April 26th, 2021 by Estefania Puricelli (NASA Harvest), Mary Mitkish (NASA Harvest), Inbal Becker-Reshef (NASA Harvest)
How Earth observation technologies are revolutionizing global agri-food systems.
Nowadays, various forms of technology play a fundamental role in our daily routines. From the use of GPS to navigate to different places, to sharing pictures using just our smartphones, to automatic vacuum cleaners, there are endless examples. Even when it comes to the food we eat there have been so many technological advances and an immeasurable amount of research developed, particularly in the past few decades.
But one common technological thread that we can find within all of these different aspects of our lives is Earth Observation Technologies (EOT) and remote sensing. Numerous Earth observing (EO) satellites have been launched into space by NASA and other international space agencies within the last decade, serving to collect (or “remotely sense”) data about our home planet, which helps experts around the world to better understand our ecosystems and environment. We’ll take a look at agricultural production, international food markets, and global food security as an example of the importance of this remotely sensed Earth observation data.
There are several steps and many factors involved from the time a crop is grown on a plot of land to the availability of certain crops and the price volatilities eventually seen in the corresponding food market system. Because of the complicated paths of the agricultural supply chain, it is often easier to understand this relationship if we think about it as a collective “agri-food system.” Over the past decades, agri-food systems have evolved in so many aspects, but so have the EO technologies that make it easier to understand and monitor every step of this complex chain, bringing transparency and efficiency to the overall system. According to the United Nations definition, the food system includes many different factors such as: food-related resources, various food system inputs, production, transport, processing, manufacturing, retailing, and food consumption, all in addition to the overall agri-food system impacts on environment, health, and society. Many of the changes in our food systems are attributed to external and internal forces as well as feedback mechanisms among these forces.
Let’s start from the beginning—from the farm and from the plot. With the help of satellites and EOT, producers have a better understanding of weather patterns and can plan the optimal time to sow their land. With GPS-based technologies, producers can use precision agriculture to specifically apply only the needed amount of fertilizers, herbicides, and seeds. Once everything is in the ground, farmers can remotely monitor how the crops are developing from anywhere in the world, and they can even estimate the potential yield they can expect at harvest time months in advance of actual harvesting dates. Such tools are extremely important for successful financial planning. While those crops are still developing, we can use EOT to monitor agro-meteorological conditions such as temperature and precipitation, and their impacts on crop conditions—all of which help us understand if today’s conditions are following the average trends seen in previous seasons. If anomalies occur, the EOT data can warn us well in advance of a potentially catastrophic harvest season which enables earlier mitigation actions.
Meanwhile, as that same crop is growing, many different market actors around the world are trying to estimate how much of that crop will be successfully produced, not only in that specific region but also throughout the whole country and elsewhere around the globe. Consequently, accurate early estimates of crop area and crop yields are fundamental to the efficient and transparent functioning of global agricultural markets. And guess what? Remote sensing technologies play a critical role here too, providing key pieces of information for analyzing and developing estimates at different scales for crop area and yield, making use of NASA satellite data from missions such as MODIS, Landsat-8, and SMAP, with additional launches on the way (i.e. NISAR, Landsat-9).
At a national level, having accurate and timely information helps policy makers to enact more suitable mitigation plans in case they need to take action if there is an unusual shock to the food system (i.e. extreme weather events, food price changes, a global pandemic, etc.), not only for countries whose economies depend on those crops to be exported but also those countries who rely on imports for their food security. EOT can also help to put many farmers literally on the map, and provide policymakers with timely tools and resources to help them. National governments can assess various crop situations using EOT and are currently monitoring the crop conditions with existing National Crop Monitors. Because the agricultural system on a whole is so dynamic, remote sensing technology can also help update estimates of crop production at a national level as the state of the agrifood system changes.
At a global level, with the help of information provided by satellite data, the early warning community (including the GEOGLAM Crop Monitor for Early Warning and various humanitarian organizations) have a better understanding of the current situation and can utilize early warning information for early action to more efficiently allocate aid where and when needed. Crop production estimates play an important role in price volatility in different markets, both domestically and internationally, which highlights the need for accurate, cost-effective, and frequent agrifood system monitoring. International communities, such as the Agricultural Market Information System (AMIS), have been launched by the G20 in an effort to monitor commodity price volatility. For example, AMIS publishes a crop monitor bulletin in their monthly reports as a way of providing more transparency into agricultural markets. Timely and accurate assessments of crop harvest quantities can have a positive impact in avoiding extreme price volatility.
But going back to our crop, remote-sensing data and advanced computing capacity improve yield estimates, subsequently improving the financial planning of the producers (i.e. both small- and large-scale farming operations). Remote sensing technologies also improve field-scale nutrient management, not only helping improve sustainability of crop production but also saving on costs for the farmers. Accurate crop area estimates, especially those estimates with a regional approach, help different types of companies—including seed providers, fertilizers, herbicide producers, agriculture services, machinery companies, and many others—to better plan their marketing and distribution strategies. This information creates a more efficient allocation of resources, with benefits also being seen in often under-represented rural areas.
Moving along the food cycle to the post-harvesting stage, if there was a problem with the harvested crop then insurance providers can use remote sensing technologies to assess the situation from anywhere, minimizing cost and emissions of visiting the plots themselves. Remarkably, remote-sensing data can reduce the transaction costs associated with monitoring traditional insurance contracts, reinvigorating rural finance markets.
But the role of remote sensing technology doesn’t finish when the crop is harvested. Once the crop is out of the ground and for example, sent to the grain elevator, we can track the exact location of the vessels on which those grains are traveling and their destinations. Most recently, we were able to use Planet satellite images to spot the movements of the cargo ship that halted global trade and created a massive bottleneck in the global supply chain.
Overall, Earth observation technologies are making the interlinkages of the complex and sometimes fragile agrifood systems more transparent, accurate, and efficient, while also helping to reduce transaction cost and shedding light on the information asymmetry challenges. It almost goes without saying that science, innovation, and technology have huge impacts on understanding changes to food systems. Markets, trade, and related infrastructures—increasingly combined with digitization—are today cutting across internal and external drivers of food systems’ change (Von Braun, 2020) and digital technologies will undoubtedly continue to increase efficiency in the design and delivery of agricultural policies (Schroeder, Lampietti, & Elabed, 2021). EOT has the capacity to enable a rapid assessment of different shocks, providing a powerful tool for decision makers across the agricultural, government, and humanitarian spectrums.
Von Braun, J. A. (2020, October 26). Food Systems – Definition, Concept and Application for the UN Food Systems Summit.
Schroeder, K., Lampietti, J., Elabed, G. (2021, March 16). What’s Cooking : Digital Transformation of the Agrifood System.
March 19th, 2021 by Alyssa Whitcraft (NASA Harvest), John Keniston (NASA Harvest), Mary Mitkish (NASA Harvest)
Satellite Data for Agricultural Monitoring
Food security and agriculture is probably not the first thing that comes to mind when you think about satellites being launched into space and orbiting planet Earth. You might even be surprised to learn that NASA, best known for its space exploration, actually has over a dozen satellite missions dedicated to monitoring food production, land changes, and vegetation. Truth be told, the data provided by these Earth observing satellites provides critical information for analyzing the state of food production and food security around the world. From space, we can see things like the NDVI (namely, green light reflectance) of plants which is indicative of crop health, crop damage resulting from natural disasters (such as the 2020 Iowa derecho), and even the amount of moisture stored deep within farmland soil. However, compiling these large satellite datasets into deep time series to monitor interannual and within-season changes in vegetation requires a lot of skill, computational power, and time.
The Legacy Global Agricultural Monitoring System Gets an Update
In the early days of satellite Earth observations, the University of Maryland worked together with NASA and USDA to develop the first web-based platform to enable near-real-time monitoring of global croplands, enabling users across the globe to track crop conditions as growing seasons unfolded (re. Monitoring Global Croplands with Coarse Resolution Earth Observations: The Global Agriculture Monitoring (GLAM) Project). After years of operational use and a lot of valuable feedback from partners, the time came to redesign this system – known as Global Agricultural Monitoring or GLAM – to be faster, more flexible, and to capitalize new datasets coming online and new computing architectures available. Thanks to funding from NASA ESDS and NASA Applied Sciences, the team at NASA Harvest (NASA’s Food Security and Agriculture Program) has accomplished just that.
New GLAM System Features, Datasets, and Functionalities
Now lightning-fast due to back-end developments that drastically reduce processing time, the new GLAM system includes many new functions. Users can access the publicly-available web-interface from anywhere in the world, as all processing takes place on “the cloud” and does not rely on local bandwidth to compile datasets. They can use the interface to pan around the globe to a region of interest. They can select an administrative boundary or draw a custom polygon to investigate. They can select from a variety of cropland and crop type masks to target relevant croplands, and then create custom time series charts with the ability to calculate mean and anomaly for datasets over time. Because cloud cover is a common issue in many agricultural regions and cultivation periods, users can customize their cloud cover tolerance in plotting and displaying imagery as well. Furthermore, users also have the ability to overplot crop calendar information on their time series plots, which provides important context to compare crop conditions between years.
In addition to these new functions and features, the interface includes multiple new datasets to help analysts glean insight into the drivers behind anomalous crop conditions throughout the world. Currently, the new GLAM system includes 8-day and 16-day NDVI datasets from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS), CHIRPS Precipitation datasets from the University of California Santa Barbara Climate Hazards Center, Soil Water Index from the Copernicus Global Land Service, and Temperature datasets from NASA’s MERRA-2 product, with more datasets on the way.
In addition to the existing global interface, there are also national GLAM system implementations planned and already operational. First up is the Conab Brasil customized GLAM interface, which was developed and tested hand-in-hand with NASA Harvest counterparts at Conab. Conab is a public company, operationally mandated by the Ministry of Agriculture, Livestock and Food Supply to produce area, yield and production estimates regarding several major commodity crops to the Brazilian government. While users can still view and implement all of the same types of datasets offered in the global system, the Brasil GLAM website offers additional features. While the global site does include regional statistics at the subnational level, the Brazilian site goes beyond the subnational level. In response to Brazilian administrative regional needs, this application includes extra Brazil-specific crop masks as well as extra levels of analysis.
The updated GLAM system has already been widely used to flag potential crop impacts especially in Argentina, as featured by the country’s largest media outlet La Nacion. In addition to the system’s current capabilities, planned features include the ability to upload custom geometry (for example, shp, kml, geojson) to retrieve dataset statistics and visualization of dataset anomaly in order to provide users with an indication of the relative performance of a crop over time. The team is also preparing to integrate moderate resolution satellite data sets, for example Landsat, Sentinel, and the Hybrid Landsat-Sentinel products, to move us into this new era of continuous land monitoring at 30 meters or finer resolution. Additionally, the serverless architecture enables us to quickly spin up and launch new GLAM systems for other regions and users.
Are you interested in your own GLAM system? Contact project PI Alyssa Whitcraft.
How We Accomplished It
By using Amazon Web Services (AWS) and “serverless” architecture, the NASA Harvest team has been able to create both a web application and application programming interface (API) that provides on-the-fly data projection, masking, color table application, image differencing, and statistics calculation. As a result, it is not necessary to pre-generate all permutations of the NDVI datasets that would previously have been generated in on-premises architecture. Likewise, it is also not necessary to reproject the global NDVI datasets with the new GLAM system. The on-the-fly capabilities reduce the storage demand to the volume of only the NDVI files (less than ten terabytes) as opposed to the hundreds of terabytes required by the previous on-premises architecture version. In addition, because these functions are done on the spot, the collection of datasets, crop masks, and color ramps can be arbitrary. Therefore, the system does not need to be limited to NDVI and can be expanded to include precipitation, soil moisture, or any other raster dataset.
December 16th, 2020 by Andrea Nicolau, SERVIR-Mekong Regional Science Associate
On November 23, the Royal Thai Government’s Pollution Control Department (PCD) and SERVIR-Mekong launched the Mekong Air Quality Explorer (AQE) tool at a press event in Bangkok, Thailand. Due to ongoing COVID restrictions, my colleagues from the SERVIR Science Coordination Office (SCO) and I participated in the event remotely. In working with the SERVIR-Mekong hub, I learned that poor air quality in Southeast Asia is a recurring problem that has lingered for over a decade. In contrast to smaller numbers of ground monitoring stations, Earth observations have proven essential to provide consistent and accurate air quality information. Co-developed by SERVIR-Mekong, PCD, and the Geo-Informatics and Space Technology Development Agency (GISTDA), the AQE uses Earth observation inputs in a web-based platform that forecasts and monitors air quality in the Lower Mekong region.
The press event took place at the Ministry of Natural Resources and Environment in Bangkok, and included remarks, a tool demo, and a Q&A session—and I got to watch it all live online. With about 40 participants, the event featured speakers such as: Mr. Athapol Charoenshunsa, PCD Director General and Chairman of the event; Dr. Steven G. Olive, Mission Director of USAID’s Regional Development Mission for Asia (RDMA); Dr. Lawrence Friedl, Director of NASA Earth Science Division’s Applied Science Program (through a pre-recorded video); and Mr. Aslam Perwaiz, Deputy Executive Director of the Asian Disaster Preparedness Center (ADPC). Additionally, GISTDA, the National Research Council of Thailand, the Prime Minister’s Delivery Unit, the Thai Health Promotion Foundation, the Department of Health, and about 20 local media channels and newspapers were at the event.
Collaboration with Local and Regional Partners
As some background, SERVIR, a joint NASA and USAID program, works with leading regional organizations to help countries worldwide use Earth observations and geospatial technologies to address environmental challenges. Led by the ADPC, SERVIR-Mekong works with organizations in countries across the Lower Mekong, including Thailand. I wasn’t working with the Mekong hub at the time, but in April of 2019, SERVIR-Mekong initiated a collaboration with local authorities to improve air quality monitoring and forecasting. The release event builds on nearly two years of research, trainings, and collaborative development in the area of air quality monitoring and forecasting. As Aekkapol Aekakkararungroj, a Remote Sensing and GIS Specialist from ADPC and my co-worker, stated: “Tackling air pollution needs to be done right now. This requires cooperation from grassroots to policy makers. Earth observation technology from space is one of the most important tools to bridge the gap—to help them better communicate, and collaboratively manage the situation on the ground.”
Research and Development for Air Quality Monitoring and Forecasting
I learned a lot about Air Quality monitoring from Dr. Gupta, the lead scientist of the AQE. He told me that in the past two decades, satellite observations of atmospheric aerosols and trace gases have been used to address surface air pollution issues. NASA has invested significant resources in researching and developing data products ready to be used in applications. The products used for the AQE are created through a research and analysis (R&A) project of NASA’s Science of Terra, Aqua and Suomi-NPP program (PI – Dr. Pawan Gupta). The R&A project focuses on air quality research and data product development for the Indian-Subcontinent, which has been expanded to include Thailand in collaboration with SERVIR. “AQE is an excellent example where NASA’s R&A program collaborated with NASA’s Applied Science program to use science for real-life application,” said Dr. Gupta.
The AQE uses aerosol and meteorological forecasts from NASA’s existing advanced climate model called the Goddard Earth Observing System (GEOS). GEOS assimilates millions of daily Earth observations and provides global forecasts up to 10 days in advance under its forward processing (FP) stream. Forecasts using the GEOS system are experimental and are produced for research purposes only. Therefore, the AQE uses NASA’s global aerosol forecasts informed by satellite observations, real-time ground monitoring data from the PCD, and an advanced machine learning algorithm to provide three hourly air quality forecasts for the next three days. The machine learning algorithm helps calibrate global forecasts with local conditions and provide better accuracy. This all sounds super cool, right?
In addition, AQE has real-time satellite imagery, fire detection, and aerosol retrievals from NASA’s MODIS and VIIRS sensors. These near real-time products can help human forecasters evaluate model forecasted fields for improved decision making on final forecast outputs. AQE also has gridded historical (past two decades) data on aerosols and fires developed under the R&A project (Gupta et al., 2020). The historical datasets can evaluate change over time and help understand the impact of any significant policy changes on emissions in the region.
Capacity Building Through Training at Partner Institutions
In addition to app development, the SERVIR-Mekong team has supported training and youth outreach efforts. Dr. Gupta conducted two training sessions on the Remote Sensing of Air Quality: the first for the ADPC in July 2019, and the second in August 2020 for the PCD and GISTDA, in which I had the chance to participate. The latter was supported by NASA Applied Remote Sensing Training and the Committee of Earth Observation Satellites. These trainings built technical capabilities within Thai institutions on satellite remote sensing basics, the use of satellite data for air quality applications, and advantages and limitations of satellite datasets. These trainings were also used to introduce and get feedback on AQE while still under development.
To increase youth engagement in the work, in February of 2020, SERVIR-Mekong, PCD, USAID, and the Department of State’s Young Southeast Asian Leaders Initiative hosted Smogathon Thailand 2020. The event brought together young professionals, students, and technical experts to tackle air pollution using satellite data.
Development of an Online Visualization System – An Air Quality Explorer
The AQE started as a simple visualization tool for historical aerosols and fire data to support a R&A project. Around the same time, SERVIR-Mekong showed an interest in air quality applications in the Lower Mekong region, which motivated the SERVIR team to further develop this visualization tool and include other data sets. The collaboration with PCD allowed us to use their ground monitoring data, which then combined with GEOS reanalysis (MERRA2) to develop machine learning (ML) models. These ML models were evaluated against independent datasets using a 10-fold validation strategy. Finally, an ensemble model is used to calculate surface PM2.5 for the entire region. These ML models were implemented in automatic data processing, which generate three hourly air quality index maps for the next three days following Thai air quality standards. In addition to historical data and forecasting, near real-time satellite data layers from NASA’s Land, Atmosphere Near real-time Capabilities for EO (LANCE) were incorporated in the tool. The AQE had been through testing and improvement for almost a year with all the partners, including PCD, GISTDA, and SERVIR-Mekong, before it was adopted by PCD and became part of the Thai air quality management system.
The adoption of AQE by the PCD is an excellent example of how NASA’s science, research, and data are being applied around the world to address real-life problems. The AQE is also a first step by PCD in adopting Earth observations to complement and fill gaps in ground-based air quality monitoring systems. The AQE also addresses air quality forecasting, a gap in their air quality management program.
Since the adoption and release, PCD has already made several improvement requests for the AQE tool to serve their air quality needs. This includes improved spatial resolution, expanding the regional coverage to include neighboring countries to understand transboundary pollution, and including data on more air pollutants such as ozone, SO2, and NO2. There is also an opportunity to explore air quality observations by geostationary satellites by JAXA (i.e., Himawari-8/9) and KARI (i.e., GEMS) for the region. SERVIR-Mekong and SCO will continue to work on these aspects in close collaboration with PCD and GISTDA. I’m really excited to contribute to this effort and see the direction the AQE will take in the future!
NASA recently completed its senior review process for Earth science missions and CYGNSS has been approved to continue science operations through 2023. Another senior review will occur then. The constellation of eight spacecraft are healthy and operating nominally in their orbits 524 kilometers above the Earth. They continue to make 24/7 measurements of ocean surface winds, both globally and in tropical cyclones, to help understand meteorological processes and improve numerical weather forecasts. Over land, measurements of flood inundation, water body extent, and soil moisture are used in hydrological process studies and for disaster monitoring.
Our ability to forecast the strengthening of hurricanes has been improved by CYGNSS measurements of their inner core winds, which allow us to track the transfer of energy from the warm ocean water into the atmosphere in the form of latent heat flux. Currently, CYGNSS data are being used in re-analysis investigations; that is, revisiting old hurricane forecasts with new CYGNSS data added to see how the predictions are improved. Eventually, the goal is to use the data directly in operational forecasting by NOAA’s National Hurricane Center. CYGNSS wind observations can also be used to provide better estimates of the location of the hurricane, as reported in 2019 in the Journal of Applied Meteorology and Climatology.
Measurements by CYGNSS over land are used to determine soil moisture content under dense vegetation. This, in turn, allows for the identification of conditions conducive to major locust outbreaks in East Africa. Locating locust breeding grounds before the insects are mature and able to fly provides a unique opportunity for eradication efforts on the ground. This capability is being investigated through a partnership between members of the CYGNSS science team, NASA SERVIR, the U.S. Agency for International Development (USAID), and the United Nations Food and Agriculture Organization (FAO). For more information, refer to this Image of the Day published in March 2020.
With NASA’s continued support, CYGNSS will be able to continue with its investigations supporting a better understanding of and ability to forecast tropical cyclones, and to expand its investigations into novel applications over land.
September 23rd, 2020 by Melinda Webster/University of Alaska Fairbanks
Sea ice geophysicist Melinda Webster is blogging from the RV Polarstern, an icebreaker ship locked in Arctic sea ice for the MOSAiC expedition. Webster will use MOSAiC data as a blueprint to evaluate and extend the seasonal capability of data from NASA’s ICESat-2 satellite for sea ice research.
On August 17, the Polarstern reached the North Pole during its search for a new ice floe for the MOSAiC observatory, the research icebreaker Polarstern. We made surprisingly quick progress through the sea-ice pack owing to thin, warm ice conditions and a fragmented sea-ice cover. It took us less than seven days to cover 597 nautical miles (676 miles/1,087 kilometers) in a straight-line distance. The ice at the North Pole was level, seasonal ice extensively covered in dark melt ponds. I can only imagine how different it must have looked 30 years ago when thick, hummocky multiyear sea ice was more normal.
From the North Pole, we established a new ice camp at 87.717N, 104.313E on about 1.3-meter thick, first-year sea ice with numerous interconnected melt ponds. About half of the melt ponds were completely melted through to the ocean and the floe itself contained a network of old, melted cracks. These conditions required us to wear survival suits for much of our field work. After bouts of rain, snow, freezing, and melting, we were lucky enough to experience freeze-up, a time when the sea-ice cover begins to grow continuously from autumn to spring.
Two days ago, we completed our work at the snowy, frozen ice camp and began our journey to Bremerhaven, Germany. As we travel through the ice pack, we’re conducting “ice stations”–measurements collected at certain locations to capture the spatial variability in snow depth, melt pond depth, and sea ice thickness, which are important for understanding Arctic sea ice characteristics during the autumn transition. Yesterday, we observed snow cover that was about 8 centimeters deep, refrozen melt ponds, and “fields” of frost flowers on refrozen leads. Today, we’re traveling through wet first-year ice with open melt ponds covering about 25 percent of the area–a stark contrast from conditions yesterday due to a recent storm that passed through with above-freezing temperatures and rain.
As the Polarstern passes through changing ice conditions on our way southward, I can’t help but wonder what the sea-ice cover looked like 30 years ago and what it will look like 30 years from now.