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The extinction-to-backscattering ratio, popularly known as lidar (light detection and ranging) ratio of atmospheric aerosols is an important optical property, which is essential to retrieve the extinction profiles of atmospheric aerosols. Lidar satellite observations can provide the global coverage of atmospheric aerosols along with their vertical extent. NASA's Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) on board the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite is the only space-based platform available, so far, that provides the vertical profiles of extinction due to atmospheric aerosols. A physics-based theoretical approach is presented in the present paper that estimates lidar ratio values for CALIPSO aerosol models, which can be used as inputs to determine the extinction profiles of aerosols using CALIPSO data. The developed methodology was also qualified by comparing it with the lidar ratio values derived using AERONET (AErosol RObotic NETwork) datasets. Lidar ratio values for CALIPSO aerosol models were estimated in the range of 38.72 to 85.98 sr at 532 nm, whereas at 1064 nm lidar ratio varied between 20.11 to 71.11 sr depending upon the aerosol type and their size distributions. Aerosols are compositions of various particles; thus, the presence of water vapour in the atmosphere can affect the optical properties of the aerosols. Thus, the effect of relative humidity on lidar ratio was studied using Optical Properties of Aerosols and Clouds (OPAC) aerosol models, which are the standard aerosol models against the cluster-classified AERONET and CALIPSO aerosol models. Water-soluble particles contribute substantially in clean continental, clean marine, tropical marine and desert aerosol models and are hygroscopic in nature. Hygroscopic sulfate particles dominate the Antarctic aerosols during summertime. In the presence of relative humidity between 0 %–80 %, the lidar ratio values were observed to decrease from 53.59 to 47.13, from 53.66 to 47.15, from 53.70 to 47.16, and from 55.32 to 48.78 sr at 532 nm for clean continental, clean marine, tropical marine, and desert aerosols, respectively, whereas lidar ratio gradually increased from 47.13 to 51, from 47.15 to 51, from 47.16 to 51, and from 48.78 to 51.68 sr, respectively, for these aerosol models when relative humidity was between 80 %–99 %, due to constituent hygroscopic particles. In the case of Antarctic aerosols, the lidar ratio was observed to increase from 57.73 to 97.64 sr due to hygroscopic sulfate particles that backscattered heavily in the presence of water vapour at 532 nm. The soot particles dominate the polluted continental and polluted marine particles, causing an increase in lidar ratio over its corresponding clean counterpart. Similar results were observed at 1064 nm for OPAC aerosol models. [ABSTRACT FROM AUTHOR]

The extinction-to-backscattering ratio, popularly known as lidar ratio of atmospheric aerosols is an important optical property, which is essential to retrieve the extinction profiles of atmospheric aerosols. Lidar satellite observations can provide the global coverage of atmospheric aerosols along with their vertical extent. NASA's Cloud-Aerosol Lidar with Orthogonal Polarisation (CALIOP) on-board Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) is the only space-based platform available so far, that provides the vertical profiles of extinction due to atmospheric aerosols. A physics-based theoretical approach is presented in the present paper that estimates lidar ratio values for CALIPSO aerosol models, which can be used as inputs to determine the extinction profiles of aerosols using CALIPSO data. The developed methodology was also qualified by comparing it with the lidar ratio values derived using AERONET datasets. Lidar ratio for CALIPSO aerosols models were estimated in the range of 38.72 sr to 85.98 sr at 532 nm whereas, at 1064 nm lidar ratio varied between 20.11 sr to 71.11 sr depending upon the aerosol type and their size distributions. Aerosols are compositions of various particles and thus the presence of water vapour in the atmosphere can affect the optical properties of the aerosols. Thus, the effect of relative humidity on lidar ratio was studied using Optical Properties of Cloud and Aerosols software tool (OPAC) aerosol models, which are the standard aerosol models against the cluster classified AERONET and CALIPSO aerosol models. Water soluble particles contribute substantially in clean continental, clean marine, tropical marine and desert aerosol models and are hygroscopic in nature. Hygroscopic sulfate particles dominate the Antarctic aerosols during summertime. In presence of relative humidity between 0 - 80 %, the lidar ratio values were observed to decrease from 53.59 sr to 47.13 sr, 53.66 sr to 47.15 sr, 53.70 sr to 47.16 sr and 55.32 sr to 48.78 sr at 532 nm for clean continental, clean marine, tropical marine and desert aerosols respectively, whereas lidar ratio gradually increased from 47.13 sr to 51 sr, 47.15 sr to 51 sr, 47.16 sr to 51 sr and 48.78 sr to 51.68 sr respectively for these aerosol models when relative humidity was between 80 - 99 %; due to constituent hygroscopic particles. In case of Antarctic aerosols, the lidar ratio was observed to increase from 57.73 sr to 97.64 sr due to hygroscopic sulfate particles that backscattered heavily in presence of water vapour. The soot particles dominate the polluted continental and polluted marine particles causing an increase in lidar ratio over corresponding clean counterpart. Similar results were observed at 1064 nm for OPAC aerosol models. [ABSTRACT FROM AUTHOR]

This manuscript describes reflectance-based vicarious calibration exercise of the Airborne Visible InfraRed Imaging Spectrometer - Next Generation (AVIRIS-NG) sensor during its second phase of observation campaigns over the Indian subcontinent. Vicarious calibration methods are one of the significant approaches that have been practiced successfully for the absolute radiometric calibration of various spaceborne and airborne sensors to ascertain its desired performance in terms of data quality and its accuracy. Calibration campaigns were performed for AVIRIS-NG sensor during its phase-2 campaign overpass at the Desalpar (25th March 2018) and Amarapur (27th March 2018) calibration sites of Gujarat, India. Results from ground-based measurements of atmospheric conditions and surface reflectance including descriptions of the test sites are summarized in this work. Based on the in-situ data and images from the AVIRIS-NG sensor, the at-sensor apparent radiances are simulated using the 6SV radiative transfer model. The comparison shows good agreement between AVIRIS-NG measured radiance and 6SV simulated apparent radiance at the two calibration sites using desert aerosol model. The gains (spectrally) derived agree to within ∼ 2 % each other. Furthermore, an analysis is performed to determine or identify systematic and random errors, and the overall uncertainty is evaluated for reflectance-based method (total uncertainty of less than 4 % spectrally). [ABSTRACT FROM AUTHOR]

The proper radiometric calibration of the INSAT-3D and --3DR IMAGER is of utmost importance for effective weather forecasting and monitoring. Accurate and reliable images are crucial for predicting weather patterns, tracking storms, and assessing the impact of natural disasters. This research paper presents an evaluation of the radiometric calibration of the INSAT-3D and --3DR IMAGER using a reflectance-based method referenced to in-situ ground site measurements. Calibration campaigns were conducted at the Great Rann of Kutch (GROK) in Gujarat, India during February 2022 to ensure synchronous measurements of surface reflectance and atmospheric variables. In this study, the radiative transfer model called 6 SV (Second Simulation of a Satellite Signal in the Solar Spectrum Vector) was employed to simulate the Top-Of-Atmospheric (TOA) radiance. The calibration coef- ficients for the Visible (VIS) and Short Wave Infrared (SWIR) bands of the IMAGER were determined by comparing the TOA radiance obtained from the radiative transfer model with the Level 1B product of satellite data. The results convincingly demonstrate that the reflectance-based calibration method yields highly accurate and reliable radiometric calibration for both the VIS and SWIR bands of the INSAT-3D and --3DR IMAGER, with an uncertainty significantly smaller than the expected sensor degradation. Specifically, it was found that the VIS band of INSAT-3D underestimates the radiance value by 9.05%, while the VIS band of INSAT-3DR underestimates it by 4.88%. The study also discusses various sources of uncertainty and emphasizes the need for comprehensive uncertainty quantification to ensure the radiometric accuracy of the IMAGER data. Overall, the calibration results indicate that the absolute radiometric accuracy for the VIS and SWIR bands of the IMAGER on-board INSAT-3D and INSAT-3DR satellites is better than 4%. This research contributes to enhancing the reliability and usability of the INSAT-3D and --3DR IMAGER data for weather forecasting and monitoring applications. [ABSTRACT FROM AUTHOR]

Indian geostationary satellite Kalpana-1 (K1) offers a potential to capture the diurnal cycle of land surface temperature (LST) through thermal infrared channel (10.5–12.5μm) observations of the Very High Resolution Radiometer (VHRR) sensor. A study was carried out to retrieve LST by adapting a generalized single-channel (SC) algorithm (Jiménez-Muñoz and Sobrino, 2003) for the VHRR sensor over India. The basis of SC algorithm depends on the concept of Atmospheric Functions (AFs) that are dependent on transmissivity, upwelling and downwelling radiances of the atmosphere. In the present study AFs were computed for the VHRR sensor through the MODTRAN simulations based upon varying atmospheric and surface inputs. The AFs were fitted with the atmospheric columnar water vapour content and a set of coefficients was derived for LST retrieval. The K1-LST derived with the SC algorithm was validated with (a) in situ measurements at two sites located in western parts of India and (b) the MODIS LST products. Comparison of K1-LST with the in situ measurements demonstrated that SC algorithm was successful in capturing the prominent diurnal variations of 283–332K in the LST at desert and agriculture experimental sites with a rmse of 1.6K and 2.7K, respectively. Inter comparison of K1-LST and MODIS LST showed a reasonable agreement between these two retrievals up to LST of 300K, however a cold bias up to 7.9K was observed in MODIS LST for higher LST values (310–330K) over the hot desert region. [ABSTRACT FROM AUTHOR]

This study describes On-Orbit absolute radiometric calibration for the Ocean Colour Monitor2 (OCM2) onboard Oceansat-2 satellite through a vicarious calibration experiment performed at the Great Rann of Kutch calibration site in Gujarat, India, in February 2022. To achieve accurate and consistent calibration for the OCM2 channels, a reflectance-based calibration method was used which relies on synchronous in-situ measurements of surface reflectance and atmospheric parameters at the time of satellite overpass. In this exercise, the 6 SV (Second Simulation of a Satellite Signal in the Solar Spectrum Vector) radiative transfer (RT) model was used to simulate the Top Of Atmosphere (TOA) spectral radiance for the OCM2 channels. The on-orbit radiometric performance/changes were derived by comparing the 6 SV simulated TOA radiance with those of the OCM2 Level 1B (L1B) data product. The results indicate that the average gain for band 1 to band 8 was found in the range from 0.88 to 1.23 and the relative error from 1.58% to 18.79% for the OCM2 sensor. However, the Root-Mean-Square-Error (RMSE) between the OCM2 measured and the 6 SV simulated TOA radiance data range from 0.17 (µW cm−2 sr−1 nm−1) at 443 nm to 2.04 (µW cm−2 sr−1 nm−1) at 620 nm. Furthermore, we analysed in detail the various uncertainties in this approach emanating from surface reflectance, atmospheric conditions (ozone, water vapour and aerosol optical depth), aerosol-type assumption in the RT model, BRDF, and inherent accuracy of the 6 SV RT model. The overall uncertainty was within 6% estimated using the reflectance-based calibration method. [ABSTRACT FROM AUTHOR]

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 km × 30 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. [ABSTRACT FROM AUTHOR]

Presents information on the bandpass solar exoatmospheric irradiance and Rayleigh optical thickness for several categories of sensors. Categories of sensors; Characteristics of ground surface that are being quantified through the use of remote sensing data; Constituents of the atmosphere which affect radiance in the visible and near infrared wavebands; Information on the relative spectral measurement.

The present study employs temporal C-band Sentinel-1 synthetic aperture radar (SAR) data within the Google Earth Engine (GEE) platform to evaluate discriminability and estimate acreage of kharif rice across major Indian states. Utilizing multi-temporal Sentinel-1 Cband SAR data, including time-series cross-polarization vertical--horizontal channels, the research spanned states such as Punjab, Haryana, Uttar Pradesh, Madhya Pradesh, Bihar, Jharkhand, Chhattisgarh, Telangana, Andhra Pradesh, West Bengal, Odisha and Assam. Employing five machine learning algorithms on GEE, with random forest demonstrating high performance, achieved 98.59% accuracy and 0.92 kappa coefficient ( κ) in Odisha. Subsequently, the RF algorithm was applied for kharif rice acreage estimation, yielding overall accuracies from 88.48% to 97.28% and κ between 0.87 and 0.96 with deviations from reported acreage ranging from 0.95% to 12% across diverse states. The study underscores the efficacy of SAR data and machine learning within GEE for precise large-scale automated mapping of kharif rice. [ABSTRACT FROM AUTHOR]

• Plot level rice lodging assessment with ten band multi-spectral UAV imagery. • Identification of optimal spectral, textural, and colour features for lodging assessment. • RF-based classifier yields >95 % accuracy in lodging assessment with combined features. • Demonstrated superiority of present approach and model over comparable studies. Rice plant lodging leads to change in canopy structure, yield loss and creates a menace in harvest operations. In situ assessment of lodging is time consuming, labour intensive, inefficient and inaccurate. Its assessment contributes greatly in in-situ field management and damage analysis. In this study, imaging observations from ten-band (within 444–842 nm) multispectral camera at 0.06 m Ground Sampling Distance (GSD) on-board an unmanned aerial vehicle (UAV) were acquired over a rice research farm (22.7930 N and 72.57140 E), Anand, Gujarat in western part of India. A set of features such as spectral reflectance, vegetation indices, colour coordinates and index, textural parameters and combination of all these were used for discriminating lodged rice crop from standing ones. All these features were extracted and analysed to optimize the sensitive features followed by discrimination of these two classes of rice using ensemble learning based Random Forest (RF) classifier. The analysis revealed that Green, Red-edge and Near-infrared (NIR) bands showed most optimal spectral features for lodging detection. The mean texture of these bands was also found to be sensitive indicators for rice lodging. Combined features with RF classifier produced an overall accuracy of 96.1% with kappa coefficient (κ) of 0.92 followed by textural features with an overall accuracy of 93.5 % and κ of 0.86. Plot level lodging assessment revealed that lodged area varied from 0.1 % to 15.5 % of the cropped area over different plots. The results were validated with the visually interpreted lodged areas using RGB image that resulted into R2 of 0.97 with relative root mean square error (rRMSE) of 0.02 %. Our results conclude that multispectral UAV based remote sensing can help in rapid damage assessment and plot-level field management with high precision. [ABSTRACT FROM AUTHOR]

Thermal Infrared Imaging Spectrometer (TIS), which operates in the infrared spectral region (7–13 μm), is one of the five instruments on-board the Mars Orbiting Mission (MOM). TIS was designed to detect emitted thermal infrared radiation from the Martian environment, which would enable the estimation of ground temperature of the surface of Mars and also map its surface composition. TIS instrument is a grating- based spectrometer which has spatial resolution of 258 m at periapsis (372 km). TIS hardware was realized with light-weight miniaturized components (total weight 3.2 kg) with power requirement of 6 W. Observations from TIS instrument were carried out during Earth-bound manoeuvres and cruise phase operations of MOM and the results were found to be in agreement with the laboratory measurements. [ABSTRACT FROM AUTHOR]