India’s Polar Satellite Launch Vehicle, in its 42nd flight (PSLV-C40), has launched the 710 kg Cartosat-2 Series Satellite for earth observation and 30 co-passenger satellites together weighing about 613 kg at lift-off. PSLV-C40 was launched from the First Launch Pad (FLP) of Satish Dhawan Space Centre (SDSC) SHAR, Sriharikota. In its first mission of 2018, the Indian Space Research Organisation (ISRO) successfully launched its 100th satellite. The mission comes a little over four months after the space agency’s unsuccessful launch of IRNSS-1H. Prime Minister Narendra Modi, congratulating ISRO for its success, said the launch signifies the bright future of India’s space programme.
(PSLV-C40 on First Launch Pad – Evening View. Source: ISRO)
The co-passenger satellites comprise one Microsatellite and one Nanosatellite from India as well as 3 Microsatellites and 25 Nanosatellites from six countries, namely, Canada, Finland, France, Republic of Korea, UK and USA. The total weight of all the 31 satellites carried onboard PSLV-C40 is about 1323 kg.
The 28 International customer satellites are being launched as part of the commercial arrangements between Antrix Corporation Limited (Antrix), a Government of India company under Department of Space (DOS), the commercial arm of ISRO and the International customers.
Cartosat-2 Series Satellite is the primary satellite carried by PSLV-C40. This remote sensing satellite is similar in configuration to earlier satellites in the series and is intended to augment data services to the users.
The imagery sent by satellite will be useful for cartographic applications, urban and rural applications, coastal land use and regulation, utility management like road network monitoring, water distribution, creation of land use maps, change detection to bring out geographical and manmade features and various other Land Information System (LIS) as well as Geographical Information System (GIS) applications.
New technologies such as Artificial Intelligence (AI), Cloud Machine Learning, Satellite Imagery and advanced analytics are empowering small-holder farmers in India to increase their income through higher crop yield and greater price control, Microsoft India said.
(Photo Source: ICRISAT)
In a few dozen villages in Telengana, Maharashtra and Madhya Pradesh, farmers are receiving automated voice calls that tell them whether their cotton crops are at risk of a pest attack, based on weather conditions and crop stage. In Karnataka, the government can get price forecasts for essential commodities such as tur (split red gram) three months in advance for planning the Minimum Support Price (MSP).
“Sowing date as such is very critical to ensure that farmers harvest a good crop. And if it fails, it results in loss as a lot of costs are incurred for seeds, as well as the fertilizer applications,” Suhas P. Wani, Director, Asia Region, of the International Crop Research Institute for the Semi-Arid Tropics (ICRISAT), said in a Microsoft blog post.
The non-profit ICRISAT conducts agricultural research for development in Asia and sub-Saharan Africa with a wide array of partners throughout the world. In collaboration with ICRISAT, Microsoft has developed an AI-Sowing App powered by Microsoft Cortana Intelligence Suite including Machine Learning and Power BI.
“The app sends sowing advisories to participating farmers on the optimal date to sow. The best part – the farmers don’t need to install any sensors in their fields or incur any capital expenditure. All they need is a feature phone capable of receiving text messages,” the company said.
To calculate the crop-sowing period, historic climate data spanning over 30 years – from 1986 to 2015 – for the Devanakonda area in Andhra Pradesh was analysed using AI. To determine the optimal sowing period, the Moisture Adequacy Index (MAI) was calculated. MAI is the standardised measure used for assessing the degree of adequacy of rainfall and soil moisture to meet the potential water requirement of crops. This data is then downscaled to build predictability and guide farmers to pick the ideal sowing week.
This year, ICRISAT has scaled sowing insights to 4,000 farmers across Andhra Pradesh and Karnataka for the Kharif crop cycle (rainy season). Predictive analysis in agriculture is not limited to crop growing alone. The Karnataka government will start using price forecasting for agricultural commodities, in addition to sowing advisories for farmers in the state. Commodity prices for items such as tur, of which Karnataka is the second largest producer, will be predicted three months in advance for major markets in the state, Microsoft said.
Microsoft has developed a multivariate agricultural commodity price forecasting model to predict future commodity arrival and the corresponding prices. The model uses remote sensing data from geo-stationary satellite images to predict crop yields through every stage of farming. The model currently being used to predict the prices of tur, is scalable, and time efficient and can be generalised to many other regions and crops.
India has the highest net cropland area while South Asia and Europe are considered agricultural capitals of the world. A new map was released on 14th November 2017, detailing croplands worldwide in the highest resolution yet, helping to ensure global food and water security in a sustainable way.
The map establishes that there are 1.87 billion hectares of croplands in the world, which is 15 to 20 percent—or 250 to 350 million hectares (Mha)—higher than former assessments. The change is due to the more detailed understanding of large areas that were never mapped before or were inaccurately mapped as non-croplands.
Earlier studies showed either China or the United States as having the highest net cropland area, but this study shows that India ranks first, with 179.8 Mha (9.6 percent of the global net cropland area). Second is the United States with 167.8 Mha (8.9 percent), China with 165.2 Mha (8.8 percent) and Russia with 155.8 Mha (8.3 percent). Statistics of every country in the world can be viewed on an interactive map.
(This map shows cropland distribution across the world in a nominal 30-meter resolution. This is the baseline product of the GFSAD30 Project. Source: USGS)
South Asia and Europe can be considered agricultural capitals of the world due to the percentage of croplands of the total geographic area. Croplands make up more than 80 percent of Moldova, San Marino and Hungary; between 70 and 80 percent of Denmark, Ukraine, Ireland and Bangladesh; and 60 to 70 percent of the Netherlands, United Kingdom, Spain, Lithuania, Poland, Gaza Strip, Czech Republic, Italy and India. For comparison, the United States and China each have 18 percent croplands.
The study was led by the USGS and is part of the Global Food Security-Support Analysis Data @ 30-m (GFSAD30) Project. The map is built primarily from Landsat satellite imagery with 30-meter resolution, which is the highest spatial resolution of any global agricultural dataset.
Importance of Monitoring Croplands in Great Detail
“The map clearly shows individual farm fields, big or small, at any location in the world,” said Prasad Thenkabail, USGS research geographer and Principal Investigator for the GFSAD30 Project Team. “Given the high resolution of 30 meters and 0.09 hectares per pixel, a big advantage is the ability to see croplands in any country and sub-national regions, including states, provinces, districts, counties and villages.”
With the global population nearing the 7.6 billion mark and expected to reach 10 billion by 2050, it is of increasing importance to understand and monitor the state of agriculture across the world in great detail. This new research is useful to international development organizations, farmers, decision-makers, scientists and national security professionals.
“This map is a baseline and starting point for higher level assessments, such as identifying which crops are present and where, when they grow, their productivity, if lands are left fallow and whether the water source is irrigated or rain fed,” said Thenkabail. “Comparisons can be made between the present and past years as well as between one farm and another. It is invaluable to know the precise location of croplands and their dynamics to lead to informed and productive farm management.”
Critical for Water Security
Not only does this map and accompanying data have significant food security implications, but it is also critical as a baseline for assessing water security. Nearly 80 percent of all human water use across the world goes towards producing food, and this research provides insight on “crop per drop,” which is an assessment of the number of crops produced per unit of water.
Download data through the Land Processes Distributed Active Archive Center.
North Eastern Space Applications Centre (NESAC) has been providing thunderstorm nowcasting (forecasting up to 4 hours) services for North Eastern Region (NER) of India since 2015 under the North Eastern Regional node for Disaster Risk Reduction (NER-DRR) initiatives. This was done using the data from satellite imager and sounder onboard INSAT-3D / INSAT-3DR, automatic weather station data, and by analysing numerical weather forecast data. However, it was difficult to detect, track and forecast using this data alone as most of the thunderstorms being a localised event, extending only over a few tens of km and having a lifetime of less than one hour. The availability of DWR data has opened a new window for precise identification of thunderstorm weather systems, track them and forecast the probable areas which may get affected, albeit with lesser lead time.
The first S-band dual-polarimetric Doppler Weather Radar (DWR) was installed at Cherrapunjee, Meghalaya which was dedicated to the nation by Shri Narendra Modi, Hon’ble Prime Minister of India on May 27, 2016. NESAC is operating the DWR continuously since then, and the data is made available in near real-time for the public through the MOSDAC (Meteorological and Oceanographic data archival centre) and IMD websites. The DWR is calibrated at regular intervals and the data and products are being validated. It has unobstructed coverage for the entire state of Meghalaya, Tripura, Southern Assam, and part of Mizoram and Manipur. For the western and central Assam region, the DWR has coverage beyond 3-degree elevation only. The DWR also sees a large part of India’s neighbouring country, Bangladesh. The radar completes one volume scan in 11 minutes, comprising of 360-degree azimuth scan for 10 elevation angles ranging from 0.5 to 21 degrees. It also allows sector scan (in both azimuth and elevation) for high temporal observation of any event. The DWR covers a distance of 250 km (up to 500 km only for Z) with a spatial resolution of 300 m.
A thunderstorm is a pre-monsoon season (April-May) phenomenon over the NER of India. The data collected by the DWR during 2016 was used to understand the thunderstorm and storm signatures and calibrate the nowcasting model. During 2017 the nowcasting service was made operational. Severe thunderstorm nowcasting services for Southern Assam, Meghalaya, and Tripura were done primarily using the DWR data and for the rest of the NER, the earlier methodology was used. In addition to the Z (radar reflectivity), S (spectral width) and V (velocity) data collected by the DWR, extensive use of the polarimetric data like ZDR (differential reflectivity) and ρHV (Correlation coefficient) were also made to differentiate thunderstorm clouds from non-thunderstorm clouds.
The use of the Cherrapunjee DWR data has improved the thunderstorm nowcasting accuracy over Meghalaya, Southern Assam, and Tripura states. Altogether 48 severe and very severe thunderstorms were forecasted in these three states during April 1 to June 15, 2017, period. The accuracy of nowcasting was more than 90% with lead time varying from 30 minutes to more than 2 hours. The nowcasting services were disseminated through NER-DRR website and also through direct communication to the concerned at the state level.
(The DWR, Cherrapunjee coverage for an elevation angle of 3 degrees (left). Calibration of the DWR using metal sphere attached to hydrogen gas-filled balloon & Pisharoty sonde (right))
(Max V (left) and Max S (right) data from DWR, Cherrapunjee. Max V is used to estimate the velocity at which a weather system is moving and Max S gives an idea about the internal turbulence within cloud system)
High-resolution optical imaging Earth Observation Satellite (EOS) systems such as CARTOSAT provide multi-spectral remote sensing data in the visible and near-infrared (VNIR) wavelengths of the order of sub-meter to few-meters. These datasets can be used in a variety of applications, particularly associated with precise mapping, monitoring and change detection of earth’s surface, if top of the atmosphere (TOA) measurements can be properly compensated for atmospheric absorption and scattering effects. Existing physics based atmospheric correction (AC) algorithms for multi/hyperspectral remote sensing data over land involves simultaneous use of visible and short-wave infrared (SWIR) channels to derive aerosol information. Hence, such algorithms cannot be used for AC of data acquired by VNIR sensors to derive “surface reflectance”.
Towards this, Space Applications Centre, Ahmedabad has developed a new algorithm for AC of high-resolution VNIR remote sensing data in which aerosol information is retrieved from sensor measurements in VNIR channels and by selecting appropriate aerosol optical properties from a set of defined aerosol models. The algorithm uses lookup tables generated with vector radiative transfer calculations. Derived aerosol information and pre-computed lookup tables are employed to derive surface reflectance. Good quality surface reflectances have been obtained when this algorithm was applied on Cartosat-2 Series Satellite data. It is found that this algorithm significantly removes the haze from the images, making surface features distinctly visible, and hence more useable for qualitative as well as quantitative analysis and further applications.
Following figures illustrate the drastically improved quality of the images after applying the AC algorithms, where the contribution of light due to molecular scattering and scattering from thick layer of aerosol to the sensor measurement at the top the of the atmosphere is removed.
(Parts of Ahmedabad as viewed from Cartosat-2 Series Satellite on 03/11/2016)
(Cartosat-2 Series Satellite View of Ahmedabad, Satellite Area on 03/11/2016)
The new Landsat Explorer web app from Esri enables users to wield Landsat imagery to explore geology, vegetation, agriculture, and cities anywhere in the world. The app, driven by publicly accessible image services, offers a way to better visualize the planet and understand how the earth has changed over time.
(A false color band combination, where vegetation appears in red, delineates the Exumas Islands in the Bahamas. With the Scatter Plot tool, users can select two bands to plot on a graph, with the more frequent occurrences appearing on this graph in red.)
Using the app is simple: Open it in a web browser, search for a location, and apply analysis tools on the fly to get immediate, dynamic results. With no download required, Landsat Explorer users get instant, interactive access to an extensive collection of multispectral, multi temporal Landsat imagery.
Landsat satellites have been collecting information about the earth’s surface for almost 45 years. Each Landsat image contains multiple bands of spectral data gathered at different wavelengths. More than just offering pictures of the planet, Landsat’s different bands can be combined and analyzed to learn about what is happening on the ground, beyond what the eye can see.
Beyond enabling users to instantly view half a million Landsat images using different band combinations or enhancements, Landsat Explorer offers extensive analytical capabilities. The visualization and analysis tools let users do the following, all on the spot:
- Visualize the data using custom indexes and band combinations
- Filter and select specific dates to analyze and compare
- Interactively compare two images using a swipe tool
- Create custom masks
- Perform change detection
- Generate spectral and temporal profiles
- Create scatter plots using spectral bands
- Add data (city roads, for example) from ArcGIS Online
Landsat Explorer joins Esri’s existing suite of Landsat apps, including the Landsat Arctic and Antarctic Apps. Whether users answer their own questions by applying Landsat Explorer’s powerful analysis tools or take the small leap to create their own imagery apps, it’s never been simpler to instantly visualize and dynamically analyze the earth’s surface.
Member nations of the five-nation group BRICS have agreed to share spatial data on natural resources from their remote-sensing satellites.
The move is geared towards making optimal use of space assets. According to Indian Space Research Organisation (ISRO) officials the nations will be exchanging data, including images of natural resources. Though only four of them – Brazil, Russia, India and China have remote-sensing satellites in the sun-synchronous orbit, they will give data to South Africa (SA) as it does not have a satellite of its own. Top space officials of BRICS met at the United Nations Committee on the Peaceful Uses of Outer Space Scientific and Technical Subcommittee’s 54th session at Vienna in Austria from January 30 to February 10.
Through this particular agreement, BRICS will be able to share the resources and bring developing nations under the umbrella of space, opening possibilities of using excess capacities in the satellites. As the BRIC satellites spin around the earth in lower orbit, capturing enormous data on the planet and its resources in each country, they will share it in real time for mutual benefit.
India plans to use its Resourcesat-2A, launched on December 7 from its spaceport Sriharikota in Andhra Pradesh, as part of its earth observation satellite for remote sensing data services to global users.
Going forward, the space agencies of the BRICS nations plan to share similar data for tele-education, tele-medicine and a host of societal applications, utilising the excess capacity of their respective satellites for their mutual benefit.