Modelling Within-Season Variation in Light Use Efficiency Enhances Productivity Estimates for Cropland

Round 1

Reviewer 1 Report

This journal talked about creating a build-out scenario for hydrogen production facilities using GIS interpretation that is based on several indicators and characterization. They divided the production into two pathways that are biomass pathway and electrolytic pathway. Biomass pathways are divided into Gasification and Anaerobic Digestion + SMR (steam methane reforming), while Electrolytic pathways are especially talking about liquid hydrogen fuel and the refueling infrastructure development. To create the build-out scenario, several analyses are needed. This paper analyses the demand of the facilities and the location of the highest demand to deploy new facilities in certain areas. Several software was used in this paper. Such as STREET (Spatially and Temporally Resolved Energy and Environmental Tool), CalEnviroScreen (to identify communities most affected by poor air quality), and ArcGIS. This paper made maps using ArcGIS based on the characteristics of each pathway. Like they made wind maps and solar maps to identify the best location of deploying solar panel and wind turbine facilities. Several comments raised as follows:

1. Page 8-12 fig 2-6, page 15 fig 9, page 18-21 fig 8-11, page 24-27 figure 13-16: However this paper provides low-resolution map qualities which high-resolution maps are necessary for better quality and understanding. Also, there is no detailed information about how the maps were made in GIS which is necessary to match the journal-title.
2. Page 2-3 fig 1 table 1, page 7 table 2, page 14 table 3-4, page 17 table 3, page 8-13 fig 2-6 & 8, page 15-16 fig 9 & 7, page 18-27 fig 8-16: There are no legit sources of the data stated in the paper (under the table or some maps). Which cause some confusion about where the data was taken from. Like suddenly there are some tables, graphics, and maps without data source (like the time it was made, made by who, and what software they used). Also numbering in tables and maps is multiple and not sequential.
3. There are no exact explanations about the research location. As if which part of California and the geographical data of the location. (page 2 line 4 - introduction part usually mention a little about the research location, page 3 line 47 - then in a scientific journal there usually is the 3^rd section that specifically talks about “Research Location”)
4. Page 5 line 46 - part 2: The Research methodology hasn’t been explained precisely, about how to collect the data and how to process data to obtain certain results. 

Author Response

Thanks to the reviewer for their comments, responses uploaded herewith.

Author Response File: Author Response.docx

Reviewer 2 Report

The manuscript aims at improving crop GPP estimation from Landsat by parametrizing LUE or maximal LUE. The topic is not new as a few previous studies had made similar topic, which was not well introduced and discussed in the Introduction and Discussion section. The manuscript is generally easy to follow. However, the author did not clearly present this study was to estimate LUE or maximal LUE from in-situ GPP and Landsat. For instance, the figure 1 shows it was to estimate LUE rather than maximal LUE, however, some presentations such as in the Section 2.5.2 show it was to estimate maximal LUE. If it was to estimate maximal LUE for improving the daily LUE in the CASA model, the information on how to extract maximal LUE from the in-situ GPP and the GPP model (section 2.4.3) are missing. The estimated maximal LUE value from the in-situ GPP or Landsat data for different crops, sites, and seasons is missing as well. If it was to estimate maximal LUE, how to employ the temporal variability of predictor variables from satellite data and agroclimatic variables (Table 2) in the RFR model is unclear.  Figure 5 aims at discussing the contribution of predictor variables on the output in maximal LUE (daily LUE ?). However, most of the parameters (excepted to crop type) varied with crop growth, and their contributions should vary with crop growth as well.  In addition, the temporal resolution of Landsat may be 16-day, while daily fPAR from NDVI is required in the GPP model. How to make daily NDVI and other information from Landsat for the GPP model is not clear as well.

Author Response

We thank the reviewer for their comments, responses attached herewith.

Author Response File: Author Response.docx

Reviewer 3 Report

It doesn't seem to fit in that direction of Remote Sensing.
This paper seems to be an aggregate of general introductory content rather than technical content.
The authors did not interpret his academic research results, opinions, or arguments according to logic.

Author Response

We thank the reviewer for their comments. Responses attached herewith.

Author Response File: Author Response.docx

Reviewer 4 Report

The comments are attached herewith. 

Comments for author File: Comments.pdf

Author Response

We thank the reviewer for their comments. Responses attached herewith.

Author Response File: Author Response.docx

Round 2

Reviewer 3 Report

There is no disagreement about publishing.