Determining the dry boundary of the LST/FVC space for soil moisture monitoring: a semi-empirical method

Sun, Hao; Ma, Liru; Wang, Yanmei; Zhou, Baichi; Liu, Weihan; Cai, Chuangchuang; Zhou, Wei; Chen, Wei

doi:10.1080/01431161.2019.1707901

Cited by 2 publications

(1 citation statement)

References 42 publications

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“…The optical datadominated methods are chiefly based on the spectral reflectance characteristics of soil and vegetation to identify varied SM statuses [12]. The thermal data-dominated methods are primarily based on the thermal radiation or thermal inertia of soil and vegetation to describe the variation of SM [13][14][15][16]. The microwave data-dominated methods are based on the vast disparity of water and dry soil in dielectric permittivity which could lead to the great difference in the emissivity and backscattering of microwaves.…”

Section: Introductionmentioning

confidence: 99%

Series or Parallel? An Exploration in Coupling Physical Model and Machine Learning Method for Disaggregating Satellite Microwave Soil Moisture

Sun

Zhang

Zhao

2022

IEEE Trans. Geosci. Remote Sensing

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Remotely sensed soil moisture (SM) dataset with well accuracy and fine spatiotemporal resolution is very valuable in various fields. Downscaling is a promising way to obtain such an SM dataset. There are currently two basic methodologies in the downscaling with satellite datasets, i.e., the machine learning (ML) methods and the physical or semi-physical (PH) models. This study focuses on exploring feasible ways to integrate them for boosting the performance of downscaling. Here, three parallel modes i.e., arithmetic average (Ari), geometric average (Geo), and weighted average (Wei), and two series modes i.e., first PH then ML (PH-ML) and first ML then PH (ML-PH) were developed and evaluated. A representative of PH, DSCALE_mod16, and three ML algorithms Random Forest, XGBoost, and LightGBM were used in this study. Microwave SM datasets from Soil Moisture Active and Passive Mission (SMAP), satellite datasets from MODIS, and in-situ SM observations spanning from April 2015 to December 2018 were employed in the evaluation. Spatial dynamic range, energy conservation, and precision preservation analyses were conducted as evaluation methods. Results demonstrated the PH-ML series mode outperformed the other coupling modes, which was even better than the better one of the PH and ML. The PH can first generate a valuable initial estimate, and the initial estimate's advantages can be preserved while its errors can be suppressed by the ML. Resultantly, the ideology of first using the PH to obtain an initial estimate and then putting the initial estimate into the ML is suggested for further microwave SM downscaling.

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