Rain Rate Retrieval Algorithm for Conical-Scanning Microwave Imagers Aided by Random Forest, RReliefF, and Multivariate Adaptive Regression Splines (RAMARS)

This paper proposes a rain rate retrieval algorithm for conical-scanning microwave imagers (RAMARS), as an alternative to the NASA Goddard profiling (GPROF) algorithm, that does not rely on any a priori information. The fundamental basis of the RAMARS follows the concept of the GPROF algorithm, which means, being consistent with the Tropical Rainfall Measuring Mission (TRMM) precipitation radar rain rate observations, but independent of any auxiliary information. The RAMARS is built upon the combination of state-of-the-art machine learning and regression techniques, comprising of random forest algorithm, RReliefF, and multivariate adaptive regression splines. The RAMARS is applicable to both over ocean and land as well as coast surface terrains. It has been demonstrated that, when comparing with the TRMM Precipitation Radar observations, the performance of the RAMARS algorithm is comparable with the 2A12 GPROF algorithm. Furthermore, the RAMARS has been applied to two cyclonic cases, hurricane Sandy in 2012, and cyclone Mahasen in 2013, showing a very good capability to reproduce the structure and intensity of the cyclone fields. The RAMARS is highly flexible, because of its four processing components, making it extremely suitable for use to other passive microwave imagers in the global precipitation measurement (GPM) constellation

An introduction to factor analysis for radio frequency interference detection on satellite observations

A novel radio frequency interference (RFI) detection method is introduced for satellite-borne passive microwave radiometer observations. This method is based on factor analysis, in which variability among observed and correlated variables is described in terms of factors. In the present study, this method is applied to the Tropical Rainfall Measuring Mission (TRMM)/TRMM Microwave Imager (TMI) and Aqua/Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) satellite measurements over the land surface to detect the RFI signals, respectively, in 10 and 6 GHz channels. The RFI detection results are compared with other traditional methods, such as spectral difference method and principal component analysis (PCA) method. It has been found that the newly proposed method is able to detect RFI signals in the C- and X-band radiometer channels as effectively as the conventional PCA method

Assessment of SMOS Soil Moisture Retrieval Parameters Using Tau-Omega Algorithms for Soil Moisture Deficit Estimation

Soil Moisture and Ocean Salinity (SMOS) is the latest mission which provides flow of coarse resolution soil moisture data for land applications. However, the efficient retrieval of soil moisture for hydrological applications depends on optimally choosing the soil and vegetation parameters. The first stage of this work involves the evaluation of SMOS Level 2 products and then several approaches for soil moisture retrieval from SMOS brightness temperature are performed to estimate Soil Moisture Deficit (SMD). The most widely applied algorithm i.e. Single channel algorithm (SCA), based on tau-omega is used in this study for the soil moisture retrieval. In tau-omega, the soil moisture is retrieved using the Horizontal (H) polarisation following Hallikainen dielectric model, roughness parameters, Fresnel's equation and estimated Vegetation Optical Depth (tau). The roughness parameters are empirically calibrated using the numerical optimization techniques. Further to explore the improvement in retrieval models, modifications have been incorporated in the algorithms with respect to the sources of the parameters, which include effective temperatures derived from the European Center for Medium-Range Weather Forecasts (ECMWF) downscaled using the Weather Research and Forecasting (WRF)-NOAH Land Surface Model and Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) while the s is derived from MODIS Leaf Area Index (LAI). All the evaluations are performed against SMD, which is estimated using the Probability Distributed Model following a careful calibration and validation integrated with sensitivity and uncertainty analysis. The performance obtained after all those changes indicate that SCA-H using WRF-NOAH LSM downscaled ECMWF LST produces an improved performance for SMD estimation at a catchment scale

Landscape transform and spatial metrics for mapping spatiotemporal land cover dynamics using Earth Observation data-sets

Analysis of Earth observation (EO) data, often combined with geographical information systems (GIS), allows monitoring of land cover dynamics over different ecosystems, including protected or conservation sites. The aim of this study is to use contemporary technologies such as EO and GIS in synergy with fragmentation analysis, to quantify the changes in the landscape of the Rajaji National Park (RNP) during the period of 19 years (1990–2009). Several statistics such as principal component analysis (PCA) and spatial metrics are used to understand the results. PCA analysis has produced two principal components (PC) and explained 84.1% of the total variance, first component (PC1) accounted for the 57.8% of the total variance while the second component (PC2) has accounted for the 26.3% of the total variance calculated from the core area metrics, distance metrics and shape metrics. Our results suggested that notable changes happened in the RNP landscape, evidencing the requirement of taking appropriate measures to conserve this natural ecosystem

Integration Of TRMM Rainfall In Numerical Model For Pesticide Prediction In Subtropical Climate

Rain gauge data in developing countries are usually very limited, which constrains most of the hydrological modelling applications. The satellite based rainfall estimates could be a promising choice and hence can be used as a surrogate to ground-based rainfall. However, the usefulness of these products needs to be evaluated for hydrological application such as for pesticide predictions. The present study compares the contaminant transport simulation with the utilization of Tropical Rainfall Measuring Mission (TRMM) rainfall compared with rain gauge data from the field site. Through this study, transport trends of the pesticide, Thiram, a dithiocarbamate, at different time and depth in the fields under real field conditions for the wheat crop were compared to the numerical simulations using HYDRUS- 1D with the input of daily rainfall from the TRMM. The daily rainfall from TRMM has been utilized to simulate the pesticide concentration up to 60 cm vertical soil profile with the intervals of 15 cm. The simulated soil moisture content using ground based rainfall and TRMM derived rainfall measurements indicate an agreeable goodness of fit between the both. The overall analysis reveals that TRMM rainfall is promising for soil pesticide prediction in absence of ground based measurements of soil pesticide. Further, comparison of the model to measured field data of pesticides movement indicates that the modelling approach can provide reliable and useful estimates of the mass flux of water and non-volatile pesticide in vadose zone. Thus, the satellite-based rainfall products could also be useful for policy makers and planners while controlling inappropriate pesticide application under saturated and deficit soil moisture conditions

Early Blight Disease Management of Tomato through Use of Native Bioresources in Mid Hills of Himachal Pradesh

Early blight of tomato also known as target leaf spot disease caused by Alternaria solani (Ellis and Martin) Jones and Grout is important disease of tomato. To manage the disease, six plant origin pesticides and cow urine based bioresource were assessed under in vitro and field conditions. The results of present investigations revealed that cow urine and Roylea elegans (kadu) showed cent per cent mycelial growth inhibition followed by Justicia adhatoda, Vitex negundo and Allium sativum which resulted in 88.15, 84.44 and 80.00% growth inhibition at 15% concentration. To study the effect of these bioresources under field conditions five foliar sprays were given at ten days interval after commencement of disease. It was revealed that foliar application of bioresources, significantly reduced the disease severity and increased the yield. Five foliar sprays of Allium satium (garlic) at ten days interval was found most effective in decreasing the severity of disease to 56.12% followed by Justicia adhatoda (basunti), Melia azedarach (darek) and cow urine which resulted in 44.24, 38.71 and 37.92% reduction of disease respectively, whereas, leaf water extract of Vitex negundo (banah) was least significantly effective and resulted in 28.50% reduction of disease. Five successive sprays of Allium sativum (garlic) also resulted in maximum yield of fruits (18.40 kg plot-1) followed by Justicia adhatoda (basunti) (13.52 kg plot-1), cow urine (13.00 kg plot-1) and Nerium indicum (12.70 kg plot-1

A case of brain abscess caused by actinomyces mimicking glioma: A rare presentation

Actinomycosis is a rare, chronic and slowly progressive granulomatous disease, caused by filamentous Gram-positive anaerobic bacteria from Actinomycetaceae family (genus Actinomyces) belonging to endogenous flora of mucous membranes. Actinomycosis infection to the Central Nervous System is generally secondary to hematogenous dissemination from the primary infection in the lung, abdomen, pelvis or by continuity in a cervical, oral or facial infection, since it is closely related to mycobacteria in the mouth and gastrointestinal tract. Actinomyces abscess of the brain can at times be confused with space-occupying lesions (SOL), tuberculosis as was seen in this case. A biopsy is essential for the diagnosis as if treated early these patients have a good prognosis. We present a case of 28 years male presented with frontal SOL which came out as actinomycosis on biopsy