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Wheat growth profile based yield models for 12 districts of Punjab State and 16 districts of Haryana State have been developed using the normalised difference vegetation index (NDVI) derived from NOAA-11 AVHRR data of the 1993-94 cropping season. Atmospheric normalisation of AVHRR data was performed prior to deriving district-level area weighted average NDVI (AWANDVI). The invariant growth profile model suggested by Badhwar was fitted and spectral emergence date, maximum vegetative vigour, peak day value of profile, growth rate and senescence rate, area under the curve, etc. were derived. These parameters were related to the reported district-level wheat yields using multiple regression analysis. A field study was also conducted using a handheld spectro-radiometer at the research station of Punjab Agricultural University (PAU), Ludhiana. From this field experimental data, wheat growth profile parameters were derived which were compared with satellite based parameters. Inversion of the models was carried out to evaluate the results by comparing the reported and predicted wheat yields. The results indicate highly significant fitting of the NDVI profile to the Badhwar model as indicated by multiple linear correlation coefficients and Fisher test. A significant relationship between district-level wheat yields and fractional area under the curve was also observed. The overall correlation of 0.82 for Punjab and Haryana states was obtained between reported yield and growth profile derived parameters. Atmospheric normalisation resulted in improvement of prediction model statistics (R increased from 0.42 to 0.86). Evaluation of the models indicated that 10 out of 16 districts of Haryana State and 9 out of 12 districts of Punjab State showed relative deviations within 10% between reported and model predicted wheat yields.

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This paper presents the potential for soil moisture (SM) retrieval using Sentinel-1 C-band Synthetic Aperture Radar (SAR) data acquired in Interferometric Wide Swath (IW) mode along with Land Surface Temperature (LST) estimated from analysis of LANDSAT-8 digital thermal data. In this study Sentinel-1 data acquired on 27 February 2020 was downloaded from Copernicus website and LANDSAT-8 OLI data acquired on 24 February 2020 from the website https://earthexplorer.usgs.gov/.The soil samples were collected from 70 test fields in different villages of three talukas for estimating soil moisture content using the gravimetric method. The Sentinel-1 SAR microwave data was analysed using open source tools of Sentinel Application Platform (SNAP) software for estimation of backscattering coefficient. Land surface temperature estimated using Landsat-8 thermal data. The Landsat-8, Thermal infrared sensor Band-10 data and operational land imager Band-4 and Band-5 data were used in estimating LST. The Soil Moisture Index (SMI) for all field test sites was computed using the LST values. The regression analysis using σ0VV and σ0VH polarization with soil moisture indicated that σ0VV polarization was more sensitive to soil moisture content as compared to σ0VH polarization. The multiple regression analysis using field measured soil moisture (MS %) as dependent variable, and σ0VV and SMI as independent variable was carried which resulted in the coefficient of determination (R2) of 0.788, 0.777 and 0.778 for Godhra, Goghamba and Kalol talukas, respectively. These linear regression equations were used to compute the predicted soil moisture in three talukas.

India has witnessed tremendous industrialization in the last five decades. This has led to migration of masses from rural areas towards cities for jobs and businesses. With increase in the population, the demand for residences has also increased which has escalated growth of slum areas and haphazard planning in suburbs. City of Ahmedabad is one such urban metropolis in the state of Gujarat, India. Being the financial capital of Gujarat, population of the city has increased many folds since 1980s. Congested and unsustainable planning and increasing in the emissions from industries and vehicles in certain areas of the city have given birth to many climatic issues. One of these major problems is the Urban Heat Island (UHI) phenomena. This has increased the temperature by four to five degrees and has also severely affected air quality. Satellite based Remote sensing data can provide temperature information of various land use classes. Remote Sensing data along with in-situ surface measurements can help to identify urban heat island intensities and hotspots in the cities. A study on heat island characterization and isotherm mapping was taken up in Ahmedabad City. In the present study, Surface Heat Island (SHI) effect is studied using satellite data along with field measurements. Thermal infrared data from Landsat ETM band-6 have been effectively used for monitoring temperature differences of various land use classes in urban areas. The study aims to identify and study the urban hot spots using the data from LANDSAT-5 and field data collected using IR Gun in various zones of Ahmedabad City. The results of this study * Corresponding author. R. Joshi et al. 275 indicated that the surface temperature near industrial areas and dense urban areas was higher as compared to other suburban areas in the Ahmedabad City.