National Remote Sensing Centre Research Articles

Performance of Qualitative and Quantitative Models in Creating Landslide Susceptibility Map of Chaliyar River Basin

A Landslide Susceptibility Map (LSM) for Kerala's Chaliyar river basin is what this study aims to provide. Several landslides occurred in this basin as a result of the floods in 2018 and 2019. There were 592 identified landslides. Using ArcGIS 10.7 software, the landslide inventory was taken from the inventories created by the National Remote Sensing Center (NRSC) and Kerala State Disaster Management Authority (KSDMA), and the future incidence of landslides was projected by linking the landslide cause variables. Landslide inventories were split into training and validation groups in this study, with the ratios fixed at 70:30. Two models, including a qualitative one called Weighted Linear Combination (WLC) and a quantitative one (a bivariate statistical model) called Weights of Evidence (WOE) model, were used to evaluate landslide susceptibility. The following factors were employed as causative parameters: Slope, Aspect, Curvature, Relative Relief, TWI, Distance to Road, Distance to Streams, Distance to Lineaments, Land Use Land Cover, Drainage Density, Road Density, Lineament Density, Geomorphology, Soil Texture, and NDVI. The performance of the models was evaluated using the Receiver Operating Characteristic (ROC)'s Area Under the Curve (AUC). In the study, the WLC model yields an AUC success rate accuracy of 59.9%, while the WOE model yields an accuracy of 70.9%. In terms of ratio of landslide validation accuracy, the WOE model outperforms the WLC model by 11%. The anticipated landslide area is included in the landslide susceptibility map, which can be incorporated to lessen the risk of landslides in this research.

Scrutinizing the Hydrological Responses of Chennai, India Using Coupled SWAT-FEM Model under Land Use Land Cover and Climate Change Scenarios

This study implemented a coupled SWAT-FEM simulation model to evaluate the impacts of land use land cover and climate change scenarios (LCS) on the water resources of river catchments in Chennai, India. The land use land cover data were obtained by merging the source data from National Remote Sensing Centre (NRSC) and International Water Management Institute (IWMI). Climate change simulations were obtained from four global climate models (GCM), including GFDL Baseline Scenario (1981–2000), GFDL A1B Scenario (2081–2100), CCSM4 Baseline Scenario (1986–2005), and CCSM4 A1B Scenario (2081–2100). The LCS predicted temperature increases of 2.32 °C and 1.74 °C for GFDL and CCSM4, respectively, by the end of the century. The water use predictions suggested increases above 20% in the utilization of water by 2100, inferring the noticeable dynamics of inter-annual as well as inter-month variability in water resources in the river basins of Chennai soon. The study is novel through its implementation of a coupled modeling approach to improve the practicality of the SWAT-FEM model and to deliver useful projections of land and climate change impacts on hydrological responses. The results provide useful insights into how the variability in climate conditions alters the spatiotemporal water responses in catchment scales.

REMOTE SENSING TREND OF FOREST ECOSYSTEM PRODUCTIVITY IN GUJARAT USING SATELLITE IMAGERY

Forest changes are now being rapid because of the higher rate of climatic variability. Landscape-scale evaluations of forest productivity are important to understand the response of forest species to changing climate. Net primary productivity (NPP) changes over long-term (from the year 1981 through 2006) was studied in the forest ecosystems of Gujarat state, India. The NPP product used in the study is derived from National Oceanic and Atmospheric Administration's Advanced Very High Resolution Radiometer scanner imagery by National Remote Sensing Centre, Indian Space Research Organisation. Trend analysis was done using a linear regression model in different major forest types. There was a positive trend in NPP in all forest types studied. Statistically significant (p-value < 0.05) highest trend was observed in the thorn forest (+ 1.75 g C m-2 yr-1). Increase in forest greenness indicates warming. The change in forest productivity is one of the fingerprints of climate variability.

An assessment study on the impact of land use land cover on monsoon depressions over India

This study investigates the influence of LULC representation on surface meteorological conditions associated with monsoon depressions (MDs) in the Advanced Research Weather Research and Forecasting (WRF) model. A total of 18 MDs were considered during 2007–2018, with a life period of at least two days. Two simulations are performed at a 5-km grid-spacing, ingesting the LULC from the United States Geological Survey (USGS) and National Remote Sensing Centre (NRSC) for each MD case. The urban area has been increased from 0.13% in USGS to 1.21% in NRSC over the north-central and east coastal states, reducing the soil moisture (SM) errors by 0.015 m3 m−3 (25%) in the NRSC run. The SM in the NRSC run has a correlation of 0.53, which is 15% higher than that in the USGS run. The NRSC has a larger forest area (22%) than the USGS (7%) over the north-western parts and some parts of Maharashtra, which helps increase latent heat flux (LHF) by 30 Wm−2. Verifying with ERA analysis, the USGS and the NRSC simulations underestimate the LHF in most parts of India and overestimate in orographic areas. The NRSC-simulation shows fewer improvements (<5%) in LHF across the central and southern areas, unlike in the East and West parts (>13%). The surface temperature, moisture, and rainfall have been noticeably improved in the NRSC run for day-2 and day-3 simulation, unlike in USGS, while they are comparable in day-1. The spatial error of rainfall increases with forecast length, with NRSC having 10–15% less error than the USGS run. Further, the NRSC run could identify the rainfall peaks of 3 mm h−1 with a mean error of 1.5 mm h−1 as compared to 2.2 mm h−1 error in the USGS. This study demonstrates the positive impact of NRSC-developed LULC in the MD simulations over India.

Hydrological modelling through SWAT over a Himalayan catchment using high-resolution geospatial inputs

The concerns of water availability have been increasing due to population explosion, rapid industrialization, and other anthropogenic activities. This urges for an accurate estimation of streamflow at the river basin scale, which is typically carried out by the application of rainfall-runoff models. Though numerous models exist in hydrology literature, their efficiency over the mountainous catchments is generally observed to be poor, mostly due to the lack of high-quality data over such regions. This study presents an application of the widely used soil and water assessment tool (SWAT)-based hydrological modelling using high-resolution geospatial inputs over the Tehri reservoir catchment (India) located in the lower Himalayan region. The Resourcesat-2 Linear Imaging Self-Scanning System (LISS)-IV imageries of land use/land cover (LULC) and the Cartosat-1 digital elevation model (DEM) are procured from National Remote Sensing Centre (NRSC), Indian Space Research Organization (ISRO), India. The LULC and DEM have a spatial resolution of 5.8 m and 2.5 m, respectively. SWAT model is applied for a duration of 12 years (2006-2017) with 2006, 2007-2013 and 2014-2017 as the warm-up, calibration and validation periods, respectively. The results reveal an excellent performance of the model in streamflow simulation. The efficacy measures viz., Nash–Sutcliffe efficiency (NSE) and Coefficient of determination (R2) are obtained to be 0.83 and 0.84, respectively. The information reported in this study will be helpful to the water resources engineers for hydrologic modelling over the mountainous catchments.

Seasonal and annual variations of CO2 and CH4 at Shadnagar, a semi-urban site

Carbon dioxide (CO2) and methane (CH4) are the most important greenhouse gases (GHGs) due to their significant role in anthropogenic global climate change. The spatio-temporal variations of their concentration are characterized by the terrestrial biosphere, seasonal weather patterns and anthropogenic emissions. Hence, to understand the variability in regional surface GHG fluxes, high precision GHGs measurements were initiated by the National Remote Sensing Center (NRSC) of India. We report continuous CO2 and CH4measurements during 2014 to 2017 for the first time from Shadnagar, a suburban site in India. Annual mean CO2 and CH4 concentrations are 399.56 ± 5.46 ppm and 1.929 ± 0.09 ppm, respectively, for 2017. After the strong El Niño of 2015–2016, an abnormal rise in CO2 growth rate of 5.5 ppm year−1 was observed in 2017 at the study site, compared to 3.03 ppm year−1 at Mauna Loa. Thus, the repercussion of the El Niño effect diminishes the net uptake by the terrestrial biosphere accompanied by increased soil respiration. Seasonal tracer to tracer correlation between CO2 and CH4 was also analyzed to characterize the possible source-sink relationship between the species. We compared CO2 and CH4 concentrations to simulations from an atmospheric chemistry transport model (ACTM). The seasonal phases of CH4 were well captured by the ACTM, whereas the seasonal cycle amplitude of CO2 was underestimated by about 30%.

Assessment of Antenna Mounting Building Structural Strength using Microtremor Analysis

Any satellite tracking ground station requires fully steerable dish shaped antenna for fixed satellite services. Antenna foundations require special consideration, because they transmit dynamic loads to soil and foundation system, in addition to static loads due to self-weight of foundation, antenna and its accessories. Antenna foundations are of ground foundation or roof top i.e on buildings based on Radio Frequency requirements. To install antennas on existing building, the building should have required strength. To assess the building strength, building natural frequency and damping ratios are determined using Microtremor recorded data. For rotating Antennas, the required building frequency should be more than 8 Hz to avoid resonance effect of antenna structure natural frequency with servo frequency. In this study, the data is collected for building located at National Remote Sensing Centre, ISRO, Shadnagar, Hyderabad by mounting sensor at 3 locations of building. The data collected is analyzed using View 2002 software. FFT analysis is carried out to identify predominant natural modes and frequencies of the Building.

Establishment of lightning detection sensors network in India: generation of essential climate variable and characterization of cloud-to-ground lightning occurrences

A network of 25 lightning detection sensors (LDS) has been established by the National Remote Sensing Centre (NRSC), the Indian Space Research Organization (ISRO). At present, sensors are located in the northeast, east coastal, central, and southern locations of India under this network. Geolocation of the lightning occurrences is estimated using the time of arrival algorithm. These observations are compared well with the Indian Institute of Tropical Meteorology (IITM) sensor network data. The NRSC-LDS data have been used to derive essential climate variables (ECVs) and to understand cloud occurrences to ground lightning from July 2019 to November 2020. We note that December–January reported the least number of cloud-to-ground (CG) flash occurrences (flash density ~ 0.003 km−2 day−1), while August–September were the months with the most number of CG flash occurrences (flash density ~ 0.33 km−2 day−1). We also note that during the period under the scrutiny, Chhattisgarh, Jharkhand, Odisha, Maharashtra, and Madhya Pradesh states recorded the most number of CG lightning flash occurrences.

MAPPING AND CHANGE DETECTION OF MANGROVES AROUND MUMBAI USING REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEMS (GIS)

The study focuses on the mangroves in two districts namely, Mumbai and Mumbai Suburban. Mumbai, a coastal megacity, is a financial capital of the country with high population density. Mumbai is facing depletion of coastal resources due to land scarcity and large developmental projects. Thus, it is important to monitor these resources accurately and protect the stakeholders’ interest. Cloud-free satellite images of IRS P6 LISS III of 2004 and 2013 were procured from National Remote Sensing Centre (NRSC), Hyderabad. Two bands of visible and one band of NIR were utilized for landcover classification. Supervised Classification with Maximum Likelihood Estimator was used for the classification. The images were classified into various landcovers classes namely, Dense Mangroves, Sparse Mangroves and Others. Two software’s namely, ERDAS Imagine and GRAM++ were used for landcover classification and change detection analysis. It was observed that the total mangrove area in Mumbai in 2004 and 2013 was 50.52 square kilometers and 48.7 square kilometers respectively. In the year 2004 and 2013, contribution of sparse mangroves in the study area was 72.31 % and 87.06% respectively.

The Annual Cycling of Nighttime Lights in India

India is known to have unstable power supply, and many locations show an annual cycle in VIIRS Nighttime Light (VNL). In this study, autocorrelation function (ACF) analysis is used to identify the annual cycling in VNL. Two fundamentally different classification techniques are proposed to classify the ACF profile into one of the three arch types, i.e., acyclic, single peak, and dual peak. The results from the two classification techniques are closely compared to verify their output. This analysis is carried out for the entire territory of India in 15 arc second grid cells. The power stability data acquired from the India Human Development Survey (IHDS) and the Electricity Supply Monitoring Initiative (ESMI) are used to verify their relationship to the annual cycling of VNL. To further aide the analysis, land use/land class are accounted for by data from the India National Remote Sensing Center (NRSC). As a result, the contribution of power stability to VNL annual cycling in India is inconclusive due to the limitation of power stability data. Furthermore, other potential factors should be further examined.

Geospatial landslide inventory of India—an insight into occurrence and exposure on a national scale

India ranks first in the world in terms of fatal landslides. Large vulnerable area (0.42 million km2), high population density and monsoon rainfall make India’s landslide management and mitigation task difficult. Therefore, an understanding of landslide occurrences and exposure of socio-economic parameters on a national scale is essential to prioritise landslide mitigation efforts. In this paper, a database of 45,334 landslides (polygons) in India mapped by the National Remote Sensing Centre (NRSC) during the 1998–2018 period was compiled and catalogued in a WebGIS platform. High-resolution satellite data such as IRS PAN+LISS-III, Resourcesat LISS-IV Mx, Cartosat, WorldView, Pleiades and GeoEye were used to map landslides as small as 12 m2 to as big as 1,390,350 m2. GIS analysis using the landslide inventory revealed interesting results about control, exposure and pattern of landslide occurrences in India. The Northwest Himalayas contribute 66.5% of landslides in India, followed by the Northeast Himalayas (18.8%) and the Western Ghats (14.7%). The Greater Himalayan sequence consisting of high-grade metamorphic rocks has a considerable control (32%), and the Main Central Thrust is the major regional structure controlling (12%) landslides in India. In the Northeast Himalayas, the size of landslides and the slope gradient controlling landslide occurrence are less in comparison to the Northwest Himalayas and the Western Ghats. Landslides in the Western Ghats are triggered with a lesser amount of rainfall than the Himalayan regions. Exposure analysis using four key socio-economic parameters in the 145 hilly districts shows that Rudraprayag district is most affected by landslides in India. The understanding derived using the landslide database on a national scale will help to prioritise and strengthen landslide disaster risk reduction strategies in India.

Subcategories Multiple Uses of Land Recognized by Land Use and Land Cover Classification System Using LISS-III and NRSC Enumerate Data: Case Study at Medchal Mandal, Hyderabad, India

Recognition of compound land possessions in Mandal level often produces complex in precisely classifying land use and land cover (LULC) characteristics. A renovated village, as an Infrastructure development ought to adequate information on several aspects of its performance in direct to compose verdict. LULC is merely a solitary such aspect, to increase significance to conquer problems of declining environmental quality, farmlands, degradation of wetlands, the slaughter of wildlife territory, and water bodies. LISS-III (Linear Integrated self-scanning) images and NRSC (National Remote Sensing Centre) catalog improve LULC classification accuracy at the Mandal level. The results of this investigation reveal that five categories are occupied a large area, followed by buildup land, cropland, forest, industries, and water bodies, (13%, 26%, 9%, 10%, and 13%). The highest contribution is cropland and the lowest contribution is forest land. The unobstructedly available a large amount of satellite data conceding for monitoring of existing land features on a large scale. Cluster-I division contains more dominated LULC categorised villages such as Ravalkole, Gundlapochampally, Medchal, and Sreerangavam villages. LULC classification data applied for investigation of environmental sustainability and problems. This research has been recommended and support for implement rainwater harvesting structures for recharge of groundwater or collection of water in this Medchal Mandal and develop mobile education, higher education institutes, and small-scale industries in villages.

Effect of Soil Orders and Land Uses on Available Soil Nutrients in Meghalaya, India

Understanding of spatial distribution of available soil nutrients is important for sustainable land management. An attempt has been made to assess the spatial distribution of available soil nutrients under different soil orders and land uses of RiBhoi, Meghalaya, India using geo-statistical techniques. Seven Land Use Land Cover (LULC) classes were selected from LULC map on 1:50,000 scale prepared by National Remote Sensing Centre (NRSC) viz. Abandoned Jhum (AJ), Current Jhum (CJ), Deciduous Forest (DF), Double Crop (DC), Evergreen Forest (EF), Kharif Crop (KC) and Wastelands (WL). Again, three soil orders were identified by National Bureau of Soil Survey and Land Use Planning (NBSS&amp;LUP) in RiBhoi district of Meghalaya, India viz. Alfisols, Inceptisols and Ultisols. 105 soil samples were collected, 5 replicated soil samples from 21 strata derived from 7 LULC and 3 soil orders. Soil samples were analyzed for available nitrogen (N), available phosphorus (P2O5), available potassium (K2O) and available zinc (Zn) using standard procedures. One way ANOVA was carried out using IBM SPSS Statistics 20.0 software. Significance levels were tested at p≤0.05.&#x0D; N content varied from low (215.50 kg/ha) to medium (414.30 kg/ha) with mean value of 291.50 kg/ha. On the other hand, P2O5 content varied from low (19.90 kg/ha) to high (68.30 kg/ha) with mean value of 43.52 kg/ha. Similarly, K2O content varied from low (112.09 kg/ha) to high (567.84 kg/ha) with mean value of 273.68 kg/ha. Again, Zn also varied from low (0.26 ppm) to high (1.46 ppm) with mean value of 0.64 ppm.&#x0D; In Alfisols, N was found to be higher in EF, AJ &amp; CJ than DF, DC, KC and WL. KC has been found to have lower N than all other LULC classes. Higher P2O5 has been found under EF over KC and WL. AJ has been found to have higher K2O than all other LULC classes. K2O has also been found to be higher in CJ over DC, KC and WL. DF and EF have been found to have higher K2O than KC and WL. Zn has been found to be higher in EF over CJ, DC and WL.&#x0D; In Inceptisols, higher amount of N was observed under EF over all other LULC classes. Higher N has also been found under CJ over DF, DC, KC and WL. P2O5 content was found to be higher under DF over all other LULC classes. Higher P2O5 content was also found under AJ, CJ and DC than KC and WL. Higher amount of K2O has been found under AJ over all other LULC. K2O content of soil under DF was also higher than CJ, EF, KC and WL. Zn has been found to be higher under EF over all other LULC classes. Zn content under CJ has also been found to be higher than AJ, DF, KC and WL.&#x0D; In Ultsols, higher amount of N has been found under EF compared to all other LULC classes. Lowest N content was found under KC. P2O5 content was found to be higher under EF, DF and AJ over all other LULC. K2O content has been found to be higher under CJ in comparison to all other LULC classes. K2O content of EF and DF were also found to be higher than AJ, DC, KC and WL. Again, K2O content has been found to be higher under DC compared to AJ, KC and WL. Zn content under EF and AJ was found to be higher than all other LULC classes. CJ, DF, DC, KC and WL have been found to have lower Zn content.&#x0D; It has been observed that P2O5 content is significantly higher in inceptisols irrespective of LULC classes. The study has highlighted the spatial distribution of available soil nutrients as a function of soil orders and LULC. This will be a useful input in sustainable land management programmes.

Influence of eddies and tropical cyclone heat potential on intensity changes of tropical cyclones in the North Indian Ocean

Eddies are known to play a crucial role in the sudden intensification of tropical cyclones. In this study, Sea Level Anomaly (SLA) data from satellite altimetry is utilized to investigate the role of eddies on tropical cyclones intensification in the North Indian Ocean (NIO) basin. SLA data obtained from Archiving Validation and Interpretation of Satellite Data in Oceanography (AVISO), Tropical Cyclone Heat Potential (TCHP) from National Remote Sensing Centre (NRSC) and cyclone intensity data from Indian Meteorological Department (IMD) have been utilized to analyse and understand the impact of TCHP and eddies on 60 tropical cyclones in the NIO spanning the years 2001–2018. Out of these 60 cyclones, 38 were formed in the Bay of Bengal (BoB) and 22 in the Arabian Sea (AS). From the analysis, it is found that nearly 40% cyclones are affected by Warm Core Eddy (WCE) generally intensifying them, 30% are influenced by Cold Core Eddy (CCE) that generally reduce cyclone intensities and the rest 30% are affected by TCHP variations (in the absence of eddies) in the NIO region during the study period. Cyclones which do not follow the established trend of intensifying due to WCEs or increasing TCHP; and diminishing owing to CCEs or TCHP decrease were found to be governed primarily by the Translational Speed of the cyclones. WCE are mostly present during the pre-monsoon season in the NIO (both BoB and AS). This analysis confirms that consideration of the underlying eddies in the cyclone track represented by SLA, TCHP variations, Translational Speed of cyclones and the ocean barrier layer are key factors that must be considered in forecasting cyclone intensity and track.

Synthesized pansharpening using curvelet transform and adaptive neuro-fuzzy inference system

Image fusion is an important technique in remote sensing to improve visual interpretation and classification. Pansharpening is the procedure of fusing panchromatic (PAN) and multispectral images to produce high spatial and spectral resolution images. Synthesized pansharpening is performed on Linear Imaging Self-Scanning Sensor III and Advanced Wide Field Sensor data, which are freely available and provided by the National Remote Sensing Center. The Adaptive Neuro-Fuzzy Inference System (ANFIS) in multiscale transform domain for multisensor image fusion application is evaluated. The state-of-the-art method has been evaluated by various quality metrics. The computational cost of ANFIS with wavelet, contourlet, shearlet, and curvelet transform is investigated. This study proves that curvelet with ANFIS-based fusion technique outperformed state-of-the-art techniques. The application will be used to incorporate the missing spectral information in the high spatial resolution PAN image to identify objects, highlighting the regions clearly.

Inter-comparison of model, satellite and in situ tropical cyclone heat potential in the North Indian Ocean

The North Indian Ocean (NIO) experiences frequent tropical cyclones (TCs). TC heat potential (TCHP) is a major ocean parameter responsible for TC genesis and intensification changes. In this study, Indian National Centre for Ocean Information Services-Global Ocean Data Assimilation System (INCOIS-GODAS) model and satellite-derived TCHP data from National Remote Sensing Centre (NRSC) and National Oceanic and Atmospheric Administration (NOAA) are validated against TCHP from in situ profiles in the NIO during the period 2011–2013 for buoys and during 2005–2015 for Argo data. Data from eight moored buoys (6 in Bay of Bengal and 2 in Arabian Sea) under the Ocean Moored Buoy Network are used. Comparison of model and in situ TCHP yields correlation coefficients (root-mean-square errors in kJ/cm2) of 0.74 (17.75), 0.59 (15.34), 0.70 (17.68), 0.60 (22.24), 0.57 (19.52), 0.73 (17.88) and 0.77 (39.17) at buoy locations BD08, BD09, BD10, BD11, BD13, AD06 and AD10. The scatter indices between collocated TCHP values at these locations were 0.32, 0.22, 0.30, 0.30, 0.31, 0.58 and 0.41. Further, it was found that satellite-based TCHP from NRSC match better with in situ as compared to near-real-time TCHP data obtained from NOAA. TCHP from INCOIS-GODAS model, NOAA delayed time data and NRSC TCHP data set are also in good agreement with those from Argo profiles. As a case study, model and in situ TCHP were compared during a TC, “Thane” at two buoy locations (BD11 and BD13), closest to its track. The analysis revealed underestimation of model TCHP at BD11, but good correlation at BD13. This could be attributed to the existence of a strong temperature inversion at BD11. It is observed that although the model is able to capture features like barrier and inversion layers, the temperature and depth of such layers are underestimated. Further, the recovery time from the influence of TC on the ocean subsurface is also much longer in case of the model which thus needs to be fine-tuned. Seasonal comparison of TCHP from various sources with in situ estimated TCHP also shows better correlation between all the products for the pre-summer monsoon compared to the post-summer monsoon season.

Spatio-temporal distribution of CO2 mixing ratio over Bhubaneswar, Varanasi and Jodhpur of India– airborne campaign, 2016

Airborne measurements of vertical distribution of atmospheric CO2 were conducted over India in 2016 from a Super King Beechcraft 200 aircraft of National Remote Sensing Centre (NRSC), Hyderabad, employing a LICOR-7200 (LI-7200) instrument for high frequency CO2/H2O measurements and a Trimble 5700 GPS receiver for geo-tagging them. The present study analyzes the spatio-temporal distribution of CO2 mixing ratios over the cities of Bhubaneswar, Varanasi and Jodhpur during June 2016. Measurements were conducted over each location for up to 5 days each, between 0900 and 1400 h local time. Significant variation was observed in the vertical and horizontal spatial distribution of CO2 from surface to 3.2 km altitude, which was the maximum height at which the aircraft was flown, over all locations. Inter-comparison of airborne measurements against satellite remote sensing retrieved CO2 profiles from the Infrared Atmospheric Sounding Interferometer (IASI) showed underestimation of IASI by 0.5–1.5% (3–6 ppm) to an overestimation of up to 5% (about 15 ppm), with the Pearson correlation coefficient ‘r’ between airborne and IASI retrieved CO2 varying from 0.34 to 0.95 respectively. The mean relative bias between airborne and satellite measured CO2 profiles are within 1–4%. A variation of 5–15 ppm was observed between dry and wet CO2 measurements, indicating the strong influence of water vapor on CO2 mixing ratios. The CO2 mixing ratio with corresponding altitudes [maximumCO2(height)minimumCO2(height)] over Bhubaneswar, Varanasi and Jodhpur were [412.43±8.23ppm(1000m)389.42±2.66ppm(2800m),420.34±15.82ppm(800m)386.89±7.82ppm(3200m),405.74±6.77ppm(200m)383.42±5.58ppm(3200m)] respectively.

ESTABLISHMENT OF VICARIOUS CALIBRATION/VALIDATION FACILITY FOR SPACE BORNE HIGH AND MID RESOLUTION OPTICAL SENSORS

Abstract. Vicarious calibration refers to methods that make use of “invariant” natural targets of the Earth for the post-launch calibration of sensors. This process of calibration is useful for initial phase orbit operation of sensor and thereafter for validation during its operational span. This method of periodic evaluation of radiometric and geometric performance of the space-borne optical sensors and validation of derived radiance/reflectance is to ensure availability of consistent and accurate data products to the user community. This paper describes one such Cal/Val facility’s design, engineering aspects and realization at National Remote Sensing Centre (NRSC), Shadnagar. The target materials used for filling the Cal/Val site are studied for its reflectance. These targets have reflectance ranging from 9% to 53%, in the VNIR (Visible and Near Infra-Red) region. This paper also describes instruments used for calibration and homogeneity studies of targets for its invariance since operationalisation (January 2016). The last section of this paper is about reflectance based absolute radiometric calibration of medium resolution Indian Remote Sensing (IRS) sensor (Resourcesat and Cartosat series) using RT model and edge based LSF/MTF estimation of high resolution sensor carried out using the Cal/Val facility.

OHC, OMT and TCHP Products with AMSR2 SST

Ocean Heat Content (OHC) and Ocean Mean temperature (OMT) products at seven different depths between 50 and 700 m along with Tropical Cyclone Heat Potential (TCHP) are hosted at Bhuvan website of National Remote Sensing Centre (NRSC) for activities carried out under the National Information System for Climate and Environment Studies (NICES) program. These products are generated using Sea Surface Temperature (SST) and Sea Surface Height Anomaly (SSHA) over the north Indian Ocean between 40–120°E and 0–30°N on daily basis. These products have been generated till the availability of Tropical Rainfall Monitoring Mission Microwave Imager (TMI) SST. Since TMI SST is no longer available beyond April 2015, Advanced Microwave Scanning Radiometer 2 (AMSR2) derived SST is considered to continue generation of the OHC and related products. The AMSR2 derived products are found well related with TMI based estimations (R2 > 0.99) at all the depth levels of 50, 100, 150, 200, 300, 500, and 700 m depth. We have also observed a good relationship for TCHP and OMT products. The maximum Root Mean Square Error of 7.9 kJ/cm2 for OHC at 700 m depth and low scatter index (< 0.005) indicate good quality of OHC products generated with AMSR2 based SST.

A new ground-based FTIR spectrometer reference site at Shadnagar (India) and preliminary columnar retrievals of CH4 and N2O

ABSTRACTA Fourier Transform Infrared (FTIR) spectrometer has been operational since March 2014, at National Remote Sensing Centre (NRSC), Shadnagar, which is a suburban site of central India. Direct solar radiation spectra recorded by the FTIR during clear sky days at a high spectral resolution of 0.01 cm‒1 were analysed using the line-by-line radiative transfer algorithm (LBLRTA) to retrieve the initial results of column-averaged concentrations of methane (XCH4) and nitrous oxide (XN2O) in the atmosphere. Error residuals between the measured and estimated atmospheric transmissions are in the range of 0.40–0.80% for CH4 and 0.20–0.50% for N2O, respectively. The maximum (minimum) XCH4 and XN2O were, respectively, 1.88 ± 0.04 ppm (1.67 ± 0.02 ppm) and 316.15 ± 5.85 ppb (278.92 ± 4.02 ppb). Columnar concentrations of CH4 and N2O retrieved from the Infrared Atmospheric Sounding Interferometer (IASI) were compared against retrieved XCH4 and XN2O using the ground FTIR. The observed differences between the two data sets lie between −0.06 ppm and +0.10 ppm for CH4 and between +32 ppb and ‒10 ppb for N2O.