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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]

A time-series-based methodology has been used for the estimation of soil moisture from AMSR-E 10.7-GHz H-polarized brightness temperature over India. The estimated soil moisture has been validated with in situ soil moisture and compared with the NSIDC AMSR-E soil moisture product. The soil moisture estimated using the present algorithm shows improvement over NSIDC soil moisture products over India. The present algorithm shows an root-mean-squared error of 8.8% with in situ soil moisture, which is 12.9% for the NSIDC products over Indian agromet stations. The sensitivity in the soil moisture is captured well from the present algorithm; however, it is limited to regions with low to moderate vegetation cover having low temporal variability. The algorithm is location specific that is tuned over India to provide better estimates than achievable using the NSIDC global algorithm. This will help in improving the mesoscale monsoon prediction of rainfall over India. [ABSTRACT FROM PUBLISHER]

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]