NDLI: AMSR2 soil moisture downscaling using multisensor products through machine learning approach

Content Provider	IEEE Xplore Digital Library
Author	Seonyoung Park Jungho Im Sumin Park Jinyoung Rhee
Copyright Year	2015
Description	Author affiliation: Sch. of Urban Environ. Eng., Ulsan Nat. Inst. of Sci. & Technol., Ulsan, South Korea (Seonyoung Park; Jungho Im; Sumin Park) \|\| Climate Res. Dept., APEC Climate Center, Busan, South Korea (Jinyoung Rhee)
Abstract	Soil moisture is important to understand the interaction between the land and the atmosphere, and has an influence on hydrological and agricultural processes such as drought and crop yield. In-situ measurements at stations have been used to monitor soil moisture. However, data measured in the field are point-based and difficult to represent spatial distribution of soil moisture. Remote sensing techniques using microwave sensors provide spatially continuous soil moisture. The spatial resolution of remotely sensed soil moisture based on typical passive microwave sensors is coarse (e.g., tens of kilometers), which is inadequate for local or regional scale studies. In this study, AMSR2 soil moisture was downscaled to 1km using MODIS products that are closely related to soil moisture through statistical ordinary least squares (OLS) and random forest (RF) machine learning approaches. RF $(r^{2}=0.96,$ rmse=0.06) outperformed OLS $(r^{2}=0.47,$ rmse=0.16) in modeling soil moisture possibly because RF is much flexible through randomization and adopts an ensemble approach. Both approaches identified T·V (i.e., multiplication between land surface temperature and normalized difference vegetation index) and evapotranspiration. AMSR2 soil moisture produced from the VUA-NASA algorithm appeared overestimated at high elevation areas because the characteristics of ground data for validation and correction used in the algorithm were different from those in our study area. In future study, AMSR2 soil moisture based on the JAXA algorithm will be evaluated with additional input variables including land cover, elevation and precipitation.
Starting Page	1984
Ending Page	1987
File Size	438548
Page Count	4
File Format	PDF
e-ISBN	9781479979295
DOI	10.1109/IGARSS.2015.7326186
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-26
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Soil moisture Radio frequency MODIS Spatial resolution Remote sensing Microwave radiometry Soil measurements downscaling AMSR2 machine learning
Content Type	Text
Resource Type	Article

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