We thank the reviewer for the questions and comments that we would like to clarify here.

Regarding the multi-angular capabilities, so far, the model does not allow for off-nadir observation since the point spread function (PSF) is projected over the surface only from nadir. The emulators simulate the different spectroradiometric variables for multiple sun angles and nadir view. Recognizing the importance of off-nadir observation in modeling bidirectional reflectance, we seek to include off-nadir PSF projection in the next version of BOSSE. This will be stressed in the main text of the manuscript. In fact, this task is partly advanced, and we have already trained some of the multi-angular emulators, and the implementation of the remote sensing PSF needs to be developed.

Regarding the mixture of species, BOSSE species are individually simulated in the background and always correspond to a single pure pixel at the selected baseline. All the variables are thus handled at a relative resolution of 100 % (i.e., each pixel represents a single individual or a group of identical individuals). Then, this resolution can be degraded when the output (e.g., the simulated remote sensing imagery or plant trait maps) is provided to the user. In that case, the traits and spectral signals of the different nearby pixels are averaged in different ways: simple averaging for plant trait maps, simulating field sampling plots, or weighted averaging according to the PSF of the remote sensors. We have clarified this in sections 2.4.1 and 2.4.2, respectively. Additionally, any user could produce the maps at 100 % spatial resolution and aggregate the data using any approach of their choice.

We also thank the reviewer for the manuscript recommended. This is indeed a complementary approach in line with the aims of BOSSE, and we will make reference to it in the revised manuscript.

Dear Editor,

Thanks for the warning. I was not aware of this. A release of the code has been published in Zenodo under DOI: https://doi.org/10.5281/zenodo.14973471 . The DOI will be included in the manuscript during the review.

On behalf of the co-authors,

Javier Pacheco-Labrador

We thank the reviewer for the detailed review and the suggestions regarding potential improvements and developments of the model. While it is not possible to tackle them all in the first version of this model, we will implement some of the recommendations in this review, and some others in the next version of the model. The open-source nature of pyBOSSE allows the community to contribute and include new features, foreseen or not, by the authors or the reviewers of this manuscript.

Regarding the improvement of radiative transfer models, we acknowledge the limitations and anticipate future model improvements, but we cannot provide accurate timelines for all of them. The inclusion of 3D modeling has already been foreseen, and we hope we can incorporate it in the next version of the model. LiDAR and radar radiative transfer modeling may not yet be so mature and might take longer. Consider this is the first version of the model, and it already includes a wide variety of spectral signals. BOSSE v1.0 is able, in its current state, to provide answers to “simple”, yet relevant and unanswered questions. Likely, future designs could be helped by the lessons learned during the efforts to answer these questions. Furthermore, the open-source code and modular design would allow researchers interested in questions BOSSE v1.0 could not answer to implement additional features, including those suggested by the reviewer. Finally, while there is a commitment to further developing BOSSE, implementing new functionalities will depend on available funding and the community’s interest in the model. 

Regarding the inclusion of GUIs or pre-configured scenarios, we thank the reviewer for the suggestion. However, we had to balance modeling efforts and the development of interfaces. Still, this is a good idea that could be implemented once the model is more mature and we have received more feedback from potential users to understand their needs better. Regarding pre-configured scenarios, the current tutorial shows how to generate repeatable scenarios, which, in our opinion, is the most important aspect. Still, we could include a second tutorial showing how to store these scenarios in data formats accessible from GUI and non-programming platforms. 

With respect to the improvements in clarity, we will improve the description of the processes mentioned by the reviewer and include a new flowchart focused on phenological and trait generation. We will also include colors to facilitate the reading of the current flowcharts, a legend in Fig. 2, and define the acronyms in the figures captions. We will also include a glossary of terms as in a new table or a box.

Regarding validation, we thank the reviewer for the suggestion. However, while validation is important, please notice that BOSSE generates the spatial distribution of species and their traits in a fully randomized way, constrained by the meteorological data and the plant functional types to which each species is assigned. This feature allows for generating multiple scenarios but also makes comparing the simulations with observational datasets difficult. Validation would require having spatialized information of species distributions and a large set of their traits and biophysical and phenological properties so that the dynamics of other observables (e.g., remote sensing imagery or eddy covariance fluxes) could be compared with the simulations. However, this is not trivial, and it is precisely the lack of such information at wide scales that motivated the development of BOSSE. On the one hand, a partial lack of this information could compromise the comparison, making it impossible to understand whether the differences between simulations and observations are due to uncertainties in the unknown input parameters. On the other hand, these variables could be estimated by data assimilation, but then the validation would be circular, since it would depend on the fit and equifinality of the constrained solutions of the inverse problem. BOSSE relies on validated or at least tested models (e.g., SCOPE). Partial validations (e.g., validating only one scene) would be just validations of these models, which is not the aim of this work. Another key element is the distributions and ranges of the biophysical parameters simulated. While literature and observational data inform these, we will include in the Jupyter notebook tutorial a section dedicated to how to modify these thresholds for a scene, so that the simulations can be adjusted to more local or better-known conditions. We still acknowledge that randomness could lead to implausible simulations. For this reason, BOSSE is able to generate a large number of plots describing the temporal and spatial distribution of species traits and the sensitivity of the simulated species to meteorological variables for inspection, allowing the users to determine the plausibility or similarity of a particular scene with the behaviour of a specific study area or ecosystem type. Future developments of the model might allow for some tuning of the scene parameters to observations, but addressing this problem to provide an “improved” solution requires the BOSSE to mature and some understanding of the requirements from the community.

With respect to the emulation uncertainty, we have tested not only the uncertainty in the variables predicted by the emulators in fully independent “test” datasets, but we have also propagated these uncertainties to functional diversity metrics such as Rao Q or the variance-based partitioning of diversity for all of them. These results are presented in Table S6.1 of the supplementary material. We will stress this point in the main text of the manuscript. Notice that this is a step further with respect to former similar simulations in the context of plant functional diversity (e.g., Pacheco-Labrador et al., 2022, Ludwig et al., 2024), or other ecological applications (e.g., Katteborn et al. 2017).

Finally, computational feasibility is indeed an important point that BOSSE takes care of by replacing expensive models with emulators. Following the reviewer’s recommendation, we will provide an additional script to benchmark the computational cost on any machine prior to the large-scale use of the model to improve the user’s understanding of computational resources and time

References:

* Kattenborn, T., Fassnacht, F.E., Pierce, S., Lopatin, J., Grime, J.P., & Schmidtlein, S. (2017). Linking plant strategies and plant traits derived by radiative transfer modelling. Journal of Vegetation Science, 28, 717-727

* Ludwig, A., Doktor, D., & Feilhauer, H. (2024). Is spectral pixel-to-pixel variation a reliable indicator of grassland biodiversity? A systematic assessment of the spectral variation hypothesis using spatial simulation experiments. Remote Sensing of Environment, 302, 113988

* Pacheco-Labrador, J., Migliavacca, M., Ma, X., Mahecha, M.D., Carvalhais, N., Weber, U., Benavides, R., Bouriaud, O., Barnoaiea, I., Coomes, D.A., Bohn, F.J., Kraemer, G., Heiden, U., Huth, A., & Wirth, C. (2022). Challenging the link between functional and spectral diversity with radiative transfer modeling and data. Remote Sensing of Environment, 280, 113170