Penetration Analysis of SAR Signals in the C and L Bands for Wheat, Maize, and Grasslands
Abstract
:1. Introduction
2. Study Site and Dataset Description
2.1. Study Site
2.2. Dataset Description
2.2.1. SAR Data
2.2.2. Sentinel-2 Images
2.2.3. In Situ Measurements
3. Data Analysis
3.1. Sensitivity of L-Band to SSM for Wheat and Grasslands
3.2. Sensitivity of the C-Band to SSM for Wheat and Grassland
3.3. Temporal Behavior of the L- and C-Bands According to the SSM and NDVI for Wheat and Grasslands
3.4. Temporal Behavior of C-Band for Maize
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Cepuder, P.; Nolz, R. Irrigation management by means of soil moisture sensor technologies. J. Water Land Dev. 2007, 11, 79–90. [Google Scholar] [CrossRef]
- Aubert, M.; Baghdadi, N.; Zribi, M.; Douaoui, A.; Loumagne, C.; Baup, F.; El Hajj, M.; Garrigues, S. Analysis of TerraSAR-X data sensitivity to bare soil moisture, roughness, composition and soil crust. Remote Sens. Environ. 2011, 115, 1801–1810. [Google Scholar] [CrossRef] [Green Version]
- Baghdadi, N.; Gaultier, S.; King, C. Retrieving surface roughness and soil moisture from SAR data using neural networks. In Proceedings of the Retrieval of Bio-and Geo-Physical Parameters from SAR Data for Land Applications, Sheffield, UK, 11–14 September 2001; ESTEC Publishing Division: Noordwijk, The Netherlands, 2002; Volume 475, pp. 315–319. [Google Scholar]
- Srivastava, H.S.; Patel, P.; Sharma, Y.; Navalgund, R.R. Large-area soil moisture estimation using multi-incidence-angle RADARSAT-1 SAR data. IEEE Trans. Geosci. Remote Sens. 2009, 47, 2528–2535. [Google Scholar] [CrossRef]
- Baghdadi, N.; Cresson, R.; El Hajj, M.; Ludwig, R.; La Jeunesse, I. Estimation of soil parameters over bare agriculture areas from C-band polarimetric SAR data using neural networks. Hydrol. Earth Syst. Sci. 2012, 16, 1607–1621. [Google Scholar] [CrossRef] [Green Version]
- Zribi, M.; Saux-Picart, S.; André, C.; Descroix, L.; Ottle, C.; Kallel, A. Soil moisture mapping based on ASAR/ENVISAT radar data over a Sahelian region. Int. J. Remote Sens. 2007, 28, 3547–3565. [Google Scholar] [CrossRef]
- Zribi, M.; Baghdadi, N.; Holah, N.; Fafin, O. New methodology for soil surface moisture estimation and its application to ENVISAT-ASAR multi-incidence data inversion. Remote Sens. Environ. 2005, 96, 485–496. [Google Scholar] [CrossRef]
- Zribi, M.; Gorrab, A.; Baghdadi, N. A new soil roughness parameter for the modelling of radar backscattering over bare soil. Remote Sens. Environ. 2014, 152, 62–73. [Google Scholar] [CrossRef] [Green Version]
- Tomer, S.K.; Al Bitar, A.; Sekhar, M.; Zribi, M.; Bandyopadhyay, S.; Sreelash, K.; Sharma, A.K.; Corgne, S.; Kerr, Y. Retrieval and multi-scale validation of soil moisture from multi-temporal SAR data in a semi-arid tropical region. Remote Sens. 2015, 7, 8128–8153. [Google Scholar] [CrossRef]
- Zribi, M.; Dechambre, M. A new empirical model to retrieve soil moisture and roughness from C-band radar data. Remote Sens. Environ. 2002, 84, 42–52. [Google Scholar] [CrossRef]
- Aubert, M.; Baghdadi, N.N.; Zribi, M.; Ose, K.; El Hajj, M.; Vaudour, E.; Gonzalez-Sosa, E. Toward an Operational Bare Soil Moisture Mapping Using TerraSAR-X Data Acquired Over Agricultural Areas. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2013, 6, 900–916. [Google Scholar] [CrossRef] [Green Version]
- Baghdadi, N.; Saba, E.; Aubert, M.; Zribi, M.; Baup, F. Evaluation of Radar Backscattering Models IEM, Oh, and Dubois for SAR Data in X-Band Over Bare Soils. IEEE Geosci. Remote Sens. Lett. 2011, 8, 1160–1164. [Google Scholar] [CrossRef]
- Baghdadi, N.; Cresson, R.; Pottier, E.; Aubert, M.; Zribi, M.; Jacome, A.; Benabdallah, S. A potential use for the C-band polarimetric SAR parameters to characterize the soil surface over bare agriculture fields. IEEE Trans. Geosci. Remote Sens. 2012, 50, 3844–3858. [Google Scholar] [CrossRef]
- Baghdadi, N.; Aubert, M.; Zribi, M. Use of TerraSAR-X data to retrieve soil moisture over bare soil agricultural fields. IEEE Geosci. Remote Sens. Lett. 2012, 9, 512–516. [Google Scholar] [CrossRef]
- Zribi, M.; André, C.; Decharme, B. A method for soil moisture estimation in Western Africa based on the ERS scatterometer. IEEE Trans. Geosci. Remote Sens. 2008, 46, 438–448. [Google Scholar] [CrossRef]
- El Hajj, M.; Baghdadi, N.; Belaud, G.; Zribi, M.; Cheviron, B.; Courault, D.; Hagolle, O.; Charron, F. Irrigated grassland monitoring using a time series of terraSAR-X and COSMO-skyMed X-Band SAR Data. Remote Sens. 2014, 6, 10002–10032. [Google Scholar] [CrossRef]
- El Hajj, M.; Baghdadi, N.; Zribi, M.; Bazzi, H. Synergic use of Sentinel-1 and Sentinel-2 images for operational soil moisture mapping at high spatial resolution over agricultural areas. Remote Sens. 2017, 9, 1292. [Google Scholar] [CrossRef]
- El Hajj, M.; Baghdadi, N.; Zribi, M.; Belaud, G.; Cheviron, B.; Courault, D.; Charron, F. Soil moisture retrieval over irrigated grassland using X-band SAR data. Remote Sens. Environ. 2016, 176, 202–218. [Google Scholar] [CrossRef] [Green Version]
- Baghdadi, N.; EL Hajj, M.; Zribi, M.; Fayad, I. Coupling SAR C-Band and Optical Data for Soil Moisture and Leaf Area Index Retrieval Over Irrigated Grasslands. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2015, 9, 1229–1243. [Google Scholar] [CrossRef]
- King, C.; Lecomte, V.; Le Bissonnais, Y.; Baghdadi, N.; Souchère, V.; Cerdan, O. Remote-sensing data as an alternative input for the ‘STREAM’runoff model. Catena 2005, 62, 125–135. [Google Scholar] [CrossRef]
- Geudtner, D.; Torres, R.; Snoeij, P.; Davidson, M.; Rommen, B. Sentinel-1 System capabilities and applications. In Proceedings of the 2014 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Quebec City, QC, Canada, 13–18 July 2014; pp. 1457–1460. [Google Scholar]
- Paloscia, S.; Pettinato, S.; Santi, E.; Notarnicola, C.; Pasolli, L.; Reppucci, A. Soil moisture mapping using Sentinel-1 images: Algorithm and preliminary validation. Remote Sens. Environ. 2013, 134, 234–248. [Google Scholar] [CrossRef]
- Mattia, F.; Balenzano, A.; Satalino, G.; Lovergine, F.; Loew, A.; Peng, J.; Wegmuller, U.; Santoro, M.; Cartus, O.; Dabrowska-Zielinska, K. Sentinel-1 high resolution soil moisture. In Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA, 23–28 July 2017; pp. 5533–5536. [Google Scholar]
- Gao, Q.; Zribi, M.; Escorihuela, M.J.; Baghdadi, N. Synergetic use of Sentinel-1 and Sentinel-2 data for soil moisture mapping at 100 m resolution. Sensors 2017, 17, 1966. [Google Scholar] [CrossRef] [PubMed]
- Baghdadi, N.; El Hajj, M.; Zribi, M.; Bousbih, S. Calibration of the Water Cloud Model at C-Band for Winter Crop Fields and Grasslands. Remote Sens. 2017, 9, 969. [Google Scholar] [CrossRef]
- Joseph, A.T.; van der Velde, R.; O’neill, P.E.; Lang, R.; Gish, T. Effects of corn on C-and L-band radar backscatter: A correction method for soil moisture retrieval. Remote Sens. Environ. 2010, 114, 2417–2430. [Google Scholar] [CrossRef]
- Ulaby, F.T.; Moore, R.K.; Fung, A.K. Microwave Remote Sensing: Active and Passive, vol. III, Volume Scattering and Emission Theory, Advanced Systems and Applications; Artech House: Dedham, MA, USA, 1986; pp. 1797–1848. [Google Scholar]
- Kerr, Y.H.; Waldteufel, P.; Wigneron, J.-P.; Delwart, S.; Cabot, F.; Boutin, J.; Escorihuela, M.-J.; Font, J.; Reul, N.; Gruhier, C. The SMOS mission: New tool for monitoring key elements ofthe global water cycle. Proc. IEEE 2010, 98, 666–687. [Google Scholar] [CrossRef]
- Entekhabi, D.; Njoku, E.G.; Neill, P.E.; Kellogg, K.H.; Crow, W.T.; Edelstein, W.N.; Entin, J.K.; Goodman, S.D.; Jackson, T.J.; Johnson, J. The soil moisture active passive (SMAP) mission. Proc. IEEE 2010, 98, 704–716. [Google Scholar] [CrossRef]
- Narvekar, P.S.; Entekhabi, D.; Kim, S.-B.; Njoku, E.G. Soil moisture retrieval using L-band radar observations. IEEE Trans. Geosci. Remote Sens. 2015, 53, 3492–3506. [Google Scholar] [CrossRef]
- Bruscantini, C.A.; Konings, A.G.; Narvekar, P.S.; McColl, K.A.; Entekhabi, D.; Grings, F.M.; Karszenbaum, H. L-band radar soil moisture retrieval without ancillary information. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2015, 8, 5526–5540. [Google Scholar] [CrossRef]
- Lievens, H.; Vernieuwe, H.; Alvarez-Mozos, J.; De Baets, B.; Verhoest, N.E. Error in radar-derived soil moisture due to roughness parameterization: An analysis based on synthetical surface profiles. Sensors 2009, 9, 1067–1093. [Google Scholar] [CrossRef]
- Sol, G. L’état des sols de France. Groupement d’intérêt scientifique sur les sols. 2011, p. 188. Available online: https://www.researchgate.net/publication/281387497_L’etat_des_sols_de_France (accessed on 13 November 2018).
- Baghdadi, N.; Bernier, M.; Gauthier, R.; Neeson, I. Evaluation of C-band SAR data for wetlands mapping. Int. J. Remote Sens. 2001, 22, 71–88. [Google Scholar] [CrossRef]
- Topouzelis, K.; Singha, S.; Kitsiou, D. Incidence angle normalization of Wide Swath SAR data for oceanographic applications. Open Geosci. 2016, 8, 450–464. [Google Scholar] [CrossRef]
- Baghdadi, N.; Boyer, N.; Todoroff, P.; El Hajj, M.; Bégué, A. Potential of SAR sensors TerraSAR-X, ASAR/ENVISAT and PALSAR/ALOS for monitoring sugarcane crops on Reunion Island. Remote Sens. Environ. 2009, 113, 1724–1738. [Google Scholar] [CrossRef]
- Mladenova, I.; Lakshmi, V.; Walker, J.P.; Panciera, R.; Wagner, W.; Doubkova, M. Validation of the ASAR global monitoring mode soil moisture product using the NAFE’05 data set. IEEE Trans. Geosci. Remote Sens. 2010, 48, 2498–2508. [Google Scholar] [CrossRef]
- Ardila, J.P.; Tolpekin, V.; Bijker, W. Angular backscatter variation in L-band ALOS ScanSAR images of tropical forest areas. IEEE Geosci. Remote Sens. Lett. 2010, 7, 821–825. [Google Scholar] [CrossRef]
- Hagolle, O.; Huc, M.; Pascual, D.V.; Dedieu, G. A multi-temporal method for cloud detection, applied to FORMOSAT-2, VENµS, LANDSAT and SENTINEL-2 images. Remote Sens. Environ. 2010, 114, 1747–1755. [Google Scholar] [CrossRef] [Green Version]
- Hagolle, O.; Huc, M.; Villa Pascual, D.; Dedieu, G. 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. 2015, 7, 2668–2691. [Google Scholar] [CrossRef]
- Brown, S.C.; Quegan, S.; Morrison, K.; Bennett, J.C.; Cookmartin, G. High-resolution measurements of scattering in wheat canopies-Implications for crop parameter retrieval. IEEE Trans. Geosci. Remote Sens. 2003, 41, 1602–1610. [Google Scholar] [CrossRef]
- Picard, G.; Le Toan, T.; Mattia, F. Understanding C-band radar backscatter from wheat canopy using a multiple-scattering coherent model. IEEE Trans. Geosci. Remote Sens. 2003, 41, 1583–1591. [Google Scholar] [CrossRef]
- Mattia, F.; Le Toan, T.; Picard, G.; Posa, F.I.; D’Alessio, A.; Notarnicola, C.; Gatti, A.M.; Rinaldi, M.; Satalino, G.; Pasquariello, G. Multitemporal C-band radar measurements on wheat fields. IEEE Trans. Geosci. Remote Sens. 2003, 41, 1551–1560. [Google Scholar] [CrossRef]
- Cookmartin, G.; Saich, P.; Quegan, S.; Cordey, R.; Burgess-Allen, P.; Sowter, A. Modeling microwave interactions with crops and comparison with ERS-2 SAR observations. IEEE Trans. Geosci. Remote Sens. 2000, 38, 658–670. [Google Scholar] [CrossRef]
- Del Frate, F.; Ferrazzoli, P.; Guerriero, L.; Strozzi, T.; Wegmuller, U.; Cookmartin, G.; Quegan, S. Wheat cycle monitoring using radar data and a neural network trained by a model. IEEE Trans. Geosci. Remote Sens. 2004, 42, 35–44. [Google Scholar] [CrossRef]
- Macelloni, G.; Paloscia, S.; Pampaloni, P.; Marliani, F.; Gai, M. The relationship between the backscattering coefficient and the biomass of narrow and broad leaf crops. IEEE Trans. Geosci. Remote Sens. 2001, 39, 873–884. [Google Scholar] [CrossRef]
- Chauhan, N.S.; Le Vine, D.M.; Lang, R.H. Discrete scatter model for microwave radar and radiometer response to corn: Comparison of theory and data. IEEE Trans. Geosci. Remote Sens. 1994, 32, 416–426. [Google Scholar] [CrossRef]
- Chiu, T.; Sarabandi, K. Electromagnetic scattering from short branching vegetation. IEEE Trans. Geosci. Remote Sens. 2000, 38, 911–925. [Google Scholar] [CrossRef] [Green Version]
- Stiles, J.M.; Sarabandi, K. Electromagnetic scattering from grassland. I. A fully phase-coherent scattering model. IEEE Trans. Geosci. Remote Sens. 2000, 38, 339–348. [Google Scholar] [CrossRef]
SAR Dates dd/mm/yyyy | Polarizations | Incidence Angle (°) | Pixel Size m × m |
---|---|---|---|
02/10/2016 | HH+HV | ~37.5 | 6 × 6 |
19/02/2017 | HH+HV | ~37.3 | 6 × 6 |
02/04/2017 | HH+HV+VH+VV | ~37.4 | 3 × 3 |
09/05/2017 | HH+HV+VH+VV | ~27.4 | 3 × 3 |
16/07/2017 | HH | ~40.5 | 3 × 3 |
15/09/2017 | HH | ~31.1 | 3 × 3 |
01/10/2017 | HH+HV | ~37.5 | 6 × 6 |
27/05/2018 | HH+HV | ~37.5 | 6 × 6 |
22/06/2018 | HH+HV | ~30.9 | 6 × 6 |
01/07/2018 | HH+HV | ~40.5 | 6 × 6 |
15/07/2018 | HH+HV | ~40.5 | 6 × 6 |
26/08/2018 | HH+HV | ~40.5 | 6 × 6 |
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El Hajj, M.; Baghdadi, N.; Bazzi, H.; Zribi, M. Penetration Analysis of SAR Signals in the C and L Bands for Wheat, Maize, and Grasslands. Remote Sens. 2019, 11, 31. https://doi.org/10.3390/rs11010031
El Hajj M, Baghdadi N, Bazzi H, Zribi M. Penetration Analysis of SAR Signals in the C and L Bands for Wheat, Maize, and Grasslands. Remote Sensing. 2019; 11(1):31. https://doi.org/10.3390/rs11010031
Chicago/Turabian StyleEl Hajj, Mohammad, Nicolas Baghdadi, Hassan Bazzi, and Mehrez Zribi. 2019. "Penetration Analysis of SAR Signals in the C and L Bands for Wheat, Maize, and Grasslands" Remote Sensing 11, no. 1: 31. https://doi.org/10.3390/rs11010031
APA StyleEl 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 Sensing, 11(1), 31. https://doi.org/10.3390/rs11010031