General comment:

Drought monitoring and early warning systems are valuable tools to enhance our understanding and better inform relevant authorities to act early and effectively to mitigate adverse effects that drought may bring to food security systems. Implementing strategies aimed at improving the reliability of Drought monitoring and early warning systems remains key. Authors of this manuscript have implemented an approach of assimilating remotely sensed data (LAI and soil moisture) to a land surface model (Noah-MP) to improve drought monitoring in the MENA region. The study is interesting and valuable not only for the MENA region but could also be applied in other regions experiencing drought challenges. Thus, the manuscript could contribute to valuable knowledge that this journal aims for. However, I have noted some scientific concerns (approaches and discussions) in the manuscript that could lower the quality of this manuscript at the current state.

Specific scientific comment:

1. Application of SMAP-DA must have failed due to assumptions taken in the model which were, highly likely, not representative of actual position on the ground. Model failure, especially from SSM-DA, was therefore almost certain. For this reason, the authors then focused on quantifying how data assimilation differentiates the categorization of drought and reproduces the evolution, duration, and intensity of past drought events as an indirect way to evaluate its impact on root zone soil moisture, which is good. However, the authors should first better highlight more on these assumptions, such as assuming soil is always irrigated to field capacity (lines 168 to 169) and uncertainty in irrigation information (irrigation frequency/timing and amount), as possible reasons behind DA failure. Could better representation of actual ground information in the model lead to improvements in model simulation after implementing SSM-DA? So in my view the authors should explicitly state that the reasons behind model failure was due to missing or limited in-situ data and local information and the decision to set the model with conditions that may not be the actual position on the ground.

2. Authors should demonstrate or reference other studies on accuracy and uncertainty of the RS datasets, such as MODIS, used in this study.

3. In line 149 to 152, the authors relied on irrigation maps from global datasets. The study area is relatively small. Why couldn’t they generate more accurate irrigation maps? Did they attempt to validate the irrigation map from global dataset?

4. In lines 306 to 310, the authors noted spatial variability of improvements in results obtained but failed to discuss the reasons behind these? Could they discuss this?

5. Authors have stated in lines 351 to 352 that overestimation of transpiration during summertime, especially for croplands, likely due to misrepresentation of the vegetation seasonality. Why were there no attempts to better represent the vegetation seasonality in the model?

6. Authors have stated in line 431 that limited in-situ soil data was available. what were the sources of soil moisture data? Can they also include these data in the manuscript? what about using rainfall data as proxy for soil moisture, standardised precipitation index? did they attempt to use this?

7. Why are all figures placed at the end of the manuscript rather than at their rightful positions? This approach makes it difficult for the reader to follow smoothly.

8. Line 411, authors are referring to the figure 2(b), representing the difference of correlation (R) – computed as DA minus OL for the period of 2015-2019 for LAI-DA in terms of transpiration? why specifically stratify this figure?

Technical corrections:

1. Line 65, separate DA and reference using separate brackets.

2. Line 191, state SSM-DA and SSM-DA_irr separately and not as SSM-DA (SSM-DA_irr). line 197 it is well stated LAI-DA and LAI-DA_irr.

3. Line 214, correct the superscript of degree in 0.05o

4. Line 256, SIF is not defined but then defined later in line 260. it should be defined at this point

5. Line 392, place irr as a subscript in LAI-DA_irr

General Comments:

This manuscript analyses the impact of assimilating LAI and soil moisture data from remote sensing (separately) on the water and carbon fluxes simulated by a land surface model (Noah-MP) and how drought categorization is affected as a result. The impact of irrigated areas is also included in the analysis through a simple irrigation module. This is a very relevant topic as early drought warning can help implement mitigation measures to reduce adverse impacts. Irrigation is often not included in LSMs, and its inclusion here is valuable in bringing  focus to the difficulties encountered in its inclusion in models, in particular in data scarce regions.  

The work is well written and the evidence is clearly and convincingly presented.

Specific Comments/Questions:

There is no description of the downscaling of the SMAP/upscaling of the MODIS LAI data. A discussion on the spatial resolution of the datasets is also missing: what could be its impact relative to 1. the model grid and 2. the landscape fragmentation (in particular size of irrigated areas)?

The parameterization of the irrigated areas and module deserves more discussion: what could be the impact of the uncertainty of the global datasets used? How about irrigation amounts which assume reaching of field capacity? I also do not see a mention of matching the irrigated areas to the land use map, were irrigated areas only applied to pixels identified as cropped/partially cropped?

There is a brief mention of loss of information due to the rescaling of the soil moisture product to the model climatology but I think this deserves further discussion. What is the impact of rescaling the SSM to a model run which does not include irrigation? In particular when there is also an attempt to use the SSM to improve the irrigation run. A comparison of the SMAP cdf over an irrigated vs. non-irrigated pixel would be an interesting starting point.

Was a simultaneous assimilation of LAI and SSM tested? If not, why not? Do you see any potential for the inclusion of both to improve the results?

Technical Comments

l.268: ‘for the’ should be removed (or rephrase sentence, unclear)

l.300: ‘with the percent change over 20%’ is awkward. Suggestion: ‘with a relative improvement of over 20%’

458-460: I assume the higher LAI leads to increased transpiration. The sentence structure implies the opposite effect. Add ‘for LAI-DA’ for clarity at the end of the sentence