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Downscaling of AMSR-E soil moisture with MODIS products using machine learning approaches

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Abstract

Passive microwave remotely sensed soil moisture products, such as Advanced Microwave Scanning Radiometer on the Earth Observing System (AMSR-E) data, have been routinely used to monitor global soil moisture patterns. However, they are often limited in their ability to provide reliable spatial distribution data for soil moisture due to their coarse spatial resolutions. In this study, three machine learning approaches—random forest, boosted regression trees, and Cubist—were examined for the downscaling of AMSR-E soil moisture (25 × 25 km) data over two regions (South Korea and Australia) with different climatic characteristics using moderate resolution imaging spectroradiometer products (1 km), including surface albedo, land surface temperature (LST), Normalized Difference Vegetation Index, Enhanced Vegetation Index, Leaf Area Index, and evapotranspiration (ET). Results showed that the random forest approach was superior to the other machine learning models for downscaling AMSR-E soil moisture data in terms of the correlation coefficient [r = 0.71/0.84 (South Korea/Australia) for random forest, 0.75/0.77 for boosted regression trees, and 0.70/0.61 for Cubist] and root-mean-square error (RMSE = 0.049/0.057, 0.052/0.078, and 0.051/0.063, respectively) through cross-validation. The ET and LST were identified as the most influential among the six input parameters when estimating AMSR-E soil moisture for South Korea, while ET, albedo, and LST were very useful for Australia. In overall, the downscaled soil moisture with 1 km resolution yielded a higher correlation with in situ observations than the original AMSR-E soil moisture data. The latter appeared higher than the downscaled data in forested areas, possibly due to the overestimation of soil moisture by passive microwave sensors over forests, which implies that downscaling can mitigate such overestimation of soil moisture.

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Acknowledgments

This research was supported by the National Space Lab Program and Technology Development Program to Solve Climate Changes through the National Foundation of Korea (NRF), funded by the Ministry of Science, ICT, and Future Planning (Grant: NRF-2013M1A3A3A02042391 and NRF-2012M1A2A2671851). This research was also supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2013R1A1A2A10004743). The authors would like to express their gratitude to the Global Change Master Directory for providing Advanced Microwave Scanning Radiometer on the Earth Observing System (AMSR-E) products. We are also grateful to the Rural Development Administration (RDA) for providing access to soil moisture data.

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Authors and Affiliations

  1. School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea

    Jungho Im & Seonyoung Park

  2. Climate Research Department, APEC Climate Center, Busan, Republic of Korea

    Jinyoung Rhee

  3. School of Civil, Architectural, and Environmental System Engineering, Sungkyunkwan University, Suwon, Republic of Korea

    Jongjin Baik

  4. Water Resources and Remote Sensing Laboratory, Department of Water Resources, Graduate School of Water Resources, Sungkyunkwan University, 2066, Seobu-ro, Jangan-gu, Suwon, Gyeonggi-do, 440-746, Republic of Korea

    Minha Choi

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Correspondence to Minha Choi.

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Im, J., Park, S., Rhee, J. et al. Downscaling of AMSR-E soil moisture with MODIS products using machine learning approaches. Environ Earth Sci 75, 1120 (2016). https://doi.org/10.1007/s12665-016-5917-6

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