Soil moisture extraction using Microwave Imagery (Case Study: Behshahr, Mazandaran)
الموضوعات : فصلنامه علمی پژوهشی سنجش از دور راداری و نوری و سیستم اطلاعات جغرافیایی
1 - Department of GIS & RS, Yazd Branch, Islamic Azad University, Yazd, Iran
الکلمات المفتاحية: Backscatter Coefficient, Soil Moisture, SAR, Sentinel-1,
ملخص المقالة :
Objective: Estimate the large part of the soil surface to calculate its moisture is very important for agriculture since it would improve food security. In current study, four radar images of Sentinel-1 are employed to observe soil moisture in Miyankale Peninsula where is located in Behshahr, Mazandaran province.
Methods: These data are collected since 1394 until 1395 in both VV and VH polarization while imagery mode is Global mode. Soil texture, vegetation disturbs microwaves responses therefore the images are processed to eradicate vegetation effect, then backscatter coefficient calculated.
Results: These backscatters connect to statistical information gathered by field sampling (hygrometer device) to determine volumetric soil moisture in Miyankale Peninsula. The results show 0.79 for R2 (coefficient of determination) between volumetric moisture and backscatter; 0.62 for R2, between vegetation and backscatter, which confirm the vegetation effect on detecting moisture of soil.
Conclusion: This effect is removed from backscatter. In this study, Global mode in SAR data is appropriate for spares vegetation areas.
Babayan, A., Homaei, M., and Nowruz, AS., (2013). Estimation of Surface Soil Moisture Using ENVISAT / ASAR Radar Images, Journal of Water Research in Agriculture, 27 (4), pp. 622-611. https://journals.iau.ir/article_665927_04b010dd7d0c510562fb66445c43e278.pdf
Behyar, m.b., (2014). Evaluation of Soil Moisture in Isfahan Province by AMSR-E, Geographic Quarterly Journal, 29 (112), pp. 8-1. DOI:10.2136/vzj2013.05.0093
Khan mohammadi, F., Homaei, M., and Nowruz, AS a., (2014). Estimation of Soil Moisture Using Vegetation Indices and Soil Temperature and Normalized Moisture Index Using MODIS Images, Journal of Soil and Water Resources Conservation, 4 (2), pp. 9-1. DOI: 20.1001.1.22517480.1393.4.2.4.5
Mesri, a. Kamkarrowhani, A. Arab Amiri, A (2013): Determination of Soil Moisture Content Using GPR Method and Comparison of Results with Laboratory Results, Case Study: Shahroud University Agricultural University Campus, 8th Conference of Engineering and Environmental Geology of Iran, Ferdowsi University of Mashhad, p. 7. https://www.sid.ir/paper/852261/fa
Mirmazlomi, S.M. Sahebi, M. R., (2014): Estimation of soil moisture content using SAR data with emphasis on surface re-diffusion models, PhD thesis of Civil Engineering, Mapping Engineering, Khaje Nasir-e-Din Tusi University of Technology, Pages 110. https://elmnet.ir/doc/10787550-32212
Norozi Aghdam, A., Behbahani, M.R., Rahimi Khob, ., Aghighi, H., (2008): Surface Soil Moisture Model Using NDVI Index (Case Study: Rangelands of Khorasan Province), Journal of Environmental Studies, 2006, No. 48, Pages 127-136, DOI: 20.1001.1.10258620.1387.34.48.12.1
Altese. E, Bolognani. O, Mancini. M, and Troch, P.A, (1996): Retrieving soil moisture over bare soil from ERS1 Synthetic Aperture Radar data: sensitivity analysis based on theoretical surface scattering model and field data, Water Resources Research J, 32: 653-661. DOI: 10.1029/95WR03638
Baghdadi. N, Aubert. M and Zribi. M, (2012): Use of TerraSAR-X data to retrieve soil moisture over bare soil agricultural fields. IEEE Geoscience and Remote Sensing Letters, 9: 3-12. https://hal.science/hal-00704953/document
Balik Sanli. F, Kurucu. Y, Tolga Esetlili. M, Abdikan. S, (2008): Soil Moisture Estimation from Radarsat -1, Asar and Palsar Data In Agricultural Fields Of Menemen Plane Of Western Turkey. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XXXVII. Part B7. https://www.isprs.org/proceedings/xxxvii/congress/7_pdf/2_wg-vii-2/02.pdf
Brocca. L.S, Hasenauer. T, Lacava. F, Melone. T, Moramarco. W, Wagner. W, Dorigo. P, Matgen. J, Martínez-Fernández. P, Llorens. J, Latron. C and Martin. M, (2011): Soil moisture estimation through ASCAT and AMSR-E sensors: an intercomparison and validation study across Europe, Remote Sensing of Environment J., 115:3390–3408. DOI:10.1016/j.rse.2011.08.003
Das. N.N, Mohanty. B.P and Njoku. E.G, (2008): A Markov chain Monte Carlo algorithm for upscaled SVAT modeling to evaluate sattelite-based soil moisture measurements,Water Resources Research, 44:W05416. DOI:10.1029/2007WR006472
Entekhabi. D, Nakamura. H and Njoku. E.G, (1994): Solving the inverse problem for soil moisture and temperature profiles by the sequential assimilation of multifrequency remotely sensed observations. IEEE Trans. Geosci, Remote Sensing, 32: 438-448. DOI: 10.1109/36.295058
Kolassa. J, Gentine. P, Prigent. C, Aires. F, (2016): Soil moisture retrieval from AMSR-E and ASCAT microwave observation synergy. Part 1: Satellite data analysis, Remote Sensing of Environment, 173: 1–14. DOI:10.1016/j.rse.2015.11.011
Lievens, H. Martens, B. Verhoest, N.E.C. Hahn, S. Reichle, R.H. Miralles, D.G. (2017): Assimilation of global radar backscatter and radiometer brightness temperature observations to improve soil moisture and land evaporation estimates, Remote Sensing of Environment 189: 194–210. https://doi.org/10.1016/j.rse.2016.11.022
Lievens. H and Verhoest. N.E.C., (2012): Spatial and temporal soil moisture estimation from RADARSAT-2 imagery over Flevoland, The Netherlands, Journal of Hydrology, 456: 44-56. DOI:10.1016/j.jhydrol.2012.06.013
Mecklenburg. S, Drusch. M, Kaleschke. L, Rodriguez-Fernandez. N, Reul. N, Kerr.Y, Font. J, Martin-Neira. M, Oliva. R, Daganzo-Eusebio. E, Grant. J.P, Sabia. R, Macelloni. G, Rautiainen. K, Fauste. J, de Rosnay. P, Munoz-Sabater. J, Verhoest. N, Lievens. H, Delwart. S, Crapolicchio. R, de la Fuente. A, Kornberg. M, (2015): ESA's Soil Moisture and Ocean Salinity mission: From science tooperational applications, Remote Sensing of Environment. RSE-09658: No of Pages 16. DOI: 10.1016/j.rse.2015.12.025
Minchella, Andrea, (2016): SNAP Sentinel-1 in a Nutshell, – “ESA SNAP-Sentinel-1 Training Course”Satellite Applications Catapult - Electron Building, Harwell, Oxf. https://www.academia.edu/26646467/SNAP_Sentinel_1_in_a_Nutshell
Moran. M. S, McElroy. Stephen, Watts. Joseph M and Peters-Lidard. Christa D, (2005): Radar Remote Sensing for Estimation of Surface Soil Moisture at the Watershed Scale, Radar Remote Sensing for Estimation of Surface Soil Moisture at The Watershed Scale, p:17. https://www.researchgate.net/publication/255606424_Radar_Remote_Sensing_for_Estimation_of_Surface_Soil_Moisture_at_the_Watershed_Scale
Sanli. F.B, Kurucu. Y, Esetlili. M.T and Abdikana. S, (2008): Soil moisture estimation from RADARSAT-1, ASAR and PALSAR data in agricultural fiels of Menemen plain of western Turkey.The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Part B7, Beijing. https://www.researchgate.net/publication/232269197_SOIL_MOISTURE_ESTIMATION_FROM_RADARSAT_-1_ASAR_AND_PALSAR_DATA_IN_AGRICULTURAL_FIELDS_OF_MENEMEN_PLANE_OF_WESTERN_TURKEY
Thain. C, Barstow. R, Ramsbottom. D, Lim. P, Wong. T, (2011). Sentinel-1Product Specification, Ref: S1-RS-MDA-52-744, Issue/Revision: 2/2. https://journals.iau.ir/article_665927_04b010dd7d0c510562fb66445c43e278.pdf
