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http://dx.doi.org/10.7780/kjrs.2022.38.5.1.11

Assessment of Stand-alone Utilization of Sentinel-1 SAR for High Resolution Soil Moisture Retrieval Using Machine Learning  

Jeong, Jaehwan (Center for Built Environment, Sungkyunkwan University)
Cho, Seongkeun (Department of Water Resources, Sungkyunkwan University)
Jeon, Hyunho (Department of Global Smart City, Sungkyunkwan University)
Lee, Seulchan (Department of Water Resources, Sungkyunkwan University)
Choi, Minha (Department of Water Resources, Sungkyunkwan University)
Publication Information
Korean Journal of Remote Sensing / v.38, no.5_1, 2022 , pp. 571-585 More about this Journal
Abstract
As the threat of natural disasters such as droughts, floods, forest fires, and landslides increases due to climate change, social demand for high-resolution soil moisture retrieval, such as Synthetic Aperture Radar (SAR), is also increasing. However, the domestic environment has a high proportion of mountainous topography, making it challenging to retrieve soil moisture from SAR data. This study evaluated the usability of Sentinel-1 SAR, which is applied with the Artificial Neural Network (ANN) technique, to retrieve soil moisture. It was confirmed that the backscattering coefficient obtained from Sentinel-1 significantly correlated with soil moisture behavior, and the possibility of stand-alone use to correct vegetation effects without using auxiliary data observed from other satellites or observatories. However, there was a large difference in the characteristics of each site and topographic group. In particular, when the model learned on the mountain and at flat land cross-applied, the soil moisture could not be properly simulated. In addition, when the number of learning points was increased to solve this problem, the soil moisture retrieval model was smoothed. As a result, the overall correlation coefficient of all sites improved, but errors at individual sites gradually increased. Therefore, systematic research must be conducted in order to widely apply high-resolution SAR soil moisture data. It is expected that it can be effectively used in various fields if the scope of learning sites and application targets are specifically limited.
Keywords
Sentinel-1; Synthetic aperture radar; Soil moisture; Machine learning; Artificial neural network;
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1 Dorigo, W., W. Wagner, R. Hohensinn, S. Hahn, C. Paulik, A. Xaver, A. Gruber, M. Drusch, S. Mecklenburg, and P. van Oevelen, 2011. The International Soil Moisture Network: a data hosting facility for global in situ soil moisture measurements, Hydrology and Earth System Sciences, 15(5): 1675-1698. https://doi.org/10.5194/hess-15-1675-2011   DOI
2 Gao, Q., M. Zribi, M.J. Escorihuela, and N. Baghdadi, 2017. Synergetic use of Sentinel-1 and Sentinel-2 data for soil moisture mapping at 100 m resolution, Sensors, 17(9): 1966. https://doi.org/10.3390/s17091966   DOI
3 Gherboudj, I., R. Magagi, A.A. Berg, and B. Toth, 2011. Soil moisture retrieval over agricultural fields from multi-polarized and multi-angular RADARSAT-2 SAR data, Remote Sensing of Environment, 115(1): 33-43. https://doi.org/10.1016/j.rse.2010.07.011   DOI
4 Imhoff, M.L., 1995. A theoretical analysis of the effect of forest structure on synthetic aperture radar backscatter and the remote sensing of biomass, IEEE Transactions on Geoscience and Remote Sensing, 33(2): 341-351. https://doi.org/10.1109/TGRS.1995.8746015   DOI
5 Jeong, J., S. Cho, J. Baik, and M. Choi, 2018. A Study on the Establishment of a Korean Soil Moisture Network, (2): Measurement of Intermediate-Scale Soil Moisture Using a Cosmic-Ray Sensor, Journal of the Korean Society of Hazard Mitigation, 18(7): 83-91 (in Korean with English abstract). https://doi.org/10.9798/KOSHAM.2018.18.7.83   DOI
6 Kim, S., J. Jeong, M. Zohaib, and M. Choi, 2018. Spatial disaggregation of ASCAT soil moisture under all sky condition using support vector machine, Stochastic Environmental Research and Risk Assessment, 32(12): 3455-3473. https://doi.org/10.1007/s00477-018-1620-3   DOI
7 Park, S.-E., W.M. Moon, and E. Pottier, 2012. Assessment of scattering mechanism of polarimetric SAR signal from mountainous forest areas, IEEE Transactions on Geoscience and Remote Sensing, 50(11): 4711-4719. https://doi.org/10.1109/TGRS.2012.2194153   DOI
8 Patel, P., H.S. Srivastava, S. Panigrahy, and J.S. Parihar, 2006. Comparative evaluation of the sensitivity of multi-polarized multi-frequency SAR backscatter to plant density, International Journal of Remote Sensing, 27(2): 293-305. https://doi.org/10.1080/01431160500214050   DOI
9 Le Toan, T., A. Beaudoin, J. Riom, and D. Guyon, 1992. Relating forest biomass to SAR data, IEEE Transactions on Geoscience and Remote Sensing, 30(2): 403-411. https://doi.org/10.1109/36.134089   DOI
10 Kim, S., T. Lee, B. Chun, Y. Jung, W.S. Jang, C. Sur, and Y. Shin, 2020. Estimation of High-Resolution Soil Moisture Using Sentinel-1A/B SAR and Soil Moisture Data Assimilation Scheme, Journal of Korean Society of Agricultural Engineer, 62(6): 11-20 (in Korean with English abstract). https://doi.org/10.5389/KSAE.2020.62.6.011   DOI
11 Lee, S.-J., S. Hong, J. Cho, and Y.-W. Lee, 2017. Experimental Retrieval of Soil Moisture for Cropland in South Korea Using Sentinel-1 SAR Data, Korean Journal of Remote Sensing, 33(6): 947-960 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2017.33.6.1.4   DOI
12 Li, J. and S. Wang, 2018. Using SAR-derived vegetation descriptors in a water cloud model to improve soil moisture retrieval, Remote Sensing, 10(9): 1370. https://doi.org/10.3390/rs10091370   DOI
13 Mandal, D., V. Kumar, D. Ratha, S. Dey, A. Bhattacharya, J.M. Lopez-Sanchez, H. McNairn, and Y.S. Rao, 2020. Dual polarimetric radar vegetation index for crop growth monitoring using sentinel-1 SAR data, Remote Sensing of Environment, 247: 111954. https://doi.org/10.1016/j.rse.2020.111954   DOI
14 Mermoz, S., M. Rejou-Mechain, L. Villard, T. Le Toan, V. Rossi, and S. Gourlet-Fleury, 2015. Decrease of L-band SAR backscatter with biomass of dense forests, Remote Sensing of Environment, 159: 307-317. https://doi.org/10.1016/j.rse.2014.12.019   DOI
15 Svoray, T. and M. Shoshany, 2002. SAR-based estimation of areal aboveground biomass, (AAB) of herbaceous vegetation in the semi-arid zone: A modification of the water-cloud model, International Journal of Remote Sensing, 23(19): 4089-4100. https://doi.org/10.1080/01431160110115924   DOI
16 Shi, J., J. Wang, A.Y. Hsu, P.E. O'Neill, and E.T. Engman, 1997. Estimation of bare surface soil moisture and surface roughness parameter using L-band SAR image data, IEEE Transactions on Geoscience and Remote Sensing, 35(5): 1254-1266. https://doi.org/10.1109/36.628792   DOI
17 Nguyen, H.H., S. Cho, J. Jeong, and M. Choi, 2021. A D-vine copula quantile regression approach for soil moisture retrieval from dual polarimetric SAR Sentinel-1 over vegetated terrains, Remote Sensing of Environment, 255: 112283. https://doi.org/10.1016/j.rse.2021.112283   DOI
18 Notarnicola, C. and F. Posa, 2007. Inferring vegetation water content from C-and L-band SAR images, IEEE Transactions on Geoscience and Remote Sensing, 45(10): 3165-3171. https://doi.org/10.1109/TGRS.2007.903698   DOI
19 Orimoloye, I.R., J.A. Belle, A.O. Olusola, E.T. Busayo, and O.O. Ololade, 2021. Spatial assessment of drought disasters, vulnerability, severity and water shortages: a potential drought disaster mitigation strategy, Natural Hazards, 105(3): 2735-2754. https://doi.org/10.1007/s11069-020-04421-x   DOI
20 Paloscia, S., S. Pettinato, E. Santi, C. Notarnicola, L. Pasolli, and A. Reppucci, 2013. Soil moisture mapping using Sentinel-1 images: Algorithm and preliminary validation, Remote Sensing of Environment, 134: 234-248. https://doi.org/10.1016/j.rse.2013.02.027   DOI
21 Tingsanchali, T., 2012. Urban flood disaster management, Procedia Engineering, 32: 25-37. https://doi.org/10.1016/j.proeng.2012.01.1233   DOI
22 Cho, S., J. Jeong, S. Lee, and M. Choi, 2020. Estimation of soil moisture based on sentinel-1 SAR data: focusing on cropland and grassland area, Journal of Korea Water Resources Association, 53(11): 973-983 (in Korean with English abstract). https://doi.org/10.3741/JKWRA.2020.53.11.973   DOI
23 Torres, R., P. Snoeij, D. Geudtner, D. Bibby, M. Davidson, E. Attema, P. Potin, B. Rommen, N. Floury, and M. Brown, 2012. GMES Sentinel-1 mission, Remote Sensing of Environment, 120: 9-24. https://doi.org/10.1016/j.rse.2011.05.028   DOI
24 Van Aalst, M.K., 2006. The impacts of climate change on the risk of natural disasters, Disasters, 30(1): 5-18. https://doi.org/10.1111/j.1467-9523.2006.00303.x   DOI
25 Zhao, W. and Z.-L. Li, 2013. Sensitivity study of soil moisture on the temporal evolution of surface temperature over bare surfaces, International Journal of Remote Sensing, 34(9-10): 3314-3331. https://doi.org/10.1080/01431161.2012.716532   DOI
26 Bindlish, R. and A.P. Barros, 2001. Parameterization of vegetation backscatter in radar-based, soil moisture estimation, Remote Sensing of Environment, 76(1): 130-137. https://doi.org/10.1016/S0034-4257(00)00200-5   DOI
27 Bousbih, S., M. Zribi, Z. Lili-Chabaane, N. Baghdadi, M. E. Hajj, Q. Gao, and B. Mougenot, 2017. Potential of Sentinel-1 radar data for the assessment of soil and cereal cover parameters, Sensors, 17(11): 2617. https://doi.org/10.3390/s17112617   DOI
28 Brocca, L., A. Tarpanelli, P. Filippucci, W. Dorigo, F. Zaussinger, A. Gruber, and D. Fernandez-Prieto, 2018. How much water is used for irrigation? A new approach exploiting coarse resolution satellite soil moisture products, International Journal of Applied Earth Observation and Geoinformation, 73: 752-766. https://doi.org/10.1016/j.jag.2018.08.023   DOI
29 Chatterjee, S., N. Dey, and S. Sen, 2020. Soil moisture quantity prediction using optimized neural supported model for sustainable agricultural applications, Sustainable Computing: Informatics and Systems, 28: 100279. https://doi.org/10.1016/j.suscom.2018.09.002   DOI
30 Chen, J.M., G. Pavlic, L. Brown, J. Cihlar, S. Leblanc, H. White, R. Hall, D. Peddle, D. King, and J. Trofymow, 2002. Derivation and validation of Canada-wide coarse-resolution leaf area index maps using high-resolution satellite imagery and ground measurements, Remote Sensing of Environment, 80(1): 165-184. https://doi.org/10.1016/S0034-4257(01)00300-5   DOI
31 De Roo, R.D., Y. Du, F.T. Ulaby, and M.C. Dobson, 2001. A semi-empirical backscattering model at L-band and C-band for a soybean canopy with soil moisture inversion, IEEE Transactions on Geoscience and Remote Sensing, 39(4): 864-872. https://doi.org/10.1109/36.917912   DOI
32 Ahmad, S., A. Kalra, and H. Stephen, 2010. Estimating soil moisture using remote sensing data: A machine learning approach, Advances in Water Resources, 33(1): 69-80. https://doi.org/10.1016/j.advwatres.2009.10.008   DOI
33 Alexakis, D.D., F.-D.K. Mexis, A.-E.K. Vozinaki, I.N. Daliakopoulos, and I. K. Tsanis, 2017. Soil moisture content estimation based on Sentinel-1 and auxiliary earth observation products. A hydrological approach, Sensors, 17(6): 1455. https://doi.org/10.3390/s17061455   DOI
34 Baghdadi, N., M.E. Hajj, M. Zribi, and S. Bousbih, 2017. Calibration of the water cloud model at C-band for winter crop fields and grasslands, Remote Sensing, 9(9): 969. https://doi.org/10.3390/rs9090969   DOI
35 Ward, P.J., M.C. de Ruiter, J. Mard, K. Schroter, A. Van Loon, T. Veldkamp, N. von Uexkull, N. Wanders, A. AghaKouchak, and K. Arnbjerg-Nielsen, 2020. The need to integrate flood and drought disaster risk reduction strategies, Water Security, 11: 100070. https://doi.org/10.1016/j.wasec.2020.100070   DOI
36 Alvioli, M., M. Melillo, F. Guzzetti, M. Rossi, E. Palazzi, J. von Hardenberg, M.T. Brunetti, and S. Peruccacci, 2018. Implications of climate change on landslide hazard in Central Italy, Science of The Total Environment, 630: 1528-1543. https://doi.org/10.1016/j.scitotenv.2018.02.315   DOI
37 Balenzano, A., F. Mattia, G. Satalino, and M.W. Davidson, 2010. Dense temporal series of C- and L-band SAR data for soil moisture retrieval over agricultural crops, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 4(2): 439-450. https://doi.org/10.1109/JSTARS.2010.2052916   DOI
38 Bauer-Marschallinger, B., S. Cao, C. Navacchi, V. Freeman, F. Reuss, D. Geudtner, B. Rommen, F. C. Vega, P. Snoeij, and E. Attema, 2021. The normalised Sentinel-1 Global Backscatter Model, mapping Earth's land surface with C-band microwaves, Scientific Data, 8(1): 1-18. https://doi.org/10.6084/m9.figshare.16432983   DOI
39 Cho, S., J. Jeong, S. Lee, and M. Choi, 2021. Estimation of soil moisture based on Sentinel-1 SAR data: Assessment of soil moisture estimation in different vegetation condition, Journal of Korea Water Resources Association, 54(2): 81-91 (in Korean with English abstract). https://doi.org/10.3741/JKWRA.2021.54.2.81   DOI
40 Chung, J., Y. Lee, J. Kim, C. Jung, and S. Kim, 2022. Soil Moisture Content Estimation Based on Sentinel-1 SAR Imagery Using an Artificial Neural Network and Hydrological Components, Remote Sensing, 14(3): 465. https://doi.org/10.3390/rs14030465   DOI
41 Bhogapurapu, N., S. Dey, S. Homayouni, A. Bhattacharya, and Y. Rao, 2022. Field-scale soil moisture estimation using sentinel-1 GRD SAR data, Advances in Space Research, Article in Press. https://doi.org/10.1016/j.asr.2022.03.019   DOI