InSAR can map coastal sinking at fine scales critical for flood-risk. A recent paper claimed this technology is unreliable for slow subsidence, potentially undermining its policy use. We show that this conclusion stems from methodological flaws: original data were coarsened before analysis, validation used an unrepresentative GNSS network, and uncertainty threshold is self-contradictory. We confirm InSAR performs robustly and technology remains a vital tool for protecting coastal communities.

The shrinking of glacier frees large areas worldwide in alpine environment which are progressively colonized by the vegetation. Monitoring this process often relies on in situ botanical observations which are challenging to obtain. Here, we quantify accurately the uncertainty of the vegetation cover measured by optical images from Unmanned Aerial Vehicles. We find that, despite biases, this increasingly used technology extend and complete the data collected during such field campaign.

Monitoring sea ice is important for understanding climate change and supporting safe travel in the Arctic, but creating detailed maps usually requires time-consuming manual work. In this study, we trained a computer model to learn from large amounts of unlabelled satellite images before using only a small number of labelled examples. This approach produced accurate ice maps while greatly reducing the need for manual labelling, making large-scale monitoring more practical.