期刊
REMOTE SENSING
卷 14, 期 15, 页码 -出版社
MDPI
DOI: 10.3390/rs14153812
关键词
soil moisture; local scale; SMAP; Planet SuperDove; Google Earth Engine; machine learning; CDF matching
类别
资金
- National Aeronautics and Space Administration (NASA) [80NSSC21K1350]
This research proposes a new method for estimating high-resolution soil moisture using multi-sensor fusion, machine-learning, and cumulative distribution function matching. The method shows favorable accuracy and sensitivity in the Australian Yanco region, capturing soil moisture distributions under different vegetation biomass gradients and irrigation regimes.
A capability for mapping meter-level resolution soil moisture with frequent temporal sampling over large regions is essential for quantifying local-scale environmental heterogeneity and eco-hydrologic behavior. However, available surface soil moisture (SSM) products generally involve much coarser grain sizes ranging from 30 m to several 10 s of kilometers. Hence, a new method is proposed to estimate 3-m resolution SSM using a combination of multi-sensor fusion, machine-learning (ML), and Cumulative Distribution Function (CDF) matching approaches. This method established favorable SSM correspondence between 3-m pixels and overlying 9-km grid cells from overlapping Planet SuperDove (PSD) observations and NASA Soil Moisture Active-Passive (SMAP) mission products. The resulting 3-m SSM predictions showed improved accuracy by reducing absolute bias and RMSE by similar to 0.01 cm(3)/cm(3) over the original SMAP data in relation to in situ soil moisture measurements for the Australian Yanco region while preserving the high sampling frequency (1-3 day global revisit) and sensitivity to surface wetness (R 0.865) from SMAP. Heterogeneous soil moisture distributions varying with vegetation biomass gradients and irrigation regimes were generally captured within a selected study area. Further algorithm refinement and implementation for regional applications will allow for improvement in water resources management, precision agriculture, and disaster forecasts and responses.
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