Evaluation of Soil Salinity, Actual Evapotranspiration and Soil Moisture Using Remote Sensing (Case Study: Herat Dry Region)

Subject Areas : Article frome a thesis

Fatemeh Firozi ¹ , Hossein Malakinezhad ² , Noorallah Nikpour ³

1 - Post-doctorate, Department of Natural resources, University of Yazd, Iran.
2 - Professor Department of Natural resources, University of Yazd, Iran
3 - Expert in Geomorphology, Faculty of Geography and Environmental Planning, Sistan and Baluchestan University, Zahedan, Iran.

Received: 2020-12-19 Accepted : 2021-10-19 Published : 2023-01-21

Keywords: Vegetation Index, soil salinity, soil moisture, arid and semi-arid region, actual evapotranspiration,

Abstract :

Abstract Introduction: Many factors are involved in agricultural development such as climatic conditions, soil moisture, evapotranspiration and etc. For their effectiveness, it is necessary to examine the key parameters. Timely and accurate monitoring of these committees with the help of satellite imagery is a necessity in this regard. Herat plain is one of the plains where soil salinity and lack of moisture has led to a critical situation of gardens and agricultural lands. Methods: In this research, we have tried to study soil salinity, soil moisture and actual evapotranspiration using the MODIS sensor data for the four months of February, May, August and November 2017. Findings: The first stage of vegetation survey shows 0.4 in May (growing season). While the maximum land surface temperature was recorded in August (54 ° C) and May (45.15 ° C). Then, in the next step, using the results of two indicators of vegetation and land surface temperature, the humidity of the area is investigated by TVDI. The humidity of the region was divided into five classes from zero to 0.5, which indicates the low soil moisture and dryness in the Herat plain. Finally, due to the dryness of the area and to verify the TVDI method, field soil samples were taken from different parts of Herat and especially its agricultural lands to estimate the soil salinity (EC, PH and soil moisture). The results showed that the soil moisture content of the samples at a depth of 5 cm above the ground varies between 0 and 0.3. Also, out of 12 soil samples, 6 samples have saline soils and one sample has saline-acid soils. Of course, it is also important to note that some of the agricultural lands whose soils are in the saline group are dry and left to their own devices. Finally, the study of actual evapotranspiration with the SEBAL algorithm showed that in this region, despite the lack of moisture, actual evapotranspiration is very high, especially in the hot month of August.

References:

1.       Alvipanah, S.K. 2013. Application of remote sensing in earth sciences (soil sciences). Tehran University Press. [In Persian].

2.       Behbahani, S. M. R., 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, 5:39-47.(Persian)

3.       Brown, C.E., Pezeshki, S.R., DeLaune, R.D. 2006. The effects of salinity and soil drying on nutrient uptake and growth of Spartina alterniflora in a simulated tidal system. Environmental and Experimental Botany, 58: 140-148.

4.       Carlson, T. N., Gillies, R. R., Perry, E. M. 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, 9:161-173.

5.       Carlson, T.N., Gillies, R.R., Schmugge, T.J. 1995. An Interpretation of Methodologies for Indirect Measurement of Soil-Water Content. Agricultural and Forest Meteorology, 77: 191-205.

6.       Dadrasi Sabzevar, A. M., Akhondali, F., Radmanesh, A., Noorozi, A. A. 2017. Estimation of spatial distribution of soil surface moisture in arid and semi-arid regions using remote sensing data. Iranian Journal of Range and Desert Research, 24: 370-382.

7.       Fabriiki Orang ,S., Mehrabad Pourbanab S. 2017. Evaluation of changes in physiological and biochemical traits in ancestral and evolutionary wheat species under water stress. Environmental tensions in agricultural sciences, 4(2): 802-791. [In Persian].

8.       Farooq, M., Wahid, A., Kobayashi, N., Fujita, D., Basra, S.M.A. 2009. Plant drought stress: effects, mechanisms and management. Agronomy for Sustainable Development, 29: 185–212.

9.       Foroughi, H., Naseri ,A.A.,Boroomandnasab, S., Hamzeh, S., scott, B.J. 2018. Presenting a New Method for Soil-moisture Estimation Using Optical Remotely-sensed Imagery. Iranian Journal of Soil and Water Research, 50: 641-652.(Persian)

10.   Franklin, P, R., Gardner, B., Mitchell, R. L. (2010). Physiology of crop plants. Scientific Press. 336pp.

11.   Friedl, M.A., Davis, F.W. 1994. Sources of variation in radiometric surface temperature over a tall grass prairie. Remote Sensing of Environment, 48: 1–17.

12.   GAO, Y., Long, D., Li, Z. 2008. Estimation of daily evapotranspiration from remotely sensed data under complex terrain over the upper Chao river basin in north China. International Journal of Remote Sensing, 11(1): 3295-3315.

13.   Ghassemi, F., Jakeman, A.J., Nix, H.A.1995. Salinisation Of land and water resource. Australia,  1:28-35.

14.   Goetz, S. J. 1997. Multi-sensor analysis of NDVI, surface tempera and biophysical variables at a mixed grassland site. International Journal of Remote Sensing, 18: 71-94.

15.   Habibipour, A. 2011. Predicted drought in Yazd synoptic stations using the decision tree mode. Master's thesis, Yazd University.

16.   Hao, F., Zhang, X., Ouyang, W., Skidmore, A. K., Toxopeus, A. G. 2012. Vegetation NDVI Linked to Temperature in the Upper Catchments of Yellow River. Environmental Modeling & Assessment, 17: 389-398.

17.   Huang, Y., Tian, Q,., Du ,L., Sun, S. 2013. Analysis of Spatial-Temporal Variation of Agricultural Drought and Its Response to ENSO over the Past 30 Years in the Huang-Huai-Hai Region. China Terr Atmos Ocean Sci, 24: 745-759.

18.   Lalehzari, E., Abaspour, A. R. 2017. An Assessment of the Role of Large Reservoir Dams on Local Climate using Remote Sensing Techniques (Case Study: Baft Reservoir Dam) .GEJ, 8: 35-43.

19.   Lambin, E.G., Ehrlich, D. 1996.The Surface Temperature-vegetation Index Space for Land Cover and Land-cover Change Analysis. Int. J. Remote Sensing, 17: 463-487.

20.   Lingtong, D., Song, N., Liu, K., Hou, J., Hu ,Y., Zhu, Y., Wang, L., Guo, Y. 2017. Comparison of Two Simulation Methods of the Temperature Vegetation Dryness Index (TVDI) for Drought Monitoring in Semi-Arid Regions of China. Remote sensing, 9: 2-19.

21.   Mattia, F., Satalino, G., Pauwels, V.R.N., Loew, A. 2008. Soil moisture retrieval through a merging of multi-temporal L-band SAR data and hydrologic modeling. Hydrology and Earth System Sciences, 5:3479–3515.

22.   McVicar, T.R., Jupp, D.L.B. 1998. The current and potential operational use of remote sensing to aid decisions on drought exceptional circumstances in Australia: a review. Agricultural System, 57: 399–468.

23.   Metternicht, G. I., Zinck, J. A. 1996. Modeling salining-alkalinity classes for mapping salt-affected topsoils in the semi-arid valleys of Cochabama (Bolivia). ITC Journal, 1: 125-135.

24.   Mir Yaqubzadeh, M.H., Soleimani, K., Habib Nejadroshen, M., Shahidi, K., Abbaspour, K., Akhwan, S. 2013. Determining and evaluating actual evapotranspiration using remote sensing data; A case study of Tamr watershed, Golestan. Irrigation and Water Engineering of iran, 15(1): 89-102. [In Persian].

25.   Moghimi, H., Zare Abadi Iqbal, M. 2014. Qualitative zoning of Herat aquifer (Yazd province) for agricultural uses using GIS First National Conference on Application of Advanced Spatial Analysis Models (Remote Sensing and GIS) in Land Preparation. Islamic Azad University of Yazd Iran, (Persian)

26.   Mohamed, E.S., Abdelraouf Ali, M., Shirbeny, E.L., Abutaleb, K., Sameh, M .2019. Mapping soil moisture and their correlation with crop pattern usingremotely sensed data in arid region. The Egyptian Journal of Remote Sensing and Space Sciences, doi.org/10.1016/j.ejrs.2019.04.003.

27.   Moran, M. S., Hymer, D. C., Qi, J., Sano, E. E. 2000. Soil moisture evaluation using multi-temporal synthetic aperture radar (SAR) in semiarid rangeland. Agricultural and Forest meteorology, 105:69-80.

28.   Moran, M.S., Peters-Lidard, C.D., Watts ,J.M., McElroy, S. 2004. Estimating soil moisture at the watershed scale with satellite-based radar and land surface models Can. Journal Remote Sens, 30: 805-826.

29.   Nowroozi, M., Tabatabaei, S. H., Nouri, R. 2018. Water use efficiency of wheat under simultaneous water and salinity stress conditions by using date palm leaves biochar. Soil and Water Sciences, 7: 20-36. (Persian)

30.   Penuelas, J., Filella, I., Biel, C., Serrano, L., Save, R. 1993. The reflectance at the 950–970mm region as an indicator of plant water status. International Journal of Remote Sensing, 14:1887–1905.

31.   Peters, A.J., Rundquist, D.C., Wilhite, D.A. 1991. Satellite detection of the geographic core of the 1988 Nebraska drought. Agricultural and ForestMeteorology, 57: 1-3.

32.   Pirasteh-Anosheh, H., Emam, Y .,Kazemeini, S. A., Dehghany, F. 2017. Effect of Irrigation Water Salinity on Soil Moisture and Salinity during Growing Season, Barley Yield and Its Water Productivity. Journal of Soil Research (Soil and Water Sciences), 31:155-167 .(Persian)

33.   Price, J.C. 1990. Using Spatial Context in Satellite Data to Infer Regional Scale Evapotranspiration. IEEE Transactions on Geoscience and Remote, 28: 940-948.

34.   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 journal of photogrammetry and remote sensing, 83:94-103.

35.   Ranjbar Gholam, H., Pirste Anoushe, H. 2014. A look at salinity research in Iran with an emphasis on improving crop production. Journal of Agricultural Sciences of Iran, 17 (2):178-165 [In Persian].

36.   Ruiz, D., Martínez, V., Cerdá, A. 1997. Citrus response to salinity: Growth and nutrient uptake. Tree Physio, 17: 141-150.

37.   Sandholt, I., Rasmussena, K., Andersenb ,J. 2002. A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote Sensing of Environment, 79: 213–224.

38.   Scheftic, W. D., Cummins, K. L., Krider, E. P.,Sternberg, B. K., Goodrich, D., Moran, S., Scott, R. 2008. Wide-area soil moisture estimation using the propagation of lightning generated lowfrequency electromagnetic signals. 20th internationallightning detection conference, 140-148.

39.   Spaeth, S.c., Randau, H.c., Sinclair, T.R., Vendeland, J.S. 1984. Stability of soybean harvest index. Agron Journal,79:482-486.

40.   Stefanov, W. L., Netzband, M. 2005. Assessment of ASTER land cover and NDVI data at multiple scales for ecological characterization of an arid urban center. Remote Sensing of Environment, 99: 31–43.

41.   Su, H., Wood, E.F., Wojcik, R., McCabe, M. 2006. Sensitivity Analysis of Regional Scale Evapotranspiration Predictions to the Forcing Data. American Geophysical Union, Fall Meeting 2007, abstract #H31A-02.

42.   Tang, H., Li, Z. L. 2013. Quantitative Remote Sensing in Thermal Infrared: Theory and Applications.Springer Science & Business Media, 1:1-10.

43.   Wan, Z., Wang, P., Li, X. 2004. Using MODIS land surface temperature and normalized difference vegetation index products for monitoring drought in the southern Great Plains, USA. International Journal of Remote Sensing, 25: 61–72.

44.   Wang, X., Xie, H., Guan, H., Zhou, X. 2007. Different responses of MODIS-derived NDVI to root-zone soil moisture in semi-arid and humid regions. Journal of Hydrology, 340:12 24.

45.    Sanaei-Neja,  S.H., Noori,  S.,  Hashemi nia, M. 2011.Estimation of Evapotranspiration Using Satellite Image Data in Mashhad area. Journal water and soil, 25 (3): 540-547 [In Persian]