Validation of SMAP Satellite-Based Soil Moisture in Different Land Uses of Simineh-Zarrineh (Bokan) Basin

Document Type : Research Paper

Authors

1 Ph.D Student, Soil Science Department, Faculty of Agriculture, University of Zanjan, Zanjan, Iran

2 Professor, Soil Science Department, Faculty of Agriculture, University of Zanjan, Zanjan, Iran

3 Professor, Soil Science Department, Faculty of Agriculture and Naturan Resource, University of Tehran, Karaj, Iran

4 Professor, Hydrology and Remote Sensing Laboratory, USDA, Maryland, USA

5 Director of Research, Hydrology Group of the Research Institute for Geo-Hydrological Protection, Perugia , Italy

Abstract

Soil moisture is a key variable in determinant terrestrial systems for water and energy exchanges between the earth's surface and the atmosphere. In this study, the soil moisture data of SMAP satellite at different times and land uses were validated through four indices of correlation coefficient, root mean square error, unbiased root mean square error, and mean difference in 2017. For this purpose, Simineh-Zarineh basin located in the south and southeast of Urmia Lake, which is the largest sub-basin of the Urmia Lake basin, was investigated. The total study area is about 1762500 hectares. The spatial and temporal resolution of the SMAP satellite is 9 square kilometers and three days. Therefore, 287 ground points on a grid were selected for sampling in the study area. The results showed that the SMAP satellite data with ground observation data on December 3 and April 3 had a maximum RMSD value of 0.25 to 0.35 cm3 cm-3. The results revealed that the soil moisture data of SMAP satellite with RMSD values between 0.18 to 0.33 cm3 cm-3 and ubRMSE between 0.17 to 0.33 cm3 cm-3 show better performances correspond to ground data. The highest correlations and the lowest RMSD value were observed in July 3rd and September 13th, respectively. The lowest RMSD and the highest correlation for dryland was observed on April 3rd. In July 3rd the highest correlation was observed in all land uses, and among them the highest correlation was observed in dryland.

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Ahmed, A., Zhang, Y., & Nichols, S. (2011). Review and evaluation of remote sensing methods for soil-moisture estimation. SPIE reviews2(1), 028001.
Albergel, C., Dorigo, W., Balsamo, G., Muñoz-Sabater, J., de Rosnay, P., Isaksen, L., and Wagner, W. (2013). Monitoring multi-decadal satellite earth observation of soil moisture products through land surface reanalyses. Remote Sensing of Environment138, 77-89.
Al-Yaari, A., Wigneron, J. P., Ducharne, A., Kerr, Y., De Rosnay, P., De Jeu, R., ... & Richaume, P. (2014). Global-scale evaluation of two satellite-based passive microwave soil moisture datasets (SMOS and AMSR-E) with respect to Land Data Assimilation System estimates. Remote Sensing of Environment149, 181-195.
Baghdadi, N., Aubert, M., & Zribi, M. (2011). Use of TerraSAR-X data to retrieve soil moisture over bare soil agricultural fields. IEEE Geoscience and Remote Sensing Letters9(3), 512-516.
Brocca, L., Melone, F., Moramarco, T., Wagner, W., & Hasenauer, S. (2010). ASCAT soil wetness index validation through in situ and modeled soil moisture data in central Italy. Remote Sensing of Environment114(11), 2745-2755.
Das, N. N., Entekhabi, D., Dunbar, R. S., Colliander, A., Chen, F., Crow, W. & Cosh, M. H. (2018). The SMAP mission combined active-passive soil moisture product at 9 km and 3 km spatial resolutions. Remote Sensing of Environment211, 204-217.
Dorigo, W., de Jeu, R., Chung, D., Parinussa, R., Liu, Y., Wagner, W., & Fernández‐Prieto, D. (2012). Evaluating global trends (1988–2010) in harmonized multi‐satellite surface soil moisture. Geophysical Research Letters39(18).
Entekhabi, D., Njoku, E. G., O'Neill, P. E., Kellogg, K. H., Crow, W. T., Edelstein, W. N., ... & Kimball, J. (2010). The soil moisture active passive (SMAP) mission. Proceedings of the IEEE98(5), 704-716.
Escorihuela, M. J., & Quintana-Seguí, P. (2016). Comparison of remote sensing and simulated soil moisture datasets in Mediterranean landscapes. Remote sensing of environment180, 99-114.
Mekonnen, D. F. (2009). Satellite remote sensing for soil moisture estimation: Gumara catchment. Ethiopia Satellite remote sensing for soil moisture estimation: Gumara catchment, Ethiopia.
Miralles, D. G., Crow, W. T., & Cosh, M. H. (2010). Estimating spatial sampling errors in coarse-scale soil moisture estimates derived from point-scale observations. Journal of Hydrometeorology11(6), 1423-1429.
Safari, N., Zarghami, M., & Szidarovszky, F. (2014). Nash bargaining and leader–follower models in water allocation: Application to the Zarrinehrud River basin, Iran. Applied Mathematical Modelling38(7-8), 1959-1968.
Santi, E., Paloscia, S., Pettinato, S., Brocca, L., Ciabatta, L., & Entekhabi, D. (2018). Integration of microwave data from SMAP and AMSR2 for soil moisture monitoring in Italy. Remote Sensing of Environment212, 21-30.
Seneviratne, S. I., Corti, T., Davin, E. L., Hirschi, M., Jaeger, E. B., Lehner, I., and Teuling, A. J. (2010). Investigating soil moisture–climate interactions in a changing climate: A review, Earth. Rev., 99, 125–161.
Wang, L., & Qu, J. J. (2009). Satellite remote sensing applications for surface soil moisture monitoring: A review. Frontiers of Earth Science in China3(2), 237-247.
Wang, X., Wang, B., Xu, X., Liu, T., Duan, Y., & Zhao, Y. (2018). Spatial and temporal variations in surface soil moisture and vegetation cover in the Loess Plateau from 2000 to 2015. Ecological indicators95, 320-330.
Zaman, M. R., Morid, S., & Delavar, M. (2016). Evaluating climate adaptation strategies on agricultural production in the Siminehrud catchment and inflow into Lake Urmia, Iran using SWAT within an OECD framework. Agricultural Systems147, 98-110.
Zhang, J., Zhou, L., Ma, R., Jia, Y., Yang, F., Zhou, H., & Cao, X. (2019). Influence of soil moisture content and soil and water conservation measures on time to runoff initiation under different rainfall intensities. CATENA182, 104172.
Zhu, Q., Liao, K., Xu, Y., Yang, G., Wu, S., & Zhou, S. (2013). Monitoring and prediction of soil moisture spatial–temporal variations from a hydropedological perspective: a review. Soil Research50(8), 625-637.