Response to reviewer 1

We would like to thank the reviewer for his meaningful comment that we have taken into account. See our point-by-point response below

1. 167: this is called the « auto-correlation function »

Right. The name was changed to “auto-correlation function” in the new version of the manuscript.

1. 182: replace « backscatter » by « radar backscattering response »

Done.

1. §3.1.4: Add references detailing the used GLAI estimation method.

Two references were added as requested by the reviewer: Duchemin et al., 2006; Khabba et al., 2009.

1. 241-256: Add a reference advocating this data processing chain

The reference to Frison and Lardeux, 2018 was added in the new version of the manuscript. The same processing is also used by Bousbih et al. (2017).

1. 259-260: Give the number of pixels involved in the average process, the corresponding ELN estimation, and the corresponding error on the estimated backscattering coefficient.

Right. In response to the reviewer comment, the number of pixels and the error on the backscattering coefficient is now added in the new version of the manuscript. By contrast, we didn’t understand what ELN means.

1. 261-293: - Add a reference advocating this data processing chain

The reference to Veci, 2015 is added in the new version of the manuscript.

1. - Specify the spatial neigbourhood dimension in range and azimuth that is used.

The spatial neighborhood dimension range*azimuth was specified at line 289.

1. 298: Give details nabout the atmospheric corrections made for the Sentinel-2 level-2A products (the atmospheric radiative transfer model as well as the atmospheric gazeous and aerosols concentrations that are used)

The correction chain used for S2 correction is named MAYA and has been developed by Hagolle et al, 2015. The atmospheric corrections are performed in three steps:

1-The satellite top-of-atmosphere (TOA) reflectances are corrected from the absorption by the atmospheric gas molecules usingthe absorption part of the Simplified Model for Atmospheric Correction (SMAC) method by Rahman et al., 1994. The concentrations of the ozone, the oxygen and the water vapor are obtained from satellite data (ozone) and meteorological data (water vapor, pressure).

2- The detection of the clouds (and cloud’s shadows) is based on the multi-temporal cloud detection method proposed by Hagolle et al., 2010 .

3-The estimation of the aerosol optical thickness (AOT) relies on a hybrid method merging the criteria of a multi-spectral method with the multi-temporal technique developed initially for the VENµS satellite mission by Hagolle et al., 2010. The AOT is used along with the surface altitude, the viewing geometry and the wavelength in the parameterization of look-up tables for the conversion of TOA reflectances already corrected in step “1” into surface reflectances. The look-up tables are provided by the successive orders of scattering code (Lenobel et al., 2007) used in the modeling of molecular and aerosol scattering effects. A different look-up table is computed for each aerosol model.

In response to the reviewer comment, the processing chain is now described in the new version of the manuscript.

1. 378: Precise that Polarization Ratio consist in the ratio between Sigma0_VH / Sigma0_VV

Agree. This is now specified in the new version of the manuscript.

1. Fig. 9, 10, A3-A8: Are these temporal profiles similar than those already published in litterature? A comparison should be welcome.

Agree. Similar temporal profiles were partly already published. For the backscattering coefficient, the temporal behavior of wheat over a growing season at C-band was found by several authors to be characterized by four stages; i) the signal is first governed by soil moisture dynamic during the first growing stage, ii) The backscattering coefficient decreases in a econd step under the effect of canopy attenuation until the heading stage when it reaches a minimum value and iii) it increases again gradually in response to the development of the head creating a thin very wet layer at the top of the canopy favoring volume backscattering; iv) Finally, backscattering decreases during senescence in response to the soil and the vegetation drying. The first part differs from one study to another given the differences in soil hydric condition and surface roughness of the sites. After this period, the behavior of the signal is overall similar to the profiles obtained by Cookmartin et al. (2000), El Hajj et al. (2019), Nasrallah et al. (2019) and Veloso et al. (2017). With the development of vegetation, the decrease caused by the canopy attenuation has been observed by several authors before, as indicated in the manuscript (Cookmartin et al., 2000; Mattia et al., 2003; Picard et al., 2003; Wang et al., 2018). After heading, the increase of the backscattering coefficient at C-band was reported first by Ulaby and Batlivala (1976). Ulaby et al. (1986) suggested that an additional term needs to be added to the traditional three terms model (volume scattering from vegetation, soil attenuated and interaction soil-vegetation) to properly represent wheat backscattering after heading. The increase after heading has then be observed and attributed to the appearance of the ears followed by the grain by numerous authors (Brown et al., 2003; El Hajj et al., 2019; Mattia et al., 2003; Patel et al., 2006; Veloso et al., 2017; Ouaadi et al., 2020). In response to the reviewer comment, the comparison to literature has been strengthened in the new version of the manuscript.

For the interferometric coherence, only a few time series have been presented and analyzed on wheat crops to our knowledge. In line with the time series illustrated in this work (Figures 10 and A8), Santoro et al., 2010 demonstrates using the ERS–Envisat Tandem mission that coherence measurements of vegetated fields are always below the level of bare soils coherence. During the period of vegetation, the observed degradation/decrease of coherence with wheat development have been illustrated by Blaes and Defourny (2003) and Engdahl et al. (2001) even that the number of data was limited (less than six data along the season). In response to the reviewer comment, this is now specified in the new version of the manuscript.

References

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Bousbih, S., Zribi, M., Lili-Chabaane, Z., Baghdadi, N., El Hajj, M., Gao, Q., Mougenot, B., 2017. Potential of sentinel-1 radar data for the assessment of soil and cereal cover parameters. Sensors (Switzerland) 17. https://doi.org/10.3390/s17112617

Brown, S.C.M., Quegan, S., Morrison, K., Bennett, J.C., Cookmartin, G., 2003. High-resolution measurements of scattering in wheat canopies - Implications for crop parameter retrieval. IEEE Trans. Geosci. Remote Sens. 41, 1602–1610. https://doi.org/10.1109/TGRS.2003.814132

Cookmartin, G., Saich, P., Quegan, S., Cordey, R., Burgess-Alien, P., Sowter, A., 2000. Modeling microwave interactions with crops and comparison with ERS2 SAR observations. IEEE Trans. Geosci. Remote Sens. 38, 658–670. https://doi.org/10.1109/36.841996

Duchemin, B., Hadria, R., Erraki, S., Boulet, G., Maisongrande, P., Chehbouni, A., Escadafal, R., Ezzahar, J., Hoedjes, J.C.B., Kharrou, M.H., Khabba, S., Mougenot, B., Olioso, A., Rodriguez, J.C., Simonneaux, V., 2006. Monitoring wheat phenology and irrigation in Central Morocco: On the use of relationships between evapotranspiration, crops coefficients, leaf area index and remotely-sensed vegetation indices. Agric. Water Manag. 79, 1–27. https://doi.org/10.1016/j.agwat.2005.02.013

El Hajj, M., Baghdadi, N., Bazzi, H., Zribi, M., 2019. Penetration Analysis of SAR Signals in the C and L Bands for Wheat, Maize, and Grasslands. Remote Sens. 11, 22–24. https://doi.org/10.3390/rs11010031

Engdahl, M.E., Borgeaud, M., Member, S., Rast, M., 2001. The Use of ERS-1 / 2 Tandem Interferometric Coherence in the Estimation of Agricultural Crop Heights. IEEE Trans. Geosci. Remote Sens. 39, 1799–1806. https://doi.org/0.1109/36.942558

Frison, P.-L., Lardeux, C., 2018. Vegetation Cartography from Sentinel-1 Radar Images, in: Baghdadi, N., Mallet, C., Zribi, M. (Eds.), QGIS and Applications in Agriculture and Forest. p. 350. https://doi.org/10.1002/9781119457107.ch6

Hagolle, O., Huc, M., Pascual, D.V., Dedieu, G., 2015. A multi-temporal and multi-spectral method to estimate aerosol optical thickness over land, for the atmospheric correction of FormoSat-2, LandSat, VENμS and Sentinel-2 images. Remote Sens. 7, 2668–2691. https://doi.org/10.3390/rs70302668

Hagolle, O., Huc, M., Pascual, D.V., Dedieu, G., 2010. A multi-temporal method for cloud detection, applied to FORMOSAT-2, VENμS, LANDSAT and SENTINEL-2 images. Remote Sens. Environ. 114, 1747–1755. https://doi.org/10.1016/j.rse.2010.03.002

Khabba, S., Duchemin, B., Hadria, R., Er-Raki, S., Ezzahar, J., Chehbouni, A., Lahrouni, A., Hanich, L., 2009. Evaluation of digital Hemispherical Photography and Plant Canopy Analyzer for Measuring Vegetation Area Index of Orange Orchards. J. Agron. 8, 67–72. https://doi.org/10.3923/ja.2009.67.72

Lenoble, J., Herman, M., Deuzé, J.L., Lafrance, B., Santer, R., Tanré, D., 2007. A successive order of scattering code for solving the vector equation of transfer in the earth’s atmosphere with aerosols. J. Quant. Spectrosc. Radiat. Transf. 107, 479–507. https://doi.org/10.1016/j.jqsrt.2007.03.010

Mattia, F., Le Toan, T., Picard, G., Posa, F.I., D’Alessio, A., Notarnicola, C., Gatti, A.M., Rinaldi, M., Satalino, G., Pasquariello, G., 2003. Multitemporal C-band radar measurements on wheat fields. IEEE Trans. Geosci. Remote Sens. 41, 1551–1560. https://doi.org/10.1109/TGRS.2003.813531

Nasrallah, A., Baghdadi, N., Hajj, M. El, Darwish, T., Belhouchette, H., Faour, G., Darwich, S., Mhawej, M., 2019. Sentinel-1 Data for Winter Wheat Phenology Monitoring and Mapping. Remote Sens. 11, 1–29.

Ouaadi, N., Jarlan, L., Ezzahar, J., Zribi, M., Khabba, S., Bouras, E., Bousbih, S., Frison, P., 2020. Monitoring of wheat crops using the backscattering coe ffi cient and the interferometric coherence derived from Sentinel-1 in semi-arid areas. Remote Sens. Environ. 251. https://doi.org/10.1016/j.rse.2020.112050

Patel, P., Srivastava, H.S., Navalgund, R.R., 2006. Estimating wheat yield: an approach for estimating number of grains using cross-polarised ENVISAT-1 ASAR data. Microw. Remote Sens. Atmos. Environ. V 6410, 641009. https://doi.org/10.1117/12.693930

Picard, G., Toan, T. Le, Mattia, F., 2003. Understanding C-Band Radar Backscatter From Wheat Canopy Using a Multiple-Scattering Coherent Model. IEEE Trans. Geosci. Remote Sens. 41, 1583–1591. https://doi.org/10.1109/TGRS.2003.813353

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Santoro, M., Wegmüller, U., Askne, J.I.H., 2010. Signatures of ERS-Envisat interferometric SAR coherence and phase of short vegetation: An analysis in the case of maize fields. IEEE Trans. Geosci. Remote Sens. 48, 1702–1713. https://doi.org/10.1109/TGRS.2009.2034257

Ulaby, F., Moore, R., Fung, A., 1986. Microwave remote sensing active and passive-volume III: from theory to applications.

Ulaby, F.T., Batlivala,  percy P., 1976. Optimum Radar Parameters for Mapping Soil Moisture. IEEE Trans. Geosci. Electron. GE-14, 81–93.

Veci, L., 2015. Sentinel-1 Toolbox: TOPS Interferometry Tutorial.

Veloso, A., Mermoz, S., Bouvet, A., Le Toan, T., Planells, M., Dejoux, J.F., Ceschia, E., 2017. Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications. Remote Sens. Environ. 199, 415–426. https://doi.org/10.1016/j.rse.2017.07.015

Wang, H., Magagi, R., Goïta, K., 2018. Potential of a two-component polarimetric decomposition at C-band for soil moisture retrieval over agricultural fields. Remote Sens. Environ. 217, 38–51. https://doi.org/10.1016/j.rse.2018.08.003

 Response to reviewer 2 

We would like to thank the reviewer for his interest in reviewing the paper. All comments are considered in the new version of the manuscript and have been addressed in detail below:

1. 130-132: why the depth of sensors for F1, F2 and F3 is different?

Ok. Thank you.

We had a limited number of sensors during the growing season 2016-2017 and 2017-2018 explaining the different experimental design between F1 and F2. In response to the reviewer’s comment, this is now detailed in the new version of the manuscript.

1. 162 “the first stage of wheat ….” Do you mean the sowing or emergence?

The reviewer is right. It was not clear in the previous version of the manuscript. We meant “the first stages of wheat” when the ground is not completely covered by vegetation. It corresponds to the period from emergence of wheat to early tillering. This is now clearly indicated in the new version of the manuscript.

1. Figure 10d needs to add the unit of VWC, FAGB in y axis.

Agree. The unit is the same for VWC, FAGB and AGB (kg/m²). The three variables were grouped under the nomination biomass on the y-axis for simplification in the previous version. In response to the reviewer’s comment, the label of the y-axis was changed to “ABG, FABG and VWC (kg/m²)”. The same was done for Fig. A6-A8.

1. The reference need to be modified carefully. For example, Line 551 with Uppercase article title;

Thank you. All the references were checked and modified when needed.

1. Line 146 I guess it is 0.018?

Yes, RMSE=0.018 m³/m^3. Thank you, the comma is replaced by a dot in the new version of the manuscript.

1. Add reference for the vegetation water content process.

The reference to Gherboudj et al. (2011) is added in the new version of the manuscript.

1. How do you consider the effect of precipitation on the surface roughness?

In addition to irrigation, rain is supposed to impact slightly the roughness in the beginning of the crop season (before the wheat covers the soil) as the rows are directly exposed to rainfall. During this period, the roughness is measured every week/two weeks to take into account the effect of precipitation and irrigation. After this period, the roughness is assumed to be constant. Indeed, it has been shown in literature that after sowing (no soil works happened), roughness is only affected by very limited temporal variations (Bousbih et al., 2017) and it is generally kept constant during the crop season (El Hajj et al., 2016; Gherboudj et al., 2011; Gorrab et al., 2015; Ouaadi et al., 2020). In response to the reviewer comment, this is now clarified in the new version of the manuscript.

1. How to measure the surface roughness during the extension growth stage?

With a pin profiler, the measurements of surface roughness when the canopy covers the soil are almost impossible explaining why the data base extends during the first stages of wheat growth. It is assumed to be constant after this time (see response to point above).

1. Under what condition will you start irrigation?

The irrigation process is driven by the farmer based on evapotranspiration demand computed with the FAO-56 simple approach  (Allen et al., 1998). The timing of irrigation is determined by the farmer according to the available workforce, the occurrence of rain... This is now clarified in the new version of the manuscript.

1. How many times of field observations do you made? and can you list the specific information of each filed campaign?

The numbers of field’s campaigns are 26, 18 and 16 campaigns during 2016-2017, 2017-2018 and 2018-2019 seasons, respectively. A table summarizes the campaign details is added in the new version of the manuscript in response to the reviewer suggestion.

References:

Bousbih, S., Zribi, M., Lili-Chabaane, Z., Baghdadi, N., El Hajj, M., Gao, Q., Mougenot, B., 2017. Potential of sentinel-1 radar data for the assessment of soil and cereal cover parameters. Sensors (Switzerland) 17. https://doi.org/10.3390/s17112617

El Hajj, M., Baghdadi, N., Zribi, M., Belaud, G., Cheviron, B., Courault, D. and Charron, F.: Soil moisture retrieval over irrigated grassland using X-band SAR data, Remote Sens. Environ., 176, 202–218, doi:10.1016/j.rse.2016.01.027, 2016.

Gherboudj, I., Magagi, R., Berg, A. A. and Toth, B.: Soil moisture retrieval over agricultural fields from multi-polarized and multi-angular RADARSAT-2 SAR data, Remote Sens. Environ., 115(1), 33–43, doi:10.1016/j.rse.2010.07.011, 2011.

Gorrab, A., Zribi, M., Baghdadi, N., Mougenot, B., Fanise, P. and Chabaane, Z. L.: Retrieval of both soil moisture and texture using TerraSAR-X images, Remote Sens., 7(8), 10098–10116, doi:10.3390/rs70810098, 2015.

Ouaadi, N., Jarlan, L., Ezzahar, J., Zribi, M., Khabba, S., Bouras, E., Bousbih, S. and Frison, P.: Monitoring of wheat crops using the backscattering coe ffi cient and the interferometric coherence derived from Sentinel-1 in semi-arid areas, Remote Sens. Environ., 251(C),