Satellite-based Earth Observation Research Articles

Deformation monitoring and thematic mapping of the Badaling Great Wall using very high-resolution interferometric synthetic aperture radar data

The preventive monitoring and sustainable conservation of large-scale cultural heritage sites require satellite-based Earth observations. In this study, we present the first monitoring and thematic mapping results of the Badaling Great Wall (Beijing, China) using deformation indicators calculated from very high-resolution (VHR) multi-temporal spotlight TerraSAR-X data. The proposed coarse–fine search algorithm achieved high computational efficiency for calculating the unknown parameters of the two-tier network persistent scatterer synthetic aperture radar (SAR) interferometry (PSInSAR) approach. The spatiotemporal deformation anomalies, characterized by the absolute velocity, deformation deviation and acceleration, are informative to identify suspected hotspots for prioritizing monitoring activities. We provide an understandable method for thematic mapping and subsequent sustainable conservation of heritage sites by synergistically exploiting impacts from natural degradation and the tourism industry. We determine that the optimum tourist capacity of the site could be 1.0 million per month based on comparing pre- and post-COVID data (2019–2020). This study demonstrates the potential and performance of spaceborne PSInSAR tools for the intelligent management of large-scale architectural heritage sites by integrating InSAR deformation products with environmental and social data.

A Lithosphere–Atmosphere–Ionosphere Coupling Phenomenon Observed Before M 7.7 Jamaica Earthquake

The satellite-based earth observations have become an appropriate instrumental approach in monitoring the natural hazards among modern-day researchers. This study presents a multi-parameter approach using precursors of different physical nature defining the states of atmosphere and the ionosphere in terms of temporal and spatial variations about 5 days before the impending M 7.7 Jamaican earthquake (EQ). We performed a comprehensive analysis from the surface to the ionosphere at different altitude levels by analyzing the different datasets comprising, surface air temperature, relative humidity, total column water vapor, air pressure, Outgoing Longwave Radiations (OLR), and the total electron content of the global ionosphere maps (GIM-TEC). We observed a sharp increment in the atmospheric chemical potential (ACP) due to the increased radon activity that led to an abrupt decrement in the atmospheric relative humidity and, consequently, increased OLR that provides strong evidence of the air ionization production around the epicenter of M 7.7 EQ. Moreover, to check the periodicity of these atmospheric parameters, we performed a confutation analysis by meticulously analyzing these parameters in the same month and region for the previous 5 years during the non-existence of any major seismicity. This technique confirmed that the simultaneous atmospheric variations observed before the Jamaica EQ are not cyclic in the absence of significant seismic activities. The ionospheric conditions have also shown consistency with atmospheric disturbances, as depletions in GIM-TECs, having an amplitude of 4 TECU, are observed over the epicenter for 6 h (LT = 13–19 (-– UT)) on January 23, 2020. Additionally, the vertical ionospheric and atmospheric profiles from FORMOSAT-7/COSMIC-2 (F7/C2), at different altitudes (75–225 km) over the EQ epicenter, showed significant depletions on January 23, 2020. These TEC variations are observed to be an effect of the vertical seismogenic electric field due to the production of the air ionization at the atmospheric boundary layer by increased radon activity around the seismic preparation zone. The existence of these co-located synchronized atmospheric and ionospheric anomalies is explicitly and persistently local over a small region of the epicenter of Jamaican EQ that could be considered as potential short-term precursors. Also, these multi-observed anomalies will contribute to the physical explanation of the Lithosphere-Atmosphere–Ionosphere Coupling (LAIC) model.

Exploitation of optical and SAR amplitude imagery for landslide identification: a case study from Sikkim, Northeast India

Detection and mapping of landslides is one of the most important techniques used for reducing the impact of natural disasters especially in the Himalaya, owing to its high amount of tectonic deformation, seismicity, and unfavorable climatic conditions. Moreover, the northeastern part of the Himalaya, severely affected by landslides every monsoon, is poorly studied. The information on the inventories is inhomogeneous and lacking. In this context, satellite-based earth observation data, which has significantly advanced in the last decade and often serves as a potential source for data collection, monitoring, and damage assessment for disasters in a short time span, has been implemented. Keeping in mind the above framework, this study aims to exploit the potentials of Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 optical imagery for identifying new landslides in vegetated and hilly areas of the northeastern part of India. In order to assess the potentials of our data and methodology, a landslide event which occurred on 13 August 2016 13:30 h (IST) in North Sikkim, India, triggered due to rainfall has been explored in detail. The landslide also resulted in the formation of a lake, 2.2 km in length and 290 m in width. Difficulty in procurement of cloud-free datasets immediately after the event led us to the use of Sentinel-1 SAR backscatter data, to assess its potential for this purpose. It is observed that the potential of SAR amplitude imagery is limited to different aspects as per the sensor look direction during the mode of acquisition. Furthermore, the present study also incorporates a change detection algorithm to evaluate the performance of the Sudden Landslide Identification Product (SLIP) model to identify new landslides using Sentinel-2 multispectral imagery. Overall, the results exhibit that integrated usage of both optical and SAR amplitude imagery may provide a plethora of information for identification and mapping of new landslides for damage assessment and early warning. All the above results combined together suggest this method for rapid identification of landslides in the Himalayan terrain with special emphasis on the northeastern part of the Himalaya. The automation of this method for future operational usage is also suggested.

Spring and autumn phenology across the Tibetan Plateau inferred from normalized difference vegetation index and solar-induced chlorophyll fluorescence

ABSTRACT Plant phenology is a key parameter for accurately modeling ecosystem dynamics. Limited by scarce ground observations and benefiting from the rapid growth of satellite-based Earth observations, satellite data have been widely used for broad-scale phenology studies. Commonly used reflectance vegetation indices represent the emergence and senescence of photosynthetic structures (leaves), but not necessarily that of photosynthetic activities. Leveraging data of the recently emerging solar-induced chlorophyll fluorescence (SIF) that is directly related to photosynthesis, and the traditional MODIS Normalized Difference Vegetation Index (NDVI), we investigated the similarities and differences on the start and end of the growing season (SOS and EOS, respectively) of the Tibetan Plateau. We found similar spatiotemporal patterns in SIF-based SOS (SOSSIF) and NDVI-based SOS (SOSNDVI). These spatial patterns were mainly driven by temperature in the east and by precipitation in the west. Yet the two satellite products produced different spatial patterns in EOS, likely due to their different climate dependencies. Our work demonstrates the value of big Earth data for discovering broad-scale spatiotemporal patterns, especially on regions with scarce field data. This study provides insights into extending the definition of phenology and fosters a deeper understanding of ecosystem dynamics from big data.

A Survey of Active Learning for Quantifying Vegetation Traits from Terrestrial Earth Observation Data.

The current exponential increase of spatiotemporally explicit data streams from satellitebased Earth observation missions offers promising opportunities for global vegetation monitoring. Intelligent sampling through active learning (AL) heuristics provides a pathway for fast inference of essential vegetation variables by means of hybrid retrieval approaches, i.e., machine learning regression algorithms trained by radiative transfer model (RTM) simulations. In this study we summarize AL theory and perform a brief systematic literature survey about AL heuristics used in the context of Earth observation regression problems over terrestrial targets. Across all relevant studies it appeared that: (i) retrieval accuracy of AL-optimized training data sets outperformed models trained over large randomly sampled data sets, and (ii) Euclidean distance-based (EBD) diversity method tends to be the most efficient AL technique in terms of accuracy and computational demand. Additionally, a case study is presented based on experimental data employing both uncertainty and diversity AL criteria. Hereby, a a simulated training data base by the PROSAIL-PRO canopy RTM is used to demonstrate the benefit of AL techniques for the estimation of total leaf carotenoid content (Cxc) and leaf water content (Cw). Gaussian process regression (GPR) was incorporated to minimize and optimize the training data set with AL. Training the GPR algorithm on optimally AL-based sampled data sets led to improved variable retrievals compared to training on full data pools, which is further demonstrated on a mapping example. From these findings we can recommend the use of AL-based sub-sampling procedures to select the most informative samples out of large training data pools. This will not only optimize regression accuracy due to exclusion of redundant information, but also speed up processing time and reduce final model size of kernel-based machine learning regression algorithms, such as GPR. With this study we want to encourage further testing and implementation of AL sampling methods for hybrid retrieval workflows. AL can contribute to the solution of regression problems within the framework of operational vegetation monitoring using satellite imaging spectroscopy data, and may strongly facilitate data processing for cloud-computing platforms.

Fine-grained, spatiotemporal datasets measuring 200 years of land development in the United States.

The collection, processing, and analysis of remote sensing data since the early 1970s has rapidly improved our understanding of change on the Earth’s surface. While satellite-based Earth observation has proven to be of vast scientific value, these data are typically confined to recent decades of observation and often lack important thematic detail. Here, we advance in this arena by constructing new spatially explicit settlement data for the United States that extend back to the early 19th century and are consistently enumerated at fine spatial and temporal granularity (i.e. 250m spatial and 5-year temporal resolution). We create these time series using a large, novel building-stock database to extract and map retrospective, fine-grained spatial distributions of built-up properties in the conterminous United States from 1810 to 2015. From our data extraction, we analyse and publish a series of gridded geospatial datasets that enable novel retrospective historical analysis of the built environment at an unprecedented spatial and temporal resolution. The datasets are part of the Historical Settlement Data Compilation for the United States (https://dataverse.harvard.edu/dataverse/hisdacus, last access: 25 January 2021) and are available at https://doi.org/10.7910/DVN/YSWMDR (Uhl and Leyk, 2020a), https://doi.org/10.7910/DVN/SJ213V (Uhl and Leyk, 2020b), and https://doi.org/10.7910/DVN/J6CYUJ (Uhl and Leyk, 2020c).

Special Issue on Ground-based Measurements for Satellite-based Earth Observation Data

Special Issue on Ground-based Measurements for Satellite-based Earth Observation Data

The short life of the volcanic island New Late\u2019iki (Tonga) analyzed by multi-sensor remote sensing data

Satellite-based Earth observation plays a key role for monitoring volcanoes, especially those which are located in remote areas and which very often are not observed by a terrestrial monitoring network. In our study we jointly analyzed data from thermal (Moderate Resolution Imaging Spectrometer MODIS and Visible Infrared Imaging Radiometer Suite VIIRS), optical (Operational Land Imager and Multispectral Instrument) and synthetic aperture radar (SAR) (Sentinel-1 and TerraSAR-X) satellite sensors to investigate the mid-October 2019 surtseyan eruption at Late’iki Volcano, located on the Tonga Volcanic Arc. During the eruption, the remains of an older volcanic island formed in 1995 collapsed and a new volcanic island, called New Late’iki was formed. After the 12 days long lasting eruption, we observed a rapid change of the island’s shape and size, and an erosion of this newly formed volcanic island, which was reclaimed by the ocean two months after the eruption ceased. This fast erosion of New Late’iki Island is in strong contrast to the over 25 years long survival of the volcanic island formed in 1995.

Remote Sensing of Ecosystem Structure: Fusing Passive and Active Remotely Sensed Data to Characterize a Deltaic Wetland Landscape

A project was constructed to integrate remotely sensed data from multiple sensors and platforms to characterize range of ecosystem characteristics in the Peace–Athabasca Delta in Northern Alberta, Canada. The objective of this project was to provide a framework for the processing of multisensor data to extract ecosystem information describing complex deltaic wetland environments. The data used in this study was based on a passive satellite-based earth observation multispectral sensor (Sentinel-2) and airborne discrete light detection and ranging (LiDAR). The data processing strategy adopted here allowed us to employ a data mining approach to grouping of the input variables into ecologically meaningful clusters. Using this approach, we described not only the reflective characteristics of the cover, but also ascribe vertical and horizontal structure, thereby differentiating spectrally similar, but ecologically distinct, ground features. This methodology provides a framework for assessing the impact of ecosystems on radiance, as measured by Earth observing systems, where it forms the basis for sampling and analysis. This final point will be the focus of future work.

Earth Observation Based Monitoring of Forests in Germany: A Review

Forests in Germany cover around 11.4 million hectares and, thus, a share of 32% of Germany’s surface area. Therefore, forests shape the character of the country’s cultural landscape. Germany’s forests fulfil a variety of functions for nature and society, and also play an important role in the context of climate levelling. Climate change, manifested via rising temperatures and current weather extremes, has a negative impact on the health and development of forests. Within the last five years, severe storms, extreme drought, and heat waves, and the subsequent mass reproduction of bark beetles have all seriously affected Germany’s forests. Facing the current dramatic extent of forest damage and the emerging long-term consequences, the effort to preserve forests in Germany, along with their diversity and productivity, is an indispensable task for the government. Several German ministries have and plan to initiate measures supporting forest health. Quantitative data is one means for sound decision-making to ensure the monitoring of the forest and to improve the monitoring of forest damage. In addition to existing forest monitoring systems, such as the federal forest inventory, the national crown condition survey, and the national forest soil inventory, systematic surveys of forest condition and vulnerability at the national scale can be expanded with the help of a satellite-based earth observation. In this review, we analysed and categorized all research studies published in the last 20 years that focus on the remote sensing of forests in Germany. For this study, 166 citation indexed research publications have been thoroughly analysed with respect to publication frequency, location of studies undertaken, spatial and temporal scale, coverage of the studies, satellite sensors employed, thematic foci of the studies, and overall outcomes, allowing us to identify major research and geoinformation product gaps.

Characterizing the Error and Bias of Remotely Sensed LAI Products: An Example for Tropical and Subtropical Evergreen Forests in South China

Leaf area is a key parameter underpinning ecosystem carbon, water and energy exchanges via photosynthesis, transpiration and absorption of radiation, from local to global scales. Satellite-based Earth Observation (EO) can provide estimates of leaf area index (LAI) with global coverage and high temporal frequency. However, the error and bias contained within these EO products and their variation in time and across spatial resolutions remain poorly understood. Here, we used nearly 8000 in situ measurements of LAI from six forest environments in southern China to evaluate the magnitude, uncertainty, and dynamics of three widely used EO LAI products. The finer spatial resolution GEOV3 PROBA-V 300 m LAI product best estimates the observed LAI from a multi-site dataset (R2 = 0.45, bias = −0.54 m2 m−2, RMSE = 1.21 m2 m−2) and importantly captures canopy dynamics well, including the amplitude and phase. The GEOV2 PROBA-V 1 km LAI product performed the next best (R2 = 0.36, bias = −2.04 m2 m−2, RMSE = 2.32 m2 m−2) followed by MODIS 500 m LAI (R2 = 0.20, bias = −1.47 m2 m−2, RMSE = 2.29 m2 m−2). The MODIS 500 m product did not capture the temporal dynamics observed in situ across southern China. The uncertainties estimated by each of the EO products are substantially smaller (3–5 times) than the observed bias for EO products against in situ measurements. Thus, reported product uncertainties are substantially underestimated and do not fully account for their total uncertainty. Overall, our analysis indicates that both the retrieval algorithm and spatial resolution play an important role in accurately estimating LAI for the dense canopy forests in Southern China. When constraining models of the carbon cycle and other ecosystem processes are run, studies should assume that current EO product LAI uncertainty estimates underestimate their true uncertainty value.

A multinomial logistic model-based land use and land cover classification for the South Asian Association for Regional Cooperation nations using Moderate Resolution Imaging Spectroradiometer product

The land-use and land-cover (LULC) mapping is crucial for the management and planning of a nation. The land use and land cover are the two separate terminologies often used interchangeably. We mapped LULC for the nations of South Asian Association for Regional Cooperation represented by Afghanistan, Bangladesh, Bhutan, India, Nepal, the Maldives, Pakistan and Sri Lanka covering an area of ca. 5,100,000 km2. Mapping LULC is a cumbersome process for a larger area; nevertheless, in recent decades the satellite-based Earth observation has provided a great impetus and various approaches have been adopted to map LULC. In this study, we used the multinomial logit model (Mnlogit) to classify Moderate Resolution Imaging Spectroradiometer (MODIS)-derived time-series data for mapping LULC. The MODIS-archived images for the years 2004–2007 and 2016 were used to map LULC for corresponding years. For the same years, maps were available from IGBP, FAO and NR Census, India, for comparison; hence, these years were selected. The classification of images for each of the years was found to be more consistent and accurate in comparison with the existing LULC maps of IGBP and FAO, and hence a robust approach. The method can be adopted to map global resources. We achieved overall accuracy ranging from 76 to 82% with kappa statistic (K-hat) ranging from 0.64 to 0.73, which is better than previous works. The study demonstrates an effective way to utilize remote sensing data for mapping LULC for a larger area with reasonable accuracy.

The Satellite-based Earth Observation Laboratory, Department of Earth Science, School of Education, Waseda University

The Satellite-based Earth Observation Laboratory, Department of Earth Science, School of Education, Waseda University

Deriving the Reservoir Conditions for Better Water Resource Management Using Satellite-Based Earth Observations in the Lower Mekong River Basin

The Mekong River basin supported a large population and ecosystem with abundant water and nutrient supply. However, the impoundments in the river can substantially alter the flow downstream and its timing. Using limited observations, this study demonstrated an approach to derive dam characteristics, including storage and flow rate, from remote-sensing-based data. Global Reservoir and Lake Monitor (GRLM), River-Lake Hydrology (RLH), and ICESat-GLAS, which generated altimetry from Jason series and inundation areas from Landsat 8, were used to estimate the reservoir surface area and change in storage over time. The inflow simulated by the variable infiltration capacity (VIC) model from 2008 to 2016 and the reservoir storage change were used in the mass balance equation to calculate outflows for three dams in the basin. Estimated reservoir total storage closely resembled the observed data, with a Nash-Sutcliffe efficiency and coefficient of determination more than 0.90 and 0.95, respectively. An average decrease of 55% in outflows was estimated during the wet season and an increase of up to 94% in the dry season for the Lam Pao. The estimated decrease in outflows during the wet season was 70% and 60% for Sirindhorn and Ubol Ratana, respectively, along with a 36% increase in the dry season for Sirindhorn. Basin-wide demand for evapotranspiration, about 935 mm, implicitly matched with the annual water diversion from 1000 to 2300 million m3. From the storage–discharge rating curves, minimum storage was also evident in the monsoon season (June–July), and it reached the highest in November. This study demonstrated the utility of remote sensing products to assess the impacts of dams on flows in the Mekong River basin.

Analysis of Long-Term Moon-Based Observation Characteristics for Arctic and Antarctic

The Antarctic and Arctic have always been critical areas of earth science research and are sensitive to global climate change. Global climate change exhibits diversity characteristics on both temporal and spatial scales. Since the Moon-based earth observation platform could provide large-scale, multi-angle, and long-term measurements complementary to the satellite-based Earth observation data, it is necessary to study the observation characteristics of this new platform. With deepening understanding of Moon-based observations, we have seen its good observation ability in the middle and low latitudes of the Earth’s surface, but for polar regions, we need to further study the observation characteristics of this platform. Based on the above objectives, we used the Moon-based Earth observation geometric model to quantify the geometric relationship between the Sun, Moon, and Earth. Assuming the sensor is at the center of the nearside of the Moon, the coverage characteristics of the earth feature points are counted. The observation intervals, access frequency, and the angle information of each point during 100 years were obtained, and the variation rule was analyzed. The research showed that the lunar platform could carry out ideal observations for the polar regions. For the North and South poles, a continuous observation duration of 14.5 days could be obtained, and as the latitude decreased, the duration time was reduced to less than one day at the latitude of 65° in each hemisphere. The dominant observation time of the North Pole is concentrated from mid-March to mid-September, and for the South Pole, it is the rest of the year, and as the latitude decreases, it extends outward from both sides. The annual coverage time and frequency will change with the relationship between the Moon and the Earth. This study also proves that the Moon-based observation has multi-angle observation advantages for the Arctic and the Antarctic areas, which can help better understand large-scale geoscientific phenomena. The above findings indicate that the Moon-based observation can be applied as a new type of remote sensing technology to the observation field of the Earth’s polar regions.

Framework for agricultural performance assessment based on MODIS multitemporal data

We present a hierarchical classification framework for automated detection and mapping of spatial patterns of agricultural performance using satellite-based Earth observation data exemplified for the Aral Sea Basin (ASB) in Central Asia. The core element of the framework is the derivation of a composite agricultural performance index which is composed of different subindicators taking into account cropping intensity, crop diversity, crop rotations, fallow land frequency, land utilization, water use efficiency, and water availability. We derive these subindicators from net primary productivity and evapotranspiration data obtained from the MODIS sensor on board the Terra satellite during the observation period from 2000 to 2016, as well as from cropland maps created through multiannual classification of normalized difference vegetation index (NDVI). We classified pixel-based NDVI time series covering more than 8 × 106 ha of irrigated cropland based on a hierarchical approach concatenating unsupervised and supervised classification techniques to automatically generate and refine training labels, which are then used to train a decision fusion classifier, achieving an average overall accuracy of 78%. The results give unprecedented insights into spatial patterns of agricultural performance in the ASB. The proposed method is transferable and applicable for global-scale mapping, and the results of this remote sensing-aided assessment can provide important information for regional agricultural planning purposes.

Diode-pumped Alexandrite laser for next generation satellite-based earth observation lidar

In this work, the design of a diode-pumped Alexandrite ring laser in Q-switched single-longitudinal-mode (SLM) operation for a spaceborne lidar mission is presented. The laser is pumped by a self-developed fiber-coupled laser diode pump device and yields a pulse energy of 1.7 mJ at a repetition rate of 500 Hz with an excellent beam quality of M2 < 1.1. By seeding the resonator with a narrow band diode laser, SLM operation with a linewidth of approximately 10 MHz is achieved. The electro-optical efficiency of 2% is the highest achieved for all Alexandrite lasers in SLM operation and reasonable for space operation. The performance analysis as well as benchmarking with the space-qualified mounting technology points out the TRL and the remaining effort for the development of the technology. An estimation of the requirements for a spaceborne resonance lidar mission underlines the suitability of such a lidar system with a diode-pumped Alexandrite laser as the beam source.

Global 5G wireless networks threaten weather forecasts.

Next-generation mobile technology could interfere with crucial satellite-based Earth observations. Next-generation mobile technology could interfere with crucial satellite-based Earth observations.

Synergizing global tools to monitor progress towards land degradation neutrality: Trends.Earth and the World Overview of Conservation Approaches and Technologies sustainable land management database

As part of the Sustainable Development Goals, countries are striving to achieve by 2030 a land degradation neutral world. Land degradation neutrality (LDN) is the state whereby the amount and quality of land resources remains stable or increases within specified temporal and spatial scales. Achieving this will require the uptake of sustainable land management (SLM) practices to increase the sustainable provision of ecosystem goods and services the human population will require. It will also require the development of systematic, robust, and validated methods for tracking progress at project, subnational and national scales. However, to date, no systematic comparison between the SLM practices and the indicators proposed for monitoring LDN has been performed. In this article, we used the United Nations Convention to Combat Desertification primary recommended global sustainable land management database of World Overview of Conservation Approaches and Technologies (WOCAT), and an innovative tool designed to assess and monitor land condition via changes in land productivity, Trends.Earth, to evaluate the agreement between self-reported sustainable land management technologies and indicators derived from satellite-based earth observations. We found that a combination of two primary productivity indicators derived from annual integrals of normalized difference vegetation indices (NDVI), trajectory and state, were able to identify increases in primary productivity in the locations where the SLM practices are implemented in comparison to control sites where SLM practices are not known to have occurred. Moreover, different SLM practices showed unique responses in terms of proportional area which experienced increase, decrease, or remained stable terms of primary productivity. We also found that the time since establishment of the SLM technology was critical for identifying improvements in the SLM sites, as only technologies with more than 10 years since implementation show statistically significant improvements. Our results show that satellite-derived land productivity indicators are successful at detecting the impacts of SLM practices on primary productivity, positioning them as essential elements of the monitoring and assessment tools needed to track land condition to assure the achievement of a land degradation neutral world.

CAPACITY BUILDING FOR RS &amp; GIS TECHNOLOGY APPLICATIONS: STUDENT REMOTE SENSING AND GEOSPATIAL PROGRAMME IN ACADEMIC INSTITUTIONS

Abstract. Digital India program aims to transform India into a knowledge economy with Smart Cities / Smart Villages etc. It depends significantly on Remote Sensing (RS) and Geographic Information Systems (GIS). It requires enormous computing Infrastructure and RS-GIS trained professionals along with Information and Communications Technology (ICT). Satellite based Earth Observation (EO) data volumes are increasing at a rate of several Terabytes a Day in heterogeneous formats (different spatial and temporal resolutions in optical, thermal and microwave). The recent trends towards free and open access policy creates unprecedented opportunities for both industrial and academic research communities for Big Data applications.During the last decade, Engineering Institutions across the nation have improved significantly in terms of high end computing, Wi-Fi campuses and qualified faculty to help students. Agricultural Universities/Colleges are also making extensive use of RS-GIS technology. Many research papers are also being published by students from Engineering / Agricultural Institutions in the domain of RS–GIS. Here lies the scope for enhanced collaboration between ISRO / Industry and Academia in the form of Student Remote Sensing and Geospatial programme (SRSGP) for scaling up operational services to sustain Digital India and boost the Geospatial Readiness Index of the nation. It will expand the hands-on experience of the student community and enrich their job-readiness in the industry and lead to development of trained RS-GIS professionals. It has the potential to make Digital India the global destination for RS-GIS applications similar to how Y-2K made India the global IT-destination.