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SCATSAT-1 measures the backscattering coefficient over land surfaces, which is a function of vegetation structure, surface roughness, soil moisture content, incidence angle and dielectric properties of vegetation. Scatterometer image reconstruction techniques provide fine resolution data to explore the emerging applications over land by using ambiguous backscatter from land. In this paper, 2 km resolution products of ISRO's scatterometer SCATSAT-1 are exploited for land target detection, rice crop phenology stages detection for kharif and rabi seasons and estimation of relative soil moisture over parts of India. Temporal and spatial backscatter changes are due to seasonal and changes in Land Use Land Cover. Crop phenology stages such as transplanting, maximum tillering, panicle emergence and physiological maturity stages are identified by analysing SCATSAT-1 time series along with NDVI and findings are supported by appropriate ground truth observations and crop cutting experiment (CCE) data. The relative soil moisture change detection is validated with in situ ground truth measurements using Hydraprobe, carried for SCATSAT-1 ascending and descending passes. [ABSTRACT FROM AUTHOR]

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]

Abstract: The AMSR-E observed brightness temperatures over India have been analyzed to study the impact of manmade radio frequency interference (RFI) sources for the two dry months April and May from 2003 to 2010. It is observed that the brightness temperature values far exceed 300K over different locations of India indicating the presence of RFI. It is pronounced over the urban areas. The regions over Punjab, Haryana and Delhi show a marked increase in spread of the RFI affected areas. The RFI affected areas have increased from 15% to 30% from 2003 to 2007 and decreased thereafter. A maximum brightness temperature of 353K is observed in April 2007 indicating very high level of RFI. [Copyright &y& Elsevier]

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]

The time of forcing of spatial LAI to crop models at single or multiple stages is important to simulate crop biomass and yield in varying agro-climatic conditions and scales. The high temporal resolution (5-day) by Advanced Wide Field Sensor (AWiFS) on-board Resourcesat-1 Satellite IRS-P6 with 56 m spatial resolution and large swath (740 km) has substantially increased the availability of regional clear sky optical remote sensing data. The present study aimed at developing empirical vegetation index VI-LAI models for wheat using AWiFS optical data in four bands and in-situ measurements sampled over five different agro-climatic regions (ACRs) during 2005-2006 followed by validation during 2006-2007. While nonlinear relations exist for all the three normalized indices such as normalized difference vegetation index (NDVI), normalized difference water index (NDWI) and Green NDVI, linear relation was the best fit for ratio vegetation index (RVI). Both NDVI and RVI models generally showed better correlation ranges (0.65-0.84 for NDVI and 0.37-0.76 for RVI) than other indices. The common NDVI-LAI model was found to produce lower root mean square errors (RMSE) between 0.5 and 1.1 from pooled model than those between 0.5 and 1.32 from regional models. The rate of substantial increase in errors from NDVI-LAI model (RMSE of modeled LAI: 0.85 to 1.28) as compared to RVI-LAI model (RMSE of modeled LAI: 1.12 to 1.17) at LAI greater than 3, than below 3 revealed the early saturation of NDVI than RVI. It is therefore recommended that LAI estimates can be used to force crop simulation model upto early vegetative stage based on NDVI and maximum vegetative to reproductive stages based on RVI. [ABSTRACT FROM AUTHOR]

An algorithm for night time fog detection using satellite data was used to study 2009 and 2010 fog episodes over the North Indian plains. The algorithm employs a bispectral thresholding technique involving brightness temperature difference (BTD) between two spectral channels: 3.9 and 10.75 µm, combining radiative transfer simulations and satellite data. The prolonged fog episode of January 2010 was analysed in detail using this algorithm in conjunction with the meteorological parameters and it was found that upper tropospheric long waves passed over this region during the fog episode. These waves affect the circulation close to the surface and appear to have played an important role in the formation and persistence of fog during 2010. The present thresholding method can be used to monitor night time fog over the Indian subcontinent on an operational basis using the forthcoming geostationary satellite INSAT-3D. Copyright © 2011 Royal Meteorological Society [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]

One of the important payloads on-board the joint Indo-French Megha-Tropiques satellite is the Scanner for Radiation Budget (ScaRaB). It is dedicated for monitoring the Earth Radiation Budget (ERB) parameters at Top of Atmosphere (TOA). In this article, details of the algorithm used for computing two important ERB components, namely TOA reflected shortwave and emitted longwave fluxes from ScaRaB radiance measurements are presented along with preliminary cross-satellite validation results. The ScaRaB flux computation algorithm is similar to the one used in the ERB Experiment. The maximum likelihood estimation algorithm is used for identification of different Earth scenes and cloud types. First, the raw radiances are corrected for spectral filtering effects followed by implementation of scene-type dependent angular correction to deduce shortwave and longwave fluxes. The instantaneous TOA flux data derived from ScaRaB radiance measurements are compared with similar data available from Clouds and Earth's Radiant Energy System (CERES) onboard Aqua and Terra satellites. Preliminary comparison confined to two months period (September-October 2012) using the two satellites suggests that the ScaRaB data are in good agreement with the CERES data. The bias-corrected root mean square difference in ScaRaB longwave flux is 4.7 and 5.3 Wm-2 with respect to CERES on-board Aqua and Terra satellites respectively. For ScaRaB shortwave flux, it is 25.9 Wm-2 and 25.5 Wm-2 with respect to CERES onboard Aqua and Terra satellites respectively. A detailed comparison of ScaRaB TOA flux data with more than one-year of CERES data is already initiated. Results from the preliminary comparison exercise suggest that the ScaRaB data can be used with confidence for ERB studies. [ABSTRACT FROM AUTHOR]