Methodology for determining the physical parameters of ground plane by the results of the optical and radar data fusion

Authors

  • Mykhailo Svideniuk Scientific Centre for Aerospace Research of the Earth of the Institute of Geological Sciences of the National Academy of Sciences of Ukraine, Kyiv, Ukraine https://orcid.org/0000-0003-2167-3522

DOI:

https://doi.org/10.36023/ujrs.2021.8.3.197

Keywords:

optical and radar data, soil moisture, permittivity, surface roughness, physical temperature, thermal emissivity, supreme measurements, ground truth

Abstract

The methodology of multispectral optical and dual-polarized radar satellite data fusion for soils physical parameters estimation is developed. In particular, the methodology comprises relative permittivity estimation based on the Integral Equation Model (IEM) by using high resolution Sentinel-1 GRDH radar data. The calibration of ε was provided based on the compensation of soil acidity and temperature destabilizing effects. High-resolution multispectral images PlanetScope were used for vegetation indices and thermal emissivity estimation. Both, low-resolution MODIS and medium resolution Landsat-7/8 ETM+/TIRS thermal infrared images were processed in order to estimate ground plane thermodynamic temperature. An investigated approach for the radar signal depolarization compensation is based on local signal deviations and surface roughness estimation. The relief heterogeneity is restored based on the medium-resolution digital terrain elevation model ALOS AWD3D. Aiming to evaluate the accuracy of a soil moisture estimation model designed based on the presented methodology, ground truth measurements were carried out. Specifically, they included soil samples retrieving for the gravimetric soil moisture. In addition, the soil acidity and temperature were measured by applying the GM1312 differential thermometer and WALCOM multifunction device. The estimated parameters and ground truth data were used in order to retrieve the soil moisture based on the multivatiative regression dependence. Root mean square error of soil moisture retrieving was estimated as 4,73 %. Such accuracy is completely acceptable for the soil moisture monitoring of natural-reserved fund territories

References

Belyaeva T.A., Bobrov P.P., Kondratieva O.V. (2013). The changes of soils dielectric properties caused by the increasing of bound water concentration. Siberian Aerospace Journal, 5 (51), 92-95. (In Russian)

Voronin A.N. Data fusion methods. (2014). Cybernetics and system analysis, p. 78-84

Hryvachevskyi A.P. Analysis of the methods of signal data fusion of partial spectral channels in the monitoring systems of objects and scenes. (2020). Bulletin of the Lviv Polytechnic National University, 818, 55-61. (In Ukrainian).

Hryvachevskyi A.P, Fabirovskyy S. E. (2017). Matching up of images which formed by sensors of different physical nature in the process of signal fusion in multispectral monitoring systems. Bulletin of the Lviv Polytechnic National University, 874, 73–80. (In Ukrainian).

Dagurov P.N., Dobrynin S.I., Dmitriev A.V., Chimitdorzhiev N.N. (2016). Phase model of the scattering of microwaves by layer with rough boundaries. Optika Atmosfery i Okeana, 29(7), 585-591. doi:10.15372/AOO20160709. (In Russian)

ISO 11465-2001. Soil quality. Determination of dry matter and moisture content. Gravimetric method. ISO 11465:1993, IDТ). 2002. 13 p. (In Ukrainian).

L. G. Kosolapova. Temperature dependences of moist soils dielectric permittivity. Experiments and modeling. XVI International Scientific Conference «Reshetnyvsky Readings». Krasnoyarsk: SibGau, 2012, p. 214-215. (In Russian)

Laktionov I.S., Vovna A.V. (2014a). Method of reducing the soil moisture meter additional error for the botanical garden greenhouses. Scientific Efforts of Donetsk National Technical University, 27(2), 183-191. Available online: http://nbuv.gov.ua/UJRN/Npdntu_ota_2014_2_24. (In Russian)

Laktionov I.S., Vovna A.V. (2014b). Method for the efficiency improving of the soil moisture meter. Scientific Efforts of Donetsk National Technical University, 24(4), 81-87. (In Russian)

Lyalko V.I., Wulfson L.D., Kotlar A.L., Ryabokonenko A.D., Freilicher V.D. Subsurface remote sensing in the P-band for the determination of soil water content in different landscape and climatic conditions. Proceedings of the I Scientific Conference “Earth and space sciences for society”, 25-27 June 2007, p. 15-18. (In Russian)

Matus S.K. (2014). Information-measuring system of data acquisition and control of soil moisture reserves. Herald of the National University of Water and Environmental Engineering, Scientific Efforts of “Technical sciences” Series, 2(66), 198-208. Available online: http://ep3.nuwm.edu.ua/2349/1/Vt6626.pdf. (In Ukrainian)

Plotnikov O.M., Mikitenko V.I. (2017). Data fusion method with the previous finding informative area on scene. Herald of Khmelnytskyi National University, Technical sciences, 1, 196-201. Available online: http://nbuv.gov.ua/UJRN/Vchnu_tekh_2017_1_39. (In Ukrainian)

Pozdnyakov A.I., Gulalyev, Ch.G. Electrophysical properties of some soils. Moscow-Baku, Adilogolu, 2004, 240 p. (In Russian)

Prudius I.N., Lazen L.V., Semenov S.O. (2008). Multilevel fusiom of images in remote sensing systems. Bulletin of Lviv Polytechnic National University, 618, 3–10. Available online: http://ena.lp.edu.ua:8080/handle/ntb/33957. (In Ukrainian)

Stankevich S.A., Pylypchuk V.V., Lubskyi M.S., Krylova H.B. (2016). Accuracy assessment of the temperature of artificial and natural Earth’s surfaces determining by infrared satellite imagery. Space Science and Technology, 22(4), 19-28. Available online: https://www.mao.kiev.ua/biblio/jscans/knit/2016-22/knit-2016-22-4-02-Stankevich.pdf. (In Ukrainian)

Tyurin Yu. N. (2010). Multidimensional statistical analysis: geometric theory, Teor. Veroyatnost. i Primenen., 55(1), 36–58. doi:10.4213/tvp4175. (In Russian)

Fomichev A.A., Uspensky V.B., Rushman K.Yu., Pugachev R.V. (2005). Information fusion in an integrated navigation system with the incomplete constellation of navigation satellites. nformation Processing Systems, 8 (48), 284-290. (In Russian)

Shaiko SG, Micheeva MP Electrochemistry: Study guide for students of higher technical educational institutions. Donetsk National Technical University: Noulage, 2013, 226. ISBN 978-617-579-231-5.

ALOS Global Digital Surface Model (DSM) «ALOS World 3D-30m» (AW3D30) Ver. 3.1 Product Description. Earth Observation Research Center Japan Aerospace Exploration Agency (JAXA EORC), 2020. 16 p. Available online.

Álvarez-Mozos J., González-Audícana M., Casalí J., Larrañaga A. (2008). Effective versus measured correlation length for radar-based surface soil moisture retrieval. International Journal of Remote Sensing, 29(17-18), 5397-5408. doi:10.1080/ 01431160802036367.

Babaeian E., Sadeghi M., Jones S. B., Montzka C., Vereecken H., Tuller M. (2019). Ground, proximal, and satellite remote sensing of soil moisture. Rev. Geophys., 57, 530–616. doi:10.1029/2018RG000618.

Baghdadi N., Zribi M., Paloscia S., Verhoest, N. E., Lievens H., Baup F., Mattia F. (2015). Semi-empirical calibration of the integral equation model for co-polarized L-band backscattering. Remote Sensing, 7(10), 13626–13640. doi:10.3390/rs71013626.

Baghdadi N., Choker M., Zribi M., Hajj M.E., Paloscia S., Verhoest N.E.C., Lievens H., Baup F., Mattia F. (2016). A new empirical model for radar scattering from bare soil surfaces. Remote Sensing, 8(11), 920. doi:10.3390/rs8110920.

Baghdadi N., El Hajj M., Choker M., Zribi M., Bazzi H., Vaudour E., Gilliot J.-M., Ebengo D.M. (2018). Potential of Sentinel-1 Images for estimating the soil roughness over bare agricultural soils. Water, 10, 131. https://doi.org/10.3390/w10020131.

Baghdadi, N., Hajj, M. E., & Zribi, M. (2019). An Operational High Resolution Soil Moisture Retrieval Algorithm Using Sentinel-1 Images. 2019 PhotonIcs & Electromagnetics Research Symposium - Spring (PIERS-Spring). doi:10.1109/piers-spring46901.2019.9017477.

Barnes R., Brown S., Lykke K., Guenther B., Butler J., Schwarting T., Turpie K., Moyer D., DeLuccia F., Moeller C. (2015). Comparison of two methodologies for calibrating satellite instruments in the visible and near-infrared. Appl. Opt., 54(35), 10376-10396. https://doi.org/10.1364/AO.54.010376.

Barsi J.A., Barker J.L., Schott J.R.. An Atmospheric Correction Parameter Calculator for a Single Thermal Band Earth-Sensing Instrument. IGARSS03, 21-25 July 2003, Centre de Congres Pierre Baudis, Toulouse, France, p. 3014- 3016. doi:10.1109/igarss.2003.1294665.

Bousbih, S., Zribi, M., El Hajj, M., Baghdadi, N., Chabaane, Z. L., Fanise, P., & Boulet, G. (2019). Sentinel-1 and Sentinel-2 Data for Soil Moisture and Irrigation Mapping Over Semi-Arid Region. IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. doi:10.1109/igarss.2019.8897883.

Carlson T., Ripley D. (1997). On the Relation between NDVI, Fractional Vegetation Cover, and Leaf Area Index. Remote Sensing of Environment, 62, 241-252. 10.1016/S0034-4257(97)00104-1.

Choker M., Baghdadi N., Mehrez Zribi, Mohammad El Hajj, S. Paloscia, Verhoest N.E.C., Lievens H., Mattia F. (2017). Evaluation of the Oh, Dubois and IEM Backscatter Models Using a Large Dataset of SAR Data and Experimental Soil Measurements. Water, Vol. 9(1), No. 38, 27 p. https://doi.org/10.3390/w9010038.

Crow W.T., Wagner W., Naeimi V. (2010). The Impact of Radar Incidence Angle on Soil-Moisture-Retrieval Skill. IEEE Geoscience and Remote Sensing Letters, 7(3), 501–505. doi:10.1109/lgrs.2010.2040134 .

Dobson M. C., Ulaby F. T. (1986). Active microwave soil moisture research. IEEE Trans. Geosci. Remote Sens., GE-24(1), 23–26. doi:10.1109/tgrs.1986.289585.

Druce D., Tong X., Lei X., Guo T., Kittel C.M.M., Grogan K., Tottrup C. (2021). An optical and SAR based fusion approach for mapping surface water dynamics over Mainland China. Remote Sensing, 13(9). 1663 p. DOI: 10.3390/rs13091663

Dubois P. C., Van Zyl J., Engman T. (1995). Measuring soil moisture with imaging radars. IEEE Trans. Geosci. Remote Sensing, 33, 915–926. doi:10.1109/36.406677.

El Hajj, M., Baghdadi N., Zribi M., Bazzi H. (2017). Synergic use of Sentinel-1 and Sentinel-2 images for operational soil moisture mapping at high spatial resolution over agricultural areas. Remote Sens., 9, 1292. https://doi.org/10.3390/rs9121292.

European Space Agency. Sentinel-1: Radiometric Calibration of S-1 Level-1 Products Generated by the S-1 IPF. Technical Note (ESA-EOPG-CSCOP-TN-0002, May 2015). Nuno Miranda, P.J., Eds. Meadows. Available online: https://sentinels.copernicus.eu.

European Space Agency. Sentinel-1 Product Specification (S1-RS-MDA-52-7441, February 2020). Issue 3(7), 197 p. Available online: https://sentinels.copernicus.eu/documents/247904/1877131/Sentinel-1-Product-Specification.pdf/49c514c3-1574-4d94-aae2-d8061a3baebd?t=1584020315000.

Evans I. (1980). An Integrated System of Terrain Analysis and Slope Mapping. Zeitschrift Fiir Geomorphologie Supplement-Band, 36, 274-295.

Ezzahar J., Ouaadi N., Zribi M., Elfarkh J., Aouade G., Khabba S., Er-Raki S., Chehbouni A., Jarlan L. (2020). Evaluation of backscattering models and support vector machine for the retrieval of bare soil moisture from Sentinel-1 data. Remote Sensing, 12(1), A.72, 20. doi: 10.3390/rs12010072.

Fung A. K. Microwave Scattering and Emission Models and Their Applications. Artech House, Incorporated, 1994, 594 p. ISBN: 9780890065235.

Gorrab A., Zribi M., Baghdadi N., Mougenot, B., Fanise P., Chabaane Z.L. (2015). Retrieval of both soil moisture and texture using TerraSAR-X images. Remote Sens., 7(8), 10098–10116. doi:10.3390/rs70810098

Hajnsek I., Jagdhuber T., Schon H., Papathanassiou K. P. (2009). Potential of estimating soil moisture under vegetation cover by means of PolSAR. IEEE Trans. Geosci. Remote Sens., 47. P. 442–454. doi:10.1109/tgrs.2008.2009642.

Hsieh C., Fung A., Nesti G., Coppo P. (1997). A Further Study of the IEM Surface Scattering Model.. IEEE Trans. on Geosci and Remote Sensing, 35(4), 901-909. JRC15003

Huete A. R., Liu H. Q., Batchily K., Leeuwen van W. (1997). A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sensing of Environment, 59(3), 440−451. doi:10.1016/s0034-4257(96)00112-5.

Ibarra-Castanedo C., González D., Klein M., Pilla M., Vallerand S., Maldague X. (2004). Infrared image processing and data analysis. Infrared Phys. and Technol, 46(1-2), 75—83. doi:10.1016/j.infrared.2004.03.01.

Itti L., Koch C., Niebur E. A model of saliency based visual attention for rapid scene analysis. (1998). EEE Transactions on Pattern Analysis and Machine Intelligence, 20(11). P. 1254-1259. DOI:10.1109/34.730558.

Jagdhuber T. Soil Parameter Retrieval under Vegetation Cover Using SAR Polarimetry. University of Potsdam: Potsdam, Germany, 2012, 270 p.

Kedem B., Victor De Oliveira V., Sverchkov M. Statistical Data Fusion. Singapore: World Scientific Publishing, 2017, 200 p. https://doi.org/10.1142/10282

Kim J., Mohanty B. P. (2017). A physically based hydrological connectivity algorithm for describing spatial patterns of soil moisture in the unsaturated zone. Journal of Geophysical Research: Atmospheres, 122(4), 2096–2114. doi:10.1002/2016jd025591

Kondratov P., Ohanesyan A., Tkachenko V., Prudyus I., Lazko L., Hryvachevskyi A. Detection and allotment of the objects based on multispectral monitoring. Modern Problems of Radio Engineering, Telecommunications, and Computer Science (TCSET'2016): Proceedings of the XIIIth International Conference, 2016, 259–262.

Liu M., Liu X., Dong X., Zhao B., Zou X., Wu L., Wei H. (2020). An Improved Spatiotemporal Data Fusion Method Using Surface Heterogeneity Information Based on ESTARFM. Remote Sens., 12(21), 3673. https://doi.org/10.3390/rs12213673.

Lyalko V.I., Vulfson L.D., Kotlyar A.L., Shevchenko V.N., Ryabokonenko A.D., Blumberg D.G., Freilikher V. (2000). Soil Moisture (Water-Content) Assessment by an Airborne Scatterometer: The Chernobyl Disaster Area and the Negev Desert. Remote Sensing of Environment, 71(3), 309–319. doi:10.1016/s0034-4257(99)00087-5.

Macelloni G., Nesti G., Pampaloni P., Sigismondi S., Tarchi D.,Lolli S. (2000). Experimental validation of surface scattering and emission models. IEEE Transactions on Geoscience and Remote Sensing, 38(1), 459–469. doi:10.1109/36.823941.

Nguyen H., Cressie N., Braverman A. (2012). Spatial statistical data fusion for remote sensing applications. Jour. American Statistical Association, 107(499), 1004-1018. doi:10.1080/01621459.2012.694717.

Oguro, Y., Ito, S., Tsuchiya, K. (2011). Comparisons of Brightness Temperatures of Landsat-7/ETM+ and Terra/MODIS around Hotien Oasis in the Taklimakan Desert. Applied and Environmental Soil Science, 1–11. doi:10.1155/2011/948135.

Quantitative retrieval of soil moisture content and surface roughness from multipolarized radar observations of bare soil surfaces. IEEE Trans. Geosci. Remote Sens., 42, 596–601. doi:10.1109/tgrs.2003.821065.

Olsson L., Barbosa H., Bhadwal S., Cowie A., Delusca K., Flores-Renteria D., Hermans K., Jobbagy E., Kurz W., Li D., Sonwa D.J., Stringer L. (2019). Land Degradation. In: Climate Change and Land: an IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems [P.R. Shukla, J. Skea, E. Calvo Buendia, V. Masson-Delmotte, H.-O. Pörtner, D. C. Roberts, P. Zhai, R. Slade, S. Connors, R. van Diemen, M. Ferrat, E. Haughey, S. Luz, S. Neogi, M. Pathak, J. Petzold, J. Portugal Pereira, P. Vyas, E. Huntley, K. Kissick, M. Belkacemi, J. Malley, (eds.)]. In press. URL: https://www.ipcc.ch/site/assets/uploads/sites/4/2019/11/07_Chapter-4.pdf.

Palombo A., Pascucci S., Loperte A., Lettino A., Castaldi F., Muolo M.R., Santini F. (2019). Soil Moisture Retrieval by Integrating TASI-600 Airborne Thermal Data, WorldView 2 Satellite Data and Field Measurements: Petacciato Case Study. Sensors, 19, 1515. https://doi.org/10.3390/s19071515.

Panciera R., Tanase M.A., Lowell K., Walker J.P. (2014). Evaluation of IEM, Dubois, and Oh radar backscatter models using airborne L-band SAR. IEEE Trans. Geosci. Remote Sens., 52(8), 4966–4979. doi:10.1109/tgrs.2013.2286203

Pasolli L., Notarnicola C., Bertoldi G. Bruzzone, L. Remelgado R., Greifeneder F., Niedrist G., Chiesa S.D, Tappeiner U., Zebisch, M. (2015). Estimation of Soil Moisture in Mountain Areas Using SVR Technique Applied to Multiscale Active Radar Images at C-Band. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(1), 262–283. doi:10.1109/jstars.2014.2378795.

Peng M., Zhang L., Sun X., Cen Y., Zhao X. A. (2020). Fast Three-Dimensional Convolutional Neural Network-Based Spatiotemporal Fusion Method (STF3DCNN) Using a Spatial-Temporal-Spectral Dataset. Remote Sens., 12(23), 3888. https://doi.org/10.3390/rs12233888.

Planet Imagery Product Specifications. February 2021, Available online. https://www.planet.com/products.

Rees G. Physical principles of remote sensing. New York: Cambridge University Press, 2013, 492 p. ISBN: 9781107004733

Robinson D.A., Campbell C.S., Hopmans J.W., Hornbuckle B.K., Jones S.B., Knight R., Ogden F., Selker J., Wendroth O. (2008). Soil Moisture Measurement for Ecological and Hydrological Watershed-Scale Observatories: A Review. Vadose Zone Journal, 7, 358-389. https://doi.org/10.2136/vzj2007.0143.

Rowlandson et al, 2018Rowlandson T.L., Berg A.A., Bullock P.R., Hanis-Gervais K., Ojo E.R., Cosh M.H., Powers J., McNairn H. (2018). Temporal transferability of soil moisture calibration equations. J. Hydrol., 56, 349–358. doi:10.1016/j.jhydrol.2017.11.023.

Schreier G. SAR Geocoding: Data and Systems. Wichmann, 1993, 435 p.

Small D., Schubert A. Guide to S-1 Geocoding, UZH-S1-GC-AD, Issue 1.10, March 2019. 42 p. Available online: https://sentinel.esa.int/documents/247904/0/Guide-to-Sentinel-1-Geocoding.pdf/e0450150-b4e9-4b2d-9b32-dadf989d3bd3.

Stankevich S. A., Kozlova A. A., Piestova I. O., Lubskyi M. S. Leaf area index estimation of forest using sentinel-1 C-band SAR data. IEEE Microwaves, Radar and Remote Sensing Symposium (MRRS), 2017, 253-256. doi: 10.1109/MRRS.2017.8075075.

Stephen H., Ahmad S., Piechota T. C., Tang C. (2010). Relating surface backscatter response from TRMM precipitation radar to soil moisture: results over a semi-arid region. Hydrol. Earth Syst. Sci., 14, 193–204. https://doi.org/10.5194/hess-14-193-2010, 2010.

Ulaby F.T., Moore R.K., Fung A.K. (1986). Microwave Remote Sensing: Active and Passive. Vol. 2. Radar Remote Sensing and Surface Scattering and Emission Theory. Ch. 12, Artech House Publishers, Norwood, 962-966.

United Nations General Assembly, Principles Relating to Remote Sensing of the Earth from Space,, 1986, accessed on April 10, 2006. URL: https://www.unoosa.org/oosa/en/ourwork/spacelaw/principles/remote-sensing-principles.html.

Van de Griend A. A., Owe M. (1993). On the relationship between thermal emissivity and the normalized difference vegetation index for natural surfaces. Int. J. Remote Sensing, 14(6), 1119—1131. doi:10.1080/01431169308904400.

Vapnik V., Golowich S., Smola, A. J. (1997). Support vector method for function approximation, regression estimation, and signal processing. Adv. Neural Info. Processing Systems, 9, 281–287.

Vereecken, H.; Huisman, J.A.; Bogena, H.; Vanderborght, J.; Vrugt, J.A.; Hopmans, J.W. On the Value of Soil Moisture Measurements in Vadose Zone Hydrology: A Review. Water Resour. Res. 2008, 44, 4.

Verhoest N., Lievens H., Baup F., Mattia F. (2016). A new empirical model for radar scattering from bare soil surfaces. Remote Sens., 8(11), 920, doi:10.3390/rs8110920.

Wagner W., C. Pathe, D. Sabel, A. Bartsch, C. Kunzer, and K. Scipal. (2007). Wagner, Wolfgang & Pathe, Carsten & Sabel, Daniel & Bartsch, Annett & Kuenzer, Claudia & Scipal, Klaus. (2007). Experimental I km soil moisture products from Envisat ASAR for Southern Africa. Proceedings of ENVISAT Symposium, ESA SP-636, July 2007, Montreux, Switzerland, 6 p.

Wilson, D. J., A. W. Western, and R. B. Grayson (2005), A terrain and data- based method for generating the spatial distribution of soil moisture, Adv. Water Resour., 28, 43–54, doi:10.1016/j.advwatres.2004.09.007.

Wood, J. The Geomorphological Characterization of Digital Elevation Models, Ph.D. Thesis, University of Leicester, Department of Geography, Leicester, UK, 1996.

Yahia O., Guida R., Iervolino P. (2021). Novel weight-based approach for soil moisture content estimation via synthetic aperture radar, multispectral and thermal infrared data fusion. Sensors, 21, 3457. https://doi.org/10.3390/s21103457.

Yang H., Zhang L.F., Zhang X., et al. (2011). Algorithm of emissivity spectrum and temperature separation based on TASI data. Journal of remote sensing, 15(6), 1242—1254. doi:10.11834/jrs.20110380.

Zhang L., Li H., Xue Z. (2020). Calibrated integral equation model for bare soil moisture retrieval of synthetic aperture radar: A case study in Linze County. Applied Sciences, 10(21), A.7921, 17 p. doi:10.3390/app10217921.

Zhang J. (2010). Multi-source remote sensing data fusion: status and trends. International Journal of Image and Data Fusion, 1(1), 5–24. doi:10.1080/19479830903561035.

Zribi M., Muddu S., Bousbih S., Al Bitar A., Tomer S.K., Baghdadi N., Bandyopadhyay S. (2019) Analysis of L-Band SAR data for soil moisture estimations over agricultural areas in the tropics. Remote Sensing, 11, 1122. https://doi.org/10.3390/rs11091122.

Zribi M., Gorrab A., Baghdadi, N. (2014). A new soil roughness parameter for the modelling of radar backscattering over bare soil. Remote Sensing of Environment, 152, 62–73. doi:10.1016/j.rse.2014.05.009

Published

2021-09-21

Issue

Section

Earth observation data applications: Challenges and tasks