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Open AccessArticle
Peer-Review Record

A New Retrieval Algorithm for Soil Moisture Index from Thermal Infrared Sensor On-Board Geostationary Satellites over Europe and Africa and Its Validation

Remote Sens. 2019, 11(17), 1968; https://doi.org/10.3390/rs11171968
Reviewer 1: Anonymous
Reviewer 2: Anonymous
Reviewer 3: Anonymous
Remote Sens. 2019, 11(17), 1968; https://doi.org/10.3390/rs11171968
Received: 23 July 2019 / Revised: 14 August 2019 / Accepted: 16 August 2019 / Published: 21 August 2019
(This article belongs to the Special Issue Remote Sensing of Regional Soil Moisture)

Round 1

Reviewer 1 Report

Dear authors,

your submitted paper is a very good one to my mind, and since there was almost nothing to change, I simply copied my comments for the editor. The very few comments can be found below.

The authors present a retrieval system based on an approach to measure soil moisture from SEVIRI on the geostationary Meteosats. They make use of the relationship between changes in land surface temperature and absolute soil moisture. Since for this method IR channels have to be used instead of microwave channels, cloud cover and Aerosol loading have a stronger effect on the quality of the derived soil moisture. On the other hand, the spatial resolution is much higher than for microwave-based methods and also the spatial coverage is better.

This approach is competitive with traditional microwave retrievals in average performance and it can be considered to give a somewhat complementary information because the regions for higher and poorer quality differ for the two types of sensors.

The study is very well conducted, all necessary statistical measures are present, the data base is very comprehensive and the validation data sources are extensive and spatially good distributed over SEVIRI’s field of view. The discussion is good and reveals the causes of better or poorer quality in some regions.

 

I have only some very minor comments:

 

- line 156: the citation seems to missing.

- lines 317-318: one “annual” too much, I think

-line 443: I suggest to put this sentence directly into the abstract, this gives a very good overview over the properties of the data:

“...and is designed to produce surface soil moisture with less than 1 day delay over clear sky and non-steady cloudy (over 10% of the time) conditions...”

Author Response

Dear reviewer,

thank you for your positive feedback! We have taken into account your specific comments in the revised manuscript:

line 156: the citation seems to missing. the citation is now included: Stisen et al (2008) lines 317-318: one “annual” too much, I think "Annual" has been deleted, as it was already further in the sentence, thank you for noticing.

-line 443: I suggest to put this sentence directly into the abstract, this gives a very good overview over the properties of the data:“...and is designed to produce surface soil moisture with less than 1 day delay over clear sky and non-steady cloudy (over 10% of the time) conditions...”

A sentence was added at the end of the abstract with this information, as suggested.

Reviewer 2 Report

Overall comments:

The paper reports on a new retrieval algorithm for soil moisture index estimation from high-spatial resolution geostationary imagery. The evaluation of the product in different climatic zones is particularly interesting and the availability of such a product is likely to appeal to many diverse users for a variety of research projects and monitoring/assessment applications. The paper makes an important contribution and is suitable for publication after minor corrections (see detailed comments) and addressing the following overall queries:

What soil depth is the SEVIRI based soil moisture index representative of? Top 5 cm? Can the soil moisture index be combined for any benefit with SAR data from sensors such as Sentinel? Is the soil moisture index product available for research and/or applications? Are there plans to produce this operationally at LSA-SAF? What is the reason for producing the current product until 2014 and not more recent years? Have the authors fully considered issues related to the ‘thermal inertia’ and ‘triangle’ concept discussed in other published literature? For example:
Maltese, A., Capodici, F., Ciraolo, G., & Loggia, G. La. (2015). Soil Water Content Assessment: Critical Issues Concerning the Operational Application of the Triangle Method. Sensors, 15, 6699–6718. https://doi.org/10.3390/s150306699 The authors begin with a discussion of the importance of soil moisture for drought early warning systems, but this theme is abandoned after the first sentence of the abstract. What would be the recommended applications for the product, given its performance? Is it applicable to drought early warning only or can it provide useful indication of flood susceptibility or other applications?

Author Response

Dear reviewer,

thank you for your positive feedback on our work!

You can find here answers to your questions.

- What soil depth is the SEVIRI based soil moisture index representative of? Top 5 cm?

As explained in the manuscript, the assumption of surface representativity has been done. As most of the local observations of surface soil moisture were taken at 5 cm, we found a good agreement with SEVIRI soil moisture and the measurements at 5 cm depth. Several test comparison with deeper measurements were giving lower scores, confirming this surface representativity. However, further refinements of the method should probably focus on understanding to which depth the SEVIRI soil moisture is representative of, in relation with several indicators, like land cover, or climate, or soil properties.

- Can the soil moisture index be combined for any benefit with SAR data from sensors such as Sentinel?

In our study, we have shown that in some regions, the SEVIRI soil moisture index was closer to the measurements than the ASCAT derived soil moisture. In future possible work, we will probably concentrate on soil moisture products from Sentinel to confirm the complementary regional scores. Apart from the complementarity of accuracy in different regions, it would be interesting also to focus on how the temporal characteristics (high repetition rate of SEVIRI) could be used in a merged product with Sentinel. As it was also asked by the editors of the special issue, we have added some words on this avenue in the conclusion of the manuscript.

- Is the soil moisture index product available for research and/or applications? Are there plans to produce this operationally at LSA-SAF?

The product files can be asked to the authors for use/applications. In EUMETSAT's LSA-SAF, the preprocessing module of the our Evaporation & surface heat fluxes algorithm produce those maps internally (https://landsaf.ipma.pt/en/products/evapotranspiration-energy-flxs/). But, the soil moisture used is a combination of this SEVIRI indicator and the soil moisture from ECMWF's SM-Data Assimilation System H-SAF H14 (which assimilates ASCAT soil moisture). The files with the combined product is only accessible to registered beta-users. Further dissemination could be foreseen in the future if EUMETSAT and the SAFs agree on its interest to users (it may be done via direct requests to LandSAF help desk, during LandSAF workshop, or maybe via direct communication with the LSA-SAF manager, Isabel Trigo, who also co-authors this paper). 

- What is the reason for producing the current product until 2014 and not more recent years?

The product has been now extended to 2018. It may be also asked to the authors.

- Have the authors fully considered issues related to the ‘thermal inertia’ and ‘triangle’ concept discussed in other published literature? For example:
Maltese, A., Capodici, F., Ciraolo, G., & Loggia, G. La. (2015). Soil Water Content Assessment: Critical Issues Concerning the Operational Application of the Triangle Method. Sensors, 15, 6699–6718. https://doi.org/10.3390/s150306699 

We have tried to take into consideration the referenced issues of the 'thermal inertia', including problems of shading due to topography, withdrawal of outliers, withdrawal of dubious LST retrievals due to deficient cloud screening. However, there still are issues, as discussed in the manuscript, leading to scores less good in some regions, where the signal would be possibly useful. Those will be under investigation in later stages. Anyway, thanks for drawing our attention on this paper, that has been now referenced in the manuscript.

- The authors begin with a discussion of the importance of soil moisture for drought early warning systems, but this theme is abandoned after the first sentence of the abstract. What would be the recommended applications for the product, given its performance? Is it applicable to drought early warning only or can it provide useful indication of flood susceptibility or other applications?

The product is especially relevant in semi-arid to dry temperate zones for non overcasts days. It means it can be used for drought early warning, but could bring also indications of flood susceptibility in some regions. For example, we have seen very good monthly averaged representation of the flood extention of niger inner delta during the months of November in relation to normal/abnormal years. In case of rainy seasons with a lot of cloudy days, there can be sometimes gaps of a few days between estimation of soil moisture and an assimilation within a hydrological model, in absence of reliable precipitation data, could be a good solution. We therefore have added to the manuscript a sentence stating some possible applications, as you suggested.

Reviewer 3 Report

The article describes a new retrieval methodology for daily soil moisture monitoring based only on the land surface temperature observations derived from the geostationary satellites. The goals and scientific context are clear. Also, the authors clearly describe the methodology, but it would be interesting if they include a schematic diagram to illustrate it step by step.

The results are consistent with the method, and a benchmark evaluation was done to compare them with the most recent databases available.

There are minor issues requiring attention before publication. Some figures need improvement to facilitate the readers to understand and get all the relevant information on them. The text dimension inside some figures is too small, making it difficult to read. The size of some graphs is too small, making it challenging to analyze the differences between lines and data points.

The attached file shows more details.

Congratulations on the excellent work.

Comments for author File: Comments.pdf

Author Response

Dear reviewer,

We would like to thank you for your careful reading of our manuscript. We have enlarged the noted figures, added the missing information in the caption and fixed the missing elements (justification, reference) you pointed.

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