Evaluation of surface soil moisture of global products using measured data in different climates of Iran

Document Type : Complete scientific research article

Authors

1 M.Sc. Student in Water Science and Engineering, Arak University, Arak, Iran.

2 Corresponding Author, Assistant Prof., Dept. of Water Science and Engineering, Arak University, Arak, Iran and Research Institute for Water Science and Engineering, Arak University, Arak, Iran

3 Associate Prof., Dept. of Water Science and Engineering, Arak University, Arak, Iran and Research Institute for Water Science and Engineering, Arak University, Arak, Iran

Abstract

Background and Objectives: Soil moisture is one of the most important variables required from different aspects of research such as agriculture (soil water balance, irrigation schedule, agricultural drought index) and hydrology (infiltration, runoff, recharge). Although the measured soil moisture data is the most accurate data available to researchers, it has disadvantages such as the length of statistical period, missing data in some stations, point source and the inability to extend them to an area such as a basin. Soil moisture data in global products do not have these disadvantages, but their accuracy must be evaluated before using. In the present study, in order to investigate the possibility of using surface soil moisture data from global products in Iran, the accuracy of the global products databases has been evaluated. The main aim of this research is to evaluate the accuracy of surface soil moisture data of global products based on the measured data of soil moisture variable in different climates of Iran.

Materials and Methods: In this regard, the measured data of soil moisture in 43 agricultural meteorological stations across Iran were collected at 7 different depths (5, 10, 20, 30, 50, 70 and 100 cm from the soil surface) with a time step of three hours. After the preprocessing on the collected data, 14 stations from different climates of Iran with the longest and most complete soil moisture data for two depths of 5 and 10 cm with a monthly time step during the period of 2014-2021 were selected in order to compare with the data of global products. In the next step, soil moisture data in different soil layers were extracted from 5 different global products including GLEAM3.6a, ERA5, TERRA, MERRA2 and GLDAS2.1 with a monthly time scale. Surface soil moisture in the measured data, the average soil moisture at two depths of 5 and 10 cm, and in global products, the first layer of the soil surface was considered. The evaluation of the accuracy of surface soil moisture data has been done using three statistical criteria including Pearson's correlation coefficient (R), Mean Bias Error (MBE) and Normalized Root Mean Square Error (NRMSE).

Results: Generally, among the studied stations, the highest and lowest values of correlation coefficient are 0.55 and 0.04 respectively for Kahriz and Zahak stations. Between the seasons, the highest correlation has been obtained in the spring, April, equal to 0.51, and the lowest correlation has been obtained in the summer, September, equal to 0.11. The highest correlations are related to the GLEAM product, especially in the months of May, June, November and December. The results of MBE indicate that soil moisture is underestimated in most stations in humid and semi-humid climate stations (Gorgan, Karakhil and Amol stations). The GLEAM products with a range of [-0.03 ~ +0.13] has the least MBE changes among the studied products. The highest accuracy related to the ERA5 product, especially in the humid climate with NRMSE equal to 0.29. In addition, the lowest accuracy among the studied products is related to the GLDAS, especially in dry climate with NRMSE equal to 2.16.

Conclusion: The results of this research show that the accuracy of the global products of soil moisture varies according to the temporal and spatial of case study. In order to select the appropriate product with the high accuracy based on the spatiotemporal changes of soil moisture, the present research provides practical results to the researchers.

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Main Subjects


1.Hosseini-Moghari, S. M., Araghinejad, S., Ebrahimi, K., & Tourian, M. J. (2019). Introducing modified total storage deficit index (MTSDI) for drought monitoring using GRACE observations. Ecological indicators, 101, 465-475.
2.Zhang, X., Hao, Z., Singh, V. P., Zhang, Y., Feng, S., Xu, Y., & Hao, F. (2022). Drought propagation under global warming: Characteristics, approaches, processes, and controlling factors. Science of The Total Environment, 838, 156021.
3.Zhou, Z., Shi, H., Fu, Q., Ding, Y., Li, T., & Liu, S. (2021). Investigating the propagation from meteorological to hydrological drought by introducing the nonlinear dependence with directed information transfer index. Water Resources Research, 57, 8.
4.An, R., Zhang, L., Wang, Z., Quaye-Ballard, J.A., You, J., Shen, X., Gao, W., Huang, L., Zhao, Y., & Ke, Z. (2016). Validation of the ESA CCI soil moisture product in China. International journal of applied earth observation and geoinformation, 48, 28-36.
5.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.
6.Li, M., Wu, P., & Ma, Z. (2020). A comprehensive evaluation of soil moisture and soil temperature from thirdā€generation atmospheric and land reanalysis data sets. International Journal of Climatology, 40 (13), 5744-5766.
7.Xu, L., Chen, N., Zhang, X., Moradkhani, H., Zhang, C., and Hu, C. (2021). In-situ and triple-collocation based evaluations of eight global root zone soil moisture products. Remote Sensing of Environment, 254, p. 112248.
8.Liu, J., Chai, L., Dong, J., Zheng, D., Wigneron, J.P., Liu, S., Zhou, J., Xu, T., Yang, S., Song, Y., & Qu, Y. (2021). Uncertainty analysis of eleven multisource soil moisture products in the third pole environment based on the three-corned hat method. Remote sensing of environment, 255, 112225.
9.Li, L., Liu, Y., Zhu, Q., Liao, K., & Lai, X. (2022). Evaluation of nine major satellite soil moisture products in a typical subtropical monsoon region with complex land surface characteristics. International Soil and Water Conservation Research, 10 (3), 518-529.
10.Fan, L., Xing, Z., De Lannoy, G., Frappart, F., Peng, J., Zeng, J., Li, X., Yang, K., Zhao, T., Shi, J., & Ma, H. (2022). Evaluation of satellite and reanalysis estimates of surface and root-zone soil moisture in croplands of Jiangsu Province, China. Remote Sensing of Environment, 282, p. 113283.
11.Jamei, M., Baygi, M.M., Alizadeh, A., & Irannejad, P. (2017). Validation of soil moisture retrievals from SMOS microwave satellite. J Water Soil, 31 (2), 660-672. [In Persian]
12.Asadi Oskouei, E., Godarzy, L., & Helali, J. (2022). Introducing the SMAP L4 Products and Investigating the Spatio-Temporal Variability of Soil Moisture in Iran. Nivar, 46 (116), 13-25. [In Persian]
13.Motiee, H., Abdeh Kolahchi, A., & Aminian, R. (2022). Assessment of soil moisture using remote sensing ECV Database and its correlation with dust events-South and West of Iran. Iranian Journal of Soil and Water Research, 53 (7), 1531-1544. [In Persian]
14.Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration- Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. Fao, Rome, 300 (9), 5109.
15.Tsiros, I. X., Nastos, P., Proutsos, N. D., & Tsaousidis, A. (2020). Variability of the aridity index and related drought parameters in Greece using climatological data over the last century (1900–1997). Atmospheric Research, 240, 104914.
16.United Nations Educational, Scientific and Cultural Organization. (1979). Map of the world distribution of arid regions: map at scale 1:25,000,000 with explanatory note, MAB Technical Notes 7. UNESCO, Paris, 56.
17.Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., & Thépaut, J. N. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society. 146 (730), 1999-2049.
18.Martens, B., Miralles, D. G., Lievens, H., Van Der Schalie, R., De Jeu, R. A., Fernández-Prieto, D., ... & Verhoest, N. E. (2017). GLEAM v3: Satellite-based land evaporation and root-zone soil moisture. Geoscientific Model Development, 10 (5), 1903-1925.
19.Miralles, D. G., Holmes, T. R. H., De Jeu, R. A. M., Gash, J. H., Meesters, A. G. C. A., & Dolman, A. J. (2011). Global land-surface evaporation estimated from satellite-based observations. Hydrology and Earth System Sciences, 15 (2), 453-469.
20.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.
21.Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., ... & Zhao, B. (2017). The modern-era retrospective analysis for research and applications, version 2 (MERRA-2). Journal of climate, 30 (14), 5419-5454.
22.Rienecker, M. M., Suarez, M. J., Gelaro, R., Todling, R., Bacmeister, J., Liu, E., ... & Woollen, J. (2011). MERRA: NASA’s modern-era retrospective analysis for research and applications. Journal of climate, 24 (14), 3624-3648.
23.Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A., & Hegewisch, K. C. (2018). TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015. Scientific data, 5 (1), 1-12.
24.Xu, L., Chen, N., Zhang, X., Moradkhani, H., Zhang, C., & Hu, C. (2021). In-situ and triple-collocation based evaluations of eight global root zone soil moisture products. Remote Sensing of Environment, 254, p. 112248.
25.Zhang, M., Yuan, X., & Otkin, J.A. (2020). Remote sensing of the impact of flash drought events on terrestrial carbon dynamics over China. Carbon Balance and Management, 15 (1), 1-11.
26.Ji, Y., Li, Y., Yao, N., Biswas, A., Chen, X., Li, L., Pulatov, A., & Liu, F. (2022). Multivariate global agricultural drought frequency analysis using kernel density estimation. Ecological Engineering, 177, p. 106550.
27.Li, M. F., Tang, X. P., Wu, W., & Liu, H. B. (2013). General models for estimating daily global solar radiation for different solar radiation zones in mainland China. Energy Conversion and Management. 70, 139-148.