Publications

Du, JY; Kimball, JS; Bindlish, R; Walker, JP; Watts, JD (2022). Local Scale (3-m) Soil Moisture Mapping Using SMAP and Planet SuperDove. REMOTE SENSING, 14(15), 3812.

Abstract
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.

DOI:
10.3390/rs14153812

ISSN:
2072-4292