Looking 50 Years in the Future with NASA Earth Scientists
In the 50 years since the first Earth Day, the view from space has revolutionized our understanding of Earth’s interconnected atmosphere, oceans, freshwater, ice, land, ecosystems and climate that have helped find solutions to environmental challenges.
If NASA’s Earth science has changed this much in 50 years, what will it look like in 50 more years?
We asked some researchers what they thought. Here are their answers, in their own words.
Mahta Moghaddam is a professor of electrical and computer engineering at the University of Southern California. She’s building a system that helps sensors sync their measurements.
I am interested in creating new ways to observe the Earth. In particular, my team and I are building and expanding a system that will allow scientists to better study soil moisture. Soil moisture plays a vital role in the water and energy cycle and drives climate and weather patterns. When soil is wet and there is enough solar radiation, water can evaporate and form clouds, which precipitate back to Earth. Soil also feeds us – it nourishes our crops and sustains life on Earth. It’s one of the foundations of life! We need to characterize and study soil in order to feed billions of people now and in the future.
Our novel tool aims to observe changes in soil moisture using sensors that talk to each other and make decisions in real time. For instance, if one sensor in a crop field notes that soil is dry in a plot, it could corroborate it with other sensors in the area and then notify a resource manager or decision maker that an area needs water. Or if a sensor in another location senses that soil moisture is changing quickly due to rain or freeze/thaw activity, it could send a command to launch a drone or even to notify satellites to start observing a larger region. We live in one big, connected world, and can and will use many different scales of observations – local to global – from point-scale in-situ sensors to the scales that can be covered by drones, airplanes, and satellites. In just a few years from now, we might see much more vastly automated systems, with some touching not only Earth observations, but other parts of our lives, like drone deliveries of medical tests and supplies.
Odele Coddington is a scientist at the Laboratory for Atmospheric and Space Physics at the University of Colorado, Boulder. She’s building an instrument to measure how much solar energy Earth reflects back into space.
My research is focused on the Earth system response to the Sun’s energy. I spend half of my time thinking about the amount and variability of the Sun’s energy, also known as the solar irradiance. I’m particularly interested in the solar spectral irradiance, which is the study of the individual wavelengths of the Sun’s energy, like infrared and ultraviolet. On a bright, clear day, we feel the Sun’s warmth because the visible and infrared radiation penetrate Earth’s atmosphere to reach the surface. Without the Sun, we would not be able to survive. Although we’ve been monitoring solar irradiance for over 40 years, there is still much to learn about the Sun’s variability. Continuing to measure the solar irradiance 50 years from now will be as important as it is today.
I spend the other half of my time thinking about the many processes driven by the Sun’s energy both within the atmosphere and at the surface. I’m excited to build an instrument that will measure the integrated signal of these processes in the reflected solar and the emitted thermal radiation. This is my first foray into designing instrumentation and it has been so invigorating scientifically. My team is developing advanced technology that will measure Earth’s outgoing radiation at high spatial resolution and accuracy. Our instrument will be small from the onset, as opposed to reducing the size and mass of existing technology. In the future, a constellation of these instruments, launched on miniaturized spacecraft that are more flexible to implement in space, will give us more eyes in the sky for a better understanding of how processes such as clouds, wildfires and ice sheet melting, for instance, alter Earth’s outgoing energy.
Sujay Kumar is a research physical scientist at NASA’s Goddard Space Flight Center. He works on the Land Information System.
Broadly, I study the water cycle, and specifically the variability of its components. I lead the development of a modeling system called the Land Information System that isolates the land and tries to understand all the processes that move water through the landscape. We have conceptual models of land surface processes, and then we try to constrain them with satellite data to improve our understanding. The outputs are used for weather and climate modeling, water management, agricultural management and some hazard applications.
I think non-traditional and distributed platforms will become more the norm in the future. So that could be things like CubeSats and small sats that are relatively cheaper and quicker than large satellites in terms of how much time it takes to design and launch. One of the advantages is that because they are distributed, you’re not relying on a single satellite and there will be more coverage. I also think we’ll be using data from other “signals of opportunity” such as mobile phones and crowd-sourced platforms. People have figured out ways to, for example, retrieve Earth science measurements from GPS signals.
I feel like in the future we will be designing our sensors and satellites to be adaptive in terms of what the observational needs on the ground are. Say a fire or flood happens, then we will tell the satellite to look over there more intensely, more frequently so that we can benefit. Big data is a buzzword, but it’s becoming a reality. We are going to have a new mission call NISAR that’s going to collect so much data that we really have to rethink how traditional modeling systems will work. The analogy I think of is the development of a self-driving car, which is purely data driven, using tons and tons of data to train the model that drives the car. We could possibly see similar things in Earth science.
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Speaking about Sujay Kumar's last paragraph,
Realistically thinking, he's saying that we will Tell the satellites to look more intensely at something. Sure that means itll look more into the surface of whatever you're looking at, but that means we would be totally and completely relying on a machine to give us the information, yes its faster and gives us the information faster, but theoretically speaking wouldnt it be better to learn how to do that ourselves? The brain created itself and has learned to teach our bodies to cooperate with eachother to stay alive and it relies on itself already to stay alive, adapt, and evolutionize. Without a backup of that satellite, if we didnt have it, but we were still at the same level of knowledge, say it disappeared, we would have to go look at it ourselves and learn on our own and put our minds together, it'd make it harder than it already is to learn it because itd be semi new to us. Point is, yeah telling an intelligent machine to do something for us might work for a while/long time, but will it work forever? Will our own creations that are taught to be more intelligent than us, turn on us and take over? The AI's that already exist, some have emotions. Its in their programming to learn from us, and programmed to keep learning. If it gets to a point where it can keep learning and share it with other AI's, there might be one that decides to share it with "equipment" machinery. And also, considering we are creating these machines, we're putting in our knowledge as data, but the machine enhances it and does things we generally would think we cant do. But if there was a system that told us what the machine knows with its enhanced information, it would give the machine information, bounce it back to us after enhancement, we'd enhance that knowledge by learning how we can do it ourselves, (without AI) putting that information in and making a loop of enhanced info that eventually would help us to do everything organically and by hand, with first hand knowledge on everything. Say humans have limited learning abilities, i wont believe it. There are down syndrome surgeons, amazing scientists who couldnt learn well in school and still made ground quaking discoveries, no matter what the brain will find a way to adapt and grow. There are loopholes in every situation, even at the blocks. Loopholes can be seen and invisible, everything in the universe contradicts itself.
Also cubesats and small sats? Thats a very interesting thing, sure its Cheaper, and Quicker but because its cheaper and quicker, wouldnt that make it worse? Like walmart version of brand names? Rushing greatness and intelligence means it'll all be less effective, less everything, and just overall half-assed. Do you really want a half-assed object to float out into space? Sure its more flexible in terms of looking around, but when will it flake out? You might have a general idea of that but have you really scratched the surface?
Also, NISAR. Why arent you already re-thinking our current traditional modeling systems? Shouldnt we always be trying to upgrade everything? That way the factual information we receive will be more of everything at once, instead of an unbalanced amount of information about our earth. Itll be quicker if we put our all into everything, we'd finish learning about everything about our sea and planet as our universe. Yes, it'll always be unbalanced because the information about the sea and earth is limited, as theres only so much ocean n land, but there an unlimited amount of space. Our focus should be focused on every single thing around us, as well as every single thing around our planet, focused on the air we breathe and what we walk on, what we look at, if we all learned the same information through different successful learning strategies and with multiple different viewpoints and perspectives, we could be unstoppable.
Of course, this is all theoretical because theyre all thoughts, but i believe if we all put our minds together, with individually learned studies, focused on one thing, individuals who know multiple learning grounds, and individuals who know a bit of everything, we can all put together the puzzle of what we are discovering and find more pieces, slowly but surely learning everything. Then itll all come together
Idrk just some high thoughts on this post
