The paper by Wen et al. examines the performance of in-situ and satellite-derived solar-induced fluorescence (SIF) and vegetation indices in tracking intra-seasonal vegetation dynamics in an African dryland ecosystem. Overall, the paper is well-written and conveys an important finding: TROPOMI SIF aligns well with in-situ SIF measurements in the Horn of Africa (HoA), while other reflectance-based vegetation indices may overlook certain subseasonal vegetation dynamics. These findings have potential implications for understanding dryland carbon fluxes. Given the good shape of the manuscript, I have a few comments to further improve the paper.

Introduction: 

• The authors provide an insightful introduction to the importance of drylands and the role of SIF and vegetation indices. However, there is limited discussion on the existing literature concerning SIF applications for tracking dryland GPP or drought/heat stresses. I suggest the authors expand this to provide a more comprehensive background on SIF use in dryland ecosystems.
• Line 78: The IPCC reference can be more specific

Method: 

• When filtering satellite SIF measurements, it is unclear whether the authors account for the uncertainties in SIF retrievals (e.g., standard deviations of SIF) provided by the products. 

Results: 

• There are negative SIF values in both the in-situ observations and the TROPOMI SIF retrievals, as shown in Fig. 2. Since SIF should theoretically be positive, the authors should explain the source of these negative values, such as measurement errors or retrieval limitations.
• Fig. 1(h): The legend with dotted lines does not match the lines in the figure.
• Fig. 7: It might be useful to include a scatter plot showing SIF yield against variables like VPD, soil moisture, or Tair to illustrate how much environmental factors drive variation in SIF yield.

Discussion: 

• The authors could further discuss the performance of SIF in tracking dryland vegetation dynamics as reported in previous studies. For instance, Wang et al. (2022) found that NIRv performed better than SIF in capturing GPP in western U.S. drylands due to noise in SIF signals. The results of this study appear to differ somewhat from those of previous studies.

Wang, X., Biederman, J.A., Knowles, J.F., Scott, R.L., Turner, A.J., Dannenberg, M.P., Köhler, P., Frankenberg, C., Litvak, M.E., Flerchinger, G.N. and Law, B.E., 2022. Satellite solar-induced chlorophyll fluorescence and near-infrared reflectance capture complementary aspects of dryland vegetation productivity dynamics. Remote sensing of environment, 270, p.112858. 

This manuscript examines the potential of Solar-Induced Fluorescence (SIF) in detecting fast changes in vegetation function and structure in dryland ecosystems. The authors use the Horn of Africa as an example to compare the performance of different satellite SIF data products in representing the fasting-changing intra-seasonal vegetation dynamics in an abnormally wet year. First, the authors cross-compared the satellite SIF products against tower-based SIF. They found that native satellite SIF data matches tower-based SIF best. The reconstructed SIF products do not show intra-seasonal changes observed from the tower. Then, the authors use climate data and field images to explain the observed inter-seasonal changes. The results show that the native satellite SIF data is mechanistically linked to the vegetation function, which makes this data a good candidate for future real-monitoring vegetation dynamics. 

The manuscript is overall well written. Here are some major comments I have:

1. I think the manuscript is missing some detailed explanation of the choice of temporal scaling, especially since the temporal resolution is a key component in this manuscript. All results are presented as 8-day averages. However, the manuscript does not explain why 8-day is picked given other options are available. This may not be intuitive for readers who are not familiar with TROPOMI. 
2. The manuscript did not present how SIF from different spatial resolutions and footprints are cross-compared, nearest pixel? 
3. I think authors should cite papers to support their explanations for physiological changes, e.g., lines 270-271. In some locations, it would be better to include drylands/Africa-related references in addition to global-scale references, e.g., line 50. 
4. The definition of vegetation function can be ambiguous. In line 57, Li et al., 2024 used “vegetation function” to include both physiology and structure. In this manuscript, vegetation function seems to only refer to physiology. 
5. The manuscript introduces new terminology in results and discussion, such as NIRvP and SIFyield. They need to be better introduced before the results.

Some typographical suggestions: 

1. Lines 215-216 are not clear and seem to miss a word.
2. Line 221 “...variation in SIF…”, do you mean reconstructed SIF?
3. Line 243, this line needs elaborations on why anomalous vegetation dynamics are challenging to measure. Is the anomaly or vegetation dynamics in general making it challenging?

Therefore, I recommend a major revision of this manuscript before it can be accepted for publication.