An evapotranspiration model self-calibrated from remotely sensed surface soil moisture, land surface temperature and vegetation cover fraction: application to disaggregated SMOS and MODIS data

Ait Hssaine, Bouchra; Merlin, Olivier; Ezzahar, Jamal; Ojha, Nitu; Er-Raki, Salah; Khabba, Said

doi:https://doi.org/10.5194/hess-24-1781-2020

Articles | Volume 24, issue 4

https://doi.org/10.5194/hess-24-1781-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-24-1781-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 24, issue 4

Research article

|

09 Apr 2020

Research article |

| 09 Apr 2020

An evapotranspiration model self-calibrated from remotely sensed surface soil moisture, land surface temperature and vegetation cover fraction: application to disaggregated SMOS and MODIS data

Bouchra Ait Hssaine, Olivier Merlin, Jamal Ezzahar, Nitu Ojha, Salah Er-Raki, and Said Khabba

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Final revised paper (published on 09 Apr 2020)
Preprint (discussion started on 23 Apr 2019)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Review comments on evapotranspiration model self-calibration', Anonymous Referee #1, 03 Jun 2019
- AC1: 'Response to Reviewer 1', Bouchra Ait Hssaine, 18 Aug 2019
RC2: 'Review comments on "An evapotranspiration model ........ SMOS and MODIS data"', Anonymous Referee #2, 06 Jun 2019
- AC2: 'Response to Reviewer 2', Bouchra Ait Hssaine, 18 Aug 2019

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

ED: Reconsider after major revisions (further review by editor and referees) (06 Sep 2019) by Bob Su

AR by Bouchra Ait Hssaine on behalf of the Authors (08 Oct 2019) Author's response Manuscript

ED: Referee Nomination & Report Request started (18 Oct 2019) by Bob Su

RR by Anonymous Referee #2 (03 Nov 2019)

RR by Anonymous Referee #1 (07 Nov 2019)

Suggestions for revision or reasons for rejection

The revised paper titled ‘An evapotranspiration model self-calibrated from remotely sensed surface soil moisture, land surface temperature and vegetation cover fraction: application to disaggregated SMOS and MODIS data’ by Ait Hssaine have substantially improved as compared to the initial version. However, couple of points need to be addressed after which it can be accepted for publication.

Introduction: The first paragraph should be written as follows (some references need to be added):
Evapotranspiration (ET) is a crucial water flux for drought monitoring (Bhattarai et al., 2019; Gerhards et al., 2019; Mallick et al., 2014, 2016, 2018), water resource management (Please add references related to METRIC model applications in water resources management) and climate simulation (Littell et al., 2016; Molden et al., 2010) in the semi-arid ecosystems. A precise estimate of ET determines the crop water requirements, which subsequently allows the optimization of irrigation water applications (Allen et al., 1998).
Reference:
Mallick et al. (2018). Bridging Thermal Infrared Sensing and Physically-Based Evapotranspiration Modeling: From Theoretical Implementation to Validation Across an Aridity Gradient in Australian Ecosystems, Water Resources Research, 54, 3409–3435. https://doi.org/10.1029/2017WR021357.

Page 2 L13-14 (Introduction): Please correct it as,
(iii) other categories of models that integrate LST into water balance models (Olivera-Guerra et al., 2018) or into the Penman-Monteith Energy Balance (PMEB) equation to directly estimate ET (Amazirh et al., 2017; Mallick et al., 2015, 2018).

Page 2 L24 – 28 (Introduction): Please correct as follows:

Recently, Boulet et al. (2015) have developed the Soil-Plant-Atmosphere and Remote Sensing Evapotranspiration (SPARSE) model, which is similar to the TSEB model in its basic assumption, but, with additional constraints to improve the ET model performance in heterogeneous vegetation.

P4 L5 – 10: Calling it cutting edge capability is an overstatement. Please correct it as follows:

Although TSEB-SM has the capability to calibrate its main parameters from remotely sensed data, however, the real-life application needs extensive evaluation and testing. The objective of this paper is thus to demonstrate for the first time this capacity using disaggregated SMOS and MODIS (Moderate resolution imaging spectroradiometer) data.

Conclusions: Some insightful conclusions are expected. Better to give the significant conclusions in bullet points

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RR by Anonymous Referee #3 (15 Nov 2019)

Suggestions for revision or reasons for rejection

Two-source energy balance (TSEB) model based on remotely-sensed land surface temperature is an important modeling approach for estimating evapotranspiration (ET) and its components of E and T. This study aimed to use disaggregated soil moisture from SMOS to constrain the soil evaporation in TSEB and to further improve ET estimation. I do agree this is a good start point. However, the disaggregated soil moisture data should be reliable to calibrate the TSEB-SM model. If the authors failed to prove the reliability of the disaggregated soil moisture, this manuscript is just like the Ait Hssaine et al. AFM 2018b and should not be published again. After carefully read this manuscript, I think the “proof” provided in the current manuscript is not strong enough.

(1) The DisPATCh algorithm was applied to disaggregated 1km resolution soil moisture from SMOS soil moisture products. The DisPATCh algorithm is actually based on a contextual method to estimate soil and vegetation evaporative fraction. The question is whether it is reliable to apply the contextual method to such as heterogeneous farmland (See Figures 1 and 4)? It seems the authors only use one soil moisture site to do the validation. Although the authors provide a scatter in Figure 3, this is not sufficient.

(2) The manuscript failed to provide related information to the DisPATCh algorithm when applying the algorithm to the study area. How the dry and wet edge associated with the DisPATCh algorithm been determined in this study? The EFs derived from empirical contextual based DisPATCh algorithm is applied to disaggregated SMOS soil moisture, and the soil moisture is applied to constraint a more physically based TSEB ET model. What is the difference between EFs derived from DisPATCh and TSEB? Which model is more reliable?

(3) It is clear the study area is very small from the Map scale in Figure 1, and the MODIS products with coarse resolution may not be a good choice in this area.I should recommend the authors try to use other high-resolution satellite images.

(4) As a two-source model, TSEB can provide ET components (E and T) as well. The authors only provide validation results for H and LE. It should be very interesting if the E/T partition can be provided for the original TSEB and the calibrated one.

(6) More consideration should be taken to the smoothed PT coefficient in Figure 6. It seems the points are quite scatter.

Other specific comments
(1) P6_line6, “the revised soil temperature” –may be retrieved ?
(2) Table 2，the variable for the first equation should be soil latent heat flux. Soil heat flux should be G.

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ED: Publish subject to minor revisions (review by editor) (08 Jan 2020) by Bob Su

AR by Bouchra Ait Hssaine on behalf of the Authors (01 Feb 2020) Author's response

ED: Publish as is (08 Mar 2020) by Bob Su