Back in the 1960s and 1970s, scientists were familiar with data from weather satellites and the kinds of questions they could answer. But what else could be seen from space? And how useful was it?
So, when the first Landsat satellite was launched on 23rd July 1972, the biggest questions were about the type of sensors, how useful the data were and the kinds of applications that could use the data. That’s why the first satellite carried 2 sensors that would be used to monitor the Earth’s landmass - an RBV camera to capture images and an experimental multi-spectral scanning (MSS) sensor that scientists hoped would be useful but had no idea if it would be. And, in the fun and annoying way that science often works - scientists discovered that the experimental MSS sensor produced data that were much more useful and accurate than the RBV camera! So, while the RBV camera system was carried by the next 3 satellites in the Landsat program, the MSS data were what scientists focused on.
NASA asked a team of over 300 scientists from all over the world to discover different applications of the Landsat data to their fields. And the scientists found that satellite data could be used for all kinds of applications - identifying different crops from space so that governments could make better decisions about their agricultural sector, monitoring the behavior of cyclones and storm systems, mapping deforestation, exploring changes in how land was used by people around the world and even identifying unknown mountains in the Arctic and Antarctic.
The Landsat program continued adding more sensors over the years - with Landsat 7 having the most accurate and detailed data of all the satellites. The data were finally unlocked and offered for free in 2008 - and since then, we’ve seen an explosion of both commercial and scientific uses for the data. Fun fact - Google Earth uses Landsat data to visualize the planet!
Today, we have data from all kinds of sources - satellites, aircraft, drones, and submarines.All these come under the banner of remote sensing - that is, any data collected at a distance from the source. And they are equipped with a wide variety of sensors that help us collect different types of data at different resolutions and solve a range of problems.Together they help us understand different aspects of our planet - what happens on the surface, below ground, in the ocean, in the atmosphere, in forests, in crop fields - all kinds of things.
What do Google, Climate Corporation, early stage startups in farm robotics, and researchers trying to figure out how to feed the world sustainably have in common? They’re all grappling with one of the toughest challenges of working with natural systems - how do you work with data that is sparse, unevenly distributed and with systems that have so many connections and interactions with other systems? Before the advent of cheap sensors that are connected to phones, easily accessible satellite data and drones that can fly over fields quickly and inexpensively - scientists in companies and academia worked on developing plant and crop models that incorporated as many aspects of the farm and as much data as was available so that they could understand and predict what was likely to happen on the field. Understandably, the forecasts took some time to produce and as the models grew more complex, issues about how to estimate model parameters and the uncertainty associated with the resul
A mid-sized data center consumes around 300,000 gallons of water a day, or about as much as 1,000 U.S. households; About 20% of data centers in the United States already rely on watersheds that are under moderate to high stress from drought and other factors; Operating a data center often requires a tradeoff between water use and energy use; And in a survey of 122 data centers in the United States, only 16% or 20 utilities reported plans for managing water-related risks. As professionals working in the field, what can we do to solve this issue? One aspect is developing and using water models that can identify water risks at different scales - so that we can predict the risk to water supplies under a changing climate. A second is using machine learning to identify and optimize water use between all the stakeholders in the watershed - data centers, farmers, cities, other industries - so that biases and needs are brought out into the open and the key issues identified. A third, of cours
Our online community space is now open to anyone who has signed up for a free or paid course on our website! In addition to everyone who signed up for our cohort-based courses, we're now expanding it to all the members of our community. If you've already signed up for any of our courses, check your email for the invitation for the space. It's where we'll get together to talk about all things data science and clean technology related, discuss the latest research, network and make connections with other professionals in the sector. It's an invitation only , no bots and no trolls allowed space - so come on over! Here's where you can check out our courses and join our community !