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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. [ABSTRACT FROM AUTHOR]

Soil moisture is a very important boundary parameter in numerical weather prediction at different spatial and temporal scales, controlling the exchange of water and energy between the atmosphere and land surface. Satellite-based microwave radiometric observations are considered to be the best for soil moisture remote sensing because of their high sensitivity, as well as their all-weather and day–night observation capabilities with high repeativity. In this study, an attempt has been made to assess the Advanced Microwave Scanning Radiometer--Earth Observing System (AMSR-EOS) soil moisture product over India. The AMSR-E soil moisture product has been assessed using in situ soil moisture observations made by the India Meteorological Department (IMD) during the monsoon period (May–August) for the years 2002–2006 over 18 meteorological stations. Apart from assessing AMSR-E soil moisture retrieval accuracy, this study also investigates the effect of vegetation, topography and coastal water contamination, and determines the regions where the AMSR-E soil moisture product could be useful for different applications. [ABSTRACT FROM AUTHOR]

A study was carried out to estimate field-scale Leaf Area Index (LAI) using fine resolution polar orbiting IRS-1D LISS-III sensor data at 23 m spatial resolution. Three cloud-free scenes on 8 January, 2002 (D1), 30 January, 2002 (D2) and 15 January, 2003 (D3) over two sites in Gujarat, India, were acquired. The subscenes encompassing the study area were extracted and geo-registered. Surface reflectances in the red (0.62-0.68 µm) and near-infrared (NIR) (0.77-0.86 µm) bands were generated using 6S atmospheric correction code and coincident ground measurements on aerosol and water vapour. Normalized difference vegetation index (NDVI), simple ratio (SR) and soil-adjusted vegetation index (SAVI) were computed from the reflectances in the red and NIR. A total of 70 mean measured LAI datasets on wheat and tobacco were used for regression analysis and empirical models were developed between LAI and three vegetation indices (VI). Both exponential and power models gave R² between 0.53 and 0.61 except for D2 (R² between 0.04 and 0.11) when wheat was mostly at the post-anthesis stage and the VI-LAI relation seems to be influenced by canopy geometry and angular distribution of leaves. The analysis indicated that with the soil type of the study sites being different, the SAVI-based model had a smaller rms. error (R² = 0.496 and rms. error = 0.685) in estimation of LAI when compared with the SR- (R² = 0.478, rms. error = 0.698) and NDVI- (R² = 0.491, rms. error = 0.689) based models. The LAIs for the study region were estimated by inversion of empirical models and validated against ground-measured data. [ABSTRACT FROM AUTHOR]

Fog is a meteorological phenomenon in which cloud has its base near to the surface which disrupts public health and transportation due to decrease in visibility. In the present study, a collective method has been implemented for detection of fog/low clouds over the Delhi Earth Station during fog season for the years 2016 and 2017. A detailed comparison has been performed with the use of ground-based cloud-base height measurements from the Ceilometer and the Indian National Satellites (INSAT-3D/3DR)-retrieved fog product. Fog/low clouds have been effectively detected by both the Ceilometer and the INSAT-3D/3DR during single-layer cloud in the range of 0-200 m in most of the cases. Since the INSAT-3D/3DR fog product is derived under clear sky condition, fog detection during multilayer clouds is a challenging task. Results indicate that the Ceilometer can capture fog/low clouds effectively even in presence of single- and multilayer clouds. Remote-sensing technology can provide better opportunity by providing complete temporal and spatial coverage of fog. The present technique which incorporates both ground-based Ceilometer and space-based INSAT-3D/3DR measurements is observed to be more promising than other conventional methods for an improved detection and monitoring of fog/low clouds. [ABSTRACT FROM AUTHOR]