Extraction of soil moisture index (TVDI) using a scatter diagram temperature/vegetation and MODIS images

Subject Areas : Agriculture, rangeland, watershed and forestry

Salah Shahmoradi ¹ , Hamid Reza Ghafarian Malamiri ² , Mohammad Amini ³

1 - MSc. Student of Remote Sensing and Geographic Information System, Faculty of Humanities, Yazd University, Iran
2 - Assistant Professor, Department of Geography, Faculty of Humanities, Yazd University, Iran
3 - PhD Student of Remote Sensing and Geographical Information System, Department of Remote Sensing, Shahid Beheshti University, Iran

Received: 2019-04-16 Accepted : 2020-12-16 Published : 2021-03-21

Keywords: Land surface temperature (LST), remote sensing, Normalized difference vegetation index (NDVI), West of Iran, Soil surface moisture,

Abstract :

Background and Objective Soil moisture is an important parameter in controlling many processes of the climate system, one of the basic parameters of the environment and its direct impact on the plant, animal and microorganisms, its importance in the global cycle of water, energy and carbon, the energy exchange between air and soil is known for its natural water cycle (especially in the distribution of rain between surface runoff and infiltration) and the management of water and soil resources. Soil moisture plays an important role in the interactive processes between the atmosphere and the earth and global climate change. Triangular and trapezoidal methods combining thermal and visible data are the most commonly used methods for determining the amount of soil surface moisture. The aim of this study is to estimate the surface moisture of the soil (TVDI), by the triangular method in the south of West Azerbaijan province using land temperature index (LST) and vegetation index (NDVI), during 2010, 2014 and 2018. Materials and Methods The present study using MODIS timing series images, NDVI index and LST index, to estimate the surface moisture index (Temperature–Vegetation Dryness Index, TVDI), in three time periods including; the first time period from 1 January 2010 to 30 December 2010 and the second period is from 1 January 2014 to 30 December 2014 and the third period is from 1 January 2018 to 30 December 2018. During each period, 12 images were used on the 15th day of each month. Also, surface moisture was estimated by two methods, one was to establish a high regression relationship and remove the minimum temperature, and the second method was to establish a high and low regression relationship of the pixels. To evaluate the accuracy of these two methods, a regression correlation between the results of these methods with the soil surface moisture content of the Agricultural Jihad (30 points) at a depth of 5 to 15 cm was used. The reason for choosing these three years is due to the difference in high rainfall in some months of the studied years. This study was conducted in the south of the province of West Azerbaijan, which is part of the western region of Iran. Results and Discussion The evaporative triangle diagram consisting of the vegetation index and the surface temperature of the earth in 2010 from January to December month has seen many temperature changes. These same changes in the Earth's surface temperature have caused that the graphs have many changes. During the 2010 year, according to the chart, the maximum temperature was August and the minimum was January, and the maximum vegetation was May and the minimum was December. In 2014, the maximum temperature in August and the minimum in January and the maximum vegetation in May and the minimum in January and also this year were relatively warmer and drier than in 2010. The evaporative triangle chart in 2018 is rainier than the other two years studied, and the amount of vegetation and according to the graphs in this year, the maximum temperature in July and the minimum is January and the maximum vegetation is May and the minimum in January. The surface moisture level of the soil in 2010 for the western region of Iran, which is the maximum moisture level in May and the minimum in August. In most of the 2010 moisture index maps, the maximum humidity in the west and the lowest in the South of this region. The results of the moisture index maps in 2014 this year have been relatively drier than in other years studied. In 2014 has little rainfall and vegetation. Humidity changes this year are lower than in 2010. The maximum and minimum humidity in 2014 was between 0 and 0.6. The maximum humidity is June and the minimum is August. The TVDI moisture index maps for 2018 have had more moisture indicators this year than in the other two years. In 2018, heavy rains caused the vegetation to increase and the ground temperature to decrease, and this has led to an increase in the moisture index compared to 2010 and 2014. In 2018, the vegetation reached 0.89. But in other years it has been studied up to 0.7. This year, the high humidity is in May and the lowest in August. The maximum humidity during this year is in the west and the lower is in the south. The results of the TVDI index for 2010, 2014 and 2018, using the second method, the general results of this method are similar to the first method. Based on the results obtained from the accuracy of both methods, we conclude that the accuracy of the first method is better and generally simpler than the second method. In 2018, in May, according to the first method, the amount of R2 = 0.67, and also according to the second method, the amount of R2 = 0.41. Conclusion Estimation of surface soil moisture is essential for optimal management of water and soil resources. Surface soil moisture is an important variable in the water cycle of nature, which plays an important role in the global balance of water and energy through its impact on hydrological, ecological and meteorological processes. Examination of the two methods used indicates that the first method, which was also used in this research in general, has higher accuracy than the terrestrial fields due to the results of image accuracy.  In 2010, the months of May and August, according to the first method are R2 = 0.61 and 0.57. In 2010, the amount of R2 according to terrestrial data and the use of the second method in May and August are R2 = 0.43 and 0.47. Also, in 2018, the value of R2 using the first method in May is 0.66. In 2018, the value of R2 using the second method in May is 0.41. The results of the soil surface moisture index, in this study, showed that this model is able to estimate the amount of soil moisture in large geographical areas with acceptable accuracy. http://dorl.net/dor/20.1001.1.26767082.1400.12.1.3.4

References:

Alkhaier F, Su Z, Flerchinger G. 2012. Reconnoitering the effect of shallow groundwater on land surface temperature and surface energy balance using MODIS and SEBS. Hydrology and Earth System Sciences, 16(7): 1833-1844. doi:https://doi.org/10.5194/hess-16-1833-2012.

Babaeian E, Homayi M, Nowruz A. 2013. Deriving and validating point spectrotransfer functions in VIS-NIR-SWIR range to estimate soil water retention. Journal of Water and Soil Resources Conservation, 2(3): 27-41. (In Persian).

Baghdadi N, Aubert M, Cerdan O, Franchistéguy L, Viel C, Eric M, Zribi M, Desprats JF. 2007. Operational mapping of soil moisture using synthetic aperture radar data: application to the Touch basin (France). Sensors, 7(10): 2458-2483. doi:https://doi.org/10.3390/s7102458.

Carlson NT, Capehart JW, Gillies RR. 1995. A new look at the simplified method for remote sensing of daily evapotranspiration. Remote Sensing of Environment, 54(2): 161-167. doi:https://doi.org/10.1016/0034-4257(95)00139-R.

Carlson TN, Gillies RR, Perry EM. 1994. A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation cover. Remote Sensing Reviews, 9(1-2): 161-173. doi:https://doi.org/10.1080/02757259409532220.

Dashtaki Victim N, Homayi M. 2013. Estimation of soil moisture curve using transfer functions. Journal of Agricultural Sciences, 10(4): 157-166. (In Persian).

Farrokhian Firoozi A, Homayi M. 2005. Establish a point transfer function to estimate the moisture curve of gypsum soils. Journal of Agricultural Engineering Research, 6(24): 129-142. (In Persian).

Gao Z, Gao W, Chang N-B. 2011. Integrating temperature vegetation dryness index (TVDI) and regional water stress index (RWSI) for drought assessment with the aid of LANDSAT TM/ETM+ images. International Journal of Applied Earth Observation and Geoinformation, 13(3): 495-503. doi:https://doi.org/10.1016/j.jag.2010.10.005.

Homaee M, Firouzi AF. 2008. Deriving point and parametric pedotransfer functions of some gypsiferous soils. Soil Research, 46(3): 219-227.

Khodaverdiloo H, Homaee M, Martinus T, Dashtaki SG. 2011. Deriving and validating pedotransfer functions for some calcareous soils. Journal of Hydrology, 399(1): 93-99. doi:https://doi.org/10.1016/j.jhydrol.2010.12.040.

Koster RD, Dirmeyer PA, Guo Z, Bonan G, Chan E, Cox P, Gordon C, Kanae S, Kowalczyk E, Lawrence D. 2004. Regions of strong coupling between soil moisture and precipitation. Science, 305(5687): 1138-1140. doi:https://doi.org/10.1126/science.1100217.

Lunt I, Hubbard S, Rubin Y. 2005. Soil moisture content estimation using ground-penetrating radar reflection data. Journal of Hydrology, 307(1): 254-269. doi:https://doi.org/10.1016/j.jhydrol.2004.10.014.

Maduako IN, Ndukwu RI, Ifeanyichukwu C, Igbokwe O. 2017. Multi-Index Soil Moisture Estimation from Satellite Earth Observations: Comparative Evaluation of the Topographic Wetness Index (TWI), the Temperature Vegetation Dryness Index (TVDI) and the Improved TVDI (iTVDI). Journal of the Indian Society of Remote Sensing, 45(4): 631-642. doi:https://doi.org/10.1007/s12524-016-0635-9.

Mekonnen DF. 2009. Satellite remote sensing for soil moisture estimation: Gumara catchment, Ethiopia. Thesis of Geo-information Science and Earth Observation, Specialisation: (Integrated Watershed Modelling and Management). WREM Department of ITC, Enschede, the Netherlands. 120 p. In. ITC.

Navabian M, Liaqat A, Homayi M. 2003. Estimation of saturated blue conductivity using transfer functions. Journal of Agricultural Engineering Research, 16: 1-12. (In Persian).

Njoku EG, Jackson TJ, Lakshmi V, Chan TK, Nghiem SV. 2003. Soil moisture retrieval from AMSR-E. IEEE transactions on Geoscience and Remote Sensing, 41(2): 215-229. doi:https://doi.org/10.1109/TGRS.2002.808243.

Patel N, Anapashsha R, Kumar S, Saha S, Dadhwal V. 2009. Assessing potential of MODIS derived temperature/vegetation condition index (TVDI) to infer soil moisture status. International Journal of Remote Sensing, 30(1): 23-39. doi:https://doi.org/10.1080/01431160802108497.

Richards L. 1949. Methods of measuring soil moisture tension. Soil science, 68(1): 95.

Sandholt I, Rasmussen K, Andersen J. 2002. A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote Sensing of Environment, 79(2): 213-224. doi:https://doi.org/10.1016/S0034-4257(01)00274-7.

Schirmbeck LW, Fontana DC, Schirmbeck J. 2018. Two approaches to calculate TVDI in humid subtropical climate of southern Brazil. Scientia Agricola, 75(2): 111-120. doi:https://doi.org/10.1590/1678-992x-2016-0315

Wang C, Chen J, Chen X, Chen J. 2019. Identification of concealed faults in a grassland area in Inner Mongolia, China, using the temperature vegetation dryness index. Journal of Earth Science, 30(4): 853-860. doi:https://doi.org/10.1007/s12583-017-0980-9.

Wang C, Qi S, Niu Z, Wang J. 2004. Evaluating soil moisture status in China using the temperature–vegetation dryness index (TVDI). Canadian Journal of Remote Sensing, 30(5): 671-679. doi:https://doi.org/10.5589/m04-029.

Wang L, Qu JJ. 2009. Satellite remote sensing applications for surface soil moisture monitoring: A review. Frontiers of Earth Science in China, 3(2): 237-247. doi:https://doi.org/10.1007/s11707-009-0023-7.

Weidong L, Baret F, Xingfa G, Qingxi T, Lanfen Z, Bing Z. 2002. Relating soil surface moisture to reflectance. Remote Sensing of Environment, 81(2): 238-246. doi:https://doi.org/10.1016/S0034-4257(01)00347-9.

Western AW, Grayson RB. 1998. The Tarrawarra data set: Soil moisture patterns, soil characteristics, and hydrological flux measurements. Water Resources Research, 34(10): 2765-2768. doi:https://doi.org/10.1029/98WR01833.

Zhang D, Tang R, Zhao W, Tang B, Wu H, Shao K, Li Z-L. 2014. Surface soil water content estimation from thermal remote sensing based on the temporal variation of land surface temperature. Remote Sensing, 6(4): 3170-3187. doi:https://doi.org/10.3390/rs6043170.