The experimental Indian Nano-Satellite (INS)-2TD acquires data in a long-wave infrared (7 to 16 μm) region with a fairly good spatial resolution of 175 m. Our study focuses on the retrieval of land surface temperature (LST) using a physics-based generalized single-channel (GSC) algorithm for the INS-2TD observations. A total of 597,240 at-sensor radiance simulations were carried out using moderate resolution atmospheric transmittance 5.3 radiative transfer model for varying conditions pertaining to surface, atmosphere, and sensor geometry to develop and validate the GSC algorithm for broadband INS-2TD sensor. The result from simulated test dataset shows the algorithm’s consistent performance with root-mean-square error (RMSE) of 2.87 K and 0.97 R2. Pixel-to-pixel intercomparison of retrieved LST and standard LST product of Indian National Satellite (INSAT)-3D indicates a good agreement with 0.99 R2 and range of RMSE from 1.17 to 4.78 K over the six selected datasets of South-Asia. The results reveal that the retrieved INS-2TD LST products perform very well, except having a hot bias of around 4.78 K compared to INSAT-3D LST over the Himalayan mountains due to the topographic effect. These results show the overall reasonable accuracy of the retrieved LST over heterogeneous surfaces and highly dynamic atmospheric conditions.
Advanced remote sensing technologies, such as light detection and ranging (LiDAR), offer significant potential to mapping the alpine treeline ecotone (ATE) based on its actual definition (tree height ≥ 3 m) and contribute to the generation of baseline data for future change detection investigations. We propose an approach for combining LiDAR-derived absolute tree height data with elevation data to delineate the ATE in Uttarakhand, India. The approach was implemented using observations from the recently launched Global Ecosystem Dynamics Investigation system and validated with field measurements. The LiDAR-derived treeline was compared with the traditional normalized difference vegetation index (NDVI) treeline. The treeline derived from LiDAR was found to have root mean square error of ∼60 m with respect to the ground verified treeline location. The NDVI treeline was overestimated in comparison to the LiDAR treeline by an average surface distance of 290, 232, 257, and 237 m in the south, north, west, and east aspects, respectively. It is observed that the overestimation was higher at the lowest and highest elevation zones. We prove that LiDAR-based treeline mapping is an efficient method to delineate alpine treelines at a landscape scale.
Our study demonstrates the potential of Airborne Visible/Infrared Imaging Spectrometer Next Generation (AVIRIS-NG) hyperspectral data and radiative transfer modeling to retrieve the atmospheric carbon dioxide (CO2) concentration from point source emission of one of India’s major coal-fed super thermal power plants at Kota, Rajasthan, India. AVIRIS-NG data with synchronous in situ measurements were collected as a part of the first ISRO-NASA joint airborne hyperspectral science campaign in India. The method utilized in our study includes theoretical simulations of at-sensor radiance in AVIRIS-NG spectral bands through the atmospheric radiative transfer model MODTRAN. Simulations were performed for the specific scene-sensor-atmospheric conditions pertaining to AVIRIS-NG overpass over Kota site. To eliminate the interfering effect of atmospheric water vapor with CO2 concentrations, simulations pertaining to variable water vapor values varying from 0.25 to 4.5 g / cm2 were also carried out, which in turn were utilized to normalize the water vapor effects. Based upon the simulation results pertaining to the specific absorption bands of CO2 in the shortwave infrared spectral range of AVIRIS-NG radiance data, a theoretical model was established between continuum interpolated band ratio (CIBR) and CO2 concentration. The CIBR − CO2 model coefficients for each of the water vapor subranges were then applied to AVIRIS-NG L1 radiance data to obtain the CO2 concentration map of the study area. A distinct CO2 plume could be detected from the coal-fed Kota Super Thermal Power Station with the CO2 concentration (XCO2) of the order of more than 500 ppmv near the power plant stacks. A range of XCO2 from 400 to 550 ppmv was observed within the scene. Our study has provided promising results in mapping the atmospheric CO2 content from the point source using the high-resolution airborne hyperspectral AVIRIS-NG data.
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.
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.
Estimation of atmospheric aerosols by remote sensing is very important because aerosols cool/warm the Earth atmosphere
system through scattering/absorption of solar radiation. The information on aerosol optical thickness (AOT)
is also essential in atmospheric correction of satellite imagery. Due to non-uniformity and extreme reflectivity of land
targets, it is challenging to monitor aerosols over land-surfaces. This paper reports a new approach of estimating AOT
over land using dual viewing angle observations (FORE: 26 degree and AFT: -5 degree) in panchromatic channel (0.50-
0.85μm) of Cartosat-1 satellite. Differential responses of targets observed in two atmospheric path lengths through Fore
and Aft observations of Cartosat-1 were analyzed using atmospheric radiative transfer model (6S-code). A number of
forward simulations over dark to bright targets (having 1-70% reflectivity) were carried out to derive top-of-atmosphere
(TOA) reflectance for various AOT conditions to arrive at a particular reflectance condition called- cross over
reflectance (ρco: a reflectance value for which the difference between TOAAft and TOAFore equals zero). The shift in
position of ρco for dual look angle was modeled as a function of AOT. Using this method, AOT was estimated for two
sites representing clear and turbid atmosphere conditions. A cross over reflectance of 11 percent and 13 percent was
observed for clear and turbid atmospheres, respectively. Corresponding modeled AOT estimates were 0.32 and 0.78,
respectively. Validation of these estimates with the MODIS AOT showed good agreement with 0.26 in clear and 0.68 in
turbid atmosphere case. The present approach enables to retrieve single AOT value for a scene and for known aerosol
meteorology. Initial results are encouraging, however further analyses are in progress to verify it in different atmospheric
conditions.
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