Investigation of Soil Surface Moisture in Ardabil City Using Landsat 8 and Sentile 1 satellite Data

Subject Areas : Application of computer in water and soil issues

Sayyad Asghari Saraskanrod ¹ (Professor, Department of Physical Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili, Iran.)
Fariba Esfandayari Darabad ² (Professor, Department of Physical Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili, Iran.)
Elham Mollanouri ³ (Master student of Mohaghegh Ardabili University, Ardabil, Iran.)
Shiva Safary ⁴ (Master student of Mohaghegh Ardabili University, Ardabil, Iran.)

Received: 2021-11-20 Accepted : 2022-04-21 Published : 2022-06-22

Keywords: support vector regression, LST, NDVI, TOTRAM,

Abstract :

Background and Aim: Surface soil moisture is an important variable in nature's water cycle and can be affected by various factors, including temperature and soil characteristics. The use of ground sensors for measuring moisture can lead to spending time and expense and inappropriate distribution of samples on large scales. Therefore, Remote sensing observations can be an important tool in estimating soil moisture. The present study aims to use the TOTRAM model using Landsat 8 images and the SVR method using Sentile 1 images to estimate soil moisture.Methods: In the present study, two TOTRAM methods based on pixel distribution in LST- VI space and the SVR method were used to extract soil moisture using the SAR technique and Sentinel 1 data. To implement the TOTRAM method, Landsat 8 images related to 4/29/1398 and 5/30/1398 are downloaded and after extracting NDVI and LST maps, The correlation between the dependent variable of moisture and independent temperature variables and vegetation variables has been investigated using Geographically weighted regression (GWR). To implement the SVR method after acquiring Sentinel 1 images related to 31\/05\/1398 and 27\/04\/1398, Soil Moisture Data Product FLDAS and 500 meters product of Modis Satellite (MCD12q1) were called to classify land cover in the Google Earth Engine system, and maps related to soil moisture were extracted. After extracting the moisture maps the distribution of moisture using the local Moran index has been investigated. By defining this index, positive values ​​for this index represent the cluster of distribution.Results: Examination of the soil moisture map obtained by the SVR method showed the concentration of moisture in areas with vegetation and water and the change in moisture status from July to August was visible. The humidity pattern has shown the reflection of the precipitation pattern so that maximum precipitation and humidity were observed in April and in summer both precipitation and humidity components decreased. Examination of the TOTRAM method and application of the GWR method has shown a complete correlation between NDVI LST and moisture. However, the correlation between LST and humidity with B (values) and standard error (SE) of 0.995 and zero corresponding to July and 0.981 and zero corresponding to August showed the highest correlation with vegetation variable with moisture dependence parameter, which this correlation In August, with increasing the coefficient of determination of R2 to 0.997 and a significant decrease of NDVI to the value of 0.415 in July, it has increased much more. Application of Moran local index with values ​​less than 0.05 for p-value and positive values ​​for z and near positive number 1 for Moran index showed the cluster distribution of moisture variable.Conclusion: The results of TOTRAM and SVR methods showed the dependence of soil moisture status on conditions and cluster moisture distribution. According to the correlation coefficients of geographical regression, there is a greater correlation between temperature and humidity variables, especially in August, due to the decrease in vegetation density. The results of the SVR algorithm maps showed that in areas with the presence of vegetation, especially dense vegetation, we see an increase and with increasing temperature, we see a decrease in humidity. Also, the coordination of moisture patterns of the SVR algorithm and precipitation showed a direct relationship between moisture and precipitation. Considering that the SVR method uses parameters such as radar scattering intensity and land cover classification, as well as the use of Sentinel 1 radar images by this algorithm, more accurate results can be expected from this algorithm.Keywords: LST, NDVI, Support vector regression, TOTRAMBackground and Aim: Surface soil moisture is an important variable in nature's water cycle and can be affected by various factors, including temperature and soil characteristics. The use of ground sensors for measuring moisture can lead to spending time and expense and inappropriate distribution of samples on large scales. Therefore, Remote sensing observations can be an important tool in estimating soil moisture. The present study aims to use the TOTRAM model using Landsat 8 images and the SVR method using Sentile 1 images to estimate soil moisture.Methods: In the present study, two TOTRAM methods based on pixel distribution in LST- VI space and the SVR method were used to extract soil moisture using the SAR technique and Sentinel 1 data. To implement the TOTRAM method, Landsat 8 images related to 4/29/1398 and 5/30/1398 are downloaded and after extracting NDVI and LST maps, The correlation between the dependent variable of moisture and independent temperature variables and vegetation variables has been investigated using Geographically weighted regression (GWR). To implement the SVR method after acquiring Sentinel 1 images related to 31\/05\/1398 and 27\/04\/1398, Soil Moisture Data Product FLDAS and 500 meters product of Modis Satellite (MCD12q1) were called to classify land cover in the Google Earth Engine system, and maps related to soil moisture were extracted. After extracting the moisture maps the distribution of moisture using the local Moran index has been investigated. By defining this index, positive values ​​for this index represent the cluster of distribution.Results: Examination of the soil moisture map obtained by the SVR method showed the concentration of moisture in areas with vegetation and water and the change in moisture status from July to August was visible. The humidity pattern has shown the reflection of the precipitation pattern so that maximum precipitation and humidity were observed in April and in summer both precipitation and humidity components decreased. Examination of the TOTRAM method and application of the GWR method has shown a complete correlation between NDVI LST and moisture. However, the correlation between LST and humidity with B (values) and standard error (SE) of 0.995 and zero corresponding to July and 0.981 and zero corresponding to August showed the highest correlation with vegetation variable with moisture dependence parameter, which this correlation In August, with increasing the coefficient of determination of R2 to 0.997 and a significant decrease of NDVI to the value of 0.415 in July, it has increased much more. Application of Moran local index with values ​​less than 0.05 for p-value and positive values ​​for z and near positive number 1 for Moran index showed the cluster distribution of moisture variable.Conclusion: The results of TOTRAM and SVR methods showed the dependence of soil moisture status on conditions and cluster moisture distribution. According to the correlation coefficients of geographical regression, there is a greater correlation between temperature and humidity variables, especially in August, due to the decrease in vegetation density. The results of the SVR algorithm maps showed that in areas with the presence of vegetation, especially dense vegetation, we see an increase and with increasing temperature, we see a decrease in humidity. Also, the coordination of moisture patterns of the SVR algorithm and precipitation showed a direct relationship between moisture and precipitation. Considering that the SVR method uses parameters such as radar scattering intensity and land cover classification, as well as the use of Sentinel 1 radar images by this algorithm, more accurate results can be expected from this algorithm.

References:

Adab, H. 2017. Estimation of the Instantaneous Soil Surface Moisture Content in Cold Seasons by using Optical and Thermal Remote Sensing Data under Clear Sky. Water and Soil Sci, 21 (2): 175-191. [in Persian]

Ambrosone, m., Matese, a., Gennaro, s., Gioli, b., Tudoroiub, m., Genesio, l., Miglietta, f., Baronti, s., Maienza, a., Ungaro, f. and Toscano, p. 2020. Retrieving soil moisture in rainfed and irrigated fields using Sentinel-2 observations and a modified OPTRAM approach, journal Int J Appl Earth Obs Geoinformation, 102113: 1-10.

Bagheri, K., Bagheri, M., Hosein zadeh, A. A. 2019. Estimation of soil moisture using optical, thermal and radar Remote Sensing (Case Study: South of Tehran). Iran-Watershed Management Science & Engineering, 13 (47): 63-74. [in Persian]

Behbahani, S., Noroozi Aghdam, E., Rahimi Khoob, A., Aghighi, H. 2010. Assessing Surface Soil Moisture in Arid and Semiarid Rangelands Using NDVI and Meteorological Parameters. Iran-Water Resources Research, 5(3): 39-47. [in Persian]

Bruzzone, l. and Melgani, f. 2005. Robust multiple estimator system for the analysis of biophysical parameters from remotely sensed data,” IEEE Trans. Geosci. Remote Sen, 43(1): 159–174.

Casamitjana, m., Madroñero, m., Bernal-Riobo, J. and Varga d. 2020. Soil Moisture Analysis by Means of Multispectral Images According to Land Use and Spatial Resolution on Andosols in the Colombian Andes, applied science, doi:10.3390/app10165540

Carlson, t., Gillies, t. and Perry, e. 1994. A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation cover, journal Remote Sensing Reviews, 9: 161-173.

Fathololoumi, S., Vaezi, A., Alavipanah, S., Ghorbani, A. 2020 . Modeling the Influence of Biophysical Properties and Surface Topography on the Spatial Distribution of Soil Moisture in the Summer: A Case Study of Balikhli-Chay Watershed. Iranian journal of Ecohydrology, 7(3): 563-581. doi: 10.22059/ije.2020.299783.1307. [in Persian]

Feizizadeh, B., Didehban, K., Gholamnia, K. 2016. Extraction of Land Surface Temperature (LST) based on Landsat Satellite Images and Split Window Algorithm Study area: Mahabad Catchment. Scientific- Research Quarterly of Geographical Data (SEPEHR), 25(98): 171-181. doi: 10.22131/sepehr.2016.22145. [in Persian]

Greifeneder, f., Khamala, e., Sendabo, d., Wagner, w., Zebisch, m. and Farah h. 2018. Detection of soil moisture anomalies based on Sentinel-1, journal Physics and Chemistry of TheEarth, 1-24. https://www.researchgate.net/publication/329333037.

Harti, e., Lhissou, a., Chokmani, r., Ouzemou, k., Hassouna, j. and Bachaoui, m. 2016. Spatiotemporal monitoring of soil salinization in irrigated Tadla Plain (Morocco) using satellite spectral indices. International Journal of Applied Earth Observation and Geoinformation. 50: 64-73.

Hosseini Chamani, F., Farrokhian Firouzi, A. 2019. Pedotransfer Function (PTF) for Estimation Soil moisture using NDVI, land surface temperature (LST) and normalized moisture (NDMI) indices. Journal of Water and Soil Conservation, 26(4), 239-254. doi: 10.22069/jwsc.2019.15306.3053.[in Persian]

Khanmohammadi, F., Homaee, M., Noroozi, A. 2015. Soil moisture estimating with NDVI and land surface temperature and normalized moisture index using MODIS images. Journal of Water and Soil Resources Conservation, 4(2), 37-45. [in Persian]

Koohi, S., Azizian, A., Brocca, L. 2019. Calibration of VIC-3L Hydrological Model using Satellite-Based Surface Soil Moisture Datasets. Iran-Water Resources Research, 15(4): 55-67. [in Persian]

Masoodian, S., Rayatpishe, F., Keykhosravi Kiani, M. 2014. Introducing the TRMM and Asfezariprecipitation database: A comparative study. Iranian Journal of Geophysics, 8(4) :31-51. [in Persian]

McNally, a., Arsenault, k., Kumar, s., Shukla, s., Peterson, p., Wang, s., Funk, c., Peters-Lidard, c. and Verdin, v. 2017. Data Descriptor: A land data assimilation system for sub-Saharan Africa food and water security applications, SCIENTIFIC DATA. DOI: 10.1038/sdata.2017.12.

Mobasheri, m. and Amani, m. 2016. Soil moisture content assessment based on Landsat 8 red, near-infrared, and thermal channels, Journal. Appl. Remote Sens, 10(2): 1-15.

Nadian, M., Mirzaei, R., Soltani Mohammadi, S. 2018. Application of Moran'sI Autocorrelation in Spatial-Temporal Analysis of PM2.5 Pollutant (A case Study: Tehran City). Journal of Environmental Health Engineering, 5 (3) :197-213. [in Persian]

Pandey, r., Goswami, s., Sarup, j. and Matin sh. 2020. The thermal–optical trapezoid model‑based soil moisture estimation using Landsat‑8 data, journal Modeling Earth Systems and Environment, 1-9. https://doi.org/10.1007/s40808-020-00975-8.

Pasolli, l., Notarnicola, c., Bertoldi, g., Bruzzone, l., Remelgado, r., Greifeneder, f., Niedrist, g., Chiesa, s., Tappeiner, u. and Zebisch m. 2019. Estimation of Soil Moisture in Mountain Areas Using SVR Technique Applied to Multiscale Active Radar Images at C-Band, Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(1): 261-283.

Peng, w.,Wang J., Zhang, j. and Zhang y. 2020. Soil moisture estimation in the transition zone from the Chengdu Plain region to the Longmen Mountains by field measurements and LANDSAT 8 OLI/TIRS-derived indices, Arabian Journal of Geosciences. 1-15, https://doi.org/10.1007/s12517-020-5152-z.

Prashant, K. S. George, P. P. Yann, H. K. 2016. Satellite Soil Moisture Retrieval Techniques and Applications, 411 pp.

Ranjbar, S., Akhoondzadeh, M. 2020. Volumetric soil moisture estimation using Sentinel 1 and 2 satellite images. Journal of Geospatial Information Technology, 7 (4) :215-232. [in Persian]

Sari Sarraf, B., Naghizadeh, H., Rasouly, A., Jahanbakhsh, S., Babaeyan, I. 2019. Modeling and spatial analysis of snow depth in Northern Iran based on database from European Centre for Medium-Range Weather Forecasts (ECMWF). Physical Geography Research Quarterly, 51(4): 651-671. doi: 10.22059/jphgr.2019.268047.1007289. [in Persian]

Sadeghi, m., Babaeian, e., Tuller, m. and Jones, s. 2017. The optical trapezoid model: A novel approach to remote sensing of soil moisture applied to Sentinel-2 and Landsat-8 observations, Journal Remote Sensing of Environment http://dx.doi.org/10.1016/j.rse.2017.05.041 t, 52-68.

Sheikh, h., Parizadi, t., Rezaei, m., Sajadi, m. 2012. Analysis and determination of physical form in Isfahan using modeling and Moran, Urban planning, 3(9): 117-174. [in Persian]

Shafian, s. and maas, s. 2015. Improvement of the Trapezoid Method Using Raw Landsat Image Digital Count Data for Soil Moisture Estimation in the Texas (USA) High Plains, Sensors, doi:10.3390/s150101925, 1925-1944.

SUTARIYA, s., HIRAPARA, a., MEHERBANALI, m., TIWARI, m., SINGH, v. and KALUBARME, m. 2021. Soil Moisture Estimation using Sentinel-1 SAR data and Land Surface Temperature in Panchmahal district, Gujarat State, 8(1): 2148-9173.

Yousefzadeh, A., Zeynali, B., Valizadeh Kamran, K., Asghari Sarskanrood, S. 2019. The Estimation of Soil Moisture Using the New Visible Trapezoidal Model for Simineh Basin Using Images of Landsat 8 Satellite. Hydrogeomorphology, 6(18): 181-205. [in Persian]

Yadav, s., Singh, p., Pal Singh Jadaun, s., Kumar, n. and Upadhyay, r. 2019. SOIL moisture analysis of lalitpur district uttar pradesh india using landsat and sentinel data The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 3(6): 1-7.