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

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Shahmordadi Salah; Ghafarian Malmiri Hamid Reza; AMINI MOHAMMAD

Title

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

Author(s)

Shahmordadi Salah | Ghafarian Malmiri Hamid Reza | AMINI MOHAMMAD | Issue Writer Certificate

Keywords

Remote sensingQ2

Soil surface moistureQ1

Land surface temperature (LST)Q1

Normalized difference vegetation index (NDVI)Q1

West of IranQ1

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 the 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.

No record.

APA: Copy

Shahmordadi, Salah, Ghafarian Malmiri, Hamid Reza, & AMINI, MOHAMMAD. (2021). Extraction of soil moisture index (TVDI) using a scatter diagram temperature / vegetation and MODIS images. JOURNAL OF RS AND GIS FOR NATURAL RESOURCES (JOURNAL OF APPLIED RS AND GIS TECHNIQUES IN NATURAL RESOURCE SCIENCE), 12(1 (42) ), 38-62. SID. https://sid.ir/paper/400387/en

Vancouver: Copy

Shahmordadi Salah, Ghafarian Malmiri Hamid Reza, AMINI MOHAMMAD. Extraction of soil moisture index (TVDI) using a scatter diagram temperature / vegetation and MODIS images. JOURNAL OF RS AND GIS FOR NATURAL RESOURCES (JOURNAL OF APPLIED RS AND GIS TECHNIQUES IN NATURAL RESOURCE SCIENCE)[Internet]. 2021;12(1 (42) ):38-62. Available from: https://sid.ir/paper/400387/en

IEEE: Copy

Salah Shahmordadi, Hamid Reza Ghafarian Malmiri, and MOHAMMAD AMINI, “Extraction of soil moisture index (TVDI) using a scatter diagram temperature / vegetation and MODIS images,” JOURNAL OF RS AND GIS FOR NATURAL RESOURCES (JOURNAL OF APPLIED RS AND GIS TECHNIQUES IN NATURAL RESOURCE SCIENCE), vol. 12, no. 1 (42) , pp. 38–62, 2021, [Online]. Available: https://sid.ir/paper/400387/en

Scientific Information Database

ISSN: 2588-4824

Journal Paper

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