Abstract. Data from ocean color monitoring sensors at different spectral channels are available for remote sensing of radiation as seen in the given spectral windows, which is used for deriving information on various atmospheric parameters. However, recent studies have demonstrated the potential of hyperspectral (HS) data over multispectral ocean color (MSOC) data in accurately estimating phytoplankton concentration and in monitoring the coastal dynamics. We propose system spectral shape factor (SSSF)-based approach to recover the embedded HS top-of-atmosphere (TOA) radiance (TOARAD) from the MSOC data. SSSF is defined as convolution of normalized input spectrum and sensor spectral response function (SRF). The advantage of SSSF is that it decouples magnitude and spectral shape part of sensor output and enables recovery of TOARAD. To test this method, the airborne visible/infrared imaging spectrometer-next generation data are used to simulate inputs to MSOC. SRF of ocean color monitor simulated MSOC. SSSF of TOARAD is estimated using SSSF of model-based path radiance spectrum of the pixel, which is similar in spectral shape. Methodology, developed using data from five stations, is validated with data from other five stations. The procedure is successfully repeated using SRFs of sea-viewing wide-field-of-view sensor. The recovered HS data are found to be consistent with the original spectra with very small deviations in spectral angle map (<0.012  rad) spectral information divergence (<5.8  ×  10  −  5), mean percentage relative error (MPRE) of TOARAD (<0.7  %  ), and MPRE of TOA water leaving radiance (<5.8  %  ). This approach possibly opens up research for application of HS analysis on MSOC recovered spectra and for optimization of sensor configurations.

This article gives an overview of capacity development (CD) elements for fast-growing geospatial technologies and their applications (GSTA). While the Earth observation data and spatial analytics software tools are getting more open and accessible, lack of skilled workforce and institutional capacities are limiting effective applications. A systematic process is essential in realising required capacity at individual and institutional levels, in maintaining and upgrading technological infrastructures, and in updating geospatial data and strengthening human resources for sustainable supply of workforce. This article discusses on different user sectors and their competencies that are expected to meet their demands. It is also important to take stock of different modes of CD along with a variety of methods to select the appropriate suitable for the different users’ groups for efficient knowledge transfer. In addition, this article describes briefly on two methods of building curriculum design of courses based on two different body of knowledge objectives. The importance of coordinated effort in CD programs is stressed for promoting GSTA for national and international flagship development programs such as the 2030 global sustainable development agenda. It briefly discusses on the issue of gender diversity and points out ways to increase the women participation in GSTA CD programs.

The elementary objective of this current study estimates groundwater limitation in the Mehsana district of Gujarat, by the aid of downscaling GRACE (Gravity Recovery and Climate Experiment) spherical harmonics (SH) and Jet Propulsion Laboratory (JPL) mascon regional solutions data. A set of post monsoonal data from 2003 to 2019 is taken for this research and evaluated with a statistical model derived from empirical regression. The GRACE data spherical harmonics derived Terrestrial Water Storage (TWS), and analysis of regional scale solution of GRACE, JPL mascon estimates the Groundwater Storage Anomaly (GWSA). Both the GRACE solutions observe a negative anomaly of Groundwater Storage (GWS) change across the study area for these 15 years with 60.33 +/- 5.00 mm/year and 80.00 +/- 6.12 mm/year from downscaled GRACE SH and JPL mascon, respectively. Downscaling SH solutions has given 0.14 +/- 0.54 km(3)/year, 5.12 +/- 0.54 km(3)/year and 7.40 +/- 0.54 km(3)/year, and JPL mascon has derived 0.44 +/- 0.43 km(3)/year, 0.63 +/- 0.43 km(3)/year, and 0.11 +/- 0.43 km(3)/year mass loss across Northern Gujarat for 2003 to 2007, 2008 to 2013, and 2014 to 2019 respectively. Therefore, a highly correlated groundwater declining rates and mass loss exists between downscaling GRACE SH and JPL mascon. But the total correlation between downscaling GRACE SH derived GWSA and ground well data anomaly is higher compared to the correlation between JPL mascon anomaly and ground data anomaly. Also, downscaling SH has shown sensitive estimation with GWS change at a significant rate while comparing with JPL mascon derivations, by overcoming the signal loss of the filtered SH solutions. Thus, the study implies that statistical regression can downscale the GRACE SH solution successfully and is applicable for water management scale studies.

Globally, the groundwater is the most favourable and demandable freshwater resource. The threat to surface water resources and subsurface aquifer systems increased with climate change as well as surplus usage of groundwater in highly populated regions. Thus, in present day, groundwater is the primary resource for the sustainability of agriculture, industries and domestic activities in arid and semi-arid areas of the world. The overexploitation of subsurface water initiates land subduction. As water is the source of life on Earth, so it is essential to monitor and predict the capability of groundwater for secure sustainable management of subsurface water with the extreme climate conditions and population growth. The traditional way of keeping a check on groundwater level change is considering in situ or point measurements using the local network of well data. But these measurements are insufficient as hydrological models depend on the spatial data referring over large areas. Global Positioning System (GPS) and Gravity Recovery and Climate Experiment (GRACE) mission are perfect tools to overcome the drawbacks of the traditional groundwater monitoring. It measures the change in ice sheets and glaciers, near-surface and subsurface GWS changes, as well as sea-level changes by GRACE 1 mission and GRACE, Follow on (FO) mission. Most of the researches are based on GRACE satellite data to monitor GWS changes over a large-scale area as continental or regional achieved successful consequences. Although the past decade GRACE studies exhibited that GRACE solution is capable of developing accurate quantitative estimations for GWS scenarios with the high temporal resolution. Still, it restricts only to continental or regional scale studies. Therefore, most of the recent studies took the step for effective downscaling of GRACE data.

The ice sheet and glaciers of Antarctica and Greenland represent the largest sources of freshwater on planet Earth. The understanding and quantification of their dynamic properties such as albedo, precipitation, ice mass movement, and ice elevation changes are critical for the improved climate and mass balance models. The present study utilizes space-borne optical and synthetic aperture radar (SAR) imagery to measure the ice surface velocity at high spatial resolution for a part of the central Dronning Maud Land (cDML), East Antarctica. The datasets from Landsat-8 and Sentinel-1 SAR satellite are used for ice stream velocity estimation using feature-offset tracking and differential interferometric SAR (DInSAR) methods. The derived velocity products are validated with ground based stakes network at annual time scale. The fundamental ice flow laws are used to estimate the ice outflux or discharge for selected ice stream drainage basins of cDML at fluxgate locations. The ice stream basin has been delineated using combination of elevation, slope and continental scale velocity maps. The ice influx for study area is estimated using ECMWF fifth generation reanalysis (ERA5) and Regional Atmospheric Climate Model (RACMO) v2.3 model outputs. The estimated influx and outflux are in the ranges of 0.18-4.167 Gt/y and 0.201 to 1.278 Gt/y respectively, indicating net positive mass balance for the selected area.

Geo-Spatial Technology and Applications (GSTA) contributes significantly to the implementation of the UN 2030 Agenda for Sustainable Development, which seeks to 'leave no one behind'. This study attempts to assess the gender diversity in enrolment in GSTA programmes in developing countries in Asia using data available with the UN-affiliated Regional Centre in India. A survey was also conducted to gauge trainees' comfort in a complex multi-cultural, multi-linguistic classroom environment. The analysis shows that female participation in short-term courses has been increasing rather steeply over the years (1995-2019). The enrolment has reached 30% in recent years. It is also observed that the female participants have preferred (about 10%) to enrol in physical Science and Technology (ST) -oriented courses such as Remote Sensing and GIS, Satellite Meteorology and Climate Change, and Space and Atmospheric Sciences when compared to electrical Engineering and Technology-oriented Satellite Communications and Global Navigation Satellite Systems courses that would require a strong knowledge of digital signals and systems theory. The survey study with a participation of 150 trainees from 17 countries shows that women trainees have a slight edge by 9% over the male counterparts in learning ST elements of the courses. At sub-regional levels, it is observed that participants from Indian subcontinent countries (ISC) are better by 15% than participants from South-east Asian Countries (SAC) and Central Asian Countries (CAC). As far as the course structure perspective is concerned, there has been a significant difference with 70% of the ISC trainees have found the courses as well-balanced covering equally both technology and applications, while only 36% and 56% of the trainees from SAC and CAC, respectively, have found the course as balanced. Importantly, the survey has revealed that the participants from SAC and CAC have a big challenge in apprehending lectures in English, as reflected by their top scores at 18% and 17%, respectively, compared with the corresponding value of the ISC trainees being 72%. Recommendations are suggested to sustain the female enrolment in long-term GSTA training programmes. Strategies that create family-friendly learning conditions, including joint education programmes involving lead institutions of the participants' countries, are proposed to facilitate higher female participation. Furthermore, measures such as standardisation of curricula regionally at undergraduate levels and having periodic meetings of trainers are recommended in order to minimise the trainees' core competency gaps in GSTA disciplines at the regional level. (C) 2020 COSPAR. Published by Elsevier Ltd. All rights reserved.

Space science, technology, and its applications have the potential to make essential contributions to the implementation of global development agendas which encompass the 2030 Agenda for Sustainable Development, the Sendai Framework for Disaster Risk Reduction, and the Paris Agreement on Climate Change. Member States of the United Nations are ultimately responsible for implementing the development agendas at the national level. The use of Earth observation tools can provide timely and reliable input data to the Global Indicator Framework to directly follow-up and review some of the sustainable development goals 169 targets. The continued user of these tools gives Member States the capability to evaluate the effectiveness of actions taken to reach specific targets and to report on progress achieved or shortcomings that need to be addressed. For many countries, capacity development is an essential factor for making optimal use of solutions offered by space activities. A large number of stakeholders, including international, regional, and national entities, are providing relevant capacity development activities. This article provides a pragmatic conceptual framework for improved collaboration and coordination of capacity development to assist Member States to fully utilize the contributions of space science, technology, and its applications to implement the global development agendas and to benefit society. The proposed coordination of capacity development needs to be integrated into an overall results-based management approach for optimizing the use of space science, technology, and its applications in support of global development agendas.

This book covers the state-of-art image classification methods for discrimination of earth objects from remote sensing satellite data with an emphasis on fuzzy machine learning and deep learning algorithms. Both types of algorithms are described in such details that these can be implemented directly for thematic mapping of multiple-class or specific-class landcover from multispectral optical remote sensing data. These algorithms along with multi-date, multi-sensor remote sensing are capable to monitor specific stage (for e.g., phenology of growing crop) of a particular class also included. With these capabilities fuzzy machine learning algorithms have strong applications in areas like crop insurance, forest fire mapping, stubble burning, post disaster damage mapping etc. It also provides details about the temporal indices database using proposed Class Based Sensor Independent (CBSI) approach supported by practical examples. As well, this book addresses other related algorithms based on distance, kernel based as well as spatial information through Markov Random Field (MRF)/Local convolution methods to handle mixed pixels, non-linearity and noisy pixels. Further, this book covers about techniques for quantiative assessment of soft classified fraction outputs from soft classification and supported by in-house developed tool called sub-pixel multi-spectral image classifier (SMIC). It is aimed at graduate, postgraduate, research scholars and working professionals of different branches such as Geoinformation sciences, Geography, Electrical, Electronics and Computer Sciences etc., working in the fields of earth observation and satellite image processing. Learning algorithms discussed in this book may also be useful in other related fields, for example, in medical imaging. Overall, this book aims to: exclusive focus on using large range of fuzzy classification algorithms for remote sensing images; discuss ANN, CNN, RNN, and hybrid learning classifiers application on remote sensing images; describe sub-pixel multi-spectral image classifier tool (SMIC) to support discussed fuzzy and learning algorithms; explain how to assess soft classified outputs as fraction images using fuzzy error matrix (FERM) and its advance versions with FERM tool, Entropy, Correlation Coefficient, Root Mean Square Error and Receiver Operating Characteristic (ROC) methods and; combines explanation of the algorithms with case studies and practical applications.

Accurate retrieval of top of the atmosphere (TOA) radiance is a sustained research work in ocean color remote sensing. Spectral shapes of sensor input and sensor spectral response (SRF) function may significantly affect the achievable accuracy. A model is presented here to quantify the effects of spectral shapes. We also proposed an innovative method to retrieve the TOA spectral radiance that accounts for spectral shapes. Ten-fold improvement is demonstrated in retrieval accuracy using the proposed method on multiple data sets. Input spectral shape variations are simulated using spectra of TOA radiance, path radiance, exoatmospheric solar irradiance, and integrating sphere. SRF changes are simulated using SRFs of Ocean Color Monitor-2 and Sea-Viewing Wide Field-of-View Sensor. System spectral shape factor, a new parameter, is found to capture the spectral changes and helps quantitative assessment of the inaccuracy. The proposed method will be highly beneficial in deriving accurate geophysical products from the existing and upcoming ocean color remote sensing instruments. (C) 2020 Society of Photo-Optical Instrumentation Engineers (SPIE)

A widespread forest fire episode occurred over Uttarakhand during April 24–May 2, 2016. This event released large amount of carbon monoxide (CO), nitrogen dioxide (NO2) and aerosols in the pristine environment of Uttarakhand. AIRS observations showed 60–125 ppbv higher CO during fire-impacted period with respect to background CO at 925, 850 and 700 hPa. Spatial distribution of CO and fire hotspots over Uttarakhand showed high level of CO over the region of intense biomass burning specifically. Over Dehradun, rate of increase in daily average surface CO was found to be 45 ppbv/day during fire period. Average background and fire-impacted tropospheric column NO2 were found to be 1.7 × 1015 ± 5.0 × 1014 mol/cm2 and 3.0 × 1015 ± 8.5 × 1014 mol/cm2, respectively. Similarly, average background and fire-impacted aerosol optical depth (AOD) were found to be 0.47 ± 0.25 and 0.90 ± 0.35, respectively, for Terra and 0.44 ± 0.17 and 0.86 ± 0.47, respectively, for Aqua observations. Size- and shape-segregated AOD distributions showed enhancement of medium-to-coarse (radius > 0.35 µm) non-spherical particles due to fire episode. CALIPSO height-resolved aerosol subtyping showed dominance of smoke near Uttarakhand up to 10 km altitude. Interspecies correlations indicated the common sources of near-surface CO, CO aloft, AOD and NO2. A relatively poor correlation between near-surface CO and tropospheric column NO2 might be due to chemical transformation of reactive NO2 within the fire plume. Forward trajectories calculated over fire-affected region at 500 m AGL indicated that fire emissions might have influenced the air quality of southern Nepal and northern Uttar Pradesh/Bihar within lower 3 km.

Complexity and heterogeneity of urban areas lead to difficulty in urban weather simulations and climate modeling. Diversity and size of urban areas necessitate to downscale global climate models to urban scale (~ hundreds of meters) and to enhance urban parameterization in the models to realistically simulate urban weather conditions. Hence, in this study, a methodology has been developed to generate multi-class urban land use land cover (LULC) by employing Resourcesat-2 LISS IV data. Weather Research and Forecast (WRF) model which is also a mesoscale numerical weather prediction and regional climate model was utilized to downscale the meteorological parameters up to 0.5 km grid resolution. Multi-class urban LULC prepared with improved urban parameters and updated Land Surface Parameters (LSPs) was ingested in model for Delhi to evaluate the model performance in three dominant seasons, i.e., summer, monsoon and winter. Evaluation of model performance with ground observation data revealed that multi-class urban LULC along with updated LSPs provided improved RMSE values of 2.31° C, 1.79 m/s and 0.94 mbar as compared to ingestion of multi-class urban LULC only (RMSE values of 3.42° C, 3.72 m/s and 1.58 mbar) for temperature at 2 m, wind speed and surface pressure, respectively. Temperature is found to be highest in summer season (38.58° C) and lowest in winter season while relative humidity is highest in monsoon season (~ 88%) and lowest in summer season (~ 30%). The study highlights the importance of ingestion of updated LSPs along with updated multi-class urban LULC for enhanced model performance.

With the free and full access to images from Sentinel-2 satellite, the interest to use this data for quantitative retrieval of vegetation parameters is ever-increasing. LAI and chlorophyll are two key variables which are desired for studying productivity, nutrient and stress status of vegetation. Studies carried out on croplands using simulated Sentinel-2 MSI and parametric approach have identified vegetation indices (VIs) with high sensitivity to LAI and chlorophyll. To test how Sentinel-2 red-edge based VIs perform for retrieval of LAI and Chlorophyll of tropical mixed forest canopies, this study has been performed. The field measurements of LAI and chlorophyll content were recorded in a total of 28 ESUs (Elementary Sampling Units) in Bhakra range in the Tarai Central Forest Division, Uttarakhand (India). The in-situ measurements were statistically correlated with Sentinel-2VIs and strength of correlation was validated using Predicted Residual Error Sum of Squares (PRESS) statistic. Field LAI corrected for foliage dumpiness effect improved correlation of VIs with LAI. Among all VIs tested, Normalized Difference Index (NDI) offered highest positive correlation (R-2 = 0.79, p < 0.05) with LAI while Red-Edge Chlorophyll Index (RECI) (R-2 = 0.83, RMSE = 0.24 g/m(2), p < 0.05) and Simple Ratio (SR) 740/705 (R-2 = 0.79, RMSE = 0.27 g/m(2), p < 0.05) were the most closely related to chlorophyll content. VIs with red-edge and NIR combinations offered best results. (C) 2019 COSPAR. Published by Elsevier Ltd. All rights reserved.

Historical buildings are essential elements of cultural heritage. Built-up heritage is subjected to various damages over a long period of time, and it is, therefore, necessary to document such monuments by using multiple sensors, which are not only efficient but also non-complicated and effortless in terms of data acquisition and handling. Complete documentation of the monument involves recognition and mapping of such damages, if present. As the monuments are of considerable sizes and sometimes not easily accessible, recording and mapping of the damages by manual methods is both time-consuming and costly. Terrestrial laser scanning (TLS), combined with terrestrial images, can be used to document and map damages of the monuments. The present work demonstrates the practical application of TLS in the documentation of heritage monuments with a focus on damage detection using TLS point clouds and terrestrial optical data.

India is home of the largest remaining population of the Asian elephant (Elephas maximus L.) in the South and Southeast Asia. The forest loss and fragmentation is the main threat to the long-term survival of Asian elephants. In the present study, we assessed forest loss and fragmentation in the major elephant ranging provinces in India, viz., north-eastern, north-western, central, and southern since the 1930s. We quantified forest cover changes by generating and analyzing forest cover maps of 1930, 1975, and 2013, whereas fragmentation of contiguous forest areas was quantified by applying landscape metrics on the temporal forest cover maps. A total of 21.49% of the original forest cover was lost from 1930 to 1975, while another 3.19% forest cover was lost from 1975 to 2013 in the elephant ranges in India. The maximum forest loss occurred in the southern range (13,084 km2) followed by north-eastern (10,188 km2), central (5614 km2), and north-western (4030 km2) elephant ranges in the past eight decades. The forests in the central range were the most fragmented followed by southern, north-eastern, and north-western elephant ranges. The forest fragmentation in the southern range occurred at the fastest rate than central, north-eastern, and north-western ranges. The core forest areas shrunk by 39.6% from 1930 to 2013. The causative factors of forest change and situation of elephant-human conflict have been discussed. Study outcomes would be helpful in planning effective conservation strategies for Asian elephants in India.

The study aims to simulate the peri-urban growth dynamics in a growing region of India using Weights of Evidence (WOE) based cellular automata model. The growth process was expressed as a function of four causative variables corresponding to which seven data layers were generated in a Geographic Information Systems environment. The model was calibrated for the period 2000–2005 using Kappa indices and fuzzy set theory based two way comparison method. The Kappa value was 0.7, while the value of Klocation and Khisto were 0.81 and 0.93, respectively. The fuzzy similarity values increased for small to large neighbourhood sizes which showed that the model was able to simulate the contiguous and dense growth. However, for dispersed and isolated growth the model showed less accuracy. The model was validated for period 2005–2010 and revealed a Kappa value of 0.88, while value of Klocation and Khisto were 0.91 and 0.96, respectively.