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Year 2021, Volume: 8 Issue: 1, 65 - 77, 07.03.2021

Sachin Sutariya Ankur Hirapara Momin Meherbanali M.k. Tiwari Vijay Sıngh Manik Kalubarme

https://doi.org/10.30897/ijegeo.777434
Cited By: 4

Abstract

References

  • Almaw Ayele Aniley, Naveen Kumar S K, and Akshaya Kumar A. (2018). Review Article Soil Moisture Sensors in Agriculture and the possible application of nanomaterials in soil moisture sensors. Ijaert, 6(1), 134–142.
  • Amato, F., Havel, J., Gad, A., El-Zeiny, A., 2015. Remotely Sensed Soil Data Analysis Using Artificial Neural Networks: A Case Study of El-Fayoum Depression. Egypt. ISPRS International Journal of Geo-Information 4 (2), 677–696.
  • Amazirh A., Merlin Olivier, Er-Raki S., Gao Q., Rivalland V., Malbeteau Y., Khabba S., Escorihuela M. J. (2018). Retrieving surface soil moisture at high spatio-temporal resolution from a synergy between Sentinel-1 radar and Landsat thermal data: a study case over bare soil. Remote Sensing of Environment, 211, 321-337.
  • Ansari, S., & Deshmukh, R. R. (2017). Estimation of Soil Moisture Content: A Review. International Journal of Theoretical and Applied Mechanics, 12(3), 571–577.
  • Avdan, U., &Jovanovska, G. (2016). Algorithm for Automated Mapping of Land Surface Temperature Using LANDSAT 8 Satellite Data. 2016.
  • Barrett, B.; Petropoulos, G.P. (2013). Satellite Remote Sensing of Surface Soil Moisture. In Remote Sensing of Energy Fluxes and Soil Moisture Content; CRC Press: Boca Raton, FL, USA, 2013; pp. 85–120, ISBN 978-1-4665-0578-0.
  • Bezerra, B.G.; Santos, C.A.C.; Silva, B.B.; Perez-Marin, A.M.; Bezerra, M.V.C.; Berzerra, J.R.C.; Rao, T.V.R., 2013. Estimation of soil moisture in the root-zone from remote sensing data. Rev. Bras. Cienc. Solo 2013, 37, 596–603.
  • Bittelli, M. (2011). Measuring Soil Water Content: A Review. 3861(June), 293–300.
  • Chandrasekar, K. Geo-spatial Meteorological Products for Agricultural Drought Assessment, NRSC User Interaction Meet- PPT. 2016. Available online:
  • Chauhan, S.; Srivastava, H.S. Comparative evaluation of the sensitivity of multi-polarized SAR nd optical data for various land cover classed. Int. J. Remote Sens. 2016, 4, 01–14.
  • Department of the Interior U.S. Geological Survey. (2016). Landsat 8 Data Users Handbook. Nasa, 8(June),97.
  • Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., Bargellini, P. (2012). Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services. In Remote Sensing of Environment (Vol.120).
  • Esetlili, M. T., &Kurucu, Y. (2016). Determination of Main Soil Properties Using Synthetic Aperture Radar. Fresenius Environmental Bulletin, 25(1), 23–36.
  • Filipponi, F. (2019). Sentinel-1 GRD Preprocessing Workflow. Proceedings, 18(1), 11.
  • Gao, Q.; Zribi, M.; Escorihuela, M.J.; Baghdadi, N. Synergetic use of sentinel-1 and sentinel-2 data for soil moisture mapping at 100 m resolution. Sensors, 2017.
  • Karjalainen, M.; Kaartinen, H.; Hyyppä, J.; Laurila, H.; Kuittinen, R., 2004. The Use of ENVISAT Alternating Ploarization SAR Images in Agricultureal Monitoring in Compatison with RADARSAT-1 SAR Images. In Proceedings of the ISPRS Congress, Istanbul, Turkey, 12–23 July 2004.
  • Mirsoleimani, H. R., Sahebi, M. R., Baghdadi, N., & El Hajj, M. (2019). Bare soil surface moisture retrieval from sentinel-1 SAR data based on the calibrated IEM and dubois models using neural networks. Sensors (Switzerland), 19(14), 1–12.
  • Moawad, B.M., 2012. Geoscience general tool package. Max-Planck Institute fürChemie, Mainz, Germany.
  • Mohamed, E.S., Ali, Abdelraouf, El-Shirbeny, Mohammed, Abutaleba, Khaled and Shaddad, Sameh M. 2019. Mapping soil moisture and their correlation with crop pattern using remotely sensed data in arid region. The Egyptian Journal of Remote Sensing and Space Sciences xxx (xxxx) xxx (Article in Press).
  • Myhre, B. E., & Shih, S. F. (1990). Using Infrared Thermometry to Estimate Soil Water Content for a Sandy Soil. 33 (October), 1479–1486.
  • Paloscia, S., Pettinato, S., Santi, E., Notarnicola, C., Pasolli, L., & Reppucci, A. (2013). Remote Sensing of Environment Soil moisture mapping using Sentinel-1 images: Algorithm and preliminary validation. Remote Sensing of Environment, 134, 234–248.
  • Petropoulos, G., Carlson, T.N., Wooster, M.J., Islam, S. (2009). A review of T-s/VI remote sensing-based methods for the retrieval of land surface energy fluxes and soil surface moisture Prog. Phys. Geography, 33 (2009), pp. 224-250.
  • Prakash Mohan, M M, Rajitha, K. & Varma, Murari R R (2019). Integration of soil moisture as an auxiliary parameter for the anchor pixel selection process in SEBAL using Landsat 8 and Sentinel - 1A images, International Journal of Remote Sensing, 41:3, 1214-123.
  • Prakash, R.; Singh, D.; Pathak, N.P. A fusion approach to retrieve soil moisture with SAR and optical data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2012, 5, 196–206.
  • Rahimzadeh-Bajgiran, P., Berg, A.A., Champagne, C., Omasa, K., 2013. Estimation of soil moisture using optical/thermal infrared remote sensing in the Canadian Prairies. ISPRS J. Photogrammetry Remote Sens, 83, 94–103.
  • Rawat, Kishan Singh, Singh, Sudhir Kumar & Ray, Ram Lakhan (2019). An integrated approach to estimate surface soil moisture in agricultural lands, Geocarto International.
  • Reza Attarzadeh, Jalal Amini, Claudia Notarnicola and Felix Greifeneder, 2018. Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at Plot Scale. Remote Sensing, 10, 2018, pp 2 -18.
  • Sahebi, M.R.; Angles, J.; Bonn, F. (2002). A comparison of multi-polarization and multi angular approaches for estimating bare soil surface roughness from space-borne radar data. Can. J. Remote Sens, 2002, 28, 641–652.
  • Sekertekin, Aliihsan, Marangoz, Aycan Murat, and Abdikan, Saygın, 2016. Soil Moisture Mapping Using Sentinel-1A Synthetic Aperture Radar Data. International Journal of Environment and Geoinformatics 5(2): 178- 188 (2016).
  • Ulaby, F. T., Moore, R. K., and Fung, A. K. (1986). Microwave remote sensing: Active and passive; from theory to applications (Vol. Volume III, pp. 1065–2162).
  • Wagner, W., Lemoine, G., Borgeaud, M., & Rott, H. (1999b). A study of vegetation cover effects on ERS scatterometer data. IEEE Transactions on Geoscience and Remote Sensing, 37(2), 938–948.
  • Wagner, W., Lemoine, G., & Rott, H. (1999c). A method for estimating soil moisture from ERS scatterometer and soil data. Remote Sensing of Environment, 70, 191–207.
  • Wang, J.R. (1980). The dielectric properties of soil-water mixtures at microwave frequencies. Radio Sci, 1980, 15, 977–985.
  • Weng, Qihao, Lu, Dengsheng, and Schubring, Jacquelyn (2004). Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment 89(4):467–483, February 2004.
  • Yadav, Vijay Pratap, Rajendra Prasad, Bala, Ruchi, Vishwakarma, Ajeet kumar, 2019. Estimation of soil moisture through water cloud model using sentinel -1A SAR data. Proceeding URSI AP-RASC 2019, New Delhi, India, 09 - 15 March 2019.
  • Zeng, Y., Feng, Z., Xiang, N., 2004. Assessment of soil moisture using Landsat ETM+ Temperature/vegetation index in semiarid environment. IEEE, 4306–4309.
  • Zhuo, L.; Han, D. The relevance of soil moisture by remote sensing and hydrological modelling. Procedia Eng. 2016, 154, 1368–1375.
  • Zribi, M., Baghdadi, N., Holah, N., and 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.

Soil Moisture Estimation using Sentinel-1 SAR Data and Land Surface Temperature in Panchmahal District, Gujarat State

Year 2021, Volume: 8 Issue: 1, 65 - 77, 07.03.2021

Sachin Sutariya Ankur Hirapara Momin Meherbanali M.k. Tiwari Vijay Sıngh Manik Kalubarme

https://doi.org/10.30897/ijegeo.777434
Cited By: 4

Abstract

This paper presents the potential for soil moisture (SM) retrieval using Sentinel-1 C-band Synthetic Aperture Radar (SAR) data acquired in Interferometric Wide Swath (IW) mode along with Land Surface Temperature (LST) estimated from analysis of LANDSAT-8 digital thermal data. In this study Sentinel-1 data acquired on 27 February 2020 was downloaded from Copernicus website and LANDSAT-8 OLI data acquired on 24 February 2020 from the website https://earthexplorer.usgs.gov/.The soil samples were collected from 70 test fields in different villages of three talukas for estimating soil moisture content using the gravimetric method. The Sentinel-1 SAR microwave data was analysed using open source tools of Sentinel Application Platform (SNAP) software for estimation of backscattering coefficient. Land surface temperature estimated using Landsat-8 thermal data. The Landsat-8, Thermal infrared sensor Band-10 data and operational land imager Band-4 and Band-5 data were used in estimating LST. The Soil Moisture Index (SMI) for all field test sites was computed using the LST values.

The regression analysis using σ0VV and σ0VH polarization with soil moisture indicated that σ0VV polarization was more sensitive to soil moisture content as compared to σ0VH polarization. The multiple regression analysis using field measured soil moisture (MS %) as dependent variable, and σ0VV and SMI as independent variable was carried which resulted in the coefficient of determination (R2) of 0.788, 0.777 and 0.778 for Godhra, Goghamba and Kalol talukas, respectively. These linear regression equations were used to compute the predicted soil moisture in three talukas. The maps of spatial distribution of soil moisture in three talukas were generated using the respective regression equations of three talukas.

Keywords

Soil Moisture Sentinel-1 SAR data Land Surface Temperature Soil Moisture Index (SMI) Backscattering coefficient Landsat-8 OLI and TIRS data

References

  • Almaw Ayele Aniley, Naveen Kumar S K, and Akshaya Kumar A. (2018). Review Article Soil Moisture Sensors in Agriculture and the possible application of nanomaterials in soil moisture sensors. Ijaert, 6(1), 134–142.
  • Amato, F., Havel, J., Gad, A., El-Zeiny, A., 2015. Remotely Sensed Soil Data Analysis Using Artificial Neural Networks: A Case Study of El-Fayoum Depression. Egypt. ISPRS International Journal of Geo-Information 4 (2), 677–696.
  • Amazirh A., Merlin Olivier, Er-Raki S., Gao Q., Rivalland V., Malbeteau Y., Khabba S., Escorihuela M. J. (2018). Retrieving surface soil moisture at high spatio-temporal resolution from a synergy between Sentinel-1 radar and Landsat thermal data: a study case over bare soil. Remote Sensing of Environment, 211, 321-337.
  • Ansari, S., & Deshmukh, R. R. (2017). Estimation of Soil Moisture Content: A Review. International Journal of Theoretical and Applied Mechanics, 12(3), 571–577.
  • Avdan, U., &Jovanovska, G. (2016). Algorithm for Automated Mapping of Land Surface Temperature Using LANDSAT 8 Satellite Data. 2016.
  • Barrett, B.; Petropoulos, G.P. (2013). Satellite Remote Sensing of Surface Soil Moisture. In Remote Sensing of Energy Fluxes and Soil Moisture Content; CRC Press: Boca Raton, FL, USA, 2013; pp. 85–120, ISBN 978-1-4665-0578-0.
  • Bezerra, B.G.; Santos, C.A.C.; Silva, B.B.; Perez-Marin, A.M.; Bezerra, M.V.C.; Berzerra, J.R.C.; Rao, T.V.R., 2013. Estimation of soil moisture in the root-zone from remote sensing data. Rev. Bras. Cienc. Solo 2013, 37, 596–603.
  • Bittelli, M. (2011). Measuring Soil Water Content: A Review. 3861(June), 293–300.
  • Chandrasekar, K. Geo-spatial Meteorological Products for Agricultural Drought Assessment, NRSC User Interaction Meet- PPT. 2016. Available online:
  • Chauhan, S.; Srivastava, H.S. Comparative evaluation of the sensitivity of multi-polarized SAR nd optical data for various land cover classed. Int. J. Remote Sens. 2016, 4, 01–14.
  • Department of the Interior U.S. Geological Survey. (2016). Landsat 8 Data Users Handbook. Nasa, 8(June),97.
  • Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., Bargellini, P. (2012). Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services. In Remote Sensing of Environment (Vol.120).
  • Esetlili, M. T., &Kurucu, Y. (2016). Determination of Main Soil Properties Using Synthetic Aperture Radar. Fresenius Environmental Bulletin, 25(1), 23–36.
  • Filipponi, F. (2019). Sentinel-1 GRD Preprocessing Workflow. Proceedings, 18(1), 11.
  • Gao, Q.; Zribi, M.; Escorihuela, M.J.; Baghdadi, N. Synergetic use of sentinel-1 and sentinel-2 data for soil moisture mapping at 100 m resolution. Sensors, 2017.
  • Karjalainen, M.; Kaartinen, H.; Hyyppä, J.; Laurila, H.; Kuittinen, R., 2004. The Use of ENVISAT Alternating Ploarization SAR Images in Agricultureal Monitoring in Compatison with RADARSAT-1 SAR Images. In Proceedings of the ISPRS Congress, Istanbul, Turkey, 12–23 July 2004.
  • Mirsoleimani, H. R., Sahebi, M. R., Baghdadi, N., & El Hajj, M. (2019). Bare soil surface moisture retrieval from sentinel-1 SAR data based on the calibrated IEM and dubois models using neural networks. Sensors (Switzerland), 19(14), 1–12.
  • Moawad, B.M., 2012. Geoscience general tool package. Max-Planck Institute fürChemie, Mainz, Germany.
  • Mohamed, E.S., Ali, Abdelraouf, El-Shirbeny, Mohammed, Abutaleba, Khaled and Shaddad, Sameh M. 2019. Mapping soil moisture and their correlation with crop pattern using remotely sensed data in arid region. The Egyptian Journal of Remote Sensing and Space Sciences xxx (xxxx) xxx (Article in Press).
  • Myhre, B. E., & Shih, S. F. (1990). Using Infrared Thermometry to Estimate Soil Water Content for a Sandy Soil. 33 (October), 1479–1486.
  • Paloscia, S., Pettinato, S., Santi, E., Notarnicola, C., Pasolli, L., & Reppucci, A. (2013). Remote Sensing of Environment Soil moisture mapping using Sentinel-1 images: Algorithm and preliminary validation. Remote Sensing of Environment, 134, 234–248.
  • Petropoulos, G., Carlson, T.N., Wooster, M.J., Islam, S. (2009). A review of T-s/VI remote sensing-based methods for the retrieval of land surface energy fluxes and soil surface moisture Prog. Phys. Geography, 33 (2009), pp. 224-250.
  • Prakash Mohan, M M, Rajitha, K. & Varma, Murari R R (2019). Integration of soil moisture as an auxiliary parameter for the anchor pixel selection process in SEBAL using Landsat 8 and Sentinel - 1A images, International Journal of Remote Sensing, 41:3, 1214-123.
  • Prakash, R.; Singh, D.; Pathak, N.P. A fusion approach to retrieve soil moisture with SAR and optical data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2012, 5, 196–206.
  • Rahimzadeh-Bajgiran, P., Berg, A.A., Champagne, C., Omasa, K., 2013. Estimation of soil moisture using optical/thermal infrared remote sensing in the Canadian Prairies. ISPRS J. Photogrammetry Remote Sens, 83, 94–103.
  • Rawat, Kishan Singh, Singh, Sudhir Kumar & Ray, Ram Lakhan (2019). An integrated approach to estimate surface soil moisture in agricultural lands, Geocarto International.
  • Reza Attarzadeh, Jalal Amini, Claudia Notarnicola and Felix Greifeneder, 2018. Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at Plot Scale. Remote Sensing, 10, 2018, pp 2 -18.
  • Sahebi, M.R.; Angles, J.; Bonn, F. (2002). A comparison of multi-polarization and multi angular approaches for estimating bare soil surface roughness from space-borne radar data. Can. J. Remote Sens, 2002, 28, 641–652.
  • Sekertekin, Aliihsan, Marangoz, Aycan Murat, and Abdikan, Saygın, 2016. Soil Moisture Mapping Using Sentinel-1A Synthetic Aperture Radar Data. International Journal of Environment and Geoinformatics 5(2): 178- 188 (2016).
  • Ulaby, F. T., Moore, R. K., and Fung, A. K. (1986). Microwave remote sensing: Active and passive; from theory to applications (Vol. Volume III, pp. 1065–2162).
  • Wagner, W., Lemoine, G., Borgeaud, M., & Rott, H. (1999b). A study of vegetation cover effects on ERS scatterometer data. IEEE Transactions on Geoscience and Remote Sensing, 37(2), 938–948.
  • Wagner, W., Lemoine, G., & Rott, H. (1999c). A method for estimating soil moisture from ERS scatterometer and soil data. Remote Sensing of Environment, 70, 191–207.
  • Wang, J.R. (1980). The dielectric properties of soil-water mixtures at microwave frequencies. Radio Sci, 1980, 15, 977–985.
  • Weng, Qihao, Lu, Dengsheng, and Schubring, Jacquelyn (2004). Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment 89(4):467–483, February 2004.
  • Yadav, Vijay Pratap, Rajendra Prasad, Bala, Ruchi, Vishwakarma, Ajeet kumar, 2019. Estimation of soil moisture through water cloud model using sentinel -1A SAR data. Proceeding URSI AP-RASC 2019, New Delhi, India, 09 - 15 March 2019.
  • Zeng, Y., Feng, Z., Xiang, N., 2004. Assessment of soil moisture using Landsat ETM+ Temperature/vegetation index in semiarid environment. IEEE, 4306–4309.
  • Zhuo, L.; Han, D. The relevance of soil moisture by remote sensing and hydrological modelling. Procedia Eng. 2016, 154, 1368–1375.
  • Zribi, M., Baghdadi, N., Holah, N., and 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.
There are 38 citations in total.

Details

Primary Language English
Subjects Photogrammetry and Remote Sensing
Journal Section Research Articles
Authors

Sachin Sutariya 0000-0001-7912-9824

Ankur Hirapara This is me 0000-0002-1963-7197

Momin Meherbanali This is me 0000-0002-0011-8565

M.k. Tiwari This is me 0000-0003-0385-4426

Vijay Sıngh This is me

Manik Kalubarme 0000-0002-0977-7671

Publication Date March 7, 2021
Published in Issue Year 2021 Volume: 8 Issue: 1

Cite

APA Sutariya, S., Hirapara, A., Meherbanali, M., Tiwari, M., et al. (2021). Soil Moisture Estimation using Sentinel-1 SAR Data and Land Surface Temperature in Panchmahal District, Gujarat State. International Journal of Environment and Geoinformatics, 8(1), 65-77. https://doi.org/10.30897/ijegeo.777434

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