Assessment of Water Cloud Model based on SAR and optical satellite data for surface soil moisture retrievals over agricultural area

Year 2021, Volume: 10 Issue: 3, 243 - 250, 01.07.2021

Rida Khellouk Ahmed Barakat Aafaf El Jazouli Hayat Lionboui Tarik Benabdelouahab

https://doi.org/10.18393/ejss.926813

Cited By: 1

Abstract

Water availability to plants a significant role in agricultural areas, especially in arid and semi-arid areas. This research aimed to evaluate the potential of Water Cloud Model (WCM) for retrieving surface soil moisture, which is associated to water availability, in a semi-arid areas based on the combination between Sentinel-1B SAR (Synthetic Aperture Radar) and optical Sentinel-2B data. The performance of the applied model was assessed using ground observed soil moisture (0-5 cm). Accuracy evaluation was performed by the cross-validation method (k-fold), it showed a coefficients of determination (R2) of 0.65 and RMSE of 1.45%. The obtained results show a good concordance between retrieved model and ground observed surface soil moisture. In addition, this model was used for the mapping spatio-temporal variation of soil moisture at high spatial resolution in the study areas. This approach could be used by environmentalists and decision-makers as a practical tool for monitoring and estimating the change of surface moisture content.

Keywords

Remote sensing, Soil moisture, Sentinel-1B, Sentinel-2B, WCM, SAR, agricultural areas

References

• Attema, E.P.W., Ulaby, F.T., 1978. Vegetation modeled as a water cloud. Radio Science 13(2): 357-364.
• Baghdadi, N., Choker, M., Zribi, M., El-hajj, M., Paloscia, S., Verhoest, N., Lievens, H., Baup, F., Mattia, F., 2016. A New Empirical Model for Radar Scattering from Bare Soil Surfaces. Remote Sensing 8(11): 920.
• Bala, A., Rawat, K.S., Misra A., Srivastava, A., 2015. Vegetation indices mapping for Bhiwani district of Haryana (India) through LANDSAT-7ETM+ and remote sensing techniques. Journal of Applied and Natural Science 7(2): 874-879.
• Bao, Y., Lin, L., Wu, S., Deng, K.A.K., Petropoulos, G.P., 2018. Surface soil moisture retrievals over partially vegetated areas from the synergy of Sentinel-1 and Landsat 8 data using a modified water-cloud model. International Journal of Applied Earth Observation and Geoinformation 72: 76-85.
• Barakat, A., Hilali, A., El Baghdadi, M., Touhami, F., 2017. Landfill site selection with GIS-based multi-criteria evaluation technique. A case study in Béni Mellal-Khouribga Region, Morocco. Environmental Earth Sciences 76(12): 413.
• Benabdelouahab, T., Balaghi, R., Hadria, R., Lionboui, H., Minet, J., Tychon B., 2015. Monitoring surface water content using visible and short-wave infrared SPOT-5 data of wheat plots in irrigated semi-arid regions. International Journal of Remote Sensing 36(15): 4018-4036.
• Benabdelouahab, T., Derauw, D., Lionboui, H., Hadria, R., Tychon, B., Boudhar, A., Barbier, C., 2019. Using SAR data to detect wheat irrigation supply in an irrigated semi-arid area. Journal of Agricultural Science 11(1): 21-30.
• 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 17(11): 2617.
• Cassel, D.L., 2007. Re-sampling and simulation, the SAS way. Proceedings of the SAS Global Forum 2007 Conference, SAS Institute Inc., Cary, NC.
• Chu, D., 2018. MODIS remote sensing approaches to monitoring soil moisture in Tibet, China. Remote Sensing letters 9(12): 1148-1157.
• 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.
• El Hajj, M., Baghdadi, N., Zribi, M., Bazzi, H., 2018. Coupling Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping. 2018 IEEE International Geoscience and Remote Sensing Symposium 22-27 July 2018. Valencia, Spain. pp. 5537-5540.
• El Hajj, M., Baghdadi, N., Zribi, M., Belaud, G., Cheviron, B., Courault, D., Charron, F., 2016. Soil moisture retrieval over irrigated grassland using X-band SAR data. Remote Sensing of Environment 176: 202–218.
• Ennaji, W., Barakat, A., Karaoui, I., El Baghdadi, M., Arioua, A., 2018. Remote sensing approach to assess salt-affected soils in the north-east part of Tadla plain, Morocco. Geology, Ecology, and Landscapes 2(1): 22-28.
• Entekhabi, D., Reichle, R.H., Koster, R.D., Crow, W.T., 2010. Performance metrics for soil moisture retrievals and application requirements. Journal of Hydrometeorology 11(3): 832-840.
• Fung, A. K., Li, Z., Chen, K.S., 1992. Backscattering from a randomly rough dielectric surface. IEEE Transactions on Geoscience and Remote Sensing 30(2): 356–369.
• Gorrab, A., Zribi, M., Baghdadi, N., Mougenot, B., Fanise, P., Chabaane, Z., 2015. Retrieval of both soil moisture and texture using TerraSAR-X images. Remote Sensing 7(8): 10098-10116.
• Geerts, S., Raes, D., 2009. Deficit irrigation as an on-farm strategy to maximize crop water productivity in dry areas. Agricultural Water Management 96(9): 1275-1284. Hosseini, M., Saradjian, M., 2011. Soil moisture estimation based on integration of optical and SAR images. Canadian Journal of Remote Sensing 37(1): 112–121.
• Jawson, S.D., Niemann, J.D., 2007. Spatial patterns from EOF analysis of soil moisture at a large scale and their dependence on soil, land-use, and topographic properties. Advances in Water Resources 30(3): 366–381.
• Kumar, K., Rao, H.P.S., Arora, M.K., 2015. Study of water cloud model vegetation descriptors in estimating soil moisture in Solani catchment. Hydrological Processes 29(9): 2137-2148.
• Kumar, K., Hari Prasad, K.S., Arora, M.K., 2012. Estimation of water cloud model vegetation parameters using a genetic algorithm. Hydrological Sciences Journal 57(4): 776–789.
• Notarnicola, C., Angiulli, M., Posa, F., 2006. Use of radar and optical remotely sensed data for soil moisture retrieval over vegetated areas. IEEE Transactions on Geoscience and Remote Sensing 44(4): 925–935.
• 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.
• Oumenskou, H., El Baghdadi, M., Barakat, A., Aquit, M., Ennaji, W., Karroum, L.A., Aadraoui, M., 2019. Multivariate statistical analysis for spatial evaluation of physicochemical properties of agricultural soils from Beni-Amir irrigated perimeter, Tadla plain, Morocco. Geology, Ecology, and Landscapes 3(2): 83-94.
• Pablos, M., Martínez-Fernández, J., Sánchez, N., González-Zamora, Á., 2017. Temporal and spatial comparison of agricultural drought indices from moderate resolution satellite soil moisture data over northwest Spain. Remote Sensing 9(11): 1168.
• Prakash, R.; Singh, D.; Pathak, N.P., 2012. A fusion approach to retrieve soil moisture with SAR and optical data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 5(1): 196–206.
• Rawat, K.S., Singh, S.K., Pal, R.K. 2012, 2019. Synergetic methodology for estimation of soil moisture over agricultural area using Landsat-8 and Sentinel-1 satellite data. Remote Sensing Applications: Society and Environment 15: 100250.
• Khellouk, R., Barakat, A., Jazouli, A.E., Boudhar, A., Lionboui, H., Rais, J., Benabdelouahab, T., 2019. An integrated methodology for surface soil moisture estimating using remote sensing data approach. Geocarto International
• Khellouk, R., Barakat, A., Boudhar, A., Hadria, R., Lionboui, H., El Jazouli, A., Benabdelouahab, T., 2018. Spatiotemporal monitoring of surface soil moisture using optical remote sensing data: a case study in a semi-arid area. Journal of Spatial Science 65(3): 481-499.
• Sadeghi, M., Jones, S.B., Philpot, W.D., 2015. A linear physically-based model for remote sensing of soil moisture using short wave infrared bands. Remote Sensing of Environment 164: 66–76.
• Seckler, D., Barker, R., Amarasinghe, U., 1999. Water scarcity in the twenty-first century. International Journal of Water Resources Development 15(1-2): 29–42.
• Sun, L, Sun, R., Li, X., Liang, S., Zhang, R., 2012. Monitoring surface soil moistures tatus based on remotely sensed surface temperature and vegetation index information. Agricultural and Forest Meteorology 166-167: 175–187.
• Verstraeten, W.W., Veroustraete, F., van der Sande, C.J., Grootaers, I., Feyen, J., 2006. Soil moisture retrieval using thermal inertia, determined with visible and thermal spaceborne data, validated for European forests. Remote Sensing of Environment 101: 299-314.
• Whyte, A., Fredinos, K.P., Petropoulos, G.P., 2018. A new synergistic approach for monitoring wetlands using Sentinels -1 and 2 data with object-based machine learning algorithms. Environmental modelling & Software 104: 40–54.
• Yang, Y., Guan, H., Long, D., Liu, B., Qin, G., Qin, J., Batelaan, O., 2015. Estimation of surface soil moisture from thermal infrared remote sensing using an improved trapezoid method. Remote Sensing 7(7) : 8250-8270.
• Zhao, L., Yang, K., Qin, J., Chen, Y., Tang, W., Lu, H., Yang, Z.L., 2014. The scale-dependence of SMOS soil moisture accuracy and its improvement through land data assimilation in the central Tibetan Plateau. Remote Sensing of Environment 152: 345-355.
• Zhuo, W., Huang, J., Li, L., Zhang, X., Ma, H., Gao, X., Xiao, X., 2019. Assimilating soil moisture retrieved from Sentinel-1 and Sentinel-2 data into WOFOST model to improve winter wheat yield estimation. Remote Sensing 11(13):1618.
• ,Zribi, M., Kotti, F., Wagner, W., Amri, R., Shabou, M., Lili-Chabaane, Z., Baghdadi, N., 2014. Soil moisture mapping in a semiarid region, based on ASAR/Wide Swath satellite data. Water Resources Research 50(2): 823–835.