Soil Moisture Estimation Method Using Remote Sensing Technique by Landsat Satellite

Document Type : Research Article

Authors

• S.F. Mousavizadeh ¹
• H. Ansari ²
• A. R. Faridhoseini ³

¹ Ph.D. Candidate, Professor and Associate Professor, Department of Water Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran

² Professor, Department of Water Science and Engineering, Ferdowsi University of Mashhad

³ Associate Professor, Department of Water Science and Engineering, Ferdowsi University of Mashhad

10.22067/jsw.2021.71524.1070

Abstract

Introduction: In the last decade, satellite-based methods, including remote sensing and microwave methods, have been used in many studies to detect soil surface moisture regionally. Thermal remote sensing method is quite effective for checking moisture for bare soil but shows poor correlation for vegetated surfaces. In addition, there is a widespread use of this method in the presence of temperature differences during the day. Satellite imagery enables the ability to measure humidity according to the environmental conditions at the surface. Thus, compared to field measurements, remote sensing techniques are promising because they are capable of spatial measurements at a relatively low cost. Water supply is one of the main causes of evapotranspiration, which can affect it. Soil moisture can be considered as the most direct and important variable describing drought and is the main parameter describing water circulation and energy exchange between the surface and the atmosphere. Scale reduction methods for soil moisture can be divided into three main groups including satellite-based method, GIS data and model-based methods. The same methods have been used extensively in monitoring soil moisture for different spectral patterns at different wavelengths, from visible to microwave remote sensing data. Spectral reflectance decreases with increasing soil moisture in the visible and near-infrared (NIR) range. Therefore, these methods can be used to estimate soil moisture using satellite data for water budgeting and other meteorological and agricultural applications.
Materials and Methods: In this study, using the information provided by Zaki (2013), the measured humidity by the sensor was compared with the humidity obtained from the satellite. The soil moisture were measured in 16 points from an area of 13 hectares from Neyshabour plain of Khorasan Razavi province. The novelty of this study is to provide a simple method for using Landsat 7 satellite imagery to estimate the surface moisture of areas of the Earth to eliminate field sampling and optimal use for agriculture. One of the advantages of this method is the reduction of information obtained from the field as input values for crop modeling that can be used to estimate crop yield, so the moisture measured during the winter wheat crop period from November 2012 to March 2013 was used.
Results and Discussion: The placement of band numbers 3 and 4 opposite each other to calculate M, the line equation was fitted. Since satellite imagery is not performed daily by satellite, six images were extracted during the growing season. On November 12, which is actually 12 days after planting, the plant is entering the germination stage and the soil is mostly bare. Because the satellite does not receive enough reflected green light, the accuracy of the image in measuring soil moisture decreases, but after the plant grows, the green light is reflected and the amount of digital digit of band 4 is affected, as a result, the amount of moisture in the plant leaves and stem is involved in measuring soil moisture, which is consistent with the results obtained by Petropoulos et al.
Conclusion: In general, the results of this study showed that the simple and efficient Red-NIR spatial geometry model has a great ability to estimate soil surface moisture in favorable weather conditions and this method can be used for plant modeling as input data.

Keywords

• Mathematical calculations
• Red-NIR spatial geometry model
• Spectral reflection
• Soil water

20.1001.1.20084757.1400.35.6.7.2

Main Subjects

• Irrigation

---

References

• Pasolli L., Notarnicola C., Bertoldi G., Della Chiesa S., Niedrist G., Bruzzone L., et al. 2014. Soil moisture monitoring in mountain areas by using high-resolution SAR images: Results from a feasibility study. European journal soil science 65(6): 852–64.
• Verstraeten WW., Veroustraete F., Van Der Sande CJ., Grootaers I., and Feyen J. 2006. Soil moisture retrieval using thermal inertia, determined with visible and thermal spaceborne data, validated for European forests. Remote Sensing of Environment 101(3): 299–314.
• Wagner W., Naeimi V., Scipal K., Jeu R., and Martínez-Fernández J. 2007. Soil moisture from operational meteorological satellites. Hydrogeol Journal 15(1):121–31.
• Peng J., Loew A., Merlin O., and Verhoest NEC. 2017. A review of spatial downscaling of satellite remotely sensed soil moisture. Reviews of Geophysics 55(2): 341–66.
• Verhoest NEC., Lievens H., Wagner W., Álvarez-Mozos J., Moran MS., and Mattia F. 2008. On the soil roughness parameterization problem in soil moisture retrieval of bare surfaces from synthetic aperture radar. Sensors 8(7): 4213–48.
• Li B., Ti C., Zhao Y., and Yan X. 2016. Estimating soil moisture with Landsat data and its application in extracting the spatial distribution of winter flooded paddies. Remote Sensing 8(1).
• Bao Y., Lin L., Wu S., Kwal Deng KA., and Petropoulos GP. 2018. Surface soil moisture retrievals over partially vegetated areas from the synergy of Sentinel-1 and Landsat 8 data using a modified water-cloud model. International Journal of Applied Earth Observation and Geoinformation72: 76–85.
• Mohamed ES., Ali A., El-Shirbeny M., Abutaleb K., and Shaddad SM. 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 23(3) 347-353.
• Montaldo N., Fois L., and Corona R. 2021. Soil moisture estimates in a grass field using sentinel-1 radar data and an assimilation approach. Remote Sensing 13(16): 1–25.
• Rabiei S., Jalilvand E., and Tajrishy M. 2021. A method to estimate surface soil moisture and map the irrigated cropland area using sentinel-1 and sentinel-2 data. Sustainability (Switzerland), 13(20).
• Zaki M. Estimation of groundwater recharge from irrigated fields using zero flux method. Msc thesis. 2013. (In Persian with English abstract)
• Carlson TN., and Petropoulos GP. 2019. A new method for estimating of evapotranspiration and surface soil moisture from optical and thermal infrared measurements: the simplified triangle. International journal remote sensing 40(20): 7716–29.
• Tuttle SE., and Salvucci GD. 2014. A new approach for validating satellite estimates of soil moisture using large-scale precipitation: Comparing AMSR-E products. Remote Sensing of Environment 142: 207–22.
• Attema EPW., and Ulaby FT. 2016. Prevalence of anemia in patients with diabetic kidney disease. Abstract book – 29th European Diabetic Nephropathy Study Group Meeting 13(2): 357–64.
• Kumar K., Suryanarayana Rao HP., and Arora MK. 2015. Study of water cloud model vegetation descriptors in estimating soil moisture in Solani catchment. Hydrological Processes 29(9): 2137–48.
• Park SE., Jung YT., Cho JH., Moon H., and Han S hoon. 2019. Theoretical evaluation of water cloud model vegetation parameters. Remote Sens 11(8).
• de Wit A., Boogaard H., Fumagalli D., Janssen S., Knapen R., and van Kraalingen D. 2019. 25 years of the WOFOST cropping systems model. Agricultural Systems 168: 154–67.
• Zhan ZM., Qin QM., Ghulan A., and Wang DD. 2007. NIR-red spectral space based new method for soil moisture monitoring. Science in China, Series D: Earth Sciences 50(2): 283–9.
• Foroughi H., Naseri A., Nasab SB., Hamzeh S., and Scott B. 2019. Presenting a New Method for Soil-moisture Estimation Using Optical Remotely-sensed Imagery. Iranian journal of soil and water research 50(3):641–52. (In Persian with English abstract)
• Babaeian E., Sadeghi M., Franz TE., Jones S., and Tuller M. 2018. Mapping soil moisture with the OPtical TRApezoid Model (OPTRAM) based on long-term MODIS observations. Remote Sensing of Environment 211: 425–40.
• Sadeghi M., Babaeian E., Tuller M., and Jones SB. 2017. The optical trapezoid model: A novel approach to remote sensing of soil moisture applied to Sentinel-2 and Landsat-8 observations. Remote Sensing of Environment 198: 52–68.
• Richardson AJ., and Wiegand CL. 1977. Distinguishing vegetation from soil background information. Photogrammetric Engineering and Remote Sensing 43(12): 1541–52.
• Welikhe P., Quansah JE., Fall S., and McElhenney W. 2017. Estimation of Soil Moisture Percentage Using LANDSAT-based Moisture Stress Index. Journal of Remote Sensing & GIS 06(02).
• Mousavizadeh SF., Honar T., and Ahmadi SH. 2016. Assessment of the AquaCrop Model for simulating canola under different irrigation managements in a semiarid area. International Journal of Plant Production 10(4): 425–46.
• Petropoulos GP., Ireland G., Srivastava PK., and Ioannou-Katidis P. 2014. An appraisal of the accuracy of operational soil moisture estimates from SMOS MIRAS using validated in situ observations acquired in a Mediterranean environment. International Journal of Remote Sensing 35(13): 5239-5250.
• Ihlen V., USGS. 2019. Landsat 7 (L7) Data Users Handbook. USGS Landsat User Serv .7:151.
• Geng SHU., and Supit I. 1986. Agricultural and Forest Meteorology 36: 363–76.