This paper describes the utility of a bicycle as a measurement device for tracking pavement temperature. It goes into exhaustive length describing the equipment setup and the experimental method and provides access to the data. The author states that this equipment could be used to study pavement characteristics and temperatures with finer resolution than satellites.

The paper provides little discussion of how bicycles have been used for numerous local weather studies in the past, although he does provide a few references in his literature list, and does not describe how the bicycles were used in those studies. He does not describe other methods that could be used to do similar studies such as drones, and also does not mention how satellite imagery has become much more fine-scale over time and is expected to improve its resolution in the future. He describes the method of measuring air temperature compared to pavement temperature and notes that they do not correlate, but did not explain what factors might contribute to the difference. He did not attempt to compare the temperature discrepancy to the ambient weather conditions, including wind and humidity, that might be blowing the air from the land surrounding the pavement to the bicycle's path and contributing to the difference between the air and pavement temperatures.

The author spends a long time doing a correlation between the temperature at the nearest National Weather Service station but notes that the nearest station is about 24 km (14 miles) and 250 m (850 ft). In general, if a station is more than 10 miles or 100 feet different in elevation from the target location it is not considered a comparable record, so this seems like a useless enterprise, although in weather patterns that have uniform air masses, it might have some relevance. The correlations in the paper are discussed in general terms as "excellent" or "very positive" but there has been no attempt to quantify exactly how good they are so this does not seem germane to the main theme of the paper.

Another shortfall of the analysis is a description of how the use of a bicycle would make sense economically, since it is very labor and time-intensive and can cover only a small area at a time. This makes the use of a bicycle as a measuring device to be of extremely limited use. It would even more difficult if it were to be employed in a city or more urban area compared to the relatively traffic-free area of rural Montana. In those areas it might make more sense to use driverless cars like the ones Google uses for mapping, and that would also reduce the labor cost and improve safety since no humans would be involved.

I commend the author for an extremely detailed description of his equipment and his method of collecting data, because it could easily be reproduced from what information he has provided. It was clearly a labor of love. It is a very well-documented data set but of extremely limited use. Practically speaking, I cannot see that anyone would ever choose to use this method in any case that would require it since safer, less labor-intensive, and equally or more accurate methods are available using other types of technology. Comparisons of pavement temperature would be easier to do in a laboratory setting that provides an environment that could be controlled for wind, lighting, humidity, and other ambient atmospheric conditions, so it is hard to justify using this out in the open environment. If the author chooses to revise his manuscript, I would encourage him to include more descriptions of how bicycles have been used in other scientific studies, cut down the equipment and method sections considerably, remove the section on comparisons with distant NWS stations and provide some more general comments on the general pattern of agreement between NWS stations and his own measurements, and discuss what advantages using a bicycle has in comparison to other methods of collecting similar information.

The manuscript entitled “Temperature of impervious surfaces in rural Montana” has described a dataset, which was observed based on sensors onboard a bicycle covering several routes in Bozeman, Montana. The inspiration provided by this manuscript far outweighs the value of the data itself. First, it is possible to use an economical and environmentally friendly monitoring style in Earth system studies. Second, the monitoring equipment can be easily prepared for public use, and such data can cover a variety of regions of US and even different countries. Third, the author has put forward many thought-provoking questions correlated to this data. Unfortunately, the bicycle-based data in this study only covers specific routes in a small city and lacks temporal continuity. Meanwhile, the author only provides the original observations without further quality control or strict precision assessment, which makes it difficult to directly use this data. The author intends to convey a great deal of content in the manuscript, but the dataset itself is not sufficient for publication in Earth System Science Data. In my opinion, the manuscript can be submitted to a more specialized journal. Instead of the data description, the author can either highlight the monitoring methods or the major findings from this data based on quantitative analysis.

More detailed concerns are listed as follows:

1. Lines 570-577. Only using the bicycle-based data in this study is neither sufficient nor accurate for exploring the urban heat island effect. On one hand, urban systems not only contain roads/paved surfaces, but also include trees, grassland, buildings, and possibly rivers and lakes. Only using bicycle-based observations over limited regions of imperious surfaces is not sufficient to build quantitative relationships with satellite remote sensing data. Instead, sensors onboard drones may be more suitable for acquiring spatially continuous observations covering all the surface objectives in urban areas. On the other hand, the dataset generated in this study has not been calibrated based on specific standards to keep consistent with the existing datasets (e.g., air temperature from NWS sensors), nor has it been validated based on specific in-situ observations (e.g., surface temperature based on radiation observations at representative sites in the cycling routes). For example, air temperature observations using bicycle-based and NWS sensors were obtained at different heights, which may lead to large uncertainties due to large vertical variations of air temperature near the land surface. The comparison with roof-based observations can basically prove that the sensors used on the bicycle are accurate to some extent. However, the differences between observations under moving and stationary conditions have not been investigated in the study. Besides, the effects of a small portion of the bicycle front wheel have not been quantitatively evaluated nor removed, which may make the observed data under different conditions (e.g., clear sky, under tree shadows, over snow/ice surfaces) not comparable to some extent. Specifically, as mentioned by the author, the footprint of the IR sensor is within a 1.4 m circular area for surface temperature observation. As the bicycle is painted with dark colors (i.e., black and green), the temperature of the front wheel may change over time.
2. Lines 578-583. It is a widely accepted scientific consensus that pervious surfaces are cooler than impervious ones under clear-sky conditions. A quantitative comparison between the temperatures of these two surfaces to highlight the importance of using gravel surfaces in urban regions is more meaningful than the currently subjective descriptions.
3. Lines 584-591. 1) It is a small region in this study; instead of bicycle-based monitoring, observations at fixed sites can provide temporally continuous and more comprehensive data to evaluate cost-benefit estimations of tree-induced shading. The bicycle-based monitoring may be meaningful to a larger extent (e.g., major cities in the US). However, the dataset provided in this study is only a demo region, which is not sufficient to support large-scale studies. 2) Similar data on highways should be useful, but motor vehicles will be more proper platforms, instead of bicycles.
4. Lines 592-595. The UV data in this study may be quantitatively correlated with satellite remote sensing data. However, the spatial extent of such data needs to be much larger than the current one for systematic accuracy validation or calibration of satellite remote sensing data.
5. Lines 341-348. The author found that “In no case did air temperatures respond to surface temperatures”. However, this conclusion was summarized based on the observations over the same land cover type (at least over land surfaces with similar materials). Air temperature is spatially more stationary than land surface temperature, and it usually varies among different land cover types. That is, the response of air temperature to surface temperature is more distinct among different land cover types. The author needs to refer to more existing studies before drawing any conclusions.
6. If the author wants to revise the manuscript, it is better to expand the spatial extent of the data coverage, provide a more strict accuracy assessment, and include more quantitative analysis.