Soil Moisture Mapping Using Sentinel-1A Synthetic Aperture Radar Data

Year 2018, Volume: 5 Issue: 2, 178 - 188, 01.08.2018

Aliihsan Şekertekin , Aycan Murat Marangoz , Saygın Abdikan

https://doi.org/10.30897/ijegeo.425606

Cited By: 25

Abstract

The aim of this study is to estimate and map soil
moisture distribution using C-band Synthetic Aperture Radar (SAR) data.
Sentinel-1A is a new generation C-band SAR satellite, and in this study
Sentinel-1A data acquired on 24 April 2016 were used to retrieve soil moisture
map. An agricultural region in Bergama, a district of İzmir city, was chosen as
the study area. In-situ soil moisture measurements were carried out in 20 test
fields simultaneously with SAR data acquisition. The effects of soil moisture
and local incidence angle on backscattering coefficient were analyzed, and then
a multiple regression analysis was performed to generate an empirical model.
The proposed model was evaluated using statistical metrics namely coefficient
of determination (R²) and Root Mean Square Error (RMSE), and the
results were 0.84 and 2.46 %, respectively. The obtained results showed that
Sentinel-1A SAR data presented satisfying outcomes to estimate and map soil
moisture content.

Keywords

Microwave Remote Sensing, Soil Moisture Mapping, Synthetic Aperture Radar, Sentinel-1

References

• Ahmad, A., Zhang, Y., & Nichols, S. (2011). Review and evaluation of remote sensing methods for soil-moisture estimation. SPIE Reviews, 2(1), 028001. http://doi.org/10.1117/1.3534910
• Aubert, M., Baghdadi, N., Zribi, M., Douaoui, A., Loumagne, C., Baup, F., … Garrigues, S. (2011). Analysis of TerraSAR-X data sensitivity to bare soil moisture, roughness, composition and soil crust. Remote Sensing of Environment, 115(8), 1801–1810. http://doi.org/10.1016/j.rse.2011.02.021
• B. E. Myhre, & S. F. Shih. (1990). USING INFRARED THERMOMETRY TO ESTIMATE SOIL WATER CONTENT FOR A SANDY SOIL. Transactions of the ASAE, 33(5), 1479. http://doi.org/10.13031/2013.31497
• Baghdadi, N., Aubert, M., & Zribi, M. (2012). Use of TerraSAR-X Data to Retrieve Soil Moisture Over Bare Soil Agricultural Fields. IEEE Geoscience and Remote Sensing Letters, 9(3), 512–516. http://doi.org/10.1109/LGRS.2011.2173155
• Baghdadi, N., Camus, P., Beaugendre, N., Issa, O. M., Zribi, M., Desprats, J. F., … Sannier, C. (2011). Estimating Surface Soil Moisture from TerraSAR-X Data over Two Small Catchments in the Sahelian Part of Western Niger. Remote Sensing, 3(6), 1266–1283. http://doi.org/10.3390/rs3061266
• Balenzano, A., Satalino, G., Lovergine, F., Rinaldi, M., Iacobellis, V., Mastronardi, N., & Mattia, F. (2013). On the use of temporal series of L-and X-band SAR data for soil moisture retrieval. Capitanata plain case study. European Journal of Remote Sensing, 46(1), 721–737. http://doi.org/10.5721/EuJRS20134643
• Canada Center for Remote Sensing. (1976). Fundamentals of remote sensing. Resources Policy, 2(1), 65. http://doi.org/10.1016/0301-4207(76)90065-9
• Copernicus. (2018). Sentinel-1 — The SAR Imaging Constellation for Land and Ocean Services. Retrieved from https://directory.eoportal.org/web/eoportal/satellite-missions/c-missions/copernicus-sentinel-1
• Dubois, P. C., van Zyl, J., & Engman, T. (1995). Measuring soil moisture with imaging radars. IEEE Transactions on Geoscience and Remote Sensing, 33(4), 915–926. http://doi.org/10.1109/36.406677
• Esetlili, M. T., & Kurucu, Y. (2016). DETERMINATION OF MAIN SOIL PROPERTIES USING SYNTHETIC APERTURE RADAR. Fresenius Environmental Bulletin, 25(1), 23–36.
• Evett, S. R., & Parkin, G. W. (2005). Advances in Soil Water Content Sensing. Vadose Zone Journal, 4(4), 986. http://doi.org/10.2136/vzj2005.0099
• Gao, Q., Zribi, M., Escorihuela, M., & Baghdadi, N. (2017). Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at 100 m Resolution. Sensors, 17(9), 1966. http://doi.org/10.3390/s17091966
• Heilman, J. ., Kanemasu, E. ., Bagley, J. ., & Rasmussen, V. . (1977). Evaluating soil moisture and yield of winter wheat in the Great Plains using Landsat data. Remote Sensing of Environment, 6(4), 315–326. http://doi.org/10.1016/0034-4257(77)90051-7
• Jacome, A., Bernier, M., Chokmani, K., Gauthier, Y., Poulin, J., & De Sève, D. (2013). Monitoring Volumetric Surface Soil Moisture Content at the La Grande Basin Boreal Wetland by Radar Multi Polarization Data. Remote Sensing, 5(10), 4919–4941. http://doi.org/10.3390/rs5104919
• Kseneman, M., Gleich, D., & Potočnik, B. (2012). Soil-moisture estimation from TerraSAR-X data using neural networks. Machine Vision and Applications, 23(5), 937–952. http://doi.org/10.1007/s00138-011-0375-3
• Lakshmi, V. (2013). Remote Sensing of Soil Moisture. ISRN Soil Science, 2013, 1–33. http://doi.org/10.1155/2013/424178
• Lievens, H., & Verhoest, N. E. C. (2012). Spatial and temporal soil moisture estimation from RADARSAT-2 imagery over Flevoland, The Netherlands. Journal of Hydrology, 456–457, 44–56. http://doi.org/10.1016/j.jhydrol.2012.06.013
• Moran, M. S., Alonso, L., Moreno, J. F., Cendrero Mateo, M. P., de la Cruz, D. F., & Montoro, A. (2012). A RADARSAT-2 Quad-Polarized Time Series for Monitoring Crop and Soil Conditions in Barrax, Spain. IEEE Transactions on Geoscience and Remote Sensing, 50(4), 1057–1070. http://doi.org/10.1109/TGRS.2011.2166080
• Nagler, T., Rott, H., Hetzenecker, M., Wuite, J., & Potin, P. (2015). The Sentinel-1 Mission: New Opportunities for Ice Sheet Observations. Remote Sensing, 7(7), 9371–9389. http://doi.org/10.3390/rs70709371
• Oh, Y., Sarabandi, K., & Ulaby, F. T. (1992). An empirical model and an inversion technique for radar scattering from bare soil surfaces. IEEE Transactions on Geoscience and Remote Sensing, 30(2), 370–381. http://doi.org/10.1109/36.134086
• Paloscia, S., Pettinato, S., & Santi, E. (2012). Combining L and X band SAR data for estimating biomass and soil moisture of agricultural fields. European Journal of Remote Sensing, 45(1), 99–109. http://doi.org/10.5721/EuJRS20124510
• Sadeghi, M., Babaeian, E., Tuller, M., & Jones, S. B. (2017). The optical trapezoid model: A novel approach to remote sensing of soil moisture applied to Sentinel-2 and Landsat-8 observations. Remote Sensing of Environment, 198, 52–68. http://doi.org/10.1016/j.rse.2017.05.041
• Schmugge, T. J., Jackson, T. J., & McKim, H. L. (1980). Survey of methods for soil moisture determination. Water Resources Research, 16(6), 961–979. http://doi.org/10.1029/WR016i006p00961
• Şekertekin, A. (2018). Aktif Mikrodalga Uydu Görüntü Verileri Kullanılarak Toprak Neminin Belirlenmesi. PhD Thesis. Bülent Ecevit Üniversitesi, Zonguldak, Turkey.
• Sekertekin, A., Marangoz, A. M., Abdikan, S., & Esetlili, M. T. (2016). PRELIMINARY RESULTS OF ESTIMATING SOIL MOISTURE OVER BARE SOIL USING FULL-POLARIMETRIC ALOS-2 DATA. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-2/W1, 173–176. http://doi.org/10.5194/isprs-archives-XLII-2-W1-173-2016
• Shih, S. F., & Jordan, J. D. (1992). LANDSAT MID-INFRARED DATA AND GIS IN REGIONAL SURFACE SOIL-MOISTURE ASSESSMENT. Journal of the American Water Resources Association, 28(4), 713–719. http://doi.org/10.1111/j.1752-1688.1992.tb01493.x
• Srivastava, H. S., Patel, P., Sharma, Y., & Navalgund, R. R. (2009). Large-Area Soil Moisture Estimation Using Multi-Incidence-Angle RADARSAT-1 SAR Data. IEEE Transactions on Geoscience and Remote Sensing, 47(8), 2528–2535. http://doi.org/10.1109/TGRS.2009.2018448
• Ulaby, F. T., Moore, R. K., & Fung, A. K. (1986). Microwave remote sensing: Active and passive; from theory to applications (Vol. Volume III, pp. 1065–2162).
• Verhoest, N., Lievens, H., Wagner, W., Álvarez-Mozos, J., Moran, M., & Mattia, F. (2008). On the Soil Roughness Parameterization Problem in Soil Moisture Retrieval of Bare Surfaces from Synthetic Aperture Radar. Sensors, 8(7), 4213–4248. http://doi.org/10.3390/s8074213
• Weimann, A., Von Schonermark, M., Schumann, A., Jorn, P., & Gunther, R. (1998). Soil moisture estimation with ERS-1 SAR data in the East-German loess soil area. International Journal of Remote Sensing, 19(2), 237–243. http://doi.org/10.1080/014311698216224
• Zribi, M., Baghdadi, N., Holah, N., & Fafin, O. (2005). New methodology for soil surface moisture estimation and its application to ENVISAT-ASAR multi-incidence data inversion. Remote Sensing of Environment, 96(3–4), 485–496. http://doi.org/10.1016/j.rse.2005.04.005
• Zribi, M., & Dechambre, M. (2003). A new empirical model to retrieve soil moisture and roughness from C-band radar data. Remote Sensing of Environment, 84(1), 42–52. http://doi.org/10.1016/S0034-4257(02)00069-X
• Zribi, M., Kotti, F., Lili-Chabaane, Z., Baghdadi, N., Ben Issa, N., Amri, R., … Chehbouni, A. (2012). Soil Texture Estimation Over a Semiarid Area Using TerraSAR-X Radar Data. IEEE Geoscience and Remote Sensing Letters, 9(3), 353–357. http://doi.org/10.1109/LGRS.2011.2168379
• Zribi, M., Saux‐Picart, S., André, C., Descroix, L., Ottlé, C., & Kallel, A. (2007). Soil moisture mapping based on ASAR/ENVISAT radar data over a Sahelian region. International Journal of Remote Sensing, 28(16), 3547–3565. http://doi.org/10.1080/01431160601009680