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Improving SMAP soil moisture spatial resolution in different climatic conditions using remote sensing data

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Abstract

Soil moisture (SM) at the interface between the land surface and atmosphere is one of the major environmental parameters which plays an important role in hydrological applications. In this article, the SM measured by Soil Moisture Active Passive (SMAP) is downscaled from 3- to 1-km spatial resolution. The main purpose is to evaluate the performance of two different downscaling methods over a variety of climatic conditions and land cover types. These two methods, based on regression and artificial neural network (ANN), are used for enabling us to cross-validate and reliably interpret the obtained results since the number of ground measurements is not sufficient for accuracy assessment. These methods are applied over four different case studies; one is located in the USA, i.e., state of Utah (semi-arid), and the remaining three are located in Iran, i.e., Fars (arid and semi-arid), Yazd (hyper-arid), and Golestan (humid). In both methods, different combinations of input features correlated with SM including land surface temperature (LST), normalized difference vegetation index (NDVI), brightness temperatures in horizontal and vertical polarizations (TBH and TBV), shortwave infrared (SWIR), and digital elevation model (DEM) are used. It is found that the DEM does not add extra information in downscaling. The reason is due to high correlation between topography and LST. Moreover, SWIR is most likely able to model only large-scale variations of SM. The downscaled SM products are then compared to 1-km resolution SMAP SM extracted from Sentinel-1 for the study areas in Iran and in situ measurements in Utah. Both methods produce results which are considerably consistent except that the regression method adds more spatial details in the downscaled SM. The achievements illustrate that the performance of both downscaling methods is higher in areas with more homogeneous climatic conditions, i.e., Yazd and Golestan. The best evaluation metrics including correlation coefficient (R), root mean square error (RMSE), and mean absolute percentage error (MAPE) for Yazd and Golestan are R = 0.89, RMSE = 0.025 m3/m3, and MAPE = 21.13% and R = 0.93, RMSE = 0.044 m3/m3, and MAPE = 21.95%, respectively. Moreover, large model biases are associated with dense vegetated areas and high altitudes. The best downscaling accuracy in both methods over all study areas belongs to bare soil and flat regions.

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Data availability

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  • Asakereh, H., & Mazini, F. (2010). Analysis of the probability distribution for the annual precipitation in the Golestan province. Iran-Water Resources Research, 6(1), 51–55.

    Google Scholar 

  • Alemohammad, S. H., Kolassa, J., Prigent, C., Aires, F., & Gentine, P. (2018). Global downscaling of remotely sensed soil moisture using neural networks. Hydrology and Earth System Sciences, 22(10), 5341–5356.

    Article  Google Scholar 

  • Abbaszadeh, P., Moradkhani, H., & Zhan, X. (2019). Downscaling SMAP radiometer soil moisture over the CONUS using an ensemble learning method. Water Resources Research, 55(1), 324–344.

    Article  Google Scholar 

  • Babaeian, E., Sadeghi, M., Franz, T. E., Jones, S., & Tuller, M. (2018). Mapping soil moisture with the OPtical TRApezoid Model (OPTRAM) based on long-term MODIS observations. Remote Sensing of Environment, 211, 425–440.

    Article  Google Scholar 

  • Chen, Q., Fang, M., Wang, H., Xu, Z., Tang, Z., Yang, L., & Qi, S. (2020). Downscaling of satellite remote sensing soil moisture products over the tibetan plateau based on the random forest algorithm: Preliminary results. Earth and Space Science, 7, 1–16.

    Article  Google Scholar 

  • Choi, M., & Hur, Y. (2012). A microwave-optical/infrared disaggregation for improving spatial representation of soil moisture using AMSR-E and MODIS products. Remote Sensing of Environment, 124, 259–269.

    Article  Google Scholar 

  • Colliander, A., Fisher, J. B., Halverson, G., Merlin, O., Misra, S., Bindlish, R., et al. (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.

    Article  Google Scholar 

  • Das, N. N., Entekhabi, D., Njoku, E. G., Shi, J. J., Johnson, J. T., & Colliander, A. (2013). Tests of the SMAP combined radar and radiometer algorithm using airborne field campaign observations and simulated data. IEEE Transactions on Geoscience and Remote Sensing, 52(4), 2018–2028.

    Article  Google Scholar 

  • Entekhabi, D., Njoku, E. G., O'neill, P. E., Kellogg, K. H., Crow, W. T., Edelstein, W. N., et al. (2010). The soil moisture active passive (SMAP) mission. Proceedings of the IEEE, 98(5), 704–716.

    Article  Google Scholar 

  • Guevara, M., & Vargas, R. (2019). Downscaling satellite soil moisture using geomorphometry and machine learning. PLoS One, 14(9), e0219639.

    Article  CAS  Google Scholar 

  • Henderson-Sellers, A. (1996). Soil moisture: A critical focus for global change studies. Global and Planetary Change, 13(1-4), 3–9.

    Article  Google Scholar 

  • Hassan-Esfahani, L., Torres-Rua, A., Jensen, A., & McKee, M. (2015). Assessment of surface soil moisture using high-resolution multi-spectral imagery and artificial neural networks. Remote Sensing, 7(3), 2627–2646.

    Article  Google Scholar 

  • Jackson, T. J. (1993). III. Measuring surface soil moisture using passive microwave remote sensing. Hydrological Processes, 7(2), 139–152.

    Article  Google Scholar 

  • Jiang, H., Shen, H., Li, H., Lei, F., Gan, W., & Zhang, L. (2017). Evaluation of multiple downscaled microwave soil moisture products over the central Tibetan Plateau. Remote Sensing, 9(5), 402.

    Article  Google Scholar 

  • Kerr, Y. H., Al-Yaari, A., Rodriguez-Fernandez, N., Parrens, M., Molero, B., Leroux, D., et al. (2016). Overview of SMOS performance in terms of global soil moisture monitoring after six years in operation. Remote Sensing of Environment, 180, 40–63.

    Article  Google Scholar 

  • Kim, J., & Hogue, T. S. (2011). Improving spatial soil moisture representation through integration of AMSR-E and MODIS products. IEEE Transactions on Geoscience and Remote Sensing, 50(2), 446–460.

    Article  Google Scholar 

  • Kolassa, J., Aires, F., Polcher, J., Prigent, C., Jimenez, C., & Pereira, J. M. (2013). Soil Moisture retrieval from multi instrument observations: Information content analysis and retrieval methodology. Journal of Geophysical Research-Atmospheres, 118, 4847–4859.

    Article  Google Scholar 

  • Kolassa, J., Gentine, P., Prigent, C., & Aires, F. (2016). Soil moisture retrieval from AMSR-E and ASCAT microwave observation synergy. Part 1: Satellite data analysis. Remote Sensing of Environment, 173(6), 1–14.

  • Liu, Y., Jing, W., Wang, Q., & Xia, X. (2020). Generating high-resolution daily soil moisture by using spatial downscaling techniques: A comparison of six machine learning algorithms. Advances in Water Resources, 141, 103601.

    Article  Google Scholar 

  • Li, Q., Wang, Z., Shangguan, W., Li, L., Yao, Y., & Yu, F. (2021). Improved daily SMAP satellite soil moisture prediction over China using deep learning model with transfer learning. Journal of Hydrology, 600, 126698.

    Article  Google Scholar 

  • Merlin, O., Walker, J. P., Chehbouni, A., & Kerr, Y. (2008). Towards deterministic downscaling of SMOS soil moisture using MODIS derived soil evaporative efficiency. Remote Sensing of Environment, 112(10), 3935–3946.

    Article  Google Scholar 

  • Merlin, O., Al Bitar, A., Walker, J. P., & Kerr, Y. (2010). An improved algorithm for disaggregating microwave-derived soil moisture based on red, near-infrared and thermal-infrared data. Remote Sensing of Environment, 114(10), 2305–2316.

    Article  Google Scholar 

  • Merlin, O., Rudiger, C., Al Bitar, A., Richaume, P., Walker, J. P., & Kerr, Y. H. (2012). Disaggregation of SMOS soil moisture in Southeastern Australia. IEEE Transactions on Geoscience and Remote Sensing, 50(5), 1556–1571.

    Article  Google Scholar 

  • Merlin, O., Escorihuela, M. J., Mayoral, M. A., Hagolle, O., Al Bitar, A., & Kerr, Y. (2013). Self-calibrated evaporation-based disaggregation of SMOS soil moisture: An evaluation study at 3 km and 100 m resolution in Catalunya, Spain. Remote Sensing of Environment, 130, 25–38.

    Article  Google Scholar 

  • ElSaadani, M., Habib, E., Abdelhameed, A. M., & Bayoumi, M. (2021). Assessment of a spatiotemporal deep learning approach for soil moisture prediction and filling the gaps in between soil moisture observations. Frontiers n Artificial Intelligence, 4, 1–14.

    Google Scholar 

  • Piles, M., Camps, A., Vall-Llossera, M., Corbella, I., Panciera, R., Rudiger, C., et al. (2011). Downscaling SMOS-derived soil moisture using MODIS visible/infrared data. IEEE Transactions on Geoscience and Remote Sensing, 49(9), 3156–3166.

    Article  Google Scholar 

  • Piles, M., Sánchez, N., Vall-llossera, M., Camps, A., Martínez-Fernández, J., Martinez, J., & González-Gambau, V. (2014). A downscaling approach for SMOS land observations: Evaluation of high-resolution soil moisture maps over the Iberian Peninsula. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(9), 3845–3857.

    Article  Google Scholar 

  • Portal, G., Vall-Llossera, M., Piles, M., Camps, A., Chaparro, D., Pablos, M., & Rossato, L. (2018). A spatially consistent downscaling approach for SMOS using an adaptive moving window. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(6), 1883–1894.

    Article  Google Scholar 

  • Rodriíguez-Fernández, N. J., Aires, F., Richaume, P., Kerr, Y. H., Prigent, C., Kolassa, J., Cabot, F., Jimenez, C., Mahmoodi, A., & Drusch, M. (2015). Soil moisture retrieval using neural networks: Application to SMOS. IEEE Transactions on Geoscience and Remote Sensing, 53, 5991–6007.

    Article  Google Scholar 

  • Sadeghi, M., Jones, S. B., & Philpot, W. D. (2015). A linear physically-basedmodel for remote sensing of soil moisture using short wave infrared bands. Remote Sensing of Environment, 164, 66–76.

    Article  Google Scholar 

  • Sadeghi, M., Babaeian, E., Tuller, M., & Jones, S. B. (2017). The optical trapezoid model: A novel approach to remote sensing of soil moisture applied to Sentinel-2 and Landsat-8 observations. Remote Sensing of Environment, 198, 52–68.

    Article  Google Scholar 

  • Sánchez-Ruiz, S., Piles, M., Sánchez, N., Martínez-Fernández, J., Vall-llossera, M., & Camps, A. (2014). Combining SMOS with visible and near/shortwave/thermal infrared satellite data for high resolution soil moisture estimates. Journal of Hydrology, 516, 273–283.

    Article  Google Scholar 

  • Salmani-Dehaghi, N., & Samani, N. (2019). Spatiotemporal assessment of the PERSIANN family of satellite precipitation data over Fars Province. Iran. Theoretical and Applied Climatology, 138, 1333–1357.

    Article  Google Scholar 

  • Sishah, S., Abrahem, T., Azene, G., Dessalew, A., & Hundera, H. (2023). Downscaling and validating SMAP soil moisture using a machine learning algorithm over the Awash River basin, Ethiopia. PLoS One, 18(1), e0279895.

    Article  CAS  Google Scholar 

  • Van Zyl, J. J. (2001). The Shuttle Radar Topography Mission (SRTM): A breakthrough in remote sensing of topography. Acta Astronautica, 48(5-12), 559–565.

    Article  Google Scholar 

  • Xu, M., Yao, N., Yang, H., Xu, J., Hu, A., de Goncalves, L. G. G., & Liu, G. (2022). Downscaling SMAP soil moisture using a wide & deep learning method over the Continental United States. Journal of Hydrology, 609, 127784.

    Article  Google Scholar 

  • Yao, R., Yang, J., Gao, P., Shao, H., Chen, X., & Yu, S. (2013). Multivariate simulation and assessment of three dimensional spatial patterns of coastal soil salinity using ancillary variables. Fresenius Environmental Bulletin, 22(1), 39–52.

    CAS  Google Scholar 

  • Zhao, H., Li, J., Yuan, Q., Lin, L., Yue, L., & Xu, H. (2022). Downscaling of soil moisture products using deep learning: Comparison and analysis on Tibetan Plateau. Journal of Hydrology, 607, 127570.

    Article  Google Scholar 

  • Zhang, Y., Liang, S., Ma, H., He, T., Wang, Q., Li, B., Xu, J., Zhang, G., Liu, X., & Xiong, C. (2023). Generation of global 1-km daily soil moisture product from 2000 to 2020 using ensemble learning. Earth System Science Data, 15(5), 2055–2079.

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, School of Engineering, Shiraz University, Shiraz, 7134851156, Iran

    Fatemeh Imanpour & Maryam Dehghani

  2. Department of Electronics and Telecommunication Engineering, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran

    Mehran Yazdi

Authors
  1. Fatemeh Imanpour
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  2. Maryam Dehghani
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  3. Mehran Yazdi
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Contributions

Fatemeh Imanpour: methodology, validation, resources, data curation, formal analysis. Maryam Dehghani: conceptualization, writing—original draft, supervision. Mehran Yazdi: supervision, writing—review and editing.

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Correspondence to Maryam Dehghani.

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Imanpour, F., Dehghani, M. & Yazdi, M. Improving SMAP soil moisture spatial resolution in different climatic conditions using remote sensing data. Environ Monit Assess 195, 1476 (2023). https://doi.org/10.1007/s10661-023-12107-7

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