How I became Dr Robin Wilson: Part 2
At the end of the previous post in this series, I was six months into my PhD and worrying that I really needed to come up with an overarching topic/framework/story/something into which all of the various bits of research that I was doing would fit. This part is the story of how I managed to do this, albeit rather slowly!
In fact, I felt that the next year or so of my PhD went very slowly. I can only find a few notes from meetings so I’m not 100% sure what I was spending my time doing, but in general I was trying not to worry too much about the ‘overarching story’ and just get on with doing the research. At this point ‘the research’ was mostly my work on the spatial variability of the atmosphere.
When I first thought about investigating the spatial variability of the atmosphere over southern England I was pretty sure it’d be fairly easy to do: all I had to do was grab some satellite data and do some statistics. I was obviously very naive at that point in my PhD…it was actually far harder than that for a number of reasons. One major problem was that the ‘perfect data’ that I’d imagined didn’t actually exist, and all of the datasets that did exist had limitations. For example, a number of satellite datasets had lots of missing data due to cloud cover, or had poor accuracy, and ground measurements were only taken at a few sparsely distributed points.
I spent a long time writing a very detailed report on the various datasets available, how they were calculated and their accuracy. I then performed independent validations myself (as the accuracy often depended on the situation in which they were used, and I wanted to establish their accuracy over my study area), and finally actually used the datasets to get a rough idea of the spatial variability of these two parameters (AOT and PWC) over southern England. This took a long time, but got me to the stage where I was very familiar with these datasets, and gave me the opportunity to develop my data processing skills.
I then used Py6S – by then a fairly robust tool that was starting to be used by others in the field – to simulate the effects of this spatial variability on satellite images, particularly when atmospheric correction of these images was done by assuming that the atmosphere was spatially uniform. The conclusion of my report was interesting: it basically said that the spatial variability in PWC wasn’t a huge problem for multispectral satellite sensors, but that the spatial variability in AOT could lead to significant errors if it was ignored.
By the time I’d finished writing this report I was probably somewhere between one year and one and a half years into my PhD, and I was wondering where to go next. I’d originally planned that my investigation into the spatial variability of the atmosphere would be one of the ‘three prongs’ of my PhD (yes, I found some notes that I had lost when I wrote the previous article in this series!), and the others would be based around novel sensors (such as LED-based sensors) and BRDF correction of satellite/airborne data. However, I hadn’t really done much on the BRDF side of things, and I wasn’t sure exactly how the LED-based sensors would fit in to my PhD as a lot of the development work was being done by students in the Electronics department, and so I wasn’t sure how much it could be counted as ‘my’ work (I was also concerned that we’d find out that they just didn’t work!).
I spent a lot of time around this point just sitting and thinking, and scribbling vague notes about where I could go next. While doing this I kept coming back to the resolution limitations in methods for estimating AOT from satellite images, for two main reasons.
- I really wanted high-resolution data for my investigation into spatial variability, but it wasn’t available so I had to make do with 10km MODIS data instead
- My spatial variability work had shown that it was important to take into account the spatial variability in AOT over satellite images, and the only way to do this properly would be to perform a per-pixel atmospheric correction. Of course, a per-pixel atmospheric correction requires an individual estimate for AOT for each pixel in the image: and there weren’t any AOT products that had a high enough resolution to do this for sensors such as Landsat, SPOT or DMC (or up-c0ming sensors such as Sentinel-2).
The obvious answer to this was to develop a way of estimate AOT at high-resolution from satellite data – but I kept well away from this as I was pretty sure it would be impossible (or at least, very difficult, and would require skills that I didn’t have).
I tried to continue with some other work on the novel LED-based instruments, but kept thinking how these instruments would nicely complement a high-resolution AOT product, as they could be used to validate it (after all, if you create a high-resolution product, it is often difficult to find anything to validate it with). Pretty-much everything that I did kept leading me back to the desire to develop a high-resolution AOT product…
I eventually gave up trying to resist this, and started brainstorming possible ways to approach creating a high-resolution AOT product. I was pretty sure that none of the ‘standard’ approaches would work (people had tried these before and hadn’t succeeded) so I tried to think ‘outside the box’. I eventually came up with an idea – and you’ll have to wait for the next part to find out what this idea was, how I ‘sold’ it to my supervisors, and what happened next.
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This post originally appeared on Robin's Blog.
Categorised as: Academic, Remote Sensing
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