Soil moisture estimation based on MODIS NDVI and LST productions in areas with no data

eghtedarnezhad, mina; Malekinezhad, Hossein; Rafiei sardooi, Elham

doi:10.61186/jeer.14.2.9

year 14, Issue 2 (Summer 2024) E.E.R. 2024, 14(2): 141-160 | Back to browse issues page

‎ 10.61186/jeer.14.2.9

Mendeley

Zotero

RefWorks

eghtedarnezhad M, Malekinezhad H, Rafiei sardooi E. Soil moisture estimation based on MODIS NDVI and LST productions in areas with no data. E.E.R. 2024; 14 (2) :141-160
URL: http://magazine.hormozgan.ac.ir/article-1-825-en.html

Soil moisture estimation based on MODIS NDVI and LST productions in areas with no data

Mina Eghtedarnezhad

, Hossein Malekinezhad ^*

, Elham Rafiei sardooi

Department of Rangeland and Watershed Management, Faculty of Natural Resources, Yazd University, Yazd. , hmalekinezhad@yazd.ac.ir

Abstract: (1625 Views)

1- Introduction
Soil moisture can be considered in the control of desertification, agricultural activities, watershed management and optimal management of water resources. Since the country of Iran is facing many problems in these fields, the expansion of studies in the field of accurate estimation of soil moisture becomes important (Mehrabi et al., 2019). The GLDAS system may have a high error compared to the measured data in some areas. Therefore, it is necessary before the data and results of this product are used as a decision-making tool in the region. The quality of these data should be evaluated locally using ground-measured data (Polo et al, 2016 & Sanchez-Lorenzo et al, 2013 & Zhang, 2019).In this study, Terra MODIS data was used to estimate soil moisture due to higher spatial resolution (1 km). Due to the fact that it is difficult to estimate the humidity time series in the field, and radar remote sensing methods produce humidity maps with low spatial resolution. Therefore, in this study, a new method was introduced to prepare a soil moisture map with higher spatial resolution based on NDVI and LST MODIS products.The purpose of this study is to estimate soil moisture in Jiroft city using the products of the Morris sensor and NDVI and LST indices. Considering that Jiroft plain is one of the agricultural poles of Iran. Estimating the time series of soil moisture and then providing a drought index based on soil moisture is a useful method for investigating agricultural drought in the study area.
3- Results
LST and NDVI have high relative importance in arid regions (Park et al, 2016). In the study area, an algorithm based on remote sensing was used for soil moisture time series due to the lack of access to soil moisture time series. Moody's LST and NDVI products with a resolution of 1 square kilometer were used during 2007-2022. Then multiple linear regression was created using OLS method between soil moisture observations (2021 and 2022) and NDVI and LST time series data. Based on the results, both independent variables NDVI and LST were significant at the 99% level) p-value<0.01) .VIF values were less than 10. Therefore, there is no linearity between the independent variables. The standard error is small, which indicates that the estimated value is exactly the true value. The value of t statistic of two variables is higher than 2, which is considered statistically significant. In general, R² = 0.78 and it shows the accuracy of soil moisture estimation method. R² was obtained as 0.74 and 0.8 in the years 2021 and 2022, respectively, which indicates the accuracy of the predicted values.
4- Discussion & Conclusions
Estimation of spatial and temporal changes of soil moisture is an important issue in low data areas such as Iran. We proposed a multiple regression model based on Modis NDVI and LST to obtain surface soil moisture at a regional scale. The results showed that the multivariate linear regression method can be used to estimate soil moisture products with high resolution in areas with little data in the surface layers of the soil. Park et al. (2016) stated that LST and NDVI have high relative importance in arid regions. In the studied area, according to previous studies (Bai et al, 2020 & Wang et al, 2007), an algorithm based on remote sensing was used for soil moisture time series due to the lack of access to soil moisture time series became. Moody's LST and NDVI products with a resolution of 1 square kilometer were used during 2007-2022. Then multiple linear regression was created between soil moisture observations (2021 and 2022) and NDVI and LST time series data using OLS method. Based on the results of simulated soil moisture changes and the OLS method in estimating soil moisture (0-30 cm), both independent variables NDVI and LST were significant at the 99% level (p-value <0.01)). VIF values are less than 10 Therefore, there is no linearity between the independent variables. The standard error is small, which indicates that the estimated value is exactly the true value. The value of t statistic of two variables is higher than 2, which is considered statistically significant. Overall, R2 was 0.7 And it showed the accuracy of the soil moisture estimation method, which is consistent with the results of (Khanmohammadi et al, 2008 & Lin et al, 2015).

Keywords: soil moisture, Remote Sensing, NDVI, LST

Full-Text [PDF 785 kb] (365 Downloads)

Received: 2023/11/3 | Published: 2024/06/30

References

1. Adegoke, J. O., & Carleton, A. M. (2002). Relations between soil moisture and satellite vegetation indices in the US Corn Belt. Journal of hydrometeorology, 3(4), 395-405.‏ https://doi.org/10.1175/1525-7541(2002)003<0395:RBSMAS>2.0.CO;2 [DOI:10.1175/1525-7541(2002)0032.0.CO;2]

2. Altman, D. G., & Bland, J. M. (2005). Standard deviations and standard errors. Bmj, 331(7521), 903.‏ [DOI:10.1136/bmj.331.7521.903]

3. Bai, X., Zhang, L., He, C., & Zhu, Y. (2020). Estimating regional soil moisture distribution based on NDVI and land surface temperature time series data in the upstream of the Heihe River Watershed, Northwest China. Remote Sensing, 12(15), 2414. [DOI:10.3390/rs12152414]

4. ‏4. Babaeian, E., Homaee, M., & Noroozi, A. 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), 27-42 (‏ in persian).

5. Babazadeh, H., Aghdam, E. N., Aghighi, H., Shamsnia, S. A., & Dehkordi, D. K. (2012). Estimation of soil surface moisture in arid and semi arid rangelands using remotely sensed Temperature/Vegetation Index measurements (TVX)(case study: Khorasan province). Iranian Journal of Range and Desert Research, 19(1), 120-132.

6. Baghdadi, N., Aubert, M., Cerdan, O., Franchistéguy, L., Viel, C., Martin, E., ... & Desprats, J. F. (2007). Operational mapping of soil moisture using synthetic aperture radar data: application to the Touch basin (France). Sensors, 7(10), 2458-2483.‏ [DOI:10.3390/s7102458]

7. Bi, H., Ma, J., Zheng, W., & Zeng, J. (2016). Comparison of soil moisture in GLDAS model simulations and in situ observations over the Tibetan Plateau. Journal of Geophysical Research: Atmospheres, 121(6), 2658-2678.‏ [DOI:10.1002/2015JD024131]

8. Bai, X., Zhang, L., He, C., & Zhu, Y. (2020). Estimating regional soil moisture distribution based on NDVI and land surface temperature time series data in the upstream of the Heihe River Watershed, Northwest China. Remote Sensing, 12(15), 2414.‏ [DOI:10.3390/rs12152414]

9. Benedetti, R., & Rossini, P. (1993). On the use of NDVI profiles as a tool for agricultural statistics: the case study of wheat yield estimate and forecast in Emilia Romagna. Remote Sensing of Environment, 45(3), 311-326. [DOI:10.1016/0034-4257(93)90113-C]

10. Box, G. E., Hunter, W. H., & Hunter, S. (1978). Statistics for experimenters (Vol. 664). New York: John Wiley and sons.‏

11. Cai, J., Zhang, Y., Li, Y., Liang, X. S., & Jiang, T. (2017). Analyzing the characteristics of soil moisture using GLDAS data: A case study in eastern China. Applied Sciences, 7(6), 566.‏ [DOI:10.3390/app7060566]

12. 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(1-2), 161-173. [DOI:10.1080/02757259409532220]

13. Chauhan, N. S., Miller, S., & Ardanuy, P. (2003). Spaceborne soil moisture estimation at high resolution: A microwave-optical/IR synergistic approach. International Journal of Remote Sensing, 24(22), 4599-4622.‏ [DOI:10.1080/0143116031000156837]

14. Chen, Y., Yang, K., Qin, J., Zhao, L., Tang, W., & Han, M. (2013). Evaluation of AMSR‐E retrievals and GLDAS simulations against observations of a soil moisture network on the central Tibetan Plateau. Journal of Geophysical Research: Atmospheres, 118(10), 4466-4475.‏ [DOI:10.1002/jgrd.50301]

15. Colliander, A., Fisher, J. B., Halverson, G., Merlin, O., Misra, S., Bindlish, R., ... & Yueh, S. (2017). Spatial downscaling of SMAP soil moisture using MODIS land surface temperature and NDVI during SMAPVEX15. IEEE Geoscience and Remote Sensing Letters, 14(11), 2107-2111.‏ [DOI:10.1109/LGRS.2017.2753203]

16. Gutman, G. G. (1990). Towards monitoring droughts from space. Journal of climate, 3(2), 282-295.‏ https://doi.org/10.1175/1520-0442(1990)003<0282:TMDFS>2.0.CO;2 [DOI:10.1175/1520-0442(1990)0032.0.CO;2]

17. Hanley, J. A. (1987). Standard error of the kappa statistic. Psychological bulletin, 102(2), 315.‏ [DOI:10.1037/0033-2909.102.2.315]

18. Halder, B., Bandyopadhyay, J., & Banik, P. (2021). Evaluation of the climate change impact on urban heat island based on land surface temperature and geospatial indicators. International Journal of Environmental Research, 15, 819-835.‏ [DOI:10.1007/s41742-021-00356-8]

19. Hosseini Chamani, F., Farrokhian Firouzi, A., & Amerykhah, H. (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 (in persian).

20. Khanmohammadi, F., Homaee, M., & Noroozi, A. 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).

21. Khazaei, S., Raeini Sarjaz, M., Valizadeh, E., & Ghorbani, K. (2017). Estimation of Surface Soil Moisture from Satellite Images Using Vegetation and Thermal Indices. Iranian Journal of Irrigation & Drainage, 11(2), 151-162 (in persian).

22. Kim, T. K. (2015). T test as a parametric statistic. Korean journal of anesthesiology, 68(6), 540-546.‏ [DOI:10.4097/kjae.2015.68.6.540]

23. Krause, P., Boyle, D. P., & Bäse, F. (2005). Comparison of different efficiency criteria for hydrological model assessment. Advances in geosciences, 5, 89-97.‏ [DOI:10.5194/adgeo-5-89-2005]

24. Lin, M. L., Cao, Y., Juan, C. H., Chen, C. W., Hsueh, I. C., Wang, Q. B., & Lee, Y. T. (2008, July). Monitoring drought dynamics in the ejin oasis using drought indices from modis data. In IGARSS 2008-2008 IEEE International Geoscience and Remote Sensing Symposium (Vol. 4, pp. IV-834). IEEE.‏ [DOI:10.1109/IGARSS.2008.4779852]

25. Li, Z. L., Tang, B. H., Wu, H., Ren, H., Yan, G., Wan, Z., ... & Sobrino, J. A. (2013). Satellite-derived land surface temperature: Current status and perspectives. Remote sensing of environment, 131, 14-37.‏ [DOI:10.1016/j.rse.2012.12.008]

26. Mahara, G., Wang, C., Yang, K., Chen, S., Guo, J., Gao, Q., ... & Guo, X. (2016). The association between environmental factors and scarlet fever incidence in Beijing region: using GIS and spatial regression models. International Journal of Environmental Research and Public Health, 13(11), 1083.‏ [DOI:10.3390/ijerph13111083]

27. Mehrabi, M., Hamzeh, S., Alavipanah, S. K., Kiavarz, M., & Ziaee, R. (2019). Estimating soil moisture using remotely sensed data and surface energy balance system. Watershed Engineering and Management, 11(3), 759-770 (in persian).

28. Miles, J. (2014). Tolerance and variance inflation factor. Wiley statsref: statistics reference online.‏ [DOI:10.1002/9781118445112.stat06593]

29. Nourouzi, A. E., Behbahani, S., Rahimi, K. A., & Aghighi, H. (2008). Surface Soil Moisture Model with NDVI (Case Study: Rangelands of Khorasan Province). Journal of Environmental Studies, 48, 127-136.‏

30. Park, S., Im, J., Jang, E., & Rhee, J. (2016). Drought assessment and monitoring through blending of multi-sensor indices using machine learning approaches for different climate regions. Agricultural and forest meteorology, 216, 157-169.‏ [DOI:10.1016/j.agrformet.2015.10.011]

31. Patel, N. R., Anapashsha, R., Kumar, S., Saha, S. K., & Dadhwal, V. K. (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:10.1080/01431160802108497]

32. Pablon, C. Spennemann, Juan A. River, A. Celeste Saulo, Olga C.(2015) Penalba, A Comparison of GLDAS Soil Moisture Anomalies against Standardized Precipitation Index and Multisatellite Estimations over South America, Journal of Hydrometeorology, Volume 16, 158-171. [DOI:10.1175/JHM-D-13-0190.1]

33. Polo, J., Wilbert, S., Ruiz-Arias, J. A., Meyer, R., Gueymard, C., Suri, M., ... & Cebecauer, T. (2016). Preliminary survey on site-adaptation techniques for satellite-derived and reanalysis solar radiation datasets. Solar Energy, 132, 25-37. [DOI:10.1016/j.solener.2016.03.001]

34. Qin, J., Yang, K., Lu, N., Chen, Y., Zhao, L., & Han, M. (2013). Spatial upscaling of in-situ soil moisture measurements based on MODIS-derived apparent thermal inertia. Remote Sensing of Environment, 138, 1-9.‏ [DOI:10.1016/j.rse.2013.07.003]

35. Rodell, M., Houser, P. R., Jambor, U. E. A., Gottschalck, J., Mitchell, K., Meng, C. J., ... & Toll, D. (2004). The global land data assimilation system. Bulletin of the American Meteorological society, 85(3), 381-394.‏ [DOI:10.1175/BAMS-85-3-381]

36. Rui, H., Teng, W. L., Vollmer, B., Mocko, D. M., Beaudoing, H. K., & Rodell, M. (2011, December). NASA Giovanni portals for NLDAS/GLDAS online visualization, analysis, and intercomparison. In 2011 American Geophysical :union: Fall Meeting (No. GSFC. CPR. 5791.2011).‏

37. Rahimzadeh-Bajgiran, P., Omasa, K., & Shimizu, Y. (2012). Comparative evaluation of the Vegetation Dryness Index (VDI), the Temperature Vegetation Dryness Index (TVDI) and the improved TVDI (iTVDI) for water stress detection in semi-arid regions of Iran. ISPRS Journal of Photogrammetry and Remote Sensing, 68, 1-12. [DOI:10.1016/j.isprsjprs.2011.10.009]

38. Shirazi, M., Akhavan Qalibaf, M., Matinfar, H., & Nakhkesh, M. (2019). Comparison of MODIS and OLI sensor image data integration methods to improve the visibility of dust in industrial areas. Scientific Journal of Pasture and Desert Research of Iran, 26(3), 570-586 (in persian).

39. Sanchez-Lorenzo, A., Wild, M., & Trentmann, J. (2013). Validation and stability assessment of the monthly mean CM SAF surface solar radiation dataset over Europe against a homogenized surface dataset (1983-2005). Remote sensing of environment, 134, 355-366.‏ [DOI:10.1016/j.rse.2013.03.012]

40. Sen, L. K., & Shitan, M. (2002). The performance of AICC as an order selection criterion in ARMA time series models. Pertanika Journal of Science and Technology, 10(1), 25-33.‏

41. Song, C., Jia, L., & Menenti, M. (2013). Retrieving high-resolution surface soil moisture by downscaling AMSR-E brightness temperature using MODIS LST and NDVI data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(3), 935-942.‏ [DOI:10.1109/JSTARS.2013.2272053]

42. Tucker, C. J. (1989). Comparing SMMR and AVHRR data for drought monitoring. International Journal of Remote Sensing, 10(10), 1663-1672.‏ [DOI:10.1080/01431168908903997]

43. Thompson, C. G., Kim, R. S., Aloe, A. M., & Becker, B. J. (2017). Extracting the variance inflation factor and other multicollinearity diagnostics from typical regression results. Basic and Applied Social Psychology, 39(2), 81-90.‏ [DOI:10.1080/01973533.2016.1277529]

44. Wang,X. 2005. Relation between ground- based soil moisture and satellite image - based NDVI.

45. Wang, L., Qu, J. J., Zhang, S., Hao, X., & Dasgupta, S. (2007). Soil moisture estimation using MODIS and ground measurements in eastern China. International Journal of Remote Sensing, 28(6), 1413-1418.‏ [DOI:10.1080/01431160601075525]

46. Wang, L., & Qu, J. J. (2009). Satellite remote sensing applications for surface soil moisture monitoring: A review. Frontiers of Earth Science in China, 3, 237-247.‏ [DOI:10.1007/s11707-009-0023-7]

47. Wang, H., Li, X., Long, H., Xu, X., & Bao, Y. (2010). Monitoring the effects of land use and cover type changes on soil moisture using remote-sensing data: A case study in China's Yongding River basin. Catena, 82(3), 135-145.‏ [DOI:10.1016/j.catena.2010.05.008]

48. Western, A. W., & Grayson, R. B. (1998). The Tarrawarra data set: Soil moisture patterns, soil characteristics, and hydrological flux measurements. Water resources research, 34(10), 2765-2768.‏ [DOI:10.1029/98WR01833]

49. Zhao, S., Yang, Y., Qiu, G., Qin, Q., Yao, Y., Xiong, Y., & Li, C. (2010). Remote detection of bare soil moisture using a surface-temperature-based soil evaporation transfer coefficient. International Journal of Applied Earth Observation and Geoinformation, 12(5), 351-358.‏ [DOI:10.1016/j.jag.2010.04.007]

50. Zuo, J., Xu, J., Li, W., & Yang, D. (2019). Understanding shallow soil moisture variation in the data-scarce area and its relationship with climate change by GLDAS data. Plos one, 14(5), e0217020.‏ [DOI:10.1371/journal.pone.0217020]

51. Zhang, T., Stackhouse Jr, P. W., Cox, S. J., Mikovitz, J. C., & Long, C. N. (2019). Clear-sky shortwave downward flux at the Earth's surface: Ground-based data vs. satellite-based data. Journal of Quantitative Spectroscopy and Radiative Transfer, 224, 247-260.‏ [DOI:10.1016/j.jqsrt.2018.11.015]