Potential of Optical Spaceborne Sensors for the Differentiation of Plastics in the Environment

Round 1

Reviewer 1 Report

The theoretical part is well done and well written by the authors.

The only considerations I make are the following:

-please add in the discussion a part referring to how the sensors respond considering the aging of the plastics that cause microplastics spread.

-please add a summary table or a decisional workflow to let the other to choose the best sensors for a specific application.

 

Please confirm that you have the rights to publish the all pictures

 

Please submit the new revised version with the modified text in a different color.

Author Response

Thank you for taking the time to review the manuscript and provide valuable feedback! Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

This manuscript explored the potential using remote sensing images to detect the plastics. The work is meaningful and the manuscript is well structured. Some suggestions are as follows.

1.       I do not understand why a disqualified image used in Figure 10. The Landsat images cover a wide time range, a little bias from the required time would not greatly affect the classification result. Alternatively, you can use Landsat-8 or the Landsat-7 images filtered by Google Earth Engine.

2.       K-NN is not fast among all the machine learning classifiers. It is a non-parametric method and usually slower than parametric methods.

3.       The detected range of plastics is closely related to the spatial resolution of images. This part should be thoroughly discussed. For example, the result in a 30-m image may include mixed objects, and that in a 1-m image may greatly suffer from false alarm.

Author Response

Thank you for taking the time to review the manuscript and provide valuable feedback! Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

Spectral analysis, as an important tool in natural science analysis, is often used to detect the physical structure, chemical composition and other indicators of an object. Plastics are long-chain polymers with a certain structure and have unique characteristic spectra, thus they can be identified and sorted with the help of spectroscopic techniques. In response to the increasing plastic pollution, an increasing number of researchers have applied this technique to plastic waste monitoring.

In this study, Schmidt et al. propose an optimal hypothetical sensor for plastic type differentiation through a rigorous simulation study, which provides an important technical support for the future development of remote sensing technology-driven plastic waste monitoring and management. With a reliable technical approach and a successful case study, the results of this study have significant scientific value and are recommended for publication in Remote Sensing.

To further help improve the quality of the article, I only offer two suggestions in terms of the application of this technology：

1. equipped with ideal sensors, UAV remote sensing will play an important role in environmental plastic monitoring (Yang, et al. 2022). An extended discussion is suggested to address this aspect in conjunction with the results of this study.

Z. Yang et al., UAV remote sensing applications in marine monitoring: Knowledge visualization and review. Science of The Total Environment 838, 155939 (2022)

2. Despite the authors' rigorous argumentation of the type of surface coverage for the experimental design, the use of remote sensing methods can currently only address the issue of plastic coverage on the surface of land or water bodies. For a more accurate and comprehensive monitoring of plastic pollution, a combination of fieldwork methods (which can validate the remote sensing data and at the same time can add some details to the data) may be needed. It is suggested to refer to an interesting article published recently (Tian, et al., 2022) - combined with an interdisciplinary approach, the findings of this study may gain wider application.

Y. Tian et al., Can we quantify the aquatic environmental plastic load from aquaculture? Water Research 219, 118551 (2022)

Author Response

Thank you for taking the time to review the manuscript and provide valuable feedback! Please see the attachment.

Author Response File: Author Response.pdf