Mehul R. Pandya

Bandpass solar exoatmospheric irradiance and Rayleigh optical thickness of sensors on board Indian Remote Sensing Satellites-1B, -1C, -1D, and P4

This paper reports the bandpass solar exoatmospheric irradiance and Rayleigh optical thickness for six categories of sensors, namely, the linear imaging self scanning sensor (LISS)-I, -II, -III, the wide field sensor (WiFS), the panchromatic (PAN), and the ocean color monitor (OCM) which have been flown on Indian Remote Sensing Satellites (IRS)-1B, -1C, -1D, and -P4 platforms, based on laboratory measurements of relative spectral response (RSR) of each band. These estimates have been compared with similar contemporary international sensors.

Leaf area index retrieval using IRS LISS-III sensor data and validation of the MODIS LAI product over central India

This paper reports results on the LAI Retrieval and Validation Experiment (LRVE) that was conducted for two agricultural areas in Central India during the winter season of 2001-2002. The study aimed at relating field measurements of leaf area index (LAI) to spaceborne Indian Remote Sensing Satellite (IRS) Linear Imaging Self Scanning Sensor-III (LISS-III) data, preparation of site-level LAI maps, and validation of Moderate Resolution Imaging Spectroradiometer (MODIS) 1-km LAI global fields. Measurements of field-level LAI, aerosol optical thickness and water vapor were carried out on the day of LISS-III overpasses. Empirical models based on the site-specific LAI-vegetation index relation were developed and used to generate 23-m resolution LAI maps for two sites (Indore and Bhopal) covering 30 kmtimes30 km. These LAI images were degraded to 1-km spatial resolution and used for validation of the version 3 and 4 MODIS LAI products (MOD15A2). The results indicate a positive correlation (r=0.78) between LAI derived from LISS-III data and MODIS data. However an overestimate by a factor of 1.6 to 2.5 in the version 3 MODIS product is observed with root mean square error (RMSE) ranging from 0.20 to 1.26. The factor of overestimation reduces significantly by 50% and RMSE by 40% when version 4 MODIS LAI was analyzed. The improvement in accuracy was observed to be associated with the change in algorithm path adopted for retrieving version 3 and 4 MODIS LAI. Analysis of the MODIS land cover product that is an input in the MODIS LAI retrieval algorithm indicated errors in assigning land cover classes for the study sites, which could be one of the sources of error in MODIS LAI product

Spectral characteristics of sensors onboard IRS-1D and P6 satellites: Estimation and their influence on surface reflectance and NDVI

This paper reports the results of a modeling study carried out with two objectives, (1) to estimate and compare effective spectral characteristics (central wavelength, bandwidth and bandpass exo-atmospheric solar irradiance Eo) of various spectral channels of LISS-III, WiFS, LISS-III*, LISS-IV and AWiFS onboard Indian Remote Sensing Satellites IRS-ID and P6 using moment method based on the laboratory measurements of sensor spectral response, and (2) to quantify the influence of varying sensor spectral response on reflectance and Normalized Difference Vegetation Index (NDVI) measurements using surface reflectance spectra corresponding to different leaf area index conditions of crop target obtained through field experiment. Significant deviation of 4 to 14 nm in central wavelength and 1.6 to 14.07 nm in spectral width was observed for the corresponding channel of IRS sensors. Coefficient of variation of the order of 0.1 to 1.11% was noticed in Eo among various IRS sensors, which could induce a difference of 0.72 to 3.35% in the estimation of top of atmosphere reflectance for crop target. The variation in spectral response of IRS sensors implied a relative difference of the order of 0.91 to 3.38% in surface reflectance and NDVI measurements. Polynomial approximations are also provided for spectral correction that can be utilized for normalizing the artifacts introduced due to differences in spectral characteristics among IRS sensors.

Quantification and comparison of spectral characteristics of sensors on board Resourcesat-1 and Resourcesat-2 satellites

This letter presents quantification and comparison of spectral characteristics of three similar sensors, namely Linear Imaging Self-Scanning Sensor (LISS)-III, LISS-IV and Advanced Wide Field Sensor (AWiFS) on board Indian Remote Sensing (IRS) satellites Resourcesat-1 (RS1) and Resourcesat-2 (RS2). Theoretical estimates of three critical spectral characteristics, effective bandwidth (Δλ), central wavelength (λc) and bandpass solar exoatmospheric irradiance (E 0), have been computed based on the laboratory measurements of the relative spectral response (RSR) for each spectral band. A comparative analysis between two estimates computed through the moment method and the full width at half maximum (FWHM) method showed noticeable differences of the order of 4.4–14.1 nm in Δλ and λc for the corresponding band of Resourcesat sensors. A coefficient of variation (CV) of the order of 0.1–1.3% was noticed in E 0 among various Resourcesat sensors, which could induce a difference of 0.26–3.27% in the estimation of top-of-atmosphere (TOA) reflectance. The variations in spectral characteristics among the Resourcesat sensors should be taken into account when comparisons are made between measurements from sensors on board RS1 and RS2 satellites, for example, for long-term monitoring of the Earths environment.

Estimating minimum and maximum air temperature using MODIS data over Indo-Gangetic Plain

Spatially distributed air temperature data are required for climatological, hydrological and environmental studies. However, high spatial distribution patterns of air temperature are not available from meteorological stations due to its sparse network. The objective of this study was to estimate high spatial resolution minimum air temperature (Tmin) and maximum air temperature (Tmax) over the Indo-Gangetic Plain using Moderate Resolution Imaging Spectroradiometer (MODIS) data and India Meteorological Department (IMD) ground station data. Tmin was estimated by establishing an empirical relationship between IMD Tmin and night-time MODIS Land Surface Temperature (Ts). While, Tmax was estimated using the Temperature-Vegetation Index (TVX) approach. The TVX approach is based on the linear relationship between Ts and Normalized Difference Vegetation Index (NDVI) data where Tmax is estimated by extrapolating the NDVI-Ts regression line to maximum value of NDVImax for effective full vegetation cover. The present study also proposed a methodology to estimate NDVImax using IMD measured Tmax for the Indo-Gangetic Plain. Comparison of MODIS estimated Tmin with IMD measured Tmin showed mean absolute error (MAE) of 1.73°C and a root mean square error (RMSE) of 2.2°C. Analysis in the study for Tmax estimation showed that calibrated NDVImax performed well, with the MAE of 1.79°C and RMSE of 2.16°C.

Effect of WiFS viewing geometry on crop reflectance: a simulation study using SAIL model.

The Wide-Field Sensor (WiFS) on board Indian Remote Sensing Satellites (IRS-1C, IRS-P3 and IRS-1D) has a wide swath (∼810 km) and is thus able to provide high repetivity (5/3 days) data acquired from different orbits. The impact of variable viewing geometry from WiFS on crop reflectance was investigated for Delhi (77.2°E and 28.58°N) for all 122 possible acquisitions by IRS-1C during 1997. Using the SAIL simulation model, top-of-canopy red and near-infrared reflectances were simulated for partial and closed crop canopies over soils of different brightness. Results show significant variations in red and near-infrared reflectances. The effects are higher in partial canopies, and they are observed in Normalized Difference Vegetation Index (NDVI) also. For partial canopy, the seasonal variations with solar zenith angle are also significant. For closed canopy, these variations are less, and computed NDVI is relatively insensitive to viewing geometry.

Development of a scheme for atmospheric correction of Resourcesat-2 AWiFS data

a b s t r a c t This paper presents a method called SACRS2, a scheme for atmospheric correction of RS2-AWiFS (Resourcesat2-Advanced Wide Field Sensor) data. The SACRS2 is a computationally fast scheme devel- oped from a physics-based detailed radiative transfer model 6SV for correcting large amount of data from the high-repetivity AWiFS sensor. The method is based on deriving a set of equations with coefficients which depend on the spectral bands of the RS2-AWiFS sensor through forward signal simulations by 6SV. Semi-empirical formulations provided in the SMAC method with a few improvements have been used to describe various atmospheric interactions. A total of 112 coefficients for different equations are determined using the best fit equations against the computations of the 6SV. After the specific coeffi- cients for the RS2-AWiFS spectral bands are determined, the major inputs of the scheme are raw digital numbers recorded by RS2-AWiFS sensor, atmospheric columnar water vapour content, ozone content, aerosol optical thickness at 550 nm and viewing-illumination conditions. Results showed a good per- formance of the SACRS2 with a maximum relative error in the SACRS2 simulations ranged between 1% for a reflectance of 0.5 and 8.6% for reflectance of 0.05 with respect to 6SV computations. Validation of retrieved surface reflectance using the SACRS2 scheme with respect to in-situ measurements at two sites indicated a capability of this scheme to determine the surface reflectance within 10%. This is a first of its kind scheme developed for the atmospheric correction of any Indian Remote Sensing satellite data. A package containing the SACRS2 software is available on the MOSDAC website for the researchers.

Retrieval of surface reflectance from Resourcesat-2 AWiFS, LISS-3, and LISS-4 data using SACRS2 scheme

Satellite based multispectral imagery contains various quantitative information related to surface and atmosphere. To extract the accurate information about surface, we need to correct atmospheric influence which is introduced by the atmosphere. Atmospheric correction of multispectral satellite imagery is an important prerequisite to derive geophysical parameters from satellite data. In this study surface reflectance is retrieved using the Scheme for Atmospheric Correction of Resourcesat-2 (SACRS2). The SACRS2 is physics based atmospheric correction scheme developed at Space Applications Centre (SAC), ISRO based on radiative transfer model 6SV (The Second Simulation of the Satellite Signal in the Solar Spectrum vector version). SACRS2 method is easily applicable for atmospheric correction of multispectral data. A detail analysis has been carried out to retrieve surface reflectance from Resourcesat-2 AWiFS, LISS-3 and LISS-4 data using SACRS2 method. The retrieved surface reflectance from SACRS2 for AWiFS, LISS-3 and LISS-4 have been compared with in-situ measurements. The comparison showed a good match of reflectance derived by SACRS2 scheme with the in-situ measurements.

Detection of heat wave using Kalpana-1 VHRR land surface temperature product over India

Heat Waves can have notable impacts on human mortality, ecosystem, economics and energy supply. The effect of heat wave is much more intense during summer than the other seasons. During the period of April to June, spells of very hot weather occur over certain regions of India and global warming scenario may result in further increases of such temperature anomalies and corresponding heat waves conditions. In this paper, satellite observations have been used to detect the heat wave conditions prevailing over India for the period of May-June 2015. The Kalpana-1 VHRR derived land surface temperature (LST) products have been used in the analysis to detect the heat wave affected regions over India. Results from the analysis shows the detection of heat wave affected pixels over Indian land mass. It can be seen that during the study period the parts of the west India, Indo-gangetic plane, Telangana and part of Vidarbh was under severe heat wave conditions which is also confirmed with Automatic Weather Station (AWS) air temperature observations.