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Alpine treeline ecotones act as early warning systems for detecting the impact of climate change on terrestrial ecosystems. The physiognomic traits of the treeline ecotones, such as tree height, canopy cover, and leaf area, are expected to change in response to ongoing climate warming. These traits contain valuable information about the processes that govern treeline dynamics, and thus, it is important to describe them consistently along ecologically meaningful dimensions. However, conventional approaches to evaluating vegetation structure, such as those that rely on optical and synthetic aperture radar (SAR) imaging satellites, have significant limitations that limit their utility in mountainous terrain. The use of SAR imagery is constrained by shadowing and geometric distortion caused by steep terrain, while optical imaging systems are insensitive to changes in vegetation vertical structure. In contrast, Light Detection and Ranging (LiDAR) technology, offers an accurate means of estimating the three-dimensional physiognomic traits of vegetation in a mountainous alpine environment. Due to the obvious advantages of LiDAR over conventional multi-spectral imaging and radar imaging sensors, the number of LiDAR-based treeline studies has increased in recent years. In this review, we examined how LiDAR remote sensing is utilized in the alpine treeline research and evaluated the associated challenges and opportunities. Further, this study concludes by discussing the promising future of LiDAR technology, and accordingly, some recommendations are put forward. [ABSTRACT FROM AUTHOR]

MODIS Land Surface Temperature (LST) product is extensively used in agricultural studies like crop health assessment, soil moisture estimation, irrigation management, land use land cover change, air-temperature retrieval and crop water stress detection. Numerous studies have used Split Window (SW) algorithm to retrieve LST from MODIS TIR bands. Among them, some utilize Sensor View Angle Dependent (SVAD) or Columnar Water Vapor Dependent (CWVD) SW algorithm. Present study aims to make use of SVAD and CWVD SW algorithms and compare them to evaluate the LST retrieval accuracy over various land surface type. Theoretical accuracy assessment of the CWVD and SVAD algorithms demonstrates a good accuracy with the an RMSE of 1.09K and 1.42K, respectively. The experimental retrieval of LST achieves exceptionally good accuracy, with a RMSE of 1.45K in the CWVD algorithm and 1.80K in the SVAD algorithm, particularly in heterogeneous regions. In homogeneous regions, the RMSE values are 1.14K in CWVD and 1.10K in SVAD. Both algorithms exhibit satisfactory accuracy; nevertheless, the application of these algorithms may vary in agricultural contexts. Based on the obtained results and the inclusion of required parameters, we have arrived at a conclusion regarding the superior performance of the SVAD compared to the CWVD for LST retrieval. [ABSTRACT FROM AUTHOR]

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 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]

Anomalous and erratic behaviour of weather pose various challenges for agricultural community from crop sowing to post harvest. The balance between turn-around-time for farm operations and resource optimization can limit the expected losses due to unfavourable weather. In the past, thrust was given to issue agromet advisories to farmers for a group of districts and blocks primarily using medium-range weather forecast with coarser grid resolution, crop records and point observation for crop condition. The current advisory framework under Gramin Krishi Mausam Seva (GKMS) of India Meteorological Department (IMD) lacks in, near real time assessment of crop and soil conditions to improve the quality and coverage of advisories. The spectral observations from polar and geostationary satellites provide agromet products for synoptic, real-time and continuous monitoring of crops. In order to strengthen the existing advisories under GKMS, the usage of satellite derived daily agromet products in six AFMUs (Agro-Met Field Units) (382 blocks of 60 districts) was initiated by Space Applications Centre, ISRO and IMD. Several agromet products such as Normalized Difference Vegetation Index (NDVI), Potential Evapotranspiration (PET), Surface Dryness Index (SDI), Minimum and Maximum Land Surface Temperature (LST) and Surface Soil Moisture (SSM) aggregated for block and district agricultural regions were provided to all six AFMUs in user friendly format since October 2019 through a dedicated web link from VEDAS (https://vedas. sac.gov.in) geoportal. Time series and near real-time agromet products during agricultural seasons are being used to interpret crop sowing prospect, crop condition, irrigation requirement, crop stress etc. at block and district scales. Regular evaluation of these products over respective AFMUs with measured ground data showed 9% and 10% difference for PET and SSM respectively, whereas, LST showed RMSE of 2.0 K. In future, crop specific agromet parameters and their short-term forecasting are primary focus to provide value-added quality advisories at Gram Panchayat level to all AFMUs. [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) [o 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 x 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. Index Terms--Aerosol optical thickness (AOT), crops, Indian Remote Sensing satellite (IRS), leaf area index (LAI), Linear Imaging Self-Scanning Sensor (LISS), Moderate Resolution Imaging Spectroradiometer (MODIS), normalized difference vegetation index (NDVI), remote sensing, retrieval, validation.

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]