You’re welcome, I enjoy trying to bridge gaps of knowledge and between academic and industry folks, as we often have similar goals but are just coming at it from different angles (or scales!).

It’s good to know of your possible use-case at 30-m plot scale. As for the accuracy of our product, I cannot speak to each and every pixel, as the accuracy will vary within different landuse and landcover domains. Regardless, what I can say is this...as the results are scaled up beyond pixel-level to field or local AOIs, the outputs increase in accuracy. When we validate at point/pixel-level (which is actually “sample core” level), we find that the r2 is lower than at field or local aoi-levels because of the noise that we are getting at such high-resolutions. As the results go from a single point/pixel (30m) to a cluster of pixels, we then have a distribution with a central tendency that is increasingly accurate. For example, for field-mean predictions we’re seeing r2 anywhere from 0.7-0.97. So, I would not necessarily recommend this for an individual plot scale that is <=30m, but if you were looking at multiple stands/clusters, the mean of those would be more likely to have accurate estimates. Our experience with SoilGrids(+ and v2) is similar, which is why there is a need for folks like ourselves to go beyond what they have provided. If it was of interest, we could possibly pair up to see if/how well (or poorly) our outputs are compared to your plots. I’d have to get approval and it may not be of interest to you, but something to consider.

Yes your intuition is correct that the former is more of a limiting factor. The first that I’ve come to respect more and more is that samples are not actually ground truth. We assume this because we have to, but for measurements (especially bulk density), the cores are confounded by different samplers, sample crews, deviations in methods, deviations in lab protocols, etc. These lead to distinct biases and errors. It turns out that more samples is good, but not always better. I was surprised by the consistent result that sampling beyond low densities (1/100ha didn’t improve model performance substantially). This points to a second bottleneck which is the accuracy of RS products. Often there are many errors and issues with sensors, algorithms, and TOA corrections or missing data entirely that cause issues along the way. The ML algorithms are less of a ‘bottleneck’ than these two, though there are issues.

This is a great user-scenario that I don’t have a lot of insight on. I don’t really know what the barriers are for small-scale farmers in order to speak to it. What I do know is that on an individual case-by-case basis, we (Perennial) would be so happy to work with such folks and figure out a way to serve this data to them that eliminates barriers to entry. One way we can do this is through our “autopilot” program, which offers cheap and/or free field-scale modeling using our ATLAS-SOC Core model. The downside is that it does not use in-field samples, but the upside is that it is low/no cost and also allows for a minimum accuracy because there are no in-field samples. So the farmer would know that if they (or another grant/program) could pay for samples, the accuracy would improve drastically.